From ab8fc7b1abd273c4fb00831d294d961e013421b5 Mon Sep 17 00:00:00 2001
From: "penghouwen@icloud.com" <penghouwen@icloud.com>
Date: Tue, 14 Jan 2020 14:35:47 +0800
Subject: [PATCH 01/21] integrate c-darts algorithm

---
 examples/nas/cdarts/README.md                 | 110 +++++
 examples/nas/cdarts/aux_head.py               |  99 +++++
 examples/nas/cdarts/config.py                 | 135 ++++++
 examples/nas/cdarts/datasets/cifar.py         | 108 +++++
 examples/nas/cdarts/datasets/data_utils.py    | 398 ++++++++++++++++++
 examples/nas/cdarts/datasets/imagenet.py      |  95 +++++
 examples/nas/cdarts/genotypes.py              | 163 +++++++
 examples/nas/cdarts/images/cell1.png          | Bin 0 -> 28026 bytes
 examples/nas/cdarts/images/cell2.png          | Bin 0 -> 29359 bytes
 examples/nas/cdarts/images/cell3.png          | Bin 0 -> 36654 bytes
 examples/nas/cdarts/images/framework1.png     | Bin 0 -> 121056 bytes
 examples/nas/cdarts/images/framework2.png     | Bin 0 -> 112057 bytes
 examples/nas/cdarts/model.py                  | 162 +++++++
 examples/nas/cdarts/ops.py                    | 161 +++++++
 examples/nas/cdarts/retrain.py                | 153 +++++++
 examples/nas/cdarts/run_retrain_cifar.sh      |  13 +
 examples/nas/cdarts/run_search_cifar.sh       |  14 +
 examples/nas/cdarts/search.py                 |  44 ++
 examples/nas/cdarts/trainer.py                | 270 ++++++++++++
 examples/nas/cdarts/utils.py                  | 177 ++++++++
 .../pynni/nni/nas/pytorch/cdarts/__init__.py  |   5 +
 .../pynni/nni/nas/pytorch/cdarts/mutator.py   |  75 ++++
 .../pynni/nni/nas/pytorch/cdarts/trainer.py   | 203 +++++++++
 23 files changed, 2385 insertions(+)
 create mode 100644 examples/nas/cdarts/README.md
 create mode 100644 examples/nas/cdarts/aux_head.py
 create mode 100644 examples/nas/cdarts/config.py
 create mode 100644 examples/nas/cdarts/datasets/cifar.py
 create mode 100644 examples/nas/cdarts/datasets/data_utils.py
 create mode 100644 examples/nas/cdarts/datasets/imagenet.py
 create mode 100644 examples/nas/cdarts/genotypes.py
 create mode 100644 examples/nas/cdarts/images/cell1.png
 create mode 100644 examples/nas/cdarts/images/cell2.png
 create mode 100644 examples/nas/cdarts/images/cell3.png
 create mode 100644 examples/nas/cdarts/images/framework1.png
 create mode 100644 examples/nas/cdarts/images/framework2.png
 create mode 100644 examples/nas/cdarts/model.py
 create mode 100644 examples/nas/cdarts/ops.py
 create mode 100644 examples/nas/cdarts/retrain.py
 create mode 100644 examples/nas/cdarts/run_retrain_cifar.sh
 create mode 100644 examples/nas/cdarts/run_search_cifar.sh
 create mode 100644 examples/nas/cdarts/search.py
 create mode 100644 examples/nas/cdarts/trainer.py
 create mode 100644 examples/nas/cdarts/utils.py
 create mode 100644 src/sdk/pynni/nni/nas/pytorch/cdarts/__init__.py
 create mode 100644 src/sdk/pynni/nni/nas/pytorch/cdarts/mutator.py
 create mode 100644 src/sdk/pynni/nni/nas/pytorch/cdarts/trainer.py

diff --git a/examples/nas/cdarts/README.md b/examples/nas/cdarts/README.md
new file mode 100644
index 0000000000..f4421ea4b9
--- /dev/null
+++ b/examples/nas/cdarts/README.md
@@ -0,0 +1,110 @@
+# Cyclic Differentiable Architecture Search
+This is CDARTS based on the NNI platform, which currently supports CIFAR10 search and retrain. ImageNet search and retrain should also be supported, and we provide corresponding interfaces. Our reproduced results on NNI are slightly lower than the paper, but much higher than the original DARTS.
+
+<div align="center">
+  <img src="images/framework1.png" width="500px" />
+  <!-- <p>framework1.</p> -->
+</div>
+
+## Results
+#### Main CIFAR10 top1 accuracy of DARTS and DARTS reproduced by NNI
+| Order | Paper | NNI | 
+| ---- |:-------------:| :-----:|
+| 1 | 97.00 +/-0.14 | 97.22 |
+| 2 | 97.24 +/-0.09 | 97.11 |
+
+#### Main CIFAR10 top1 accuracy of CDARTS and CDARTS reproduced by NNI
+| Runs | Paper | NNI | 
+| ---- |:-------------:| :-----:|
+| 1 | 97.52 | 97.43 |
+| 2 | 97.53 | 97.46 |
+| 3 | 97.58 | 97.47 |
+
+### The normal cells searched by CDARTS(NNI) on CIFAR10
+<div align="center">
+  <img src="images/cell1.png" width="200px" />
+  <!-- <p>cell.</p> -->
+</div>
+
+<div align="center">
+  <img src="images/cell2.png" width="300px" />
+  <img src="images/cell3.png" width="300px" />
+  <!-- <p>cell.</p> -->
+</div>
+
+
+## Environments
+Tesla V100, CUDA10.0, linux 16.04, pytorch>=1.2, python3, [apex](https://github.com/NVIDIA/apex) and NNI 1.3
+
+## Data Preparation
+* [Cifar-10](https://www.cs.toronto.edu/~kriz/cifar.html)
+* [Cifar-100](https://www.cs.toronto.edu/~kriz/cifar.html)
+* [ImageNet-2012](http://www.image-net.org/)
+
+Create soft link in main dir.
+```
+ln -s $DataLocation data
+```
+
+## Installation
+* First, install python requirements.
+    ```buildoutcfg
+    pip install torch==1.2.0
+    pip install tensorboard==1.13.0
+    pip install tensorboardX==1.6
+    pip install nni==1.3.0
+    ```
+* Then, you should install apex.
+    ```buildoutcfg
+    git clone https://github.com/NVIDIA/apex
+    cd apex
+    python setup.py install --cpp_ext --cuda_ext
+    ```
+
+## Search and Retrain
+### Search
+* Main python file is ${ROOT}/search.py
+* Followings are options during training.
+    ```buildoutcfg
+    --regular_ratio             # if use regular, the ragular ratio
+    --regular_coeff             # if use regular, the regular coefficient
+    --loss_alpha                # the loss coefficient
+    --loss_T                    # the loss coefficient
+    --w_lr                      # the learning rate of the search network
+    --alpha_lr                  # the learning rate of the architecture parameters
+    --nasnet_lr                 # the learning rate of the evaluation network
+    --w_weight_decay            # the weight decay the search and the evaluation network
+    --alpha_weight_decay        # the weight decay the the architecture parameters
+    --fix_head                  # wheter to fix the paramters of auxiliary heads
+    --interactive_type          # The KD function, kl and smoothl1
+    --share_module              # Whether to share module of the two networks
+    --warmup_epochs             # the epochs to warmup the search network
+    --epochs                    # total epochs of search
+
+    ```
+* Here we present our search scripts on CIFAR10.
+    ```buildoutcfg
+    bash run_search_cifar.sh
+    ```
+
+
+### Retrain
+* Main python file is ${ROOT}/retrain.py
+* Followings are options during training.
+    ```buildoutcfg
+    --arc_checkpoint            # choose which genotype to retrain
+    --cell_file                 # path of cell genotype
+    --weight_decay              # decay of W in the Retrain-Phase
+    --lr                        # learning rate of W in the Retrain-Phase
+    --warmup_epochs             # warmup epochs 
+    --epochs                    # total retrain epochs 
+    --cutout_length             # cutout length for cifar
+    --aux_weight                # weight of auxiliary loss, 0.4 is the best option   
+    --drop_path_prob            # used for dropping path in NAS
+    --label_smooth              # label smooth ratio
+    --mixup_alpha               # ratio of mixup
+    ```
+* Here we present our train scripts on CIFAR10.
+    ```buildoutcfg
+    bash run_retrain_cifar.sh
+    ```
diff --git a/examples/nas/cdarts/aux_head.py b/examples/nas/cdarts/aux_head.py
new file mode 100644
index 0000000000..352db6a7df
--- /dev/null
+++ b/examples/nas/cdarts/aux_head.py
@@ -0,0 +1,99 @@
+import torch.nn as nn
+
+
+class DistillHeadCIFAR(nn.Module):
+
+    def __init__(self, C, size, num_classes, bn_affine=False):
+        """assuming input size 8x8 or 16x16"""
+        super(DistillHeadCIFAR, self).__init__()
+        self.features = nn.Sequential(
+            nn.ReLU(),
+            nn.AvgPool2d(size, stride=2, padding=0, count_include_pad=False),  # image size = 2 x 2 / 6 x 6
+            nn.Conv2d(C, 128, 1, bias=False),
+            nn.BatchNorm2d(128, affine=bn_affine),
+            nn.ReLU(),
+            nn.Conv2d(128, 768, 2, bias=False),
+            nn.BatchNorm2d(768, affine=bn_affine),
+            nn.ReLU()
+        )
+        self.classifier = nn.Linear(768, num_classes)
+        self.gap = nn.AdaptiveAvgPool2d(1)
+
+    def forward(self, x):
+        x = self.features(x)
+        x = self.gap(x)
+        x = self.classifier(x.view(x.size(0), -1))
+        return x
+
+
+class DistillHeadImagenet(nn.Module):
+
+    def __init__(self, C, size, num_classes, bn_affine=False):
+        """assuming input size 7x7 or 14x14"""
+        super(DistillHeadImagenet, self).__init__()
+        self.features = nn.Sequential(
+            nn.ReLU(),
+            nn.AvgPool2d(size, stride=2, padding=0, count_include_pad=False),  # image size = 2 x 2 / 6 x 6
+            nn.Conv2d(C, 128, 1, bias=False),
+            nn.BatchNorm2d(128, affine=bn_affine),
+            nn.ReLU(),
+            nn.Conv2d(128, 768, 2, bias=False),
+            nn.BatchNorm2d(768, affine=bn_affine),
+            nn.ReLU()
+        )
+        self.classifier = nn.Linear(768, num_classes)
+        self.gap = nn.AdaptiveAvgPool2d(1)
+
+    def forward(self, x):
+        x = self.features(x)
+        x = self.gap(x)
+        x = self.classifier(x.view(x.size(0), -1))
+        return x
+
+
+class AuxiliaryHeadCIFAR(nn.Module):
+
+    def __init__(self, C, size=5, num_classes=10):
+        """assuming input size 8x8"""
+        super(AuxiliaryHeadCIFAR, self).__init__()
+        self.features = nn.Sequential(
+            nn.ReLU(inplace=True),
+            nn.AvgPool2d(5, stride=3, padding=0, count_include_pad=False),  # image size = 2 x 2
+            nn.Conv2d(C, 128, 1, bias=False),
+            nn.BatchNorm2d(128),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(128, 768, 2, bias=False),
+            nn.BatchNorm2d(768),
+            nn.ReLU(inplace=True)
+        )
+        self.classifier = nn.Linear(768, num_classes)
+
+    def forward(self, x):
+        x = self.features(x)
+        x = self.classifier(x.view(x.size(0), -1))
+        return x
+
+
+class AuxiliaryHeadImageNet(nn.Module):
+
+    def __init__(self, C, size=5, num_classes=1000):
+        """assuming input size 7x7"""
+        super(AuxiliaryHeadImageNet, self).__init__()
+        self.features = nn.Sequential(
+            nn.ReLU(inplace=True),
+            nn.AvgPool2d(size, stride=2, padding=0, count_include_pad=False),
+            nn.Conv2d(C, 128, 1, bias=False),
+            nn.BatchNorm2d(128),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(128, 768, 2, bias=False),
+            # NOTE: This batchnorm was omitted in my earlier implementation due to a typo.
+            # Commenting it out for consistency with the experiments in the paper.
+            # nn.BatchNorm2d(768),
+            nn.ReLU(inplace=True)
+        )
+        self.classifier = nn.Linear(768, num_classes)
+
+    def forward(self, x):
+        x = self.features(x)
+        x = self.classifier(x.view(x.size(0), -1))
+        return x
diff --git a/examples/nas/cdarts/config.py b/examples/nas/cdarts/config.py
new file mode 100644
index 0000000000..2894edf7a8
--- /dev/null
+++ b/examples/nas/cdarts/config.py
@@ -0,0 +1,135 @@
+""" Config class for search/retrain """
+import argparse
+from functools import partial
+
+
+def get_parser(name):
+    """ make default formatted parser """
+    parser = argparse.ArgumentParser(name, formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+    # print default value always
+    parser.add_argument = partial(parser.add_argument, help=' ')
+    return parser
+
+
+class BaseConfig(argparse.Namespace):
+    def print_params(self, prtf=print):
+        prtf("")
+        prtf("Parameters:")
+        for attr, value in sorted(vars(self).items()):
+            prtf("{}={}".format(attr.upper(), value))
+        prtf("")
+
+    def as_markdown(self):
+        """ Return configs as markdown format """
+        text = "|name|value|  \n|-|-|  \n"
+        for attr, value in sorted(vars(self).items()):
+            text += "|{}|{}|  \n".format(attr, value)
+
+        return text
+
+
+class SearchConfig(BaseConfig):
+    def build_parser(self):
+        parser = get_parser("Search config")
+        ########### basic settings ############
+        parser.add_argument('--dataset', default='cifar10', choices=['cifar10', 'cifar100', 'imagenet'])
+        parser.add_argument('--n_classes', type=int, default=10)
+        parser.add_argument('--stem_multiplier', type=int, default=3)
+        parser.add_argument('--init_channels', type=int, default=16)
+        parser.add_argument('--data_dir', type=str, default='data/cifar', help='cifar dataset')
+        parser.add_argument('--output_path', type=str, default='./outputs', help='')
+        parser.add_argument('--batch_size', type=int, default=128, help='batch size')
+        parser.add_argument('--log_frequency', type=int, default=10, help='print frequency')
+        parser.add_argument('--seed', type=int, default=0, help='random seed')
+        parser.add_argument('--workers', type=int, default=4, help='# of workers')
+        parser.add_argument('--steps_per_epoch', type=int, default=None, help='how many steps per epoch, use None for one pass of dataset')
+
+        ########### learning rate ############
+        parser.add_argument('--w_lr', type=float, default=0.05, help='lr for weights')
+        parser.add_argument('--w_momentum', type=float, default=0.9, help='momentum for weights')
+        parser.add_argument('--w_weight_decay', type=float, default=3e-4, help='weight decay for weights')
+        parser.add_argument('--grad_clip', type=float, default=5., help='gradient clipping for weights')
+        parser.add_argument('--alpha_lr', type=float, default=6e-4, help='lr for alpha')
+        parser.add_argument('--alpha_weight_decay', type=float, default=1e-3, help='weight decay for alpha')
+        parser.add_argument('--nasnet_lr', type=float, default=0.1, help='lr of nasnet')
+
+        ########### alternate training ############
+        parser.add_argument('--epochs', type=int, default=32, help='# of search epochs')
+        parser.add_argument('--warmup_epochs', type=int, default=2, help='# warmup epochs of super model')
+        parser.add_argument('--loss_alpha', type=float, default=1, help='loss alpha')
+        parser.add_argument('--loss_T', type=float, default=2, help='loss temperature')
+        parser.add_argument('--interactive_type', type=str, default='kl', choices=['kl', 'smoothl1'])
+        parser.add_argument('--sync_bn', action='store_true', default=False, help='whether to sync bn')
+        parser.add_argument('--use_apex', action='store_true', default=False, help='whether to use apex')
+        parser.add_argument('--regular_ratio', type=float, default=0.5, help='regular ratio')
+        parser.add_argument('--regular_coeff', type=float, default=5, help='regular coefficient')
+        parser.add_argument('--fix_head', action='store_true', default=False, help='whether to fix head')
+        parser.add_argument('--share_module', action='store_true', default=False, help='whether to share stem and aux head')
+
+        ########### data augument ############
+        parser.add_argument('--aux_weight', type=float, default=0.4, help='auxiliary loss weight')
+        parser.add_argument('--cutout_length', type=int, default=16, help='cutout length')
+        parser.add_argument('--drop_path_prob', type=float, default=0.2, help='drop path prob')
+        parser.add_argument('--use_aa', action='store_true', default=False, help='whether to use aa')
+        parser.add_argument('--mixup_alpha', default=1., type=float, help='mixup interpolation coefficient (default: 1)')
+
+        ########### distributed ############
+        parser.add_argument("--local_rank", default=0, type=int)
+        parser.add_argument("--world_size", default=1, type=int)
+        parser.add_argument('--dist_url', default='tcp://127.0.0.1:23456', type=str, help='url used to set up distributed training')
+        parser.add_argument('--distributed', action='store_true', help='run model distributed mode')
+
+        return parser
+
+    def __init__(self):
+        parser = self.build_parser()
+        args = parser.parse_args()
+        super().__init__(**vars(args))
+
+
+class RetrainConfig(BaseConfig):
+    def build_parser(self):
+        parser = get_parser("Retrain config")
+        parser.add_argument('--dataset', default="cifar10", choices=['cifar10', 'cifar100', 'imagenet'])
+        parser.add_argument('--data_dir', type=str, default='data/cifar', help='cifar dataset')
+        parser.add_argument('--output_path', type=str, default='./outputs', help='')
+        parser.add_argument("--arc_checkpoint", default="epoch_02.json")
+        parser.add_argument('--log_frequency', type=int, default=10, help='print frequency')
+
+        ########### model settings ############
+        parser.add_argument('--n_classes', type=int, default=10)
+        parser.add_argument('--input_channels', type=int, default=3)
+        parser.add_argument('--stem_multiplier', type=int, default=3)
+        parser.add_argument('--batch_size', type=int, default=128, help='batch size')
+        parser.add_argument('--eval_batch_size', type=int, default=500, help='batch size for validation')
+        parser.add_argument('--lr', type=float, default=0.025, help='lr for weights')
+        parser.add_argument('--momentum', type=float, default=0.9, help='momentum')
+        parser.add_argument('--grad_clip', type=float, default=5., help='gradient clipping for weights')
+        parser.add_argument('--weight_decay', type=float, default=5e-4, help='weight decay')
+        parser.add_argument('--epochs', type=int, default=600, help='# of training epochs')
+        parser.add_argument('--warmup_epochs', type=int, default=5, help='# warmup')
+        parser.add_argument('--init_channels', type=int, default=36)
+        parser.add_argument('--layers', type=int, default=20, help='# of layers')
+        parser.add_argument('--seed', type=int, default=0, help='random seed')
+        parser.add_argument('--workers', type=int, default=4, help='# of workers')
+        parser.add_argument('--aux_weight', type=float, default=0.4, help='auxiliary loss weight')
+        parser.add_argument('--cutout_length', type=int, default=16, help='cutout length')
+        parser.add_argument('--label_smooth', type=float, default=0.1, help='label smoothing')
+        parser.add_argument('--drop_path_prob', type=float, default=0.3, help='drop path prob')
+
+        ########### data augmentation ############
+        parser.add_argument('--use_aa', action='store_true', default=False, help='whether to use aa')
+        parser.add_argument('--mixup_alpha', default=1., type=float, help='mixup interpolation coefficient')
+
+        ########### distributed ############
+        parser.add_argument("--local_rank", default=0, type=int)
+        parser.add_argument("--world_size", default=1, type=int)
+        parser.add_argument('--dist_url', default='tcp://127.0.0.1:23456', type=str, help='url used to set up distributed training')
+        parser.add_argument('--distributed', action='store_true', help='run model distributed mode')
+
+        return parser
+
+    def __init__(self):
+        parser = self.build_parser()
+        args = parser.parse_args()
+        super().__init__(**vars(args))
diff --git a/examples/nas/cdarts/datasets/cifar.py b/examples/nas/cdarts/datasets/cifar.py
new file mode 100644
index 0000000000..ea9a03520c
--- /dev/null
+++ b/examples/nas/cdarts/datasets/cifar.py
@@ -0,0 +1,108 @@
+import numpy as np
+import torch
+import torchvision.datasets as dset
+import torchvision.transforms as transforms
+
+from datasets.data_utils import CIFAR10Policy, Cutout
+from datasets.data_utils import SubsetDistributedSampler
+
+
+def data_transforms_cifar(config, cutout=False):
+    CIFAR_MEAN = [0.49139968, 0.48215827, 0.44653124]
+    CIFAR_STD = [0.24703233, 0.24348505, 0.26158768]
+
+    if config.use_aa:
+        train_transform = transforms.Compose([
+            transforms.RandomCrop(32, padding=4, fill=128),
+            transforms.RandomHorizontalFlip(), CIFAR10Policy(),
+            transforms.ToTensor(),
+            transforms.Normalize(CIFAR_MEAN, CIFAR_STD),
+        ])
+    else:
+        train_transform = transforms.Compose([
+            transforms.RandomCrop(32, padding=4),
+            transforms.RandomHorizontalFlip(),
+            transforms.ToTensor(),
+            transforms.Normalize(CIFAR_MEAN, CIFAR_STD),
+        ])
+
+    if cutout:
+        train_transform.transforms.append(Cutout(config.cutout_length))
+
+    valid_transform = transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Normalize(CIFAR_MEAN, CIFAR_STD),
+    ])
+    return train_transform, valid_transform
+
+
+def get_search_datasets(config):
+    dataset = config.dataset.lower()
+    if dataset == 'cifar10':
+        dset_cls = dset.CIFAR10
+        n_classes = 10
+    elif dataset == 'cifar100':
+        dset_cls = dset.CIFAR100
+        n_classes = 100
+    else:
+        raise Exception("Not support dataset!")
+
+    train_transform, valid_transform = data_transforms_cifar(config, cutout=False)
+    train_data = dset_cls(root=config.data_dir, train=True, download=True, transform=train_transform)
+    test_data = dset_cls(root=config.data_dir, train=False, download=True, transform=valid_transform)
+
+    num_train = len(train_data)
+    indices = list(range(num_train))
+    split_mid = int(np.floor(0.5 * num_train))
+
+    if config.distributed:
+        train_sampler = SubsetDistributedSampler(train_data, indices[:split_mid])
+        valid_sampler = SubsetDistributedSampler(train_data, indices[split_mid:num_train])
+    else:
+        train_sampler = torch.utils.data.sampler.SubsetRandomSampler(indices[:split_mid])
+        valid_sampler = torch.utils.data.sampler.SubsetRandomSampler(indices[split_mid:num_train])
+
+    train_loader = torch.utils.data.DataLoader(
+        train_data, batch_size=config.batch_size,
+        sampler=train_sampler,
+        pin_memory=False, num_workers=config.workers)
+
+    valid_loader = torch.utils.data.DataLoader(
+        train_data, batch_size=config.batch_size,
+        sampler=valid_sampler,
+        pin_memory=False, num_workers=config.workers)
+
+    return [train_loader, valid_loader], [train_sampler, valid_sampler]
+
+
+def get_augment_datasets(config):
+    dataset = config.dataset.lower()
+    if dataset == 'cifar10':
+        dset_cls = dset.CIFAR10
+    elif dataset == 'cifar100':
+        dset_cls = dset.CIFAR100
+    else:
+        raise Exception("Not support dataset!")
+
+    train_transform, valid_transform = data_transforms_cifar(config, cutout=True)
+    train_data = dset_cls(root=config.data_dir, train=True, download=True, transform=train_transform)
+    test_data = dset_cls(root=config.data_dir, train=False, download=True, transform=valid_transform)
+
+    if config.distributed:
+        train_sampler = torch.utils.data.distributed.DistributedSampler(train_data)
+        test_sampler = torch.utils.data.distributed.DistributedSampler(test_data)
+    else:
+        train_sampler = None
+        test_sampler = None
+
+    train_loader = torch.utils.data.DataLoader(
+        train_data, batch_size=config.batch_size,
+        sampler=train_sampler,
+        pin_memory=True, num_workers=config.workers)
+
+    test_loader = torch.utils.data.DataLoader(
+        test_data, batch_size=config.eval_batch_size,
+        sampler=test_sampler,
+        pin_memory=True, num_workers=config.workers)
+
+    return [train_loader, test_loader], [train_sampler, test_sampler]
diff --git a/examples/nas/cdarts/datasets/data_utils.py b/examples/nas/cdarts/datasets/data_utils.py
new file mode 100644
index 0000000000..5989d274f9
--- /dev/null
+++ b/examples/nas/cdarts/datasets/data_utils.py
@@ -0,0 +1,398 @@
+import math
+import random
+
+import numpy as np
+import torch
+import torch.distributed as dist
+from PIL import Image, ImageEnhance, ImageOps
+from torch.utils.data import Sampler
+
+
+class SubsetDistributedSampler(Sampler):
+    """Sampler that restricts data loading to a subset of the dataset.
+
+    It is especially useful in conjunction with
+    :class:`torch.nn.parallel.DistributedDataParallel`. In such case, each
+    process can pass a DistributedSampler instance as a DataLoader sampler,
+    and load a subset of the original dataset that is exclusive to it.
+
+    .. note::
+        Dataset is assumed to be of constant size.
+
+    Arguments:
+        dataset: Dataset used for sampling.
+        num_replicas (optional): Number of processes participating in
+            distributed training.
+        rank (optional): Rank of the current process within num_replicas.
+        shuffle (optional): If true (default), sampler will shuffle the indices
+    """
+
+    def __init__(self, dataset, indices, num_replicas=None, rank=None, shuffle=True):
+        if num_replicas is None:
+            if not dist.is_available():
+                raise RuntimeError("Requires distributed package to be available")
+            num_replicas = dist.get_world_size()
+        if rank is None:
+            if not dist.is_available():
+                raise RuntimeError("Requires distributed package to be available")
+            rank = dist.get_rank()
+        self.dataset = dataset
+        self.num_replicas = num_replicas
+        self.rank = rank
+        self.epoch = 0
+        self.indices = indices
+        self.num_samples = int(math.ceil(len(self.indices) * 1.0 / self.num_replicas))
+        self.total_size = self.num_samples * self.num_replicas
+        self.shuffle = shuffle
+
+    def __iter__(self):
+        # deterministically shuffle based on epoch
+        g = torch.Generator()
+        g.manual_seed(self.epoch)
+        if self.shuffle:
+            # indices = torch.randperm(len(self.dataset), generator=g).tolist()
+            indices = list(self.indices[i] for i in torch.randperm(len(self.indices)))
+        else:
+            # indices = list(range(len(self.dataset)))
+            indices = self.indices
+
+        # add extra samples to make it evenly divisible
+        indices += indices[:(self.total_size - len(indices))]
+        assert len(indices) == self.total_size
+
+        # subsample
+        indices = indices[self.rank:self.total_size:self.num_replicas]
+        assert len(indices) == self.num_samples
+
+        return iter(indices)
+
+    def __len__(self):
+        return self.num_samples
+
+    def set_epoch(self, epoch):
+        self.epoch = epoch
+
+
+class data_prefetcher():
+    def __init__(self, loader):
+        self.loader = iter(loader)
+        self.stream = torch.cuda.Stream()
+        self.mean = torch.tensor([0.485 * 255, 0.456 * 255, 0.406 * 255]).cuda().view(1, 3, 1, 1)
+        self.std = torch.tensor([0.229 * 255, 0.224 * 255, 0.225 * 255]).cuda().view(1, 3, 1, 1)
+        # With Amp, it isn't necessary to manually convert data to half.
+        # if args.fp16:
+        #     self.mean = self.mean.half()
+        #     self.std = self.std.half()
+        self.preload()
+
+    def preload(self):
+        try:
+            self.next_input, self.next_target = next(self.loader)
+        except StopIteration:
+            self.next_input = None
+            self.next_target = None
+            return
+        with torch.cuda.stream(self.stream):
+            self.next_input = self.next_input.cuda(non_blocking=True)
+            self.next_target = self.next_target.cuda(non_blocking=True)
+            # With Amp, it isn't necessary to manually convert data to half.
+            # if args.fp16:
+            #     self.next_input = self.next_input.half()
+            # else:
+            self.next_input = self.next_input.float()
+            self.next_input = self.next_input.sub_(self.mean).div_(self.std)
+
+    def next(self):
+        torch.cuda.current_stream().wait_stream(self.stream)
+        input = self.next_input
+        target = self.next_target
+        self.preload()
+        return input, target
+
+
+class Cutout(object):
+    def __init__(self, length):
+        self.length = length
+
+    def __call__(self, img):
+        h, w = img.size(1), img.size(2)
+        mask = np.ones((h, w), np.float32)
+        y = np.random.randint(h)
+        x = np.random.randint(w)
+
+        y1 = np.clip(y - self.length // 2, 0, h)
+        y2 = np.clip(y + self.length // 2, 0, h)
+        x1 = np.clip(x - self.length // 2, 0, w)
+        x2 = np.clip(x + self.length // 2, 0, w)
+
+        mask[y1: y2, x1: x2] = 0.
+        mask = torch.from_numpy(mask)
+        mask = mask.expand_as(img)
+        img *= mask
+
+        return img
+
+
+class ImageNetPolicy(object):
+    """ Randomly choose one of the best 24 Sub-policies on ImageNet.
+        Example:
+        >>> policy = ImageNetPolicy()
+        >>> transformed = policy(image)
+        Example as a PyTorch Transform:
+        >>> transform=transforms.Compose([
+        >>>     transforms.Resize(256),
+        >>>     ImageNetPolicy(),
+        >>>     transforms.ToTensor()])
+    """
+
+    def __init__(self, fillcolor=(128, 128, 128)):
+        self.policies = [
+            SubPolicy(0.4, "posterize", 8, 0.6, "rotate", 9, fillcolor),
+            SubPolicy(0.6, "solarize", 5, 0.6, "autocontrast", 5, fillcolor),
+            SubPolicy(0.8, "equalize", 8, 0.6, "equalize", 3, fillcolor),
+            SubPolicy(0.6, "posterize", 7, 0.6, "posterize", 6, fillcolor),
+            SubPolicy(0.4, "equalize", 7, 0.2, "solarize", 4, fillcolor),
+
+            SubPolicy(0.4, "equalize", 4, 0.8, "rotate", 8, fillcolor),
+            SubPolicy(0.6, "solarize", 3, 0.6, "equalize", 7, fillcolor),
+            SubPolicy(0.8, "posterize", 5, 1.0, "equalize", 2, fillcolor),
+            SubPolicy(0.2, "rotate", 3, 0.6, "solarize", 8, fillcolor),
+            SubPolicy(0.6, "equalize", 8, 0.4, "posterize", 6, fillcolor),
+
+            SubPolicy(0.8, "rotate", 8, 0.4, "color", 0, fillcolor),
+            SubPolicy(0.4, "rotate", 9, 0.6, "equalize", 2, fillcolor),
+            SubPolicy(0.0, "equalize", 7, 0.8, "equalize", 8, fillcolor),
+            SubPolicy(0.6, "invert", 4, 1.0, "equalize", 8, fillcolor),
+            SubPolicy(0.6, "color", 4, 1.0, "contrast", 8, fillcolor),
+
+            SubPolicy(0.8, "rotate", 8, 1.0, "color", 2, fillcolor),
+            SubPolicy(0.8, "color", 8, 0.8, "solarize", 7, fillcolor),
+            SubPolicy(0.4, "sharpness", 7, 0.6, "invert", 8, fillcolor),
+            SubPolicy(0.6, "shearX", 5, 1.0, "equalize", 9, fillcolor),
+            SubPolicy(0.4, "color", 0, 0.6, "equalize", 3, fillcolor),
+
+            SubPolicy(0.4, "equalize", 7, 0.2, "solarize", 4, fillcolor),
+            SubPolicy(0.6, "solarize", 5, 0.6, "autocontrast", 5, fillcolor),
+            SubPolicy(0.6, "invert", 4, 1.0, "equalize", 8, fillcolor),
+            SubPolicy(0.6, "color", 4, 1.0, "contrast", 8, fillcolor),
+            SubPolicy(0.8, "equalize", 8, 0.6, "equalize", 3, fillcolor)
+        ]
+
+    def __call__(self, img):
+        policy_idx = random.randint(0, len(self.policies) - 1)
+        return self.policies[policy_idx](img)
+
+    def __repr__(self):
+        return "AutoAugment ImageNet Policy"
+
+
+class CIFAR10Policy(object):
+    """ Randomly choose one of the best 25 Sub-policies on CIFAR10.
+        Example:
+        >>> policy = CIFAR10Policy()
+        >>> transformed = policy(image)
+        Example as a PyTorch Transform:
+        >>> transform=transforms.Compose([
+        >>>     transforms.Resize(256),
+        >>>     CIFAR10Policy(),
+        >>>     transforms.ToTensor()])
+    """
+
+    def __init__(self, fillcolor=(128, 128, 128)):
+        self.policies = [
+            SubPolicy(0.1, "invert", 7, 0.2, "contrast", 6, fillcolor),
+            SubPolicy(0.7, "rotate", 2, 0.3, "translateX", 9, fillcolor),
+            SubPolicy(0.8, "sharpness", 1, 0.9, "sharpness", 3, fillcolor),
+            SubPolicy(0.5, "shearY", 8, 0.7, "translateY", 9, fillcolor),
+            SubPolicy(0.5, "autocontrast", 8, 0.9, "equalize", 2, fillcolor),
+
+            SubPolicy(0.2, "shearY", 7, 0.3, "posterize", 7, fillcolor),
+            SubPolicy(0.4, "color", 3, 0.6, "brightness", 7, fillcolor),
+            SubPolicy(0.3, "sharpness", 9, 0.7, "brightness", 9, fillcolor),
+            SubPolicy(0.6, "equalize", 5, 0.5, "equalize", 1, fillcolor),
+            SubPolicy(0.6, "contrast", 7, 0.6, "sharpness", 5, fillcolor),
+
+            SubPolicy(0.7, "color", 7, 0.5, "translateX", 8, fillcolor),
+            SubPolicy(0.3, "equalize", 7, 0.4, "autocontrast", 8, fillcolor),
+            SubPolicy(0.4, "translateY", 3, 0.2, "sharpness", 6, fillcolor),
+            SubPolicy(0.9, "brightness", 6, 0.2, "color", 8, fillcolor),
+            SubPolicy(0.5, "solarize", 2, 0.0, "invert", 3, fillcolor),
+
+            SubPolicy(0.2, "equalize", 0, 0.6, "autocontrast", 0, fillcolor),
+            SubPolicy(0.2, "equalize", 8, 0.6, "equalize", 4, fillcolor),
+            SubPolicy(0.9, "color", 9, 0.6, "equalize", 6, fillcolor),
+            SubPolicy(0.8, "autocontrast", 4, 0.2, "solarize", 8, fillcolor),
+            SubPolicy(0.1, "brightness", 3, 0.7, "color", 0, fillcolor),
+
+            SubPolicy(0.4, "solarize", 5, 0.9, "autocontrast", 3, fillcolor),
+            SubPolicy(0.9, "translateY", 9, 0.7, "translateY", 9, fillcolor),
+            SubPolicy(0.9, "autocontrast", 2, 0.8, "solarize", 3, fillcolor),
+            SubPolicy(0.8, "equalize", 8, 0.1, "invert", 3, fillcolor),
+            SubPolicy(0.7, "translateY", 9, 0.9, "autocontrast", 1, fillcolor)
+        ]
+
+    def __call__(self, img):
+        policy_idx = random.randint(0, len(self.policies) - 1)
+        return self.policies[policy_idx](img)
+
+    def __repr__(self):
+        return "AutoAugment CIFAR10 Policy"
+
+
+class SVHNPolicy(object):
+    """ Randomly choose one of the best 25 Sub-policies on SVHN.
+        Example:
+        >>> policy = SVHNPolicy()
+        >>> transformed = policy(image)
+        Example as a PyTorch Transform:
+        >>> transform=transforms.Compose([
+        >>>     transforms.Resize(256),
+        >>>     SVHNPolicy(),
+        >>>     transforms.ToTensor()])
+    """
+
+    def __init__(self, fillcolor=(128, 128, 128)):
+        self.policies = [
+            SubPolicy(0.9, "shearX", 4, 0.2, "invert", 3, fillcolor),
+            SubPolicy(0.9, "shearY", 8, 0.7, "invert", 5, fillcolor),
+            SubPolicy(0.6, "equalize", 5, 0.6, "solarize", 6, fillcolor),
+            SubPolicy(0.9, "invert", 3, 0.6, "equalize", 3, fillcolor),
+            SubPolicy(0.6, "equalize", 1, 0.9, "rotate", 3, fillcolor),
+
+            SubPolicy(0.9, "shearX", 4, 0.8, "autocontrast", 3, fillcolor),
+            SubPolicy(0.9, "shearY", 8, 0.4, "invert", 5, fillcolor),
+            SubPolicy(0.9, "shearY", 5, 0.2, "solarize", 6, fillcolor),
+            SubPolicy(0.9, "invert", 6, 0.8, "autocontrast", 1, fillcolor),
+            SubPolicy(0.6, "equalize", 3, 0.9, "rotate", 3, fillcolor),
+
+            SubPolicy(0.9, "shearX", 4, 0.3, "solarize", 3, fillcolor),
+            SubPolicy(0.8, "shearY", 8, 0.7, "invert", 4, fillcolor),
+            SubPolicy(0.9, "equalize", 5, 0.6, "translateY", 6, fillcolor),
+            SubPolicy(0.9, "invert", 4, 0.6, "equalize", 7, fillcolor),
+            SubPolicy(0.3, "contrast", 3, 0.8, "rotate", 4, fillcolor),
+
+            SubPolicy(0.8, "invert", 5, 0.0, "translateY", 2, fillcolor),
+            SubPolicy(0.7, "shearY", 6, 0.4, "solarize", 8, fillcolor),
+            SubPolicy(0.6, "invert", 4, 0.8, "rotate", 4, fillcolor),
+            SubPolicy(0.3, "shearY", 7, 0.9, "translateX", 3, fillcolor),
+            SubPolicy(0.1, "shearX", 6, 0.6, "invert", 5, fillcolor),
+
+            SubPolicy(0.7, "solarize", 2, 0.6, "translateY", 7, fillcolor),
+            SubPolicy(0.8, "shearY", 4, 0.8, "invert", 8, fillcolor),
+            SubPolicy(0.7, "shearX", 9, 0.8, "translateY", 3, fillcolor),
+            SubPolicy(0.8, "shearY", 5, 0.7, "autocontrast", 3, fillcolor),
+            SubPolicy(0.7, "shearX", 2, 0.1, "invert", 5, fillcolor)
+        ]
+
+    def __call__(self, img):
+        policy_idx = random.randint(0, len(self.policies) - 1)
+        return self.policies[policy_idx](img)
+
+    def __repr__(self):
+        return "AutoAugment SVHN Policy"
+
+
+class SubPolicy(object):
+    def __init__(self, p1, operation1, magnitude_idx1, p2, operation2, magnitude_idx2, fillcolor=(128, 128, 128)):
+        ranges = {
+            "shearX": np.linspace(0, 0.3, 10),
+            "shearY": np.linspace(0, 0.3, 10),
+            "translateX": np.linspace(0, 150 / 331, 10),
+            "translateY": np.linspace(0, 150 / 331, 10),
+            "rotate": np.linspace(0, 30, 10),
+            "color": np.linspace(0.0, 0.9, 10),
+            "posterize": np.round(np.linspace(8, 4, 10), 0).astype(np.int),
+            "solarize": np.linspace(256, 0, 10),
+            "contrast": np.linspace(0.0, 0.9, 10),
+            "sharpness": np.linspace(0.0, 0.9, 10),
+            "brightness": np.linspace(0.0, 0.9, 10),
+            "autocontrast": [0] * 10,
+            "equalize": [0] * 10,
+            "invert": [0] * 10
+        }
+
+        # from https://stackoverflow.com/questions/5252170/specify-image-filling-color-when-rotating-in-python-with-pil-and-setting-expand
+        def rotate_with_fill(img, magnitude):
+            rot = img.convert("RGBA").rotate(magnitude)
+            return Image.composite(rot, Image.new("RGBA", rot.size, (128,) * 4), rot).convert(img.mode)
+
+        func = {
+            "shearX": lambda img, magnitude: img.transform(
+                img.size, Image.AFFINE, (1, magnitude * random.choice([-1, 1]), 0, 0, 1, 0),
+                Image.BICUBIC, fillcolor=fillcolor),
+            "shearY": lambda img, magnitude: img.transform(
+                img.size, Image.AFFINE, (1, 0, 0, magnitude * random.choice([-1, 1]), 1, 0),
+                Image.BICUBIC, fillcolor=fillcolor),
+            "translateX": lambda img, magnitude: img.transform(
+                img.size, Image.AFFINE, (1, 0, magnitude * img.size[0] * random.choice([-1, 1]), 0, 1, 0),
+                fillcolor=fillcolor),
+            "translateY": lambda img, magnitude: img.transform(
+                img.size, Image.AFFINE, (1, 0, 0, 0, 1, magnitude * img.size[1] * random.choice([-1, 1])),
+                fillcolor=fillcolor),
+            "rotate": lambda img, magnitude: rotate_with_fill(img, magnitude),
+            "color": lambda img, magnitude: ImageEnhance.Color(img).enhance(1 + magnitude * random.choice([-1, 1])),
+            "posterize": lambda img, magnitude: ImageOps.posterize(img, magnitude),
+            "solarize": lambda img, magnitude: ImageOps.solarize(img, magnitude),
+            "contrast": lambda img, magnitude: ImageEnhance.Contrast(img).enhance(
+                1 + magnitude * random.choice([-1, 1])),
+            "sharpness": lambda img, magnitude: ImageEnhance.Sharpness(img).enhance(
+                1 + magnitude * random.choice([-1, 1])),
+            "brightness": lambda img, magnitude: ImageEnhance.Brightness(img).enhance(
+                1 + magnitude * random.choice([-1, 1])),
+            "autocontrast": lambda img, magnitude: ImageOps.autocontrast(img),
+            "equalize": lambda img, magnitude: ImageOps.equalize(img),
+            "invert": lambda img, magnitude: ImageOps.invert(img)
+        }
+
+        self.p1 = p1
+        self.operation1 = func[operation1]
+        self.magnitude1 = ranges[operation1][magnitude_idx1]
+        self.p2 = p2
+        self.operation2 = func[operation2]
+        self.magnitude2 = ranges[operation2][magnitude_idx2]
+
+    def __call__(self, img):
+        if random.random() < self.p1:
+            img = self.operation1(img, self.magnitude1)
+        if random.random() < self.p2:
+            img = self.operation2(img, self.magnitude2)
+        return img
+
+
+def fast_collate(batch):
+    imgs = [img[0] for img in batch]
+    targets = torch.tensor([target[1] for target in batch], dtype=torch.int64)
+    w = imgs[0].size[0]
+    h = imgs[0].size[1]
+    tensor = torch.zeros((len(imgs), 3, h, w), dtype=torch.uint8)
+    for i, img in enumerate(imgs):
+        nump_array = np.asarray(img, dtype=np.uint8)
+        if (nump_array.ndim < 3):
+            nump_array = np.expand_dims(nump_array, axis=-1)
+        nump_array = np.rollaxis(nump_array, 2)
+
+        tensor[i] += torch.from_numpy(nump_array)
+
+    return tensor, targets
+
+
+def mixup_data(x, y, alpha=1.0, use_cuda=True):
+    '''Returns mixed inputs, pairs of targets, and lambda'''
+    if alpha > 0:
+        lam = np.random.beta(alpha, alpha)
+    else:
+        lam = 1
+
+    batch_size = x.size()[0]
+    if use_cuda:
+        index = torch.randperm(batch_size).cuda()
+    else:
+        index = torch.randperm(batch_size)
+
+    mixed_x = lam * x + (1 - lam) * x[index, :]
+    y_a, y_b = y, y[index]
+    return mixed_x, y_a, y_b, lam
+
+
+def mixup_criterion(criterion, pred, y_a, y_b, lam):
+    return lam * criterion(pred, y_a) + (1 - lam) * criterion(pred, y_b)
diff --git a/examples/nas/cdarts/datasets/imagenet.py b/examples/nas/cdarts/datasets/imagenet.py
new file mode 100644
index 0000000000..c803a76755
--- /dev/null
+++ b/examples/nas/cdarts/datasets/imagenet.py
@@ -0,0 +1,95 @@
+import numpy as np
+import torch
+import torchvision.datasets as dset
+import torchvision.transforms as transforms
+
+from datasets.data_utils import ImageNetPolicy
+from datasets.data_utils import SubsetDistributedSampler
+
+
+def _imagenet_dataset(config):
+    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+    train_dir = os.path.join(config.data_dir, "train")
+    test_dir = os.path.join(config.data_dir, "val")
+    if hasattr(config, "use_aa") and config.use_aa:
+        train_data = dset.ImageFolder(
+            train_dir,
+            transforms.Compose([
+                transforms.RandomResizedCrop(224),
+                transforms.RandomHorizontalFlip(),
+                ImageNetPolicy(),
+                transforms.ToTensor(),
+                normalize,
+            ]))
+    else:
+        train_data = dset.ImageFolder(
+            train_dir,
+            transforms.Compose([
+                transforms.RandomResizedCrop(224),
+                transforms.RandomHorizontalFlip(),
+                transforms.ColorJitter(
+                    brightness=0.4,
+                    contrast=0.4,
+                    saturation=0.4,
+                    hue=0.2),
+                transforms.ToTensor(),
+                normalize,
+            ]))
+
+    test_data = dset.ImageFolder(
+        test_dir,
+        transforms.Compose([
+            transforms.Resize(256),
+            transforms.CenterCrop(224),
+            transforms.ToTensor(),
+            normalize,
+        ]))
+
+    return train_data, test_data
+
+
+def get_search_datasets(config):
+    train_data, test_data = _imagenet_dataset(config)
+    num_train = len(train_data)
+    indices = list(range(num_train))
+    split_mid = int(np.floor(0.5 * num_train))
+
+    if config.distributed:
+        train_sampler = SubsetDistributedSampler(train_data, indices[:split_mid])
+        valid_sampler = SubsetDistributedSampler(train_data, indices[split_mid:num_train])
+    else:
+        train_sampler = torch.utils.data.sampler.SubsetRandomSampler(indices[:split_mid])
+        valid_sampler = torch.utils.data.sampler.SubsetRandomSampler(indices[split_mid:num_train])
+
+    train_loader = torch.utils.data.DataLoader(
+        train_data, batch_size=config.batch_size,
+        sampler=train_sampler,
+        pin_memory=True, num_workers=config.workers)
+
+    valid_loader = torch.utils.data.DataLoader(
+        train_data, batch_size=config.batch_size,
+        sampler=valid_sampler,
+        pin_memory=True, num_workers=config.workers)
+
+    return [train_loader, valid_loader], [train_sampler, valid_sampler]
+
+
+def get_augment_datasets(config):
+    train_data, test_data = _imagenet_dataset(config)
+    if config.distributed:
+        train_sampler = torch.utils.data.distributed.DistributedSampler(train_data)
+        test_sampler = torch.utils.data.distributed.DistributedSampler(test_data)
+    else:
+        train_sampler = test_sampler = None
+
+    train_loader = torch.utils.data.DataLoader(
+        train_data, batch_size=config.batch_size,
+        sampler=train_sampler,
+        pin_memory=True, num_workers=config.workers)
+
+    test_loader = torch.utils.data.DataLoader(
+        test_data, batch_size=config.batch_size,
+        sampler=test_sampler,
+        pin_memory=True, num_workers=config.workers)
+
+    return [train_loader, test_loader], [train_sampler, test_sampler]
diff --git a/examples/nas/cdarts/genotypes.py b/examples/nas/cdarts/genotypes.py
new file mode 100644
index 0000000000..7619cfb791
--- /dev/null
+++ b/examples/nas/cdarts/genotypes.py
@@ -0,0 +1,163 @@
+""" Genotypes
+    - Genotype: normal/reduce gene + normal/reduce cell output connection (concat)
+    - gene: discrete ops information (w/o output connection)
+    - dag: real ops (can be mixed or discrete, but Genotype has only discrete information itself)
+"""
+from collections import namedtuple
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+import ops
+from ops import PRIMITIVES
+
+Genotype = namedtuple('Genotype', 'normal normal_concat reduce reduce_concat')
+
+
+def to_dag(C_in, gene, reduction, bn_affine=True):
+    """ generate discrete ops from gene """
+    dag = nn.ModuleList()
+    for edges in gene:
+        row = nn.ModuleList()
+        for op_name, s_idx in edges:
+            # reduction cell & from input nodes => stride = 2
+            stride = 2 if reduction and s_idx < 2 else 1
+            op = ops.OPS[op_name](C_in, stride, bn_affine)
+            if not isinstance(op, ops.Identity):  # Identity does not use drop path
+                op = nn.Sequential(
+                    op,
+                    ops.DropPath_()
+                )
+            op.s_idx = s_idx
+            row.append(op)
+        dag.append(row)
+
+    return dag
+
+
+def from_str(s):
+    """ generate genotype from string
+    e.g. "Genotype(
+            normal=[[('sep_conv_3x3', 0), ('sep_conv_3x3', 1)],
+                    [('sep_conv_3x3', 1), ('dil_conv_3x3', 2)],
+                    [('sep_conv_3x3', 1), ('sep_conv_3x3', 2)],
+                    [('sep_conv_3x3', 1), ('dil_conv_3x3', 4)]],
+            normal_concat=range(2, 6),
+            reduce=[[('max_pool_3x3', 0), ('max_pool_3x3', 1)],
+                    [('max_pool_3x3', 0), ('skip_connect', 2)],
+                    [('max_pool_3x3', 0), ('skip_connect', 2)],
+                    [('max_pool_3x3', 0), ('skip_connect', 2)]],
+            reduce_concat=range(2, 6))"
+    """
+
+    genotype = eval(s)
+
+    return genotype
+
+
+def parse(alpha, beta, k):
+    """
+    parse continuous alpha to discrete gene.
+    alpha is ParameterList:
+    ParameterList [
+        Parameter(n_edges1, n_ops),
+        Parameter(n_edges2, n_ops),
+        ...
+    ]
+
+    beta is ParameterList:
+    ParameterList [
+        Parameter(n_edges1),
+        Parameter(n_edges2),
+        ...
+    ]
+
+    gene is list:
+    [
+        [('node1_ops_1', node_idx), ..., ('node1_ops_k', node_idx)],
+        [('node2_ops_1', node_idx), ..., ('node2_ops_k', node_idx)],
+        ...
+    ]
+    each node has two edges (k=2) in CNN.
+    """
+
+    gene = []
+    assert PRIMITIVES[-1] == 'none'  # 'none' is implemented in mutator now
+
+    # 1) Convert the mixed op to discrete edge (single op) by choosing top-1 weight edge
+    # 2) Choose top-k edges per node by edge score (top-1 weight in edge)
+    # output the connect idx[(node_idx, connect_idx, op_idx).... () ()]
+    connect_idx = []
+    for edges, w in zip(alpha, beta):
+        # edges: Tensor(n_edges, n_ops)
+        edge_max, primitive_indices = torch.topk((w.view(-1, 1) * edges)[:, :-1], 1)  # ignore 'none'
+        topk_edge_values, topk_edge_indices = torch.topk(edge_max.view(-1), k)
+        node_gene = []
+        node_idx = []
+        for edge_idx in topk_edge_indices:
+            prim_idx = primitive_indices[edge_idx]
+            prim = PRIMITIVES[prim_idx]
+            node_gene.append((prim, edge_idx.item()))
+            node_idx.append((edge_idx.item(), prim_idx.item()))
+
+        gene.append(node_gene)
+        connect_idx.append(node_idx)
+
+    return gene, connect_idx
+
+
+def parse_gumbel(alpha, beta, k):
+    """
+    parse continuous alpha to discrete gene.
+    alpha is ParameterList:
+    ParameterList [
+        Parameter(n_edges1, n_ops),
+        Parameter(n_edges2, n_ops),
+        ...
+    ]
+
+    beta is ParameterList:
+    ParameterList [
+        Parameter(n_edges1),
+        Parameter(n_edges2),
+        ...
+    ]
+
+    gene is list:
+    [
+        [('node1_ops_1', node_idx), ..., ('node1_ops_k', node_idx)],
+        [('node2_ops_1', node_idx), ..., ('node2_ops_k', node_idx)],
+        ...
+    ]
+    each node has two edges (k=2) in CNN.
+    """
+
+    gene = []
+    assert PRIMITIVES[-1] == 'none'  # assume last PRIMITIVE is 'none'
+
+    # 1) Convert the mixed op to discrete edge (single op) by choosing top-1 weight edge
+    # 2) Choose top-k edges per node by edge score (top-1 weight in edge)
+    # output the connect idx[(node_idx, connect_idx, op_idx).... () ()]
+    connect_idx = []
+    for edges, w in zip(alpha, beta):
+        # edges: Tensor(n_edges, n_ops)
+        discrete_a = F.gumbel_softmax(edges[:, :-1].reshape(-1), tau=1, hard=True)
+        for i in range(k-1):
+            discrete_a = discrete_a + F.gumbel_softmax(edges[:, :-1].reshape(-1), tau=1, hard=True)
+        discrete_a = discrete_a.reshape(-1, len(PRIMITIVES)-1)
+        reserved_edge = (discrete_a > 0).nonzero()
+
+        node_gene = []
+        node_idx = []
+        for i in range(reserved_edge.shape[0]):
+            edge_idx = reserved_edge[i][0].item()
+            prim_idx = reserved_edge[i][1].item()
+            prim = PRIMITIVES[prim_idx]
+            node_gene.append((prim, edge_idx))
+            node_idx.append((edge_idx, prim_idx))
+
+        gene.append(node_gene)
+        connect_idx.append(node_idx)
+
+    return gene, connect_idx
diff --git a/examples/nas/cdarts/images/cell1.png b/examples/nas/cdarts/images/cell1.png
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diff --git a/examples/nas/cdarts/model.py b/examples/nas/cdarts/model.py
new file mode 100644
index 0000000000..0514004a5e
--- /dev/null
+++ b/examples/nas/cdarts/model.py
@@ -0,0 +1,162 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+import ops
+import numpy as np
+from nni.nas.pytorch import mutables
+from utils import parse_results
+from aux_head import DistillHeadCIFAR, DistillHeadImagenet, AuxiliaryHeadCIFAR, AuxiliaryHeadImageNet
+
+
+class Node(nn.Module):
+    def __init__(self, node_id, num_prev_nodes, channels, num_downsample_connect):
+        super().__init__()
+        self.ops = nn.ModuleList()
+        choice_keys = []
+        for i in range(num_prev_nodes):
+            stride = 2 if i < num_downsample_connect else 1
+            choice_keys.append("{}_p{}".format(node_id, i))
+            self.ops.append(mutables.LayerChoice([ops.OPS[k](channels, stride, False) for k in ops.PRIMITIVES],
+                                                 key=choice_keys[-1]))
+        self.drop_path = ops.DropPath()
+        self.input_switch = mutables.InputChoice(choose_from=choice_keys, n_chosen=2, key="{}_switch".format(node_id))
+
+    def forward(self, prev_nodes):
+        assert len(self.ops) == len(prev_nodes)
+        out = [op(node) for op, node in zip(self.ops, prev_nodes)]
+        out = [self.drop_path(o) if o is not None else None for o in out]
+        return self.input_switch(out)
+
+
+class Cell(nn.Module):
+
+    def __init__(self, n_nodes, channels_pp, channels_p, channels, reduction_p, reduction):
+        super().__init__()
+        self.reduction = reduction
+        self.n_nodes = n_nodes
+
+        # If previous cell is reduction cell, current input size does not match with
+        # output size of cell[k-2]. So the output[k-2] should be reduced by preprocessing.
+        if reduction_p:
+            self.preproc0 = ops.FactorizedReduce(channels_pp, channels, affine=False)
+        else:
+            self.preproc0 = ops.StdConv(channels_pp, channels, 1, 1, 0, affine=False)
+        self.preproc1 = ops.StdConv(channels_p, channels, 1, 1, 0, affine=False)
+
+        # generate dag
+        self.mutable_ops = nn.ModuleList()
+        for depth in range(2, self.n_nodes + 2):
+            self.mutable_ops.append(Node("{}_n{}".format("reduce" if reduction else "normal", depth),
+                                         depth, channels, 2 if reduction else 0))
+
+    def forward(self, s0, s1):
+        # s0, s1 are the outputs of previous previous cell and previous cell, respectively.
+        tensors = [self.preproc0(s0), self.preproc1(s1)]
+        for node in self.mutable_ops:
+            cur_tensor = node(tensors)
+            tensors.append(cur_tensor)
+
+        output = torch.cat(tensors[2:], dim=1)
+        return output
+
+
+class Model(nn.Module):
+
+    def __init__(self, dataset, n_layers, in_channels=3, channels=16, n_nodes=4, retrain=False, shared_modules=None):
+        super().__init__()
+        assert dataset in ["cifar10", "imagenet"]
+        self.dataset = dataset
+        self.input_size = 32 if dataset == "cifar" else 224
+        self.in_channels = in_channels
+        self.channels = channels
+        self.n_nodes = n_nodes
+        self.aux_size = {2 * n_layers // 3: self.input_size // 4}
+        if dataset == "cifar10":
+            self.n_classes = 10
+            self.aux_head_class = AuxiliaryHeadCIFAR if retrain else DistillHeadCIFAR
+            if not retrain:
+                self.aux_size = {n_layers // 3: 6, 2 * n_layers // 3: 6}
+        elif dataset == "imagenet":
+            self.n_classes = 1000
+            self.aux_head_class = AuxiliaryHeadImageNet if retrain else DistillHeadImagenet
+            if not retrain:
+                self.aux_size = {n_layers // 3: 6, 2 * n_layers // 3: 5}
+        self.n_layers = n_layers
+        self.aux_head = nn.ModuleDict()
+        self.ensemble_param = nn.Parameter(torch.rand(len(self.aux_size) + 1) / (len(self.aux_size) + 1)) \
+            if not retrain else None
+
+        stem_multiplier = 3 if dataset == "cifar" else 1
+        c_cur = stem_multiplier * self.channels
+        self.shared_modules = {}  # do not wrap with ModuleDict
+        if shared_modules is not None:
+            self.stem = shared_modules["stem"]
+        else:
+            self.stem = nn.Sequential(
+                nn.Conv2d(in_channels, c_cur, 3, 1, 1, bias=False),
+                nn.BatchNorm2d(c_cur)
+            )
+            self.shared_modules["stem"] = self.stem
+
+        # for the first cell, stem is used for both s0 and s1
+        # [!] channels_pp and channels_p is output channel size, but c_cur is input channel size.
+        channels_pp, channels_p, c_cur = c_cur, c_cur, channels
+
+        self.cells = nn.ModuleList()
+        reduction_p, reduction = False, False
+        aux_head_count = 0
+        for i in range(n_layers):
+            reduction_p, reduction = reduction, False
+            if i in [n_layers // 3, 2 * n_layers // 3]:
+                c_cur *= 2
+                reduction = True
+
+            cell = Cell(n_nodes, channels_pp, channels_p, c_cur, reduction_p, reduction)
+            self.cells.append(cell)
+            c_cur_out = c_cur * n_nodes
+            if i in self.aux_size:
+                if shared_modules is not None:
+                    self.aux_head[str(i)] = shared_modules["aux" + str(aux_head_count)]
+                else:
+                    self.aux_head[str(i)] = self.aux_head_class(c_cur_out, self.aux_size[i], self.n_classes)
+                    self.shared_modules["aux" + str(aux_head_count)] = self.aux_head[str(i)]
+                aux_head_count += 1
+            channels_pp, channels_p = channels_p, c_cur_out
+
+        self.gap = nn.AdaptiveAvgPool2d(1)
+        self.linear = nn.Linear(channels_p, self.n_classes)
+
+    def forward(self, x):
+        s0 = s1 = self.stem(x)
+        outputs = []
+
+        for i, cell in enumerate(self.cells):
+            s0, s1 = s1, cell(s0, s1)
+            if str(i) in self.aux_head:
+                outputs.append(self.aux_head[str(i)](s1))
+
+        out = self.gap(s1)
+        out = out.view(out.size(0), -1)  # flatten
+        logits = self.linear(out)
+        outputs.append(logits)
+
+        if self.ensemble_param is None:
+            assert len(outputs) == 2
+            return outputs[1], outputs[0]
+        else:
+            em_output = torch.cat([(e * o) for e, o in zip(F.softmax(self.ensemble_param, dim=0), outputs)], 0)
+            return logits, em_output
+
+    def drop_path_prob(self, p):
+        for module in self.modules():
+            if isinstance(module, ops.DropPath):
+                module.p = p
+
+    def plot_genotype(self, results, logger):
+        genotypes = parse_results(results, self.n_nodes)
+        logger.info(genotypes)
+        return genotypes
diff --git a/examples/nas/cdarts/ops.py b/examples/nas/cdarts/ops.py
new file mode 100644
index 0000000000..403a2b6b8a
--- /dev/null
+++ b/examples/nas/cdarts/ops.py
@@ -0,0 +1,161 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+import torch
+import torch.nn as nn
+
+OPS = {
+    'none': lambda C, stride, affine: Zero(stride),
+    'avg_pool_3x3': lambda C, stride, affine: PoolWithoutBN('avg', C, 3, stride, 1, affine=affine),
+    'max_pool_3x3': lambda C, stride, affine: PoolWithoutBN('max', C, 3, stride, 1, affine=affine),
+    'skip_connect': lambda C, stride, affine: nn.Identity() if stride == 1 else FactorizedReduce(C, C, affine=affine),
+    'sep_conv_3x3': lambda C, stride, affine: SepConv(C, C, 3, stride, 1, affine=affine),
+    'sep_conv_5x5': lambda C, stride, affine: SepConv(C, C, 5, stride, 2, affine=affine),
+    'sep_conv_7x7': lambda C, stride, affine: SepConv(C, C, 7, stride, 3, affine=affine),
+    'dil_conv_3x3': lambda C, stride, affine: DilConv(C, C, 3, stride, 2, 2, affine=affine),  # 5x5
+    'dil_conv_5x5': lambda C, stride, affine: DilConv(C, C, 5, stride, 4, 2, affine=affine),  # 9x9
+    'conv_7x1_1x7': lambda C, stride, affine: FacConv(C, C, 7, stride, 3, affine=affine)
+}
+
+PRIMITIVES = [
+    'max_pool_3x3',
+    'avg_pool_3x3',
+    'skip_connect',  # identity
+    'sep_conv_3x3',
+    'sep_conv_5x5',
+    'dil_conv_3x3',
+    'dil_conv_5x5',
+]
+
+
+class DropPath(nn.Module):
+    def __init__(self, p=0.):
+        """
+        Drop path with probability.
+        Parameters
+        ----------
+        p : float
+            Probability of an path to be zeroed.
+        """
+        super().__init__()
+        self.p = p
+
+    def forward(self, x):
+        if self.training and self.p > 0.:
+            keep_prob = 1. - self.p
+            # per data point mask
+            mask = torch.zeros((x.size(0), 1, 1, 1), device=x.device).bernoulli_(keep_prob)
+            return x / keep_prob * mask
+
+        return x
+
+
+class PoolWithoutBN(nn.Module):
+    """
+    AvgPool or MaxPool with BN. `pool_type` must be `max` or `avg`.
+    """
+
+    def __init__(self, pool_type, C, kernel_size, stride, padding, affine=True):
+        super().__init__()
+        if pool_type.lower() == 'max':
+            self.pool = nn.MaxPool2d(kernel_size, stride, padding)
+        elif pool_type.lower() == 'avg':
+            self.pool = nn.AvgPool2d(kernel_size, stride, padding, count_include_pad=False)
+        else:
+            raise ValueError()
+
+    def forward(self, x):
+        out = self.pool(x)
+        return out
+
+
+class StdConv(nn.Module):
+    """
+    Standard conv: ReLU - Conv - BN
+    """
+
+    def __init__(self, C_in, C_out, kernel_size, stride, padding, affine=True):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.ReLU(),
+            nn.Conv2d(C_in, C_out, kernel_size, stride, padding, bias=False),
+            nn.BatchNorm2d(C_out, affine=affine)
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
+
+class FacConv(nn.Module):
+    """
+    Factorized conv: ReLU - Conv(Kx1) - Conv(1xK) - BN
+    """
+
+    def __init__(self, C_in, C_out, kernel_length, stride, padding, affine=True):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.ReLU(),
+            nn.Conv2d(C_in, C_in, (kernel_length, 1), stride, padding, bias=False),
+            nn.Conv2d(C_in, C_out, (1, kernel_length), stride, padding, bias=False),
+            nn.BatchNorm2d(C_out, affine=affine)
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
+
+class DilConv(nn.Module):
+    """
+    (Dilated) depthwise separable conv.
+    ReLU - (Dilated) depthwise separable - Pointwise - BN.
+    If dilation == 2, 3x3 conv => 5x5 receptive field, 5x5 conv => 9x9 receptive field.
+    """
+
+    def __init__(self, C_in, C_out, kernel_size, stride, padding, dilation, affine=True):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.ReLU(),
+            nn.Conv2d(C_in, C_in, kernel_size, stride, padding, dilation=dilation, groups=C_in,
+                      bias=False),
+            nn.Conv2d(C_in, C_out, 1, stride=1, padding=0, bias=False),
+            nn.BatchNorm2d(C_out, affine=affine)
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
+
+class SepConv(nn.Module):
+    """
+    Depthwise separable conv.
+    DilConv(dilation=1) * 2.
+    """
+
+    def __init__(self, C_in, C_out, kernel_size, stride, padding, affine=True):
+        super().__init__()
+        self.net = nn.Sequential(
+            DilConv(C_in, C_in, kernel_size, stride, padding, dilation=1, affine=affine),
+            DilConv(C_in, C_out, kernel_size, 1, padding, dilation=1, affine=affine)
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
+
+class FactorizedReduce(nn.Module):
+    """
+    Reduce feature map size by factorized pointwise (stride=2).
+    """
+
+    def __init__(self, C_in, C_out, affine=True):
+        super().__init__()
+        self.relu = nn.ReLU()
+        self.conv1 = nn.Conv2d(C_in, C_out // 2, 1, stride=2, padding=0, bias=False)
+        self.conv2 = nn.Conv2d(C_in, C_out // 2, 1, stride=2, padding=0, bias=False)
+        self.bn = nn.BatchNorm2d(C_out, affine=affine)
+
+    def forward(self, x):
+        x = self.relu(x)
+        out = torch.cat([self.conv1(x), self.conv2(x[:, :, 1:, 1:])], dim=1)
+        out = self.bn(out)
+        return out
diff --git a/examples/nas/cdarts/retrain.py b/examples/nas/cdarts/retrain.py
new file mode 100644
index 0000000000..6332342f88
--- /dev/null
+++ b/examples/nas/cdarts/retrain.py
@@ -0,0 +1,153 @@
+import json
+import logging
+import os
+import time
+from argparse import ArgumentParser
+
+import torch
+import torch.nn as nn
+
+import apex
+import datasets
+import utils
+from apex.parallel import DistributedDataParallel
+from config import RetrainConfig
+from datasets.cifar import get_augment_datasets
+from model import Model
+from nni.nas.pytorch.fixed import apply_fixed_architecture
+from nni.nas.pytorch.utils import AverageMeterGroup
+
+
+def train(logger, config, train_loader, model, optimizer, criterion, epoch, main_proc):
+    meters = AverageMeterGroup()
+    cur_lr = optimizer.param_groups[0]["lr"]
+    if main_proc:
+        logger.info("Epoch %d LR %.6f", epoch, cur_lr)
+
+    model.train()
+    for step, (x, y) in enumerate(train_loader):
+        x, y = x.cuda(non_blocking=True), y.cuda(non_blocking=True)
+        optimizer.zero_grad()
+        logits, aux_logits = model(x)
+        loss = criterion(logits, y)
+        if config.aux_weight > 0.:
+            loss += config.aux_weight * criterion(aux_logits, y)
+        loss.backward()
+        nn.utils.clip_grad_norm_(model.parameters(), config.grad_clip)
+        optimizer.step()
+
+        prec1, prec5 = utils.accuracy(logits, y, topk=(1, 5))
+        metrics = {"prec1": prec1, "prec5": prec5, "loss": loss}
+        metrics = utils.reduce_metrics(metrics, config.distributed)
+        meters.update(metrics)
+
+        if main_proc and (step % config.log_frequency == 0 or step + 1 == len(train_loader)):
+            logger.info("Epoch [%d/%d] Step [%d/%d]  %s", epoch + 1, config.epochs, step + 1, len(train_loader), meters)
+
+    if main_proc:
+        logger.info("Train: [%d/%d] Final Prec@1 %.4f Prec@5 %.4f", epoch + 1, config.epochs, meters.prec1.avg, meters.prec5.avg)
+
+
+def validate(logger, config, valid_loader, model, criterion, epoch, main_proc):
+    meters = AverageMeterGroup()
+    model.eval()
+
+    with torch.no_grad():
+        for step, (x, y) in enumerate(valid_loader):
+            x, y = x.cuda(non_blocking=True), y.cuda(non_blocking=True)
+            logits, _ = model(x)
+            loss = criterion(logits, y)
+            prec1, prec5 = utils.accuracy(logits, y, topk=(1, 5))
+            metrics = {"prec1": prec1, "prec5": prec5, "loss": loss}
+            metrics = utils.reduce_metrics(metrics, config.distributed)
+            meters.update(metrics)
+
+            if main_proc and (step % config.log_frequency == 0 or step + 1 == len(valid_loader)):
+                logger.info("Epoch [%d/%d] Step [%d/%d]  %s", epoch + 1, config.epochs, step + 1, len(valid_loader), meters)
+
+    if main_proc:
+        logger.info("Train: [%d/%d] Final Prec@1 %.4f Prec@5 %.4f", epoch + 1, config.epochs, meters.prec1.avg, meters.prec5.avg)
+    return meters.prec1.avg, meters.prec5.avg
+
+
+def main():
+    config = RetrainConfig()
+    main_proc = not config.distributed or config.local_rank == 0
+    if config.distributed:
+        torch.cuda.set_device(config.local_rank)
+        torch.distributed.init_process_group(backend='nccl', init_method=config.dist_url,
+                                             rank=config.local_rank, world_size=config.world_size)
+    if main_proc:
+        os.makedirs(config.output_path, exist_ok=True)
+    if config.distributed:
+        torch.distributed.barrier()
+    logger = utils.get_logger(os.path.join(config.output_path, 'search.log'))
+    if main_proc:
+        config.print_params(logger.info)
+    utils.reset_seed(config.seed)
+
+    loaders, samplers = get_augment_datasets(config)
+    train_loader, valid_loader = loaders
+    train_sampler, valid_sampler = samplers
+
+    model = Model(config.dataset, config.layers, in_channels=config.input_channels, channels=config.init_channels, retrain=True).cuda()
+    if config.label_smooth > 0:
+        criterion = utils.CrossEntropyLabelSmooth(config.n_classes, config.label_smooth)
+    else:
+        criterion = nn.CrossEntropyLoss()
+
+    fixed_arc_path = os.path.join(config.output_path, config.arc_checkpoint)
+    with open(fixed_arc_path, "r") as f:
+        fixed_arc = json.load(f)
+    fixed_arc = utils.encode_tensor(fixed_arc, torch.device("cuda"))
+    genotypes = utils.parse_results(fixed_arc, n_nodes=4)
+    genotypes_dict = {i: genotypes for i in range(3)}
+    apply_fixed_architecture(model, fixed_arc_path)
+    param_size = utils.param_size(model, criterion, [3, 32, 32] if 'cifar' in config.dataset else [3, 224, 224])
+
+    if main_proc:
+        logger.info("Param size: %.6f", param_size)
+        logger.info("Genotype: %s", genotypes)
+
+    # change training hyper parameters according to cell type
+    if 'cifar' in config.dataset:
+        if param_size < 3.0:
+            config.weight_decay = 3e-4
+            config.drop_path_prob = 0.2
+        elif 3.0 < param_size < 3.5:
+            config.weight_decay = 3e-4
+            config.drop_path_prob = 0.3
+        else:
+            config.weight_decay = 5e-4
+            config.drop_path_prob = 0.3
+
+    if config.distributed:
+        apex.parallel.convert_syncbn_model(model)
+        model = DistributedDataParallel(model, delay_allreduce=True)
+
+    optimizer = torch.optim.SGD(model.parameters(), config.lr, momentum=config.momentum, weight_decay=config.weight_decay)
+    lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, config.epochs, eta_min=1E-6)
+
+    best_top1 = best_top5 = 0.
+    for epoch in range(config.epochs):
+        drop_prob = config.drop_path_prob * epoch / config.epochs
+        if config.distributed:
+            model.module.drop_path_prob(drop_prob)
+        else:
+            model.drop_path_prob(drop_prob)
+        # training
+        if config.distributed:
+            train_sampler.set_epoch(epoch)
+        train(logger, config, train_loader, model, optimizer, criterion, epoch, main_proc)
+
+        # validation
+        top1, top5 = validate(logger, config, valid_loader, model, criterion, epoch, main_proc)
+        best_top1 = max(best_top1, top1)
+        best_top5 = max(best_top5, top5)
+        lr_scheduler.step()
+
+    logger.info("Final best Prec@1 = %.4f Prec@5 = %.4f", best_top1, best_top5)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/nas/cdarts/run_retrain_cifar.sh b/examples/nas/cdarts/run_retrain_cifar.sh
new file mode 100644
index 0000000000..c78fd78343
--- /dev/null
+++ b/examples/nas/cdarts/run_retrain_cifar.sh
@@ -0,0 +1,13 @@
+NGPUS=4
+SGPU=0
+EGPU=$[NGPUS+SGPU-1]
+GPU_ID=`seq -s , $SGPU $EGPU`
+CUDA_VISIBLE_DEVICES=$GPU_ID python -m torch.distributed.launch --nproc_per_node=$NGPUS retrain.py \
+    --dataset cifar10 --n_classes 10 --init_channels 36 --stem_multiplier 3 \
+    --arc_checkpoint 'epoch_31.json' \
+    --batch_size 128 --workers 1 --log_frequency 10 \
+    --world_size $NGPUS --weight_decay 5e-4 \
+    --distributed --dist_url 'tcp://127.0.0.1:26443' \
+    --lr 0.1 --warmup_epochs 0 --epochs 600 \
+    --cutout_length 16 --aux_weight 0.4 --drop_path_prob 0.3 \
+    --label_smooth 0.0 --mixup_alpha 0
diff --git a/examples/nas/cdarts/run_search_cifar.sh b/examples/nas/cdarts/run_search_cifar.sh
new file mode 100644
index 0000000000..30c9d9b669
--- /dev/null
+++ b/examples/nas/cdarts/run_search_cifar.sh
@@ -0,0 +1,14 @@
+NGPUS=4
+SGPU=0
+EGPU=$[NGPUS+SGPU-1]
+GPU_ID=`seq -s , $SGPU $EGPU`
+CUDA_VISIBLE_DEVICES=$GPU_ID python -m torch.distributed.launch --nproc_per_node=$NGPUS search.py \
+    --dataset cifar10 --n_classes 10 --init_channels 16 --stem_multiplier 3 \
+    --batch_size 64 --workers 1 --log_frequency 10 \
+    --distributed --world_size $NGPUS --dist_url 'tcp://127.0.0.1:23343' \
+    --regular_ratio 0.2 --regular_coeff 5 \
+    --loss_alpha 1 --loss_T 2 \
+    --w_lr 0.1 --alpha_lr 3e-4 --nasnet_lr 0.1 \
+    --w_weight_decay 3e-4 --alpha_weight_decay 1e-4 \
+    --share_module --interactive_type kl \
+    --warmup_epochs 2 --epochs 32
diff --git a/examples/nas/cdarts/search.py b/examples/nas/cdarts/search.py
new file mode 100644
index 0000000000..1d556831d4
--- /dev/null
+++ b/examples/nas/cdarts/search.py
@@ -0,0 +1,44 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+import logging
+import os
+import random
+import time
+
+import numpy as np
+import torch
+import torch.nn as nn
+
+import utils
+from config import SearchConfig
+from datasets.cifar import get_search_datasets
+from model import Model
+from nni.nas.pytorch.cdarts import CdartsTrainer
+
+if __name__ == "__main__":
+    config = SearchConfig()
+    main_proc = not config.distributed or config.local_rank == 0
+    if config.distributed:
+        torch.cuda.set_device(config.local_rank)
+        torch.distributed.init_process_group(backend='nccl', init_method=config.dist_url,
+                                             rank=config.local_rank, world_size=config.world_size)
+    if main_proc:
+        os.makedirs(config.output_path, exist_ok=True)
+    if config.distributed:
+        torch.distributed.barrier()
+    logger = utils.get_logger(os.path.join(config.output_path, 'search.log'))
+    if main_proc:
+        config.print_params(logger.info)
+    utils.reset_seed(config.seed)
+
+    loaders, samplers = get_search_datasets(config)
+    model_small = Model(config.dataset, 8).cuda()
+    if config.share_module:
+        model_large = Model(config.dataset, 20, shared_modules=model_small.shared_modules).cuda()
+    else:
+        model_large = Model(config.dataset, 20).cuda()
+
+    criterion = nn.CrossEntropyLoss()
+    trainer = CdartsTrainer(model_small, model_large, criterion, loaders, samplers, logger, config)
+    trainer.train()
diff --git a/examples/nas/cdarts/trainer.py b/examples/nas/cdarts/trainer.py
new file mode 100644
index 0000000000..1e9ce45b80
--- /dev/null
+++ b/examples/nas/cdarts/trainer.py
@@ -0,0 +1,270 @@
+import json
+import logging
+import os
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+import apex
+from apex.parallel import DistributedDataParallel
+from nni.nas.pytorch.darts import DartsMutator
+from nni.nas.pytorch.mutables import LayerChoice
+from nni.nas.pytorch.mutator import Mutator
+from nni.nas.pytorch.utils import AverageMeterGroup
+from utils import CyclicIterator, TorchTensorEncoder, accuracy, reduce_metrics
+
+PHASE_SMALL = "small"
+PHASE_LARGE = "large"
+
+
+class RegularizedDartsMutator(DartsMutator):
+    def reset(self):
+        raise ValueError("You should probably call `reset_with_loss`.")
+
+    def cut_choices(self, cut_num=2):
+        # `cut_choices` is implemented but not used
+        for mutable in self.mutables:
+            if isinstance(mutable, LayerChoice):
+                _, idx = torch.topk(-self.choices[mutable.key], cut_num)
+                with torch.no_grad():
+                    for i in idx:
+                        self.choices[mutable.key][i] = -float("inf")
+
+    def reset_with_loss(self):
+        self._cache, reg_loss = self.sample_search()
+        return reg_loss
+
+    def sample_search(self):
+        result = super().sample_search()
+        loss = []
+        for mutable in self.mutables:
+            if isinstance(mutable, LayerChoice):
+                def need_reg(choice):
+                    return any(t in str(type(choice)).lower() for t in ["poolwithoutbn", "identity", "dilconv"])
+
+                for i, choice in enumerate(mutable.choices):
+                    if need_reg(choice):
+                        norm = torch.abs(self.choices[mutable.key][i])
+                        if norm < 1E10:
+                            loss.append(norm)
+        if not loss:
+            return result, None
+        return result, sum(loss)
+
+    def export(self, logger):
+        result = self.sample_final()
+        if hasattr(self.model, "plot_genotype"):
+            genotypes = self.model.plot_genotype(result, logger)
+        return result, genotypes
+
+
+class RegularizedMutatorParallel(DistributedDataParallel):
+    def reset_with_loss(self):
+        result = self.module.reset_with_loss()
+        self.callback_queued = False
+        return result
+
+    def cut_choices(self, *args, **kwargs):
+        self.module.cut_choices(*args, **kwargs)
+
+    def export(self, logger):
+        return self.module.export(logger)
+
+
+class DartsDiscreteMutator(Mutator):
+
+    def __init__(self, model, parent_mutator):
+        super().__init__(model)
+        self.__dict__["parent_mutator"] = parent_mutator  # avoid parameters to be included
+
+    def sample_search(self):
+        return self.parent_mutator.sample_final()
+
+
+class InteractiveKLLoss(nn.Module):
+    def __init__(self, temperature):
+        super().__init__()
+        self.temperature = temperature
+        # self.kl_loss = nn.KLDivLoss(reduction = 'batchmean')
+        self.kl_loss = nn.KLDivLoss()
+
+    def forward(self, student, teacher):
+        return self.kl_loss(F.log_softmax(student / self.temperature, dim=1),
+                            F.softmax(teacher / self.temperature, dim=1))
+
+
+class CdartsTrainer(object):
+    def __init__(self, model_small, model_large, criterion, loaders, samplers, logger, config):
+        train_loader, valid_loader = loaders
+        train_sampler, valid_sampler = samplers
+        self.train_loader = CyclicIterator(train_loader, train_sampler, config.distributed)
+        self.valid_loader = CyclicIterator(valid_loader, valid_sampler, config.distributed)
+
+        self.regular_coeff = config.regular_coeff
+        self.regular_ratio = config.regular_ratio
+        self.warmup_epochs = config.warmup_epochs
+        self.fix_head = config.fix_head
+        self.epochs = config.epochs
+        self.steps_per_epoch = config.steps_per_epoch
+        if self.steps_per_epoch is None:
+            self.steps_per_epoch = min(len(self.train_loader), len(self.valid_loader))
+        self.loss_alpha = config.loss_alpha
+        self.grad_clip = config.grad_clip
+        if config.interactive_type == "kl":
+            self.interactive_loss = InteractiveKLLoss(config.loss_T)
+        elif config.interactive_type == "smoothl1":
+            self.interactive_loss = nn.SmoothL1Loss()
+        self.loss_T = config.loss_T
+        self.distributed = config.distributed
+        self.log_frequency = config.log_frequency
+        self.main_proc = not config.distributed or config.local_rank == 0
+
+        self.logger = logger
+        self.checkpoint_dir = config.output_path
+        if self.main_proc:
+            os.makedirs(self.checkpoint_dir, exist_ok=True)
+        if config.distributed:
+            torch.distributed.barrier()
+
+        self.model_small = model_small
+        self.model_large = model_large
+        if self.fix_head:
+            for param in self.model_small.aux_head.parameters():
+                param.requires_grad = False
+            for param in self.model_large.aux_head.parameters():
+                param.requires_grad = False
+
+        self.mutator_small = RegularizedDartsMutator(self.model_small).cuda()
+        self.mutator_large = DartsDiscreteMutator(self.model_large, self.mutator_small).cuda()
+        self.criterion = criterion
+
+        self.optimizer_small = torch.optim.SGD(self.model_small.parameters(), config.w_lr,
+                                               momentum=config.w_momentum, weight_decay=config.w_weight_decay)
+        self.optimizer_large = torch.optim.SGD(self.model_large.parameters(), config.nasnet_lr,
+                                               momentum=config.w_momentum, weight_decay=config.w_weight_decay)
+        self.optimizer_alpha = torch.optim.Adam(self.mutator_small.parameters(), config.alpha_lr,
+                                                betas=(0.5, 0.999), weight_decay=config.alpha_weight_decay)
+
+        if config.distributed:
+            apex.parallel.convert_syncbn_model(self.model_small)
+            apex.parallel.convert_syncbn_model(self.model_large)
+            self.model_small = DistributedDataParallel(self.model_small, delay_allreduce=True)
+            self.model_large = DistributedDataParallel(self.model_large, delay_allreduce=True)
+            self.mutator_small = RegularizedMutatorParallel(self.mutator_small, delay_allreduce=True)
+            if config.share_module:
+                self.model_small.callback_queued = True
+                self.model_large.callback_queued = True
+            # mutator large never gets optimized, so do not need parallelized
+
+    def warmup(self, phase, epoch):
+        assert phase in [PHASE_SMALL, PHASE_LARGE]
+        if phase == PHASE_SMALL:
+            model, optimizer = self.model_small, self.optimizer_small
+        elif phase == PHASE_LARGE:
+            model, optimizer = self.model_large, self.optimizer_large
+        model.train()
+        meters = AverageMeterGroup()
+        for step in range(self.steps_per_epoch):
+            x, y = next(self.train_loader)
+            x, y = x.cuda(), y.cuda()
+
+            optimizer.zero_grad()
+            logits_main, _ = model(x)
+            loss = self.criterion(logits_main, y)
+            loss.backward()
+
+            self._clip_grad_norm(model)
+            optimizer.step()
+            prec1, prec5 = accuracy(logits_main, y, topk=(1, 5))
+            metrics = {"prec1": prec1, "prec5": prec5, "loss": loss}
+            metrics = reduce_metrics(metrics, self.distributed)
+            meters.update(metrics)
+            if self.main_proc and (step % self.log_frequency == 0 or step + 1 == self.steps_per_epoch):
+                self.logger.info("Epoch [%d/%d] Step [%d/%d] (%s)  %s", epoch + 1, self.epochs,
+                                 step + 1, self.steps_per_epoch, phase, meters)
+
+    def _clip_grad_norm(self, model):
+        if isinstance(model, DistributedDataParallel):
+            nn.utils.clip_grad_norm_(model.module.parameters(), self.grad_clip)
+        else:
+            nn.utils.clip_grad_norm_(model.parameters(), self.grad_clip)
+
+    def _reset_nan(self, parameters):
+        with torch.no_grad():
+            for param in parameters:
+                for i, p in enumerate(param):
+                    if p != p:  # equivalent to `isnan(p)`
+                        param[i] = float("-inf")
+
+    def joint_train(self, epoch):
+        self.model_large.train()
+        self.model_small.train()
+        meters = AverageMeterGroup()
+        for step in range(self.steps_per_epoch):
+            trn_x, trn_y = next(self.train_loader)
+            val_x, val_y = next(self.valid_loader)
+            trn_x, trn_y = trn_x.cuda(), trn_y.cuda()
+            val_x, val_y = val_x.cuda(), val_y.cuda()
+
+            # step 1. optimize architecture
+            self.optimizer_alpha.zero_grad()
+            self.optimizer_large.zero_grad()
+            reg_decay = max(self.regular_coeff * (1 - float(epoch - self.warmup_epochs) / (
+                (self.epochs - self.warmup_epochs) * self.regular_ratio)), 0)
+            loss_regular = self.mutator_small.reset_with_loss()
+            if loss_regular:
+                loss_regular *= reg_decay
+            logits_search, emsemble_logits_search = self.model_small(val_x)
+            logits_main, emsemble_logits_main = self.model_large(val_x)
+            loss_cls = (self.criterion(logits_search, val_y) + self.criterion(logits_main, val_y)) / self.loss_alpha
+            loss_interactive = self.interactive_loss(emsemble_logits_search, emsemble_logits_main) * (self.loss_T ** 2) * self.loss_alpha
+            loss = loss_cls + loss_interactive + loss_regular
+            loss.backward()
+            self._clip_grad_norm(self.model_large)
+            self.optimizer_large.step()
+            self.optimizer_alpha.step()
+            # NOTE: need to call here `self._reset_nan(self.mutator_small.parameters())` if `cut_choices`
+
+            # step 2. optimize op weights
+            self.optimizer_small.zero_grad()
+            with torch.no_grad():
+                # resample architecture since parameters have been changed
+                self.mutator_small.reset_with_loss()
+            logits_search_train, _ = self.model_small(trn_x)
+            loss_weight = self.criterion(logits_search_train, trn_y)
+            loss_weight.backward()
+            self._clip_grad_norm(self.model_small)
+            self.optimizer_small.step()
+
+            metrics = {"loss_cls": loss_cls, "loss_interactive": loss_interactive,
+                       "loss_regular": loss_regular, "loss_weight": loss_weight}
+            metrics = reduce_metrics(metrics, self.distributed)
+            meters.update(metrics)
+
+            if self.main_proc and (step % self.log_frequency == 0 or step + 1 == self.steps_per_epoch):
+                self.logger.info("Epoch [%d/%d] Step [%d/%d] (joint)  %s", epoch + 1, self.epochs,
+                                 step + 1, self.steps_per_epoch, meters)
+
+    def train(self):
+        for epoch in range(self.epochs):
+            if epoch < self.warmup_epochs:
+                with torch.no_grad():  # otherwise grads will be retained on the architecture params
+                    self.mutator_small.reset_with_loss()
+                self.warmup(PHASE_SMALL, epoch)
+            else:
+                with torch.no_grad():
+                    self.mutator_large.reset()
+                self.warmup(PHASE_LARGE, epoch)
+                self.joint_train(epoch)
+
+            self.export(os.path.join(self.checkpoint_dir, "epoch_{:02d}.json".format(epoch)),
+                        os.path.join(self.checkpoint_dir, "epoch_{:02d}.genotypes".format(epoch)))
+
+    def export(self, file, genotype_file):
+        if self.main_proc:
+            mutator_export, genotypes = self.mutator_small.export(self.logger)
+            with open(file, "w") as f:
+                json.dump(mutator_export, f, indent=2, sort_keys=True, cls=TorchTensorEncoder)
+            with open(genotype_file, "w") as f:
+                f.write(str(genotypes))
diff --git a/examples/nas/cdarts/utils.py b/examples/nas/cdarts/utils.py
new file mode 100644
index 0000000000..d0b9d48a80
--- /dev/null
+++ b/examples/nas/cdarts/utils.py
@@ -0,0 +1,177 @@
+import json
+import logging
+import os
+import random
+from collections import namedtuple
+
+import numpy as np
+import torch
+import torch.distributed as dist
+import torch.nn as nn
+
+from genotypes import Genotype
+from ops import PRIMITIVES
+
+
+def get_logger(file_path):
+    """ Make python logger """
+    logger = logging.getLogger('cdarts')
+    log_format = '%(asctime)s | %(message)s'
+    formatter = logging.Formatter(log_format, datefmt='%m/%d %I:%M:%S %p')
+    file_handler = logging.FileHandler(file_path)
+    file_handler.setFormatter(formatter)
+    # stream_handler = logging.StreamHandler()
+    # stream_handler.setFormatter(formatter)
+
+    logger.addHandler(file_handler)
+    # logger.addHandler(stream_handler)
+    logger.setLevel(logging.INFO)
+
+    return logger
+
+
+class CyclicIterator:
+    def __init__(self, loader, sampler, distributed):
+        self.loader = loader
+        self.sampler = sampler
+        self.epoch = 0
+        self.distributed = distributed
+        self._next_epoch()
+
+    def _next_epoch(self):
+        if self.distributed:
+            self.sampler.set_epoch(self.epoch)
+        self.iterator = iter(self.loader)
+        self.epoch += 1
+
+    def __len__(self):
+        return len(self.loader)
+
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        try:
+            return next(self.iterator)
+        except StopIteration:
+            self._next_epoch()
+            return next(self.iterator)
+
+
+class CrossEntropyLabelSmooth(nn.Module):
+
+    def __init__(self, num_classes, epsilon):
+        super(CrossEntropyLabelSmooth, self).__init__()
+        self.num_classes = num_classes
+        self.epsilon = epsilon
+        self.logsoftmax = nn.LogSoftmax(dim=1)
+
+    def forward(self, inputs, targets):
+        log_probs = self.logsoftmax(inputs)
+        targets = torch.zeros_like(log_probs).scatter_(1, targets.unsqueeze(1), 1)
+        targets = (1 - self.epsilon) * targets + self.epsilon / self.num_classes
+        loss = (-targets * log_probs).mean(0).sum()
+        return loss
+
+
+class TorchTensorEncoder(json.JSONEncoder):
+    def default(self, o):  # pylint: disable=method-hidden
+        if isinstance(o, torch.Tensor):
+            olist = o.tolist()
+            if "bool" not in o.type().lower() and all(map(lambda d: d == 0 or d == 1, olist)):
+                _logger.warning("Every element in %s is either 0 or 1. "
+                                "You might consider convert it into bool.", olist)
+            return olist
+        return super().default(o)
+
+
+def accuracy(output, target, topk=(1,)):
+    """ Computes the precision@k for the specified values of k """
+    maxk = max(topk)
+    batch_size = target.size(0)
+
+    _, pred = output.topk(maxk, 1, True, True)
+    pred = pred.t()
+    # one-hot case
+    if target.ndimension() > 1:
+        target = target.max(1)[1]
+
+    correct = pred.eq(target.view(1, -1).expand_as(pred))
+
+    res = []
+    for k in topk:
+        correct_k = correct[:k].view(-1).float().sum(0)
+        res.append(correct_k.mul_(1.0 / batch_size))
+    return res
+
+
+def reduce_tensor(tensor):
+    rt = tensor.clone()
+    dist.all_reduce(rt, op=dist.ReduceOp.SUM)
+    rt /= float(os.environ["WORLD_SIZE"])
+    return rt
+
+
+def reduce_metrics(metrics, distributed=False):
+    if distributed:
+        return {k: reduce_tensor(v).item() for k, v in metrics.items()}
+    return {k: v.item() for k, v in metrics.items()}
+
+
+def parse_results(results, n_nodes):
+    concat = range(2, 2 + n_nodes)
+    normal_gene = []
+    reduction_gene = []
+    for i in range(n_nodes):
+        normal_node = []
+        reduction_node = []
+        for j in range(2 + i):
+            normal_key = 'normal_n{}_p{}'.format(i + 2, j)
+            reduction_key = 'reduce_n{}_p{}'.format(i + 2, j)
+            normal_op = results[normal_key].cpu().numpy()
+            reduction_op = results[reduction_key].cpu().numpy()
+            if sum(normal_op == 1):
+                normal_index = np.argmax(normal_op)
+                normal_node.append((PRIMITIVES[normal_index], j))
+            if sum(reduction_op == 1):
+                reduction_index = np.argmax(reduction_op)
+                reduction_node.append((PRIMITIVES[reduction_index], j))
+        normal_gene.append(normal_node)
+        reduction_gene.append(reduction_node)
+
+    genotypes = Genotype(normal=normal_gene, normal_concat=concat,
+                         reduce=reduction_gene, reduce_concat=concat)
+    return genotypes
+
+
+def param_size(model, loss_fn, input_size):
+    """
+    Compute parameter size in MB
+    """
+    x = torch.rand([2] + input_size).cuda()
+    y, _ = model(x)
+    target = torch.randint(model.n_classes, size=[2]).cuda()
+    loss = loss_fn(y, target)
+    loss.backward()
+    n_params = sum(np.prod(v.size()) for k, v in model.named_parameters() if not k.startswith('aux_head') and v.grad is not None)
+    return n_params / 1e6
+
+
+def encode_tensor(data, device):
+    if isinstance(data, list):
+        if all(map(lambda o: isinstance(o, bool), data)):
+            return torch.tensor(data, dtype=torch.bool, device=device)  # pylint: disable=not-callable
+        else:
+            return torch.tensor(data, dtype=torch.float, device=device)  # pylint: disable=not-callable
+    if isinstance(data, dict):
+        return {k: encode_tensor(v, device) for k, v in data.items()}
+    return data
+
+
+def reset_seed(seed):
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    np.random.seed(seed)
+    random.seed(seed)
+    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.benchmark = True
diff --git a/src/sdk/pynni/nni/nas/pytorch/cdarts/__init__.py b/src/sdk/pynni/nni/nas/pytorch/cdarts/__init__.py
new file mode 100644
index 0000000000..2d00927846
--- /dev/null
+++ b/src/sdk/pynni/nni/nas/pytorch/cdarts/__init__.py
@@ -0,0 +1,5 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+from .mutator import RegularizedDartsMutator, RegularizedMutatorParallel, DartsDiscreteMutator
+from .trainer import CdartsTrainer
\ No newline at end of file
diff --git a/src/sdk/pynni/nni/nas/pytorch/cdarts/mutator.py b/src/sdk/pynni/nni/nas/pytorch/cdarts/mutator.py
new file mode 100644
index 0000000000..d7aaa049a6
--- /dev/null
+++ b/src/sdk/pynni/nni/nas/pytorch/cdarts/mutator.py
@@ -0,0 +1,75 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+import logging
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from nni.nas.pytorch.darts import DartsMutator
+from nni.nas.pytorch.mutables import LayerChoice
+from nni.nas.pytorch.mutator import Mutator
+
+class RegularizedDartsMutator(DartsMutator):
+    def reset(self):
+        raise ValueError("You should probably call `reset_with_loss`.")
+
+    def cut_choices(self, cut_num=2):
+        # `cut_choices` is implemented but not used
+        for mutable in self.mutables:
+            if isinstance(mutable, LayerChoice):
+                _, idx = torch.topk(-self.choices[mutable.key], cut_num)
+                with torch.no_grad():
+                    for i in idx:
+                        self.choices[mutable.key][i] = -float("inf")
+
+    def reset_with_loss(self):
+        self._cache, reg_loss = self.sample_search()
+        return reg_loss
+
+    def sample_search(self):
+        result = super().sample_search()
+        loss = []
+        for mutable in self.mutables:
+            if isinstance(mutable, LayerChoice):
+                def need_reg(choice):
+                    return any(t in str(type(choice)).lower() for t in ["poolwithoutbn", "identity", "dilconv"])
+
+                for i, choice in enumerate(mutable.choices):
+                    if need_reg(choice):
+                        norm = torch.abs(self.choices[mutable.key][i])
+                        if norm < 1E10:
+                            loss.append(norm)
+        if not loss:
+            return result, None
+        return result, sum(loss)
+
+    def export(self, logger):
+        result = self.sample_final()
+        if hasattr(self.model, "plot_genotype"):
+            genotypes = self.model.plot_genotype(result, logger)
+        return result, genotypes
+
+
+class RegularizedMutatorParallel(DistributedDataParallel):
+    def reset_with_loss(self):
+        result = self.module.reset_with_loss()
+        self.callback_queued = False
+        return result
+
+    def cut_choices(self, *args, **kwargs):
+        self.module.cut_choices(*args, **kwargs)
+
+    def export(self, logger):
+        return self.module.export(logger)
+
+
+class DartsDiscreteMutator(Mutator):
+
+    def __init__(self, model, parent_mutator):
+        super().__init__(model)
+        self.__dict__["parent_mutator"] = parent_mutator  # avoid parameters to be included
+
+    def sample_search(self):
+        return self.parent_mutator.sample_final()
\ No newline at end of file
diff --git a/src/sdk/pynni/nni/nas/pytorch/cdarts/trainer.py b/src/sdk/pynni/nni/nas/pytorch/cdarts/trainer.py
new file mode 100644
index 0000000000..07fdd47649
--- /dev/null
+++ b/src/sdk/pynni/nni/nas/pytorch/cdarts/trainer.py
@@ -0,0 +1,203 @@
+import json
+import logging
+import os
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+import apex
+from apex.parallel import DistributedDataParallel
+from nni.nas.pytorch.cdarts import RegularizedDartsMutator, RegularizedMutatorParallel, DartsDiscreteMutator
+from nni.nas.pytorch.utils import AverageMeterGroup
+from utils import CyclicIterator, TorchTensorEncoder, accuracy, reduce_metrics
+
+PHASE_SMALL = "small"
+PHASE_LARGE = "large"
+
+class InteractiveKLLoss(nn.Module):
+    def __init__(self, temperature):
+        super().__init__()
+        self.temperature = temperature
+        # self.kl_loss = nn.KLDivLoss(reduction = 'batchmean')
+        self.kl_loss = nn.KLDivLoss()
+
+    def forward(self, student, teacher):
+        return self.kl_loss(F.log_softmax(student / self.temperature, dim=1),
+                            F.softmax(teacher / self.temperature, dim=1))
+
+
+class CdartsTrainer(object):
+    def __init__(self, model_small, model_large, criterion, loaders, samplers, logger, config):
+        train_loader, valid_loader = loaders
+        train_sampler, valid_sampler = samplers
+        self.train_loader = CyclicIterator(train_loader, train_sampler, config.distributed)
+        self.valid_loader = CyclicIterator(valid_loader, valid_sampler, config.distributed)
+
+        self.regular_coeff = config.regular_coeff
+        self.regular_ratio = config.regular_ratio
+        self.warmup_epochs = config.warmup_epochs
+        self.fix_head = config.fix_head
+        self.epochs = config.epochs
+        self.steps_per_epoch = config.steps_per_epoch
+        if self.steps_per_epoch is None:
+            self.steps_per_epoch = min(len(self.train_loader), len(self.valid_loader))
+        self.loss_alpha = config.loss_alpha
+        self.grad_clip = config.grad_clip
+        if config.interactive_type == "kl":
+            self.interactive_loss = InteractiveKLLoss(config.loss_T)
+        elif config.interactive_type == "smoothl1":
+            self.interactive_loss = nn.SmoothL1Loss()
+        self.loss_T = config.loss_T
+        self.distributed = config.distributed
+        self.log_frequency = config.log_frequency
+        self.main_proc = not config.distributed or config.local_rank == 0
+
+        self.logger = logger
+        self.checkpoint_dir = config.output_path
+        if self.main_proc:
+            os.makedirs(self.checkpoint_dir, exist_ok=True)
+        if config.distributed:
+            torch.distributed.barrier()
+
+        self.model_small = model_small
+        self.model_large = model_large
+        if self.fix_head:
+            for param in self.model_small.aux_head.parameters():
+                param.requires_grad = False
+            for param in self.model_large.aux_head.parameters():
+                param.requires_grad = False
+
+        self.mutator_small = RegularizedDartsMutator(self.model_small).cuda()
+        self.mutator_large = DartsDiscreteMutator(self.model_large, self.mutator_small).cuda()
+        self.criterion = criterion
+
+        self.optimizer_small = torch.optim.SGD(self.model_small.parameters(), config.w_lr,
+                                               momentum=config.w_momentum, weight_decay=config.w_weight_decay)
+        self.optimizer_large = torch.optim.SGD(self.model_large.parameters(), config.nasnet_lr,
+                                               momentum=config.w_momentum, weight_decay=config.w_weight_decay)
+        self.optimizer_alpha = torch.optim.Adam(self.mutator_small.parameters(), config.alpha_lr,
+                                                betas=(0.5, 0.999), weight_decay=config.alpha_weight_decay)
+
+        if config.distributed:
+            apex.parallel.convert_syncbn_model(self.model_small)
+            apex.parallel.convert_syncbn_model(self.model_large)
+            self.model_small = DistributedDataParallel(self.model_small, delay_allreduce=True)
+            self.model_large = DistributedDataParallel(self.model_large, delay_allreduce=True)
+            self.mutator_small = RegularizedMutatorParallel(self.mutator_small, delay_allreduce=True)
+            if config.share_module:
+                self.model_small.callback_queued = True
+                self.model_large.callback_queued = True
+            # mutator large never gets optimized, so do not need parallelized
+
+    def warmup(self, phase, epoch):
+        assert phase in [PHASE_SMALL, PHASE_LARGE]
+        if phase == PHASE_SMALL:
+            model, optimizer = self.model_small, self.optimizer_small
+        elif phase == PHASE_LARGE:
+            model, optimizer = self.model_large, self.optimizer_large
+        model.train()
+        meters = AverageMeterGroup()
+        for step in range(self.steps_per_epoch):
+            x, y = next(self.train_loader)
+            x, y = x.cuda(), y.cuda()
+
+            optimizer.zero_grad()
+            logits_main, _ = model(x)
+            loss = self.criterion(logits_main, y)
+            loss.backward()
+
+            self._clip_grad_norm(model)
+            optimizer.step()
+            prec1, prec5 = accuracy(logits_main, y, topk=(1, 5))
+            metrics = {"prec1": prec1, "prec5": prec5, "loss": loss}
+            metrics = reduce_metrics(metrics, self.distributed)
+            meters.update(metrics)
+            if self.main_proc and (step % self.log_frequency == 0 or step + 1 == self.steps_per_epoch):
+                self.logger.info("Epoch [%d/%d] Step [%d/%d] (%s)  %s", epoch + 1, self.epochs,
+                                 step + 1, self.steps_per_epoch, phase, meters)
+
+    def _clip_grad_norm(self, model):
+        if isinstance(model, DistributedDataParallel):
+            nn.utils.clip_grad_norm_(model.module.parameters(), self.grad_clip)
+        else:
+            nn.utils.clip_grad_norm_(model.parameters(), self.grad_clip)
+
+    def _reset_nan(self, parameters):
+        with torch.no_grad():
+            for param in parameters:
+                for i, p in enumerate(param):
+                    if p != p:  # equivalent to `isnan(p)`
+                        param[i] = float("-inf")
+
+    def joint_train(self, epoch):
+        self.model_large.train()
+        self.model_small.train()
+        meters = AverageMeterGroup()
+        for step in range(self.steps_per_epoch):
+            trn_x, trn_y = next(self.train_loader)
+            val_x, val_y = next(self.valid_loader)
+            trn_x, trn_y = trn_x.cuda(), trn_y.cuda()
+            val_x, val_y = val_x.cuda(), val_y.cuda()
+
+            # step 1. optimize architecture
+            self.optimizer_alpha.zero_grad()
+            self.optimizer_large.zero_grad()
+            reg_decay = max(self.regular_coeff * (1 - float(epoch - self.warmup_epochs) / (
+                (self.epochs - self.warmup_epochs) * self.regular_ratio)), 0)
+            loss_regular = self.mutator_small.reset_with_loss()
+            if loss_regular:
+                loss_regular *= reg_decay
+            logits_search, emsemble_logits_search = self.model_small(val_x)
+            logits_main, emsemble_logits_main = self.model_large(val_x)
+            loss_cls = (self.criterion(logits_search, val_y) + self.criterion(logits_main, val_y)) / self.loss_alpha
+            loss_interactive = self.interactive_loss(emsemble_logits_search, emsemble_logits_main) * (self.loss_T ** 2) * self.loss_alpha
+            loss = loss_cls + loss_interactive + loss_regular
+            loss.backward()
+            self._clip_grad_norm(self.model_large)
+            self.optimizer_large.step()
+            self.optimizer_alpha.step()
+            # NOTE: need to call here `self._reset_nan(self.mutator_small.parameters())` if `cut_choices`
+
+            # step 2. optimize op weights
+            self.optimizer_small.zero_grad()
+            with torch.no_grad():
+                # resample architecture since parameters have been changed
+                self.mutator_small.reset_with_loss()
+            logits_search_train, _ = self.model_small(trn_x)
+            loss_weight = self.criterion(logits_search_train, trn_y)
+            loss_weight.backward()
+            self._clip_grad_norm(self.model_small)
+            self.optimizer_small.step()
+
+            metrics = {"loss_cls": loss_cls, "loss_interactive": loss_interactive,
+                       "loss_regular": loss_regular, "loss_weight": loss_weight}
+            metrics = reduce_metrics(metrics, self.distributed)
+            meters.update(metrics)
+
+            if self.main_proc and (step % self.log_frequency == 0 or step + 1 == self.steps_per_epoch):
+                self.logger.info("Epoch [%d/%d] Step [%d/%d] (joint)  %s", epoch + 1, self.epochs,
+                                 step + 1, self.steps_per_epoch, meters)
+
+    def train(self):
+        for epoch in range(self.epochs):
+            if epoch < self.warmup_epochs:
+                with torch.no_grad():  # otherwise grads will be retained on the architecture params
+                    self.mutator_small.reset_with_loss()
+                self.warmup(PHASE_SMALL, epoch)
+            else:
+                with torch.no_grad():
+                    self.mutator_large.reset()
+                self.warmup(PHASE_LARGE, epoch)
+                self.joint_train(epoch)
+
+            self.export(os.path.join(self.checkpoint_dir, "epoch_{:02d}.json".format(epoch)),
+                        os.path.join(self.checkpoint_dir, "epoch_{:02d}.genotypes".format(epoch)))
+
+    def export(self, file, genotype_file):
+        if self.main_proc:
+            mutator_export, genotypes = self.mutator_small.export(self.logger)
+            with open(file, "w") as f:
+                json.dump(mutator_export, f, indent=2, sort_keys=True, cls=TorchTensorEncoder)
+            with open(genotype_file, "w") as f:
+                f.write(str(genotypes))

From 83a7aa41bc79144e504169b308e8461ed98c8d69 Mon Sep 17 00:00:00 2001
From: "penghouwen@icloud.com" <penghouwen@icloud.com>
Date: Wed, 15 Jan 2020 11:35:24 +0800
Subject: [PATCH 02/21] revise cdarts2nni after code review

---
 docs/en_US/NAS/CDARTS.md                      |  34 +++
 docs/en_US/NAS/Overview.md                    | 112 ++++----
 examples/nas/cdarts/README.md                 | 110 -------
 examples/nas/cdarts/images/cell1.png          | Bin 28026 -> 0 bytes
 examples/nas/cdarts/images/cell2.png          | Bin 29359 -> 0 bytes
 examples/nas/cdarts/images/cell3.png          | Bin 36654 -> 0 bytes
 examples/nas/cdarts/images/framework1.png     | Bin 121056 -> 0 bytes
 examples/nas/cdarts/images/framework2.png     | Bin 112057 -> 0 bytes
 examples/nas/cdarts/retrain.py                |   4 +-
 examples/nas/cdarts/run_search_cifar.sh       |   4 +-
 examples/nas/cdarts/search.py                 |   7 +-
 examples/nas/cdarts/trainer.py                | 270 ------------------
 .../pynni/nni/nas/pytorch/cdarts/mutator.py   |   5 +-
 .../pynni/nni/nas/pytorch/cdarts/trainer.py   | 198 ++++++++++---
 14 files changed, 269 insertions(+), 475 deletions(-)
 create mode 100644 docs/en_US/NAS/CDARTS.md
 delete mode 100644 examples/nas/cdarts/README.md
 delete mode 100644 examples/nas/cdarts/images/cell1.png
 delete mode 100644 examples/nas/cdarts/images/cell2.png
 delete mode 100644 examples/nas/cdarts/images/cell3.png
 delete mode 100644 examples/nas/cdarts/images/framework1.png
 delete mode 100644 examples/nas/cdarts/images/framework2.png
 delete mode 100644 examples/nas/cdarts/trainer.py

diff --git a/docs/en_US/NAS/CDARTS.md b/docs/en_US/NAS/CDARTS.md
new file mode 100644
index 0000000000..9c88d13b81
--- /dev/null
+++ b/docs/en_US/NAS/CDARTS.md
@@ -0,0 +1,34 @@
+# CDARTS
+
+## Introduction
+CDARTS builds a cyclic feedback mechanism between the search and evaluation networks. First, the search network generates an initial topology for evaluation, so that the weights of the evaluation network can be optimized. Second, the architecture topology in the search network is further optimized by the label supervision in classification, as well as the regularization from the evaluation network through feature distillation. Repeating the above cycle results in a joint optimization of the search and evaluation networks, and thus enables the evolution of the topology to fit the final evaluation network.
+
+## Reproduction Results
+This is CDARTS based on the NNI platform, which currently supports CIFAR10 search and retrain. ImageNet search and retrain should also be supported, and we provide corresponding interfaces. Our reproduced results on NNI are slightly lower than the paper, but much higher than the original DARTS. Here we show the results of three independent experiments on CIFAR10.
+| Runs | Paper | NNI | 
+| ---- |:-------------:| :-----:|
+| 1 | 97.52 | 97.44 |
+| 2 | 97.53 | 97.48 |
+| 3 | 97.58 | 97.56 |
+
+
+## Examples
+
+[Example code](https://github.com/microsoft/nni/tree/master/examples/nas/cdarts)
+
+```bash
+# In case NNI code is not cloned. If the code is cloned already, ignore this line and enter code folder.
+git clone https://github.com/Microsoft/nni.git
+
+# install apex for distributed training.
+git clone https://github.com/NVIDIA/apex
+cd apex
+python setup.py install --cpp_ext --cuda_ext
+
+# search the best architecture
+cd examples/nas/cdarts
+bash run_search_cifar.sh
+
+# train the best architecture.
+bash run_retrain_cifar.sh
+```
diff --git a/docs/en_US/NAS/Overview.md b/docs/en_US/NAS/Overview.md
index aaf6e599c3..589ea4972b 100644
--- a/docs/en_US/NAS/Overview.md
+++ b/docs/en_US/NAS/Overview.md
@@ -1,52 +1,60 @@
-# Neural Architecture Search (NAS) on NNI
-
-Automatic neural architecture search is taking an increasingly important role on finding better models. Recent research works have proved the feasibility of automatic NAS, and also found some models that could beat manually designed and tuned models. Some of representative works are [NASNet][2], [ENAS][1], [DARTS][3], [Network Morphism][4], and [Evolution][5]. There are new innovations keeping emerging.
-
-However, it takes great efforts to implement NAS algorithms, and it is hard to reuse code base of existing algorithms in new one. To facilitate NAS innovations (e.g., design and implement new NAS models, compare different NAS models side-by-side), an easy-to-use and flexible programming interface is crucial.
-
-With this motivation, our ambition is to provide a unified architecture in NNI, to accelerate innovations on NAS, and apply state-of-art algorithms on real world problems faster.
-
-With [the unified interface](./NasInterface.md), there are two different modes for the architecture search. [One](#supported-one-shot-nas-algorithms) is the so-called one-shot NAS, where a super-net is built based on search space, and using one shot training to generate good-performing child model. [The other](./NasInterface.md#classic-distributed-search) is the traditional searching approach, where each child model in search space runs as an independent trial, the performance result is sent to tuner and the tuner generates new child model.
-
-* [Supported One-shot NAS Algorithms](#supported-one-shot-nas-algorithms)
-* [Classic Distributed NAS with NNI experiment](./NasInterface.md#classic-distributed-search)
-* [NNI NAS Programming Interface](./NasInterface.md)
-
-## Supported One-shot NAS Algorithms
-
-NNI supports below NAS algorithms now and is adding more. User can reproduce an algorithm or use it on their own dataset. We also encourage users to implement other algorithms with [NNI API](#use-nni-api), to benefit more people.
-
-|Name|Brief Introduction of Algorithm|
-|---|---|
-| [ENAS](ENAS.md) | [Efficient Neural Architecture Search via Parameter Sharing](https://arxiv.org/abs/1802.03268). In ENAS, a controller learns to discover neural network architectures by searching for an optimal subgraph within a large computational graph. It uses parameter sharing between child models to achieve fast speed and excellent performance. |
-| [DARTS](DARTS.md) | [DARTS: Differentiable Architecture Search](https://arxiv.org/abs/1806.09055) introduces a novel algorithm for differentiable network architecture search on bilevel optimization. |
-| [P-DARTS](PDARTS.md) | [Progressive Differentiable Architecture Search: Bridging the Depth Gap between Search and Evaluation](https://arxiv.org/abs/1904.12760) is based on DARTS. It introduces an efficient algorithm which allows the depth of searched architectures to grow gradually during the training procedure. |
-| [SPOS](SPOS.md) | [Single Path One-Shot Neural Architecture Search with Uniform Sampling](https://arxiv.org/abs/1904.00420) constructs a simplified supernet trained with an uniform path sampling method, and applies an evolutionary algorithm to efficiently search for the best-performing architectures. |
-
-One-shot algorithms run **standalone without nnictl**. Only PyTorch version has been implemented. Tensorflow 2.x will be supported in future release.
-
-Here are some common dependencies to run the examples. PyTorch needs to be above 1.2 to use ``BoolTensor``.
-
-* NNI 1.2+
-* tensorboard
-* PyTorch 1.2+
-* git
-
-## Use NNI API
-
-NOTE, we are trying to support various NAS algorithms with unified programming interface, and it's in very experimental stage. It means the current programing interface may be updated in future.
-
-### Programming interface
-
-The programming interface of designing and searching a model is often demanded in two scenarios.
-
-1. When designing a neural network, there may be multiple operation choices on a layer, sub-model, or connection, and it's undetermined which one or combination performs  best. So, it needs an easy way to express the candidate layers or sub-models.
-2. When applying NAS on a neural network, it needs an unified way to express the search space of architectures, so that it doesn't need to update trial code for different searching algorithms.
-
-NNI proposed API is [here](https://github.com/microsoft/nni/tree/master/src/sdk/pynni/nni/nas/pytorch). And [here](https://github.com/microsoft/nni/tree/master/examples/nas/naive) is an example of NAS implementation, which bases on NNI proposed interface.
-
-[1]: https://arxiv.org/abs/1802.03268
-[2]: https://arxiv.org/abs/1707.07012
-[3]: https://arxiv.org/abs/1806.09055
-[4]: https://arxiv.org/abs/1806.10282
-[5]: https://arxiv.org/abs/1703.01041
+# Neural Architecture Search (NAS) on NNI
+
+Automatic neural architecture search is taking an increasingly important role on finding better models. Recent research works have proved the feasibility of automatic NAS, and also found some models that could beat manually designed and tuned models. Some of representative works are [NASNet][2], [ENAS][1], [DARTS][3], [Network Morphism][4], and [Evolution][5]. There are new innovations keeping emerging.
+
+However, it takes great efforts to implement NAS algorithms, and it is hard to reuse code base of existing algorithms in new one. To facilitate NAS innovations (e.g., design and implement new NAS models, compare different NAS models side-by-side), an easy-to-use and flexible programming interface is crucial.
+
+With this motivation, our ambition is to provide a unified architecture in NNI, to accelerate innovations on NAS, and apply state-of-art algorithms on real world problems faster.
+
+With [the unified interface](./NasInterface.md), there are two different modes for the architecture search. [One](#supported-one-shot-nas-algorithms) is the so-called one-shot NAS, where a super-net is built based on search space, and using one shot training to generate good-performing child model. [The other](./NasInterface.md#classic-distributed-search) is the traditional searching approach, where each child model in search space runs as an independent trial, the performance result is sent to tuner and the tuner generates new child model.
+
+* [Supported One-shot NAS Algorithms](#supported-one-shot-nas-algorithms)
+* [Classic Distributed NAS with NNI experiment](./NasInterface.md#classic-distributed-search)
+* [NNI NAS Programming Interface](./NasInterface.md)
+
+## Supported One-shot NAS Algorithms
+
+NNI supports below NAS algorithms now and is adding more. User can reproduce an algorithm or use it on their own dataset. We also encourage users to implement other algorithms with [NNI API](#use-nni-api), to benefit more people.
+
+|Name|Brief Introduction of Algorithm|
+|---|---|
+| [ENAS](ENAS.md) | [Efficient Neural Architecture Search via Parameter Sharing](https://arxiv.org/abs/1802.03268). In ENAS, a controller learns to discover neural network architectures by searching for an optimal subgraph within a large computational graph. It uses parameter sharing between child models to achieve fast speed and excellent performance. |
+| [DARTS](DARTS.md) | [DARTS: Differentiable Architecture Search](https://arxiv.org/abs/1806.09055) introduces a novel algorithm for differentiable network architecture search on bilevel optimization. |
+| [P-DARTS](PDARTS.md) | [Progressive Differentiable Architecture Search: Bridging the Depth Gap between Search and Evaluation](https://arxiv.org/abs/1904.12760) is based on DARTS. It introduces an efficient algorithm which allows the depth of searched architectures to grow gradually during the training procedure. |
+| [SPOS](SPOS.md) | [Single Path One-Shot Neural Architecture Search with Uniform Sampling](https://arxiv.org/abs/1904.00420) constructs a simplified supernet trained with an uniform path sampling method, and applies an evolutionary algorithm to efficiently search for the best-performing architectures. |
+| [CDARTS](PDARTS.md) | [Cyclic Differentiable Architecture Search](https://arxiv.org/abs/****) builds a cyclic feedback mechanism between the search and evaluation networks. It introduces a cyclic differentiable architecture search framework which integrates the two networks into a unified architecture.|
+
+One-shot algorithms run **standalone without nnictl**. Only PyTorch version has been implemented. Tensorflow 2.x will be supported in future release.
+
+Here are some common dependencies to run the examples. PyTorch needs to be above 1.2 to use ``BoolTensor``.
+
+* NNI 1.2+
+* tensorboard
+* PyTorch 1.2+
+* git
+
+## Use NNI API
+
+NOTE, we are trying to support various NAS algorithms with unified programming interface, and it's in very experimental stage. It means the current programing interface may be updated in future.
+
+### Programming interface
+
+The programming interface of designing and searching a model is often demanded in two scenarios.
+
+1. When designing a neural network, there may be multiple operation choices on a layer, sub-model, or connection, and it's undetermined which one or combination performs  best. So, it needs an easy way to express the candidate layers or sub-models.
+2. When applying NAS on a neural network, it needs an unified way to express the search space of architectures, so that it doesn't need to update trial code for different searching algorithms.
+
+NNI proposed API is [here](https://github.com/microsoft/nni/tree/master/src/sdk/pynni/nni/nas/pytorch). And [here](https://github.com/microsoft/nni/tree/master/examples/nas/naive) is an example of NAS implementation, which bases on NNI proposed interface.
+
+[1]: https://arxiv.org/abs/1802.03268
+[2]: https://arxiv.org/abs/1707.07012
+[3]: https://arxiv.org/abs/1806.09055
+[4]: https://arxiv.org/abs/1806.10282
+[5]: https://arxiv.org/abs/1703.01041
+
+## **Reference and Feedback**
+* To [report a bug](https://github.com/microsoft/nni/issues/new?template=bug-report.md) for this feature in GitHub;
+* To [file a feature or improvement request](https://github.com/microsoft/nni/issues/new?template=enhancement.md) for this feature in GitHub;
+* To know more about [Feature Engineering with NNI](https://github.com/microsoft/nni/blob/master/docs/en_US/FeatureEngineering/Overview.md);
+* To know more about [Model Compression with NNI](https://github.com/microsoft/nni/blob/master/docs/en_US/Compressor/Overview.md);
+* To know more about [Hyperparameter Tuning with NNI](https://github.com/microsoft/nni/blob/master/docs/en_US/Tuner/BuiltinTuner.md);
diff --git a/examples/nas/cdarts/README.md b/examples/nas/cdarts/README.md
deleted file mode 100644
index f4421ea4b9..0000000000
--- a/examples/nas/cdarts/README.md
+++ /dev/null
@@ -1,110 +0,0 @@
-# Cyclic Differentiable Architecture Search
-This is CDARTS based on the NNI platform, which currently supports CIFAR10 search and retrain. ImageNet search and retrain should also be supported, and we provide corresponding interfaces. Our reproduced results on NNI are slightly lower than the paper, but much higher than the original DARTS.
-
-<div align="center">
-  <img src="images/framework1.png" width="500px" />
-  <!-- <p>framework1.</p> -->
-</div>
-
-## Results
-#### Main CIFAR10 top1 accuracy of DARTS and DARTS reproduced by NNI
-| Order | Paper | NNI | 
-| ---- |:-------------:| :-----:|
-| 1 | 97.00 +/-0.14 | 97.22 |
-| 2 | 97.24 +/-0.09 | 97.11 |
-
-#### Main CIFAR10 top1 accuracy of CDARTS and CDARTS reproduced by NNI
-| Runs | Paper | NNI | 
-| ---- |:-------------:| :-----:|
-| 1 | 97.52 | 97.43 |
-| 2 | 97.53 | 97.46 |
-| 3 | 97.58 | 97.47 |
-
-### The normal cells searched by CDARTS(NNI) on CIFAR10
-<div align="center">
-  <img src="images/cell1.png" width="200px" />
-  <!-- <p>cell.</p> -->
-</div>
-
-<div align="center">
-  <img src="images/cell2.png" width="300px" />
-  <img src="images/cell3.png" width="300px" />
-  <!-- <p>cell.</p> -->
-</div>
-
-
-## Environments
-Tesla V100, CUDA10.0, linux 16.04, pytorch>=1.2, python3, [apex](https://github.com/NVIDIA/apex) and NNI 1.3
-
-## Data Preparation
-* [Cifar-10](https://www.cs.toronto.edu/~kriz/cifar.html)
-* [Cifar-100](https://www.cs.toronto.edu/~kriz/cifar.html)
-* [ImageNet-2012](http://www.image-net.org/)
-
-Create soft link in main dir.
-```
-ln -s $DataLocation data
-```
-
-## Installation
-* First, install python requirements.
-    ```buildoutcfg
-    pip install torch==1.2.0
-    pip install tensorboard==1.13.0
-    pip install tensorboardX==1.6
-    pip install nni==1.3.0
-    ```
-* Then, you should install apex.
-    ```buildoutcfg
-    git clone https://github.com/NVIDIA/apex
-    cd apex
-    python setup.py install --cpp_ext --cuda_ext
-    ```
-
-## Search and Retrain
-### Search
-* Main python file is ${ROOT}/search.py
-* Followings are options during training.
-    ```buildoutcfg
-    --regular_ratio             # if use regular, the ragular ratio
-    --regular_coeff             # if use regular, the regular coefficient
-    --loss_alpha                # the loss coefficient
-    --loss_T                    # the loss coefficient
-    --w_lr                      # the learning rate of the search network
-    --alpha_lr                  # the learning rate of the architecture parameters
-    --nasnet_lr                 # the learning rate of the evaluation network
-    --w_weight_decay            # the weight decay the search and the evaluation network
-    --alpha_weight_decay        # the weight decay the the architecture parameters
-    --fix_head                  # wheter to fix the paramters of auxiliary heads
-    --interactive_type          # The KD function, kl and smoothl1
-    --share_module              # Whether to share module of the two networks
-    --warmup_epochs             # the epochs to warmup the search network
-    --epochs                    # total epochs of search
-
-    ```
-* Here we present our search scripts on CIFAR10.
-    ```buildoutcfg
-    bash run_search_cifar.sh
-    ```
-
-
-### Retrain
-* Main python file is ${ROOT}/retrain.py
-* Followings are options during training.
-    ```buildoutcfg
-    --arc_checkpoint            # choose which genotype to retrain
-    --cell_file                 # path of cell genotype
-    --weight_decay              # decay of W in the Retrain-Phase
-    --lr                        # learning rate of W in the Retrain-Phase
-    --warmup_epochs             # warmup epochs 
-    --epochs                    # total retrain epochs 
-    --cutout_length             # cutout length for cifar
-    --aux_weight                # weight of auxiliary loss, 0.4 is the best option   
-    --drop_path_prob            # used for dropping path in NAS
-    --label_smooth              # label smooth ratio
-    --mixup_alpha               # ratio of mixup
-    ```
-* Here we present our train scripts on CIFAR10.
-    ```buildoutcfg
-    bash run_retrain_cifar.sh
-    ```
diff --git a/examples/nas/cdarts/images/cell1.png b/examples/nas/cdarts/images/cell1.png
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diff --git a/examples/nas/cdarts/retrain.py b/examples/nas/cdarts/retrain.py
index 6332342f88..453c3f8ae9 100644
--- a/examples/nas/cdarts/retrain.py
+++ b/examples/nas/cdarts/retrain.py
@@ -7,10 +7,10 @@
 import torch
 import torch.nn as nn
 
-import apex
 import datasets
 import utils
-from apex.parallel import DistributedDataParallel
+import apex # pylint: disable=import-error
+from apex.parallel import DistributedDataParallel # pylint: disable=import-error
 from config import RetrainConfig
 from datasets.cifar import get_augment_datasets
 from model import Model
diff --git a/examples/nas/cdarts/run_search_cifar.sh b/examples/nas/cdarts/run_search_cifar.sh
index 30c9d9b669..64c6b04da4 100644
--- a/examples/nas/cdarts/run_search_cifar.sh
+++ b/examples/nas/cdarts/run_search_cifar.sh
@@ -8,7 +8,7 @@ CUDA_VISIBLE_DEVICES=$GPU_ID python -m torch.distributed.launch --nproc_per_node
     --distributed --world_size $NGPUS --dist_url 'tcp://127.0.0.1:23343' \
     --regular_ratio 0.2 --regular_coeff 5 \
     --loss_alpha 1 --loss_T 2 \
-    --w_lr 0.1 --alpha_lr 3e-4 --nasnet_lr 0.1 \
-    --w_weight_decay 3e-4 --alpha_weight_decay 1e-4 \
+    --w_lr 0.2 --alpha_lr 3e-4 --nasnet_lr 0.2 \
+    --w_weight_decay 0. --alpha_weight_decay 0. \
     --share_module --interactive_type kl \
     --warmup_epochs 2 --epochs 32
diff --git a/examples/nas/cdarts/search.py b/examples/nas/cdarts/search.py
index 1d556831d4..c41f7ce1ff 100644
--- a/examples/nas/cdarts/search.py
+++ b/examples/nas/cdarts/search.py
@@ -40,5 +40,10 @@
         model_large = Model(config.dataset, 20).cuda()
 
     criterion = nn.CrossEntropyLoss()
-    trainer = CdartsTrainer(model_small, model_large, criterion, loaders, samplers, logger, config)
+    trainer = CdartsTrainer(model_small, model_large, criterion, loaders, samplers, logger,
+                            config.regular_coeff, config.regular_ratio, config.warmup_epochs, config.fix_head,
+                            config.epochs, config.steps_per_epoch, config.loss_alpha, config.loss_T, config.distributed,
+                            config.log_frequency, config.grad_clip, config.interactive_type, config.output_path,
+                            config.w_lr, config.w_momentum, config.w_weight_decay, config.alpha_lr, config.alpha_weight_decay,
+                            config.nasnet_lr, config.local_rank, config.share_module)
     trainer.train()
diff --git a/examples/nas/cdarts/trainer.py b/examples/nas/cdarts/trainer.py
deleted file mode 100644
index 1e9ce45b80..0000000000
--- a/examples/nas/cdarts/trainer.py
+++ /dev/null
@@ -1,270 +0,0 @@
-import json
-import logging
-import os
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-import apex
-from apex.parallel import DistributedDataParallel
-from nni.nas.pytorch.darts import DartsMutator
-from nni.nas.pytorch.mutables import LayerChoice
-from nni.nas.pytorch.mutator import Mutator
-from nni.nas.pytorch.utils import AverageMeterGroup
-from utils import CyclicIterator, TorchTensorEncoder, accuracy, reduce_metrics
-
-PHASE_SMALL = "small"
-PHASE_LARGE = "large"
-
-
-class RegularizedDartsMutator(DartsMutator):
-    def reset(self):
-        raise ValueError("You should probably call `reset_with_loss`.")
-
-    def cut_choices(self, cut_num=2):
-        # `cut_choices` is implemented but not used
-        for mutable in self.mutables:
-            if isinstance(mutable, LayerChoice):
-                _, idx = torch.topk(-self.choices[mutable.key], cut_num)
-                with torch.no_grad():
-                    for i in idx:
-                        self.choices[mutable.key][i] = -float("inf")
-
-    def reset_with_loss(self):
-        self._cache, reg_loss = self.sample_search()
-        return reg_loss
-
-    def sample_search(self):
-        result = super().sample_search()
-        loss = []
-        for mutable in self.mutables:
-            if isinstance(mutable, LayerChoice):
-                def need_reg(choice):
-                    return any(t in str(type(choice)).lower() for t in ["poolwithoutbn", "identity", "dilconv"])
-
-                for i, choice in enumerate(mutable.choices):
-                    if need_reg(choice):
-                        norm = torch.abs(self.choices[mutable.key][i])
-                        if norm < 1E10:
-                            loss.append(norm)
-        if not loss:
-            return result, None
-        return result, sum(loss)
-
-    def export(self, logger):
-        result = self.sample_final()
-        if hasattr(self.model, "plot_genotype"):
-            genotypes = self.model.plot_genotype(result, logger)
-        return result, genotypes
-
-
-class RegularizedMutatorParallel(DistributedDataParallel):
-    def reset_with_loss(self):
-        result = self.module.reset_with_loss()
-        self.callback_queued = False
-        return result
-
-    def cut_choices(self, *args, **kwargs):
-        self.module.cut_choices(*args, **kwargs)
-
-    def export(self, logger):
-        return self.module.export(logger)
-
-
-class DartsDiscreteMutator(Mutator):
-
-    def __init__(self, model, parent_mutator):
-        super().__init__(model)
-        self.__dict__["parent_mutator"] = parent_mutator  # avoid parameters to be included
-
-    def sample_search(self):
-        return self.parent_mutator.sample_final()
-
-
-class InteractiveKLLoss(nn.Module):
-    def __init__(self, temperature):
-        super().__init__()
-        self.temperature = temperature
-        # self.kl_loss = nn.KLDivLoss(reduction = 'batchmean')
-        self.kl_loss = nn.KLDivLoss()
-
-    def forward(self, student, teacher):
-        return self.kl_loss(F.log_softmax(student / self.temperature, dim=1),
-                            F.softmax(teacher / self.temperature, dim=1))
-
-
-class CdartsTrainer(object):
-    def __init__(self, model_small, model_large, criterion, loaders, samplers, logger, config):
-        train_loader, valid_loader = loaders
-        train_sampler, valid_sampler = samplers
-        self.train_loader = CyclicIterator(train_loader, train_sampler, config.distributed)
-        self.valid_loader = CyclicIterator(valid_loader, valid_sampler, config.distributed)
-
-        self.regular_coeff = config.regular_coeff
-        self.regular_ratio = config.regular_ratio
-        self.warmup_epochs = config.warmup_epochs
-        self.fix_head = config.fix_head
-        self.epochs = config.epochs
-        self.steps_per_epoch = config.steps_per_epoch
-        if self.steps_per_epoch is None:
-            self.steps_per_epoch = min(len(self.train_loader), len(self.valid_loader))
-        self.loss_alpha = config.loss_alpha
-        self.grad_clip = config.grad_clip
-        if config.interactive_type == "kl":
-            self.interactive_loss = InteractiveKLLoss(config.loss_T)
-        elif config.interactive_type == "smoothl1":
-            self.interactive_loss = nn.SmoothL1Loss()
-        self.loss_T = config.loss_T
-        self.distributed = config.distributed
-        self.log_frequency = config.log_frequency
-        self.main_proc = not config.distributed or config.local_rank == 0
-
-        self.logger = logger
-        self.checkpoint_dir = config.output_path
-        if self.main_proc:
-            os.makedirs(self.checkpoint_dir, exist_ok=True)
-        if config.distributed:
-            torch.distributed.barrier()
-
-        self.model_small = model_small
-        self.model_large = model_large
-        if self.fix_head:
-            for param in self.model_small.aux_head.parameters():
-                param.requires_grad = False
-            for param in self.model_large.aux_head.parameters():
-                param.requires_grad = False
-
-        self.mutator_small = RegularizedDartsMutator(self.model_small).cuda()
-        self.mutator_large = DartsDiscreteMutator(self.model_large, self.mutator_small).cuda()
-        self.criterion = criterion
-
-        self.optimizer_small = torch.optim.SGD(self.model_small.parameters(), config.w_lr,
-                                               momentum=config.w_momentum, weight_decay=config.w_weight_decay)
-        self.optimizer_large = torch.optim.SGD(self.model_large.parameters(), config.nasnet_lr,
-                                               momentum=config.w_momentum, weight_decay=config.w_weight_decay)
-        self.optimizer_alpha = torch.optim.Adam(self.mutator_small.parameters(), config.alpha_lr,
-                                                betas=(0.5, 0.999), weight_decay=config.alpha_weight_decay)
-
-        if config.distributed:
-            apex.parallel.convert_syncbn_model(self.model_small)
-            apex.parallel.convert_syncbn_model(self.model_large)
-            self.model_small = DistributedDataParallel(self.model_small, delay_allreduce=True)
-            self.model_large = DistributedDataParallel(self.model_large, delay_allreduce=True)
-            self.mutator_small = RegularizedMutatorParallel(self.mutator_small, delay_allreduce=True)
-            if config.share_module:
-                self.model_small.callback_queued = True
-                self.model_large.callback_queued = True
-            # mutator large never gets optimized, so do not need parallelized
-
-    def warmup(self, phase, epoch):
-        assert phase in [PHASE_SMALL, PHASE_LARGE]
-        if phase == PHASE_SMALL:
-            model, optimizer = self.model_small, self.optimizer_small
-        elif phase == PHASE_LARGE:
-            model, optimizer = self.model_large, self.optimizer_large
-        model.train()
-        meters = AverageMeterGroup()
-        for step in range(self.steps_per_epoch):
-            x, y = next(self.train_loader)
-            x, y = x.cuda(), y.cuda()
-
-            optimizer.zero_grad()
-            logits_main, _ = model(x)
-            loss = self.criterion(logits_main, y)
-            loss.backward()
-
-            self._clip_grad_norm(model)
-            optimizer.step()
-            prec1, prec5 = accuracy(logits_main, y, topk=(1, 5))
-            metrics = {"prec1": prec1, "prec5": prec5, "loss": loss}
-            metrics = reduce_metrics(metrics, self.distributed)
-            meters.update(metrics)
-            if self.main_proc and (step % self.log_frequency == 0 or step + 1 == self.steps_per_epoch):
-                self.logger.info("Epoch [%d/%d] Step [%d/%d] (%s)  %s", epoch + 1, self.epochs,
-                                 step + 1, self.steps_per_epoch, phase, meters)
-
-    def _clip_grad_norm(self, model):
-        if isinstance(model, DistributedDataParallel):
-            nn.utils.clip_grad_norm_(model.module.parameters(), self.grad_clip)
-        else:
-            nn.utils.clip_grad_norm_(model.parameters(), self.grad_clip)
-
-    def _reset_nan(self, parameters):
-        with torch.no_grad():
-            for param in parameters:
-                for i, p in enumerate(param):
-                    if p != p:  # equivalent to `isnan(p)`
-                        param[i] = float("-inf")
-
-    def joint_train(self, epoch):
-        self.model_large.train()
-        self.model_small.train()
-        meters = AverageMeterGroup()
-        for step in range(self.steps_per_epoch):
-            trn_x, trn_y = next(self.train_loader)
-            val_x, val_y = next(self.valid_loader)
-            trn_x, trn_y = trn_x.cuda(), trn_y.cuda()
-            val_x, val_y = val_x.cuda(), val_y.cuda()
-
-            # step 1. optimize architecture
-            self.optimizer_alpha.zero_grad()
-            self.optimizer_large.zero_grad()
-            reg_decay = max(self.regular_coeff * (1 - float(epoch - self.warmup_epochs) / (
-                (self.epochs - self.warmup_epochs) * self.regular_ratio)), 0)
-            loss_regular = self.mutator_small.reset_with_loss()
-            if loss_regular:
-                loss_regular *= reg_decay
-            logits_search, emsemble_logits_search = self.model_small(val_x)
-            logits_main, emsemble_logits_main = self.model_large(val_x)
-            loss_cls = (self.criterion(logits_search, val_y) + self.criterion(logits_main, val_y)) / self.loss_alpha
-            loss_interactive = self.interactive_loss(emsemble_logits_search, emsemble_logits_main) * (self.loss_T ** 2) * self.loss_alpha
-            loss = loss_cls + loss_interactive + loss_regular
-            loss.backward()
-            self._clip_grad_norm(self.model_large)
-            self.optimizer_large.step()
-            self.optimizer_alpha.step()
-            # NOTE: need to call here `self._reset_nan(self.mutator_small.parameters())` if `cut_choices`
-
-            # step 2. optimize op weights
-            self.optimizer_small.zero_grad()
-            with torch.no_grad():
-                # resample architecture since parameters have been changed
-                self.mutator_small.reset_with_loss()
-            logits_search_train, _ = self.model_small(trn_x)
-            loss_weight = self.criterion(logits_search_train, trn_y)
-            loss_weight.backward()
-            self._clip_grad_norm(self.model_small)
-            self.optimizer_small.step()
-
-            metrics = {"loss_cls": loss_cls, "loss_interactive": loss_interactive,
-                       "loss_regular": loss_regular, "loss_weight": loss_weight}
-            metrics = reduce_metrics(metrics, self.distributed)
-            meters.update(metrics)
-
-            if self.main_proc and (step % self.log_frequency == 0 or step + 1 == self.steps_per_epoch):
-                self.logger.info("Epoch [%d/%d] Step [%d/%d] (joint)  %s", epoch + 1, self.epochs,
-                                 step + 1, self.steps_per_epoch, meters)
-
-    def train(self):
-        for epoch in range(self.epochs):
-            if epoch < self.warmup_epochs:
-                with torch.no_grad():  # otherwise grads will be retained on the architecture params
-                    self.mutator_small.reset_with_loss()
-                self.warmup(PHASE_SMALL, epoch)
-            else:
-                with torch.no_grad():
-                    self.mutator_large.reset()
-                self.warmup(PHASE_LARGE, epoch)
-                self.joint_train(epoch)
-
-            self.export(os.path.join(self.checkpoint_dir, "epoch_{:02d}.json".format(epoch)),
-                        os.path.join(self.checkpoint_dir, "epoch_{:02d}.genotypes".format(epoch)))
-
-    def export(self, file, genotype_file):
-        if self.main_proc:
-            mutator_export, genotypes = self.mutator_small.export(self.logger)
-            with open(file, "w") as f:
-                json.dump(mutator_export, f, indent=2, sort_keys=True, cls=TorchTensorEncoder)
-            with open(genotype_file, "w") as f:
-                f.write(str(genotypes))
diff --git a/src/sdk/pynni/nni/nas/pytorch/cdarts/mutator.py b/src/sdk/pynni/nni/nas/pytorch/cdarts/mutator.py
index d7aaa049a6..dd76fa5632 100644
--- a/src/sdk/pynni/nni/nas/pytorch/cdarts/mutator.py
+++ b/src/sdk/pynni/nni/nas/pytorch/cdarts/mutator.py
@@ -1,12 +1,9 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT license.
 
-import logging
-
 import torch
-import torch.nn as nn
-import torch.nn.functional as F
 
+from apex.parallel import DistributedDataParallel # pylint: disable=import-error
 from nni.nas.pytorch.darts import DartsMutator
 from nni.nas.pytorch.mutables import LayerChoice
 from nni.nas.pytorch.mutator import Mutator
diff --git a/src/sdk/pynni/nni/nas/pytorch/cdarts/trainer.py b/src/sdk/pynni/nni/nas/pytorch/cdarts/trainer.py
index 07fdd47649..878294cfa2 100644
--- a/src/sdk/pynni/nni/nas/pytorch/cdarts/trainer.py
+++ b/src/sdk/pynni/nni/nas/pytorch/cdarts/trainer.py
@@ -1,16 +1,14 @@
 import json
-import logging
 import os
-
 import torch
 import torch.nn as nn
+import torch.distributed as dist
 import torch.nn.functional as F
+import apex # pylint: disable=import-error
 
-import apex
-from apex.parallel import DistributedDataParallel
+from apex.parallel import DistributedDataParallel # pylint: disable=import-error
 from nni.nas.pytorch.cdarts import RegularizedDartsMutator, RegularizedMutatorParallel, DartsDiscreteMutator
 from nni.nas.pytorch.utils import AverageMeterGroup
-from utils import CyclicIterator, TorchTensorEncoder, accuracy, reduce_metrics
 
 PHASE_SMALL = "small"
 PHASE_LARGE = "large"
@@ -28,36 +26,100 @@ def forward(self, student, teacher):
 
 
 class CdartsTrainer(object):
-    def __init__(self, model_small, model_large, criterion, loaders, samplers, logger, config):
+    def __init__(self, model_small, model_large, criterion, loaders, samplers, logger,
+                 regular_coeff=5, regular_ratio=0.2, warmup_epochs=2, fix_head=True,
+                 epochs=32, steps_per_epoch=None, loss_alpha=2, loss_T=2, distributed=True,
+                 log_frequency=10, grad_clip=5.0, interactive_type='kl', output_path='./outputs',
+                 w_lr=0.2, w_momentum=0.9, w_weight_decay=3e-4, alpha_lr=0.2, alpha_weight_decay=1e-4,
+                 nasnet_lr=0.2, local_rank=0, share_module=True):
+        """
+        Initialize a CdartsTrainer.
+        Parameters
+        ----------
+        model_small : nn.Module
+            PyTorch model to be trained. This is the search network of CDARTS.
+        model_large : nn.Module
+            PyTorch model to be trained. This is the evaluation network of CDARTS.
+        criterion : callable
+            Receives logits and ground truth label, return a loss tensor.
+        loaders : list
+            List of training dataset and test dataset. Will be split for training weights and architecture weights.
+        samplers : list of Dateset Samplers
+            List of training dataset and test dataset samplers.
+        logger : callable
+            The logging object. It receives the contents for logging.
+        regular_coeff : float
+            The coefficient of regular loss.
+        regular_ratio : float
+            The ratio of regular loss.
+        warmup_epochs : int
+            The epochs to warmup the search network
+        fix_head : bool
+            ``True`` if fixing the paramters of auxiliary heads, else unfix the paramters of auxiliary heads. 
+        epochs : int
+            Number of epochs planned for training.
+        steps_per_epoch : int
+            Steps of one epoch.
+        loss_alpha : float
+            The loss coefficient.
+        loss_T : float
+            The loss coefficient.
+        distributed : bool
+            ``True`` if using distributed training, else non-distributed training.
+        log_frequency : int
+            Step count per logging.
+        grad_clip : float
+            Gradient clipping for weights.
+        interactive_type : string
+            ``kl`` or ``smoothl1``.
+        output_path : string
+            Log storage path.
+        w_lr : float
+            Learning rate of the search network parameters.
+        w_momentum : float
+            Momentum of the search and the evaluation network.
+        w_weight_decay : float
+            The weight decay the search and the evaluation network parameters.
+        alpha_lr : float
+            Learning rate of the architecture parameters.
+        alpha_weight_decay : float
+            The weight decay the architecture parameters.
+        nasnet_lr : float
+            Learning rate of the evaluation network parameters.
+        local_rank : int
+            The number of thread.
+        share_module : bool
+            ``True`` if sharing the stem and auxiliary heads, else not sharing these modules.
+        """
         train_loader, valid_loader = loaders
         train_sampler, valid_sampler = samplers
-        self.train_loader = CyclicIterator(train_loader, train_sampler, config.distributed)
-        self.valid_loader = CyclicIterator(valid_loader, valid_sampler, config.distributed)
-
-        self.regular_coeff = config.regular_coeff
-        self.regular_ratio = config.regular_ratio
-        self.warmup_epochs = config.warmup_epochs
-        self.fix_head = config.fix_head
-        self.epochs = config.epochs
-        self.steps_per_epoch = config.steps_per_epoch
+        self.train_loader = CyclicIterator(train_loader, train_sampler, distributed)
+        self.valid_loader = CyclicIterator(valid_loader, valid_sampler, distributed)
+
+        self.regular_coeff = regular_coeff
+        self.regular_ratio = regular_ratio
+        self.warmup_epochs = warmup_epochs
+        self.fix_head = fix_head
+        self.epochs = epochs
+        self.steps_per_epoch = steps_per_epoch
         if self.steps_per_epoch is None:
             self.steps_per_epoch = min(len(self.train_loader), len(self.valid_loader))
-        self.loss_alpha = config.loss_alpha
-        self.grad_clip = config.grad_clip
-        if config.interactive_type == "kl":
-            self.interactive_loss = InteractiveKLLoss(config.loss_T)
-        elif config.interactive_type == "smoothl1":
+        self.loss_alpha = loss_alpha
+        self.grad_clip = grad_clip
+        if interactive_type == "kl":
+            self.interactive_loss = InteractiveKLLoss(loss_T)
+        elif interactive_type == "smoothl1":
             self.interactive_loss = nn.SmoothL1Loss()
-        self.loss_T = config.loss_T
-        self.distributed = config.distributed
-        self.log_frequency = config.log_frequency
-        self.main_proc = not config.distributed or config.local_rank == 0
+        self.loss_T = loss_T
+        self.distributed = distributed
+        self.log_frequency = log_frequency
+        self.main_proc = not distributed or local_rank == 0
 
         self.logger = logger
-        self.checkpoint_dir = config.output_path
+        self.checkpoint_dir = output_path
         if self.main_proc:
             os.makedirs(self.checkpoint_dir, exist_ok=True)
-        if config.distributed:
+        if distributed:
             torch.distributed.barrier()
 
         self.model_small = model_small
@@ -72,20 +134,20 @@ def __init__(self, model_small, model_large, criterion, loaders, samplers, logge
         self.mutator_large = DartsDiscreteMutator(self.model_large, self.mutator_small).cuda()
         self.criterion = criterion
 
-        self.optimizer_small = torch.optim.SGD(self.model_small.parameters(), config.w_lr,
-                                               momentum=config.w_momentum, weight_decay=config.w_weight_decay)
-        self.optimizer_large = torch.optim.SGD(self.model_large.parameters(), config.nasnet_lr,
-                                               momentum=config.w_momentum, weight_decay=config.w_weight_decay)
-        self.optimizer_alpha = torch.optim.Adam(self.mutator_small.parameters(), config.alpha_lr,
-                                                betas=(0.5, 0.999), weight_decay=config.alpha_weight_decay)
+        self.optimizer_small = torch.optim.SGD(self.model_small.parameters(), w_lr,
+                                               momentum=w_momentum, weight_decay=w_weight_decay)
+        self.optimizer_large = torch.optim.SGD(self.model_large.parameters(), nasnet_lr,
+                                               momentum=w_momentum, weight_decay=w_weight_decay)
+        self.optimizer_alpha = torch.optim.Adam(self.mutator_small.parameters(), alpha_lr,
+                                                betas=(0.5, 0.999), weight_decay=alpha_weight_decay)
 
-        if config.distributed:
+        if distributed:
             apex.parallel.convert_syncbn_model(self.model_small)
             apex.parallel.convert_syncbn_model(self.model_large)
             self.model_small = DistributedDataParallel(self.model_small, delay_allreduce=True)
             self.model_large = DistributedDataParallel(self.model_large, delay_allreduce=True)
             self.mutator_small = RegularizedMutatorParallel(self.mutator_small, delay_allreduce=True)
-            if config.share_module:
+            if share_module:
                 self.model_small.callback_queued = True
                 self.model_large.callback_queued = True
             # mutator large never gets optimized, so do not need parallelized
@@ -201,3 +263,71 @@ def export(self, file, genotype_file):
                 json.dump(mutator_export, f, indent=2, sort_keys=True, cls=TorchTensorEncoder)
             with open(genotype_file, "w") as f:
                 f.write(str(genotypes))
+
+class CyclicIterator:
+    def __init__(self, loader, sampler, distributed):
+        self.loader = loader
+        self.sampler = sampler
+        self.epoch = 0
+        self.distributed = distributed
+        self._next_epoch()
+
+    def _next_epoch(self):
+        if self.distributed:
+            self.sampler.set_epoch(self.epoch)
+        self.iterator = iter(self.loader)
+        self.epoch += 1
+
+    def __len__(self):
+        return len(self.loader)
+
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        try:
+            return next(self.iterator)
+        except StopIteration:
+            self._next_epoch()
+            return next(self.iterator)
+
+class TorchTensorEncoder(json.JSONEncoder):
+    def default(self, o):  # pylint: disable=method-hidden
+        if isinstance(o, torch.Tensor):
+            olist = o.tolist()
+            if "bool" not in o.type().lower() and all(map(lambda d: d == 0 or d == 1, olist)):
+                _logger.warning("Every element in %s is either 0 or 1. "
+                                "You might consider convert it into bool.", olist)
+            return olist
+        return super().default(o)
+
+def accuracy(output, target, topk=(1,)):
+    """ Computes the precision@k for the specified values of k """
+    maxk = max(topk)
+    batch_size = target.size(0)
+
+    _, pred = output.topk(maxk, 1, True, True)
+    pred = pred.t()
+    # one-hot case
+    if target.ndimension() > 1:
+        target = target.max(1)[1]
+
+    correct = pred.eq(target.view(1, -1).expand_as(pred))
+
+    res = []
+    for k in topk:
+        correct_k = correct[:k].view(-1).float().sum(0)
+        res.append(correct_k.mul_(1.0 / batch_size))
+    return res
+
+def reduce_tensor(tensor):
+    rt = tensor.clone()
+    dist.all_reduce(rt, op=dist.ReduceOp.SUM)
+    rt /= float(os.environ["WORLD_SIZE"])
+    return rt
+
+
+def reduce_metrics(metrics, distributed=False):
+    if distributed:
+        return {k: reduce_tensor(v).item() for k, v in metrics.items()}
+    return {k: v.item() for k, v in metrics.items()}

From 4543d3f5a093b87003816efec57d26972a69bb4d Mon Sep 17 00:00:00 2001
From: "penghouwen@icloud.com" <penghouwen@icloud.com>
Date: Wed, 15 Jan 2020 12:55:12 +0800
Subject: [PATCH 03/21] update readme

---
 README.md                  | 742 +++++++++++++++++++------------------
 docs/en_US/NAS/Overview.md |   2 +-
 2 files changed, 373 insertions(+), 371 deletions(-)

diff --git a/README.md b/README.md
index edf3638413..27912a6725 100644
--- a/README.md
+++ b/README.md
@@ -1,370 +1,372 @@
-<p align="center">
-<img src="docs/img/nni_logo.png" width="300"/>
-</p>
-
------------
-
-[![MIT licensed](https://img.shields.io/badge/license-MIT-brightgreen.svg)](LICENSE)
-[![Build Status](https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/Microsoft.nni)](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=6)
-[![Issues](https://img.shields.io/github/issues-raw/Microsoft/nni.svg)](https://github.com/Microsoft/nni/issues?q=is%3Aissue+is%3Aopen)
-[![Bugs](https://img.shields.io/github/issues/Microsoft/nni/bug.svg)](https://github.com/Microsoft/nni/issues?q=is%3Aissue+is%3Aopen+label%3Abug)
-[![Pull Requests](https://img.shields.io/github/issues-pr-raw/Microsoft/nni.svg)](https://github.com/Microsoft/nni/pulls?q=is%3Apr+is%3Aopen)
-[![Version](https://img.shields.io/github/release/Microsoft/nni.svg)](https://github.com/Microsoft/nni/releases) [![Join the chat at https://gitter.im/Microsoft/nni](https://badges.gitter.im/Microsoft/nni.svg)](https://gitter.im/Microsoft/nni?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
-[![Documentation Status](https://readthedocs.org/projects/nni/badge/?version=latest)](https://nni.readthedocs.io/en/latest/?badge=latest)
-
-[简体中文](README_zh_CN.md)
-
-**NNI (Neural Network Intelligence)** is a lightweight but powerful toolkit to help users **automate** <a href="docs/en_US/FeatureEngineering/Overview.md">Feature Engineering</a>, <a href="docs/en_US/NAS/Overview.md">Neural Architecture Search</a>, <a href="docs/en_US/Tuner/BuiltinTuner.md">Hyperparameter Tuning</a> and <a href="docs/en_US/Compressor/Overview.md">Model Compression</a>.
-
-The tool manages automated machine learning (AutoML) experiments, **dispatches and runs** experiments' trial jobs generated by tuning algorithms to search the best neural architecture and/or hyper-parameters in **different training environments** like <a href="docs/en_US/TrainingService/LocalMode.md">Local Machine</a>, <a href="docs/en_US/TrainingService/RemoteMachineMode.md">Remote Servers</a>, <a href="docs/en_US/TrainingService/PaiMode.md">OpenPAI</a>, <a href="docs/en_US/TrainingService/KubeflowMode.md">Kubeflow</a>, <a href="docs/en_US/TrainingService/FrameworkControllerMode.md">FrameworkController on K8S (AKS etc.)</a> and other cloud options.
-
-## **Who should consider using NNI**
-
-* Those who want to **try different AutoML algorithms** in their training code/model.
-* Those who want to run AutoML trial jobs **in different environments** to speed up search.
-* Researchers and data scientists who want to easily **implement and experiement new AutoML algorithms**, may it be: hyperparameter tuning algorithm, neural architect search algorithm or model compression algorithm.
-* ML Platform owners who want to **support AutoML in their platform**.
-
-### **NNI v1.2 has been released! &nbsp;<a href="#nni-released-reminder"><img width="48" src="docs/img/release_icon.png"></a>**
-
-## **NNI capabilities in a glance**
-NNI provides CommandLine Tool as well as an user friendly WebUI to manage training experiements. With the extensible API, you can customize your own AutoML algorithms and training services. To make it easy for new users, NNI also provides a set of build-in stat-of-the-art AutoML algorithms and out of box support for popular training platforms. 
-
-Within the following table, we summarized the current NNI capabilities, we are gradually adding new capabilities and we'd love to have your contribution.
-
-<p align="center">
-  <a href="#nni-has-been-released"><img src="docs/img/overview.svg" /></a>
-</p>
-
-<table>
-  <tbody>
-    <tr align="center" valign="bottom">
-    <td>
-      </td>
-      <td>
-        <b>Frameworks & Libraries</b>
-        <img src="docs/img/bar.png"/>
-      </td>
-      <td>
-        <b>Algorithms</b>
-        <img src="docs/img/bar.png"/>
-      </td>
-      <td>
-        <b>Training Services</b>
-        <img src="docs/img/bar.png"/>
-      </td>
-    </tr>
-    </tr>
-    <tr valign="top">
-    <td align="center" valign="middle">
-    <b>Built-in</b>
-      </td>
-      <td>
-      <ul><li><b>Supported Frameworks</b></li>
-        <ul>
-          <li>PyTorch</li>
-          <li>Keras</li>
-          <li>TensorFlow</li>
-          <li>MXNet</li>
-          <li>Caffe2</li>
-          <a href="docs/en_US/SupportedFramework_Library.md">More...</a><br/>
-        </ul>
-        </ul>
-      <ul>
-        <li><b>Supported Libraries</b></li>
-          <ul>
-           <li>Scikit-learn</li>
-           <li>XGBoost</li>
-           <li>LightGBM</li>
-           <a href="docs/en_US/SupportedFramework_Library.md">More...</a><br/>
-          </ul>
-      </ul>
-        <ul>
-        <li><b>Examples</b></li>
-         <ul>
-           <li><a href="examples/trials/mnist-pytorch">MNIST-pytorch</li></a>
-           <li><a href="examples/trials/mnist-tfv1">MNIST-tensorflow</li></a>
-           <li><a href="examples/trials/mnist-keras">MNIST-keras</li></a>
-           <li><a href="docs/en_US/TrialExample/GbdtExample.md">Auto-gbdt</a></li>
-           <li><a href="docs/en_US/TrialExample/Cifar10Examples.md">Cifar10-pytorch</li></a>
-           <li><a href="docs/en_US/TrialExample/SklearnExamples.md">Scikit-learn</a></li>
-              <a href="docs/en_US/SupportedFramework_Library.md">More...</a><br/>
-          </ul>
-        </ul>
-      </td>
-      <td align="left" >
-        <a href="docs/en_US/Tuner/BuiltinTuner.md">Hyperparameter Tuning</a>
-        <ul>
-          <b>Exhaustive search</b>
-          <ul>
-            <li><a href="docs/en_US/Tuner/BuiltinTuner.md#Random">Random Search</a></li>
-            <li><a href="docs/en_US/Tuner/BuiltinTuner.md#GridSearch">Grid Search</a></li>
-            <li><a href="docs/en_US/Tuner/BuiltinTuner.md#Batch">Batch</a></li>
-            </ul>
-          <b>Heuristic search</b>
-          <ul>
-            <li><a href="docs/en_US/Tuner/BuiltinTuner.md#Evolution">Naïve Evolution</a></li>
-            <li><a href="docs/en_US/Tuner/BuiltinTuner.md#Anneal">Anneal</a></li>  
-            <li><a href="docs/en_US/Tuner/BuiltinTuner.md#Hyperband">Hyperband</a></li>
-          </ul>
-          <b>Bayesian optimization</b>
-            <ul>
-              <li><a href="docs/en_US/Tuner/BuiltinTuner.md#BOHB">BOHB</a></li>  
-              <li><a href="docs/en_US/Tuner/BuiltinTuner.md#TPE">TPE</a></li>
-            <li><a href="docs/en_US/Tuner/BuiltinTuner.md#SMAC">SMAC</a></li> 
-            <li><a href="docs/en_US/Tuner/BuiltinTuner.md#MetisTuner">Metis Tuner</a></li>
-            <li><a href="docs/en_US/Tuner/BuiltinTuner.md#GPTuner">GP Tuner</a> </li>
-            </ul>  
-          <b>RL Based</b>
-          <ul>
-            <li><a href="docs/en_US/Tuner/BuiltinTuner.md#PPOTuner">PPO Tuner</a> </li>
-          </ul>
-        </ul>
-          <a href="docs/en_US/NAS/Overview.md">Neural Architecture Search</a>
-          <ul>                        
-            <ul>
-              <li><a href="docs/en_US/NAS/Overview.md#enas">ENAS</a></li>
-              <li><a href="docs/en_US/NAS/Overview.md#darts">DARTS</a></li>
-              <li><a href="docs/en_US/NAS/Overview.md#p-darts">P-DARTS</a></li>
-              <li><a href="docs/en_US/Tuner/BuiltinTuner.md#NetworkMorphism">Network Morphism</a> </li>
-            </ul>    
-          </ul>
-          <a href="docs/en_US/Compressor/Overview.md">Model Compression</a>
-          <ul>
-            <b>Pruning</b>
-            <ul>
-              <li><a href="docs/en_US/Compressor/Pruner.md#agp-pruner">AGP Pruner</a></li>
-              <li><a href="docs/en_US/Compressor/Pruner.md#slim-pruner">Slim Pruner</a></li>
-              <li><a href="docs/en_US/Compressor/Pruner.md#fpgm-pruner">FPGM Pruner</a></li>
-            </ul>
-            <b>Quantization</b>
-            <ul>
-              <li><a href="docs/en_US/Compressor/Quantizer.md#qat-quantizer">QAT Quantizer</a></li>
-              <li><a href="docs/en_US/Compressor/Quantizer.md#dorefa-quantizer">DoReFa Quantizer</a></li>
-            </ul>
-          </ul>
-          <a href="docs/en_US/FeatureEngineering/Overview.md">Feature Engineering (Beta)</a>
-          <ul>
-          <li><a href="docs/en_US/FeatureEngineering/GradientFeatureSelector.md">GradientFeatureSelector</a></li>
-          <li><a href="docs/en_US/FeatureEngineering/GBDTSelector.md">GBDTSelector</a></li>
-          </ul>
-          <a href="docs/en_US/Assessor/BuiltinAssessor.md">Early Stop Algorithms</a>
-          <ul>
-          <li><a href="docs/en_US/Assessor/BuiltinAssessor.md#Medianstop">Median Stop</a></li>
-          <li><a href="docs/en_US/Assessor/BuiltinAssessor.md#Curvefitting">Curve Fitting</a></li>   
-          </ul>
-      </td>
-      <td>
-      <ul>
-        <li><a href="docs/en_US/TrainingService/LocalMode.md">Local Machine</a></li>
-        <li><a href="docs/en_US/TrainingService/RemoteMachineMode.md">Remote Servers</a></li>
-        <li><b>Kubernetes based services</b></li>
-            <ul><li><a href="docs/en_US/TrainingService/PaiMode.md">OpenPAI</a></li>
-            <li><a href="docs/en_US/TrainingService/KubeflowMode.md">Kubeflow</a></li>
-            <li><a href="docs/en_US/TrainingService/FrameworkControllerMode.md">FrameworkController on K8S (AKS etc.)</a></li>
-            </ul>
-      </ul>
-      </td>
-    </tr> 
-      <tr align="center" valign="bottom">
-      </td>
-      </tr>
-      <tr valign="top">
-       <td valign="middle">
-    <b>References</b>
-      </td>
-     <td style="border-top:#FF0000 solid 0px;">
-      <ul>
-        <li><a href="docs/en_US/sdk_reference.rst">Python API</a></li>
-        <li><a href="docs/en_US/Tutorial/AnnotationSpec.md">NNI Annotation</a></li>
-         <li><a href="docs/en_US/Tutorial/Installation.md">Supported OS</a></li>
-      </ul>
-      </td>
-       <td style="border-top:#FF0000 solid 0px;">
-      <ul>
-        <li><a href="docs/en_US/Tuner/CustomizeTuner.md">CustomizeTuner</a></li>
-        <li><a href="docs/en_US/Assessor/CustomizeAssessor.md">CustomizeAssessor</a></li>
-      </ul>
-      </td>
-        <td style="border-top:#FF0000 solid 0px;">
-      <ul>
-        <li><a href="docs/en_US/TrainingService/SupportTrainingService.md">Support TrainingService</li>
-        <li><a href="docs/en_US/TrainingService/HowToImplementTrainingService.md">Implement TrainingService</a></li>
-      </ul>
-      </td>     
-    </tr> 
-  </tbody>
-</table>
-
-## **Install & Verify**
-
-**Install through pip**
-
-* We support Linux, MacOS and Windows (local, remote and pai mode) in current stage, Ubuntu 16.04 or higher, MacOS 10.14.1 along with Windows 10.1809 are tested and supported. Simply run the following `pip install` in an environment that has `python >= 3.5`.
-
-Linux and MacOS
-
-```bash
-python3 -m pip install --upgrade nni
-```
-
-Windows
-
-```bash
-python -m pip install --upgrade nni
-```
-
-Note:
-
-* `--user` can be added if you want to install NNI in your home directory, which does not require any special privileges.
-* Currently NNI on Windows support local, remote and pai mode. Anaconda or Miniconda is highly recommended to install NNI on Windows.
-* If there is any error like `Segmentation fault`, please refer to [FAQ](docs/en_US/Tutorial/FAQ.md)
-
-**Install through source code**
-
-* We support Linux (Ubuntu 16.04 or higher), MacOS (10.14.1) and Windows (10.1809) in our current stage.
-
-Linux and MacOS
-
-* Run the following commands in an environment that has `python >= 3.5`, `git` and `wget`.
-
-```bash
-    git clone -b v1.2 https://github.com/Microsoft/nni.git
-    cd nni
-    source install.sh
-```
-
-Windows
-
-* Run the following commands in an environment that has `python >=3.5`, `git` and `PowerShell`
-
-```bash
-  git clone -b v1.2 https://github.com/Microsoft/nni.git
-  cd nni
-  powershell -ExecutionPolicy Bypass -file install.ps1
-```
-
-For the system requirements of NNI, please refer to [Install NNI](docs/en_US/Tutorial/Installation.md)
-
-For NNI on Windows, please refer to [NNI on Windows](docs/en_US/Tutorial/NniOnWindows.md)
-
-**Verify install**
-
-The following example is an experiment built on TensorFlow. Make sure you have **TensorFlow 1.x installed** before running it. Note that **currently Tensorflow 2.0 is NOT supported**.
-
-* Download the examples via clone the source code.
-
-```bash
-    git clone -b v1.2 https://github.com/Microsoft/nni.git
-```
-
-Linux and MacOS
-
-* Run the MNIST example.
-
-```bash
-    nnictl create --config nni/examples/trials/mnist-tfv1/config.yml
-```
-
-Windows
-
-* Run the MNIST example.
-
-```bash
-    nnictl create --config nni\examples\trials\mnist-tfv1\config_windows.yml
-```
-
-* Wait for the message `INFO: Successfully started experiment!` in the command line. This message indicates that your experiment has been successfully started. You can explore the experiment using the `Web UI url`.
-
-```text
-INFO: Starting restful server...
-INFO: Successfully started Restful server!
-INFO: Setting local config...
-INFO: Successfully set local config!
-INFO: Starting experiment...
-INFO: Successfully started experiment!
------------------------------------------------------------------------
-The experiment id is egchD4qy
-The Web UI urls are: http://223.255.255.1:8080   http://127.0.0.1:8080
------------------------------------------------------------------------
-
-You can use these commands to get more information about the experiment
------------------------------------------------------------------------
-         commands                       description
-1. nnictl experiment show        show the information of experiments
-2. nnictl trial ls               list all of trial jobs
-3. nnictl top                    monitor the status of running experiments
-4. nnictl log stderr             show stderr log content
-5. nnictl log stdout             show stdout log content
-6. nnictl stop                   stop an experiment
-7. nnictl trial kill             kill a trial job by id
-8. nnictl --help                 get help information about nnictl
------------------------------------------------------------------------
-```
-
-* Open the `Web UI url` in your browser, you can view detail information of the experiment and all the submitted trial jobs as shown below. [Here](docs/en_US/Tutorial/WebUI.md) are more Web UI pages.
-
-<table style="border: none">
-    <th><img src="./docs/img/webui_overview_page.png" alt="drawing" width="395"/></th>
-    <th><img src="./docs/img/webui_trialdetail_page.png" alt="drawing" width="410"/></th>
-</table>
-
-## **Documentation**
-* To learn about what's NNI, read the [NNI Overview](https://nni.readthedocs.io/en/latest/Overview.html). 
-* To get yourself familiar with how to use NNI, read the [documentation](https://nni.readthedocs.io/en/latest/index.html). 
-* To get started and install NNI on your system, please refer to [Install NNI](docs/en_US/Tutorial/Installation.md).
-
-## **Contributing**
-This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.
-
-When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.
-
-This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). For more information see the Code of [Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or contact opencode@microsoft.com with any additional questions or comments.
-
-After getting familiar with contribution agreements, you are ready to create your first PR =), follow the NNI developer tutorials to get start:
-* We recommend new contributors to start with ['good first issue'](https://github.com/Microsoft/nni/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) or ['help-wanted'](https://github.com/microsoft/nni/issues?q=is%3Aopen+is%3Aissue+label%3A%22help+wanted%22), these issues are simple and easy to start.
-* [NNI developer environment installation tutorial](docs/en_US/Tutorial/SetupNniDeveloperEnvironment.md)
-* [How to debug](docs/en_US/Tutorial/HowToDebug.md)
-* [Customize your own Tuner](docs/en_US/Tuner/CustomizeTuner.md)
-* [Implement customized TrainingService](docs/en_US/TrainingService/HowToImplementTrainingService.md)
-* [Implement a new NAS trainer on NNI](https://github.com/microsoft/nni/blob/master/docs/en_US/NAS/NasInterface.md#implement-a-new-nas-trainer-on-nni)
-* [Customize your own Advisor](docs/en_US/Tuner/CustomizeAdvisor.md)
-
-## **External Repositories and References**
-With authors' permission, we listed a set of NNI usage examples and relevant articles.
-* ### **External Repositories** ###
-   * Run [ENAS](examples/tuners/enas_nni/README.md) with NNI
-   * Run [Neural Network Architecture Search](examples/trials/nas_cifar10/README.md) with NNI 
-   * [Automatic Feature Engineering](examples/feature_engineering/auto-feature-engineering/README.md) with NNI 
-   * [Hyperparameter Tuning for Matrix Factorization](https://github.com/microsoft/recommenders/blob/master/notebooks/04_model_select_and_optimize/nni_surprise_svd.ipynb) with NNI
-   * [scikit-nni](https://github.com/ksachdeva/scikit-nni) Hyper-parameter search for scikit-learn pipelines using NNI
-
-* ### **Relevant Articles** ###
-  
-  * [Hyper Parameter Optimization Comparison](docs/en_US/CommunitySharings/HpoComparision.md)
-  * [Neural Architecture Search Comparison](docs/en_US/CommunitySharings/NasComparision.md)
-  * [Parallelizing a Sequential Algorithm TPE](docs/en_US/CommunitySharings/ParallelizingTpeSearch.md)
-  * [Automatically tuning SVD with NNI](docs/en_US/CommunitySharings/RecommendersSvd.md)
-  * [Automatically tuning SPTAG with NNI](docs/en_US/CommunitySharings/SptagAutoTune.md)
-  * [Find thy hyper-parameters for scikit-learn pipelines using Microsoft NNI](https://towardsdatascience.com/find-thy-hyper-parameters-for-scikit-learn-pipelines-using-microsoft-nni-f1015b1224c1)
-  * **Blog (in Chinese)** - [AutoML tools (Advisor, NNI and Google Vizier) comparison](http://gaocegege.com/Blog/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/katib-new#%E6%80%BB%E7%BB%93%E4%B8%8E%E5%88%86%E6%9E%90) by [@gaocegege](https://github.com/gaocegege) - 总结与分析 section of design and implementation of kubeflow/katib
-
-## **Feedback**
-* Discuss on the NNI [Gitter](https://gitter.im/Microsoft/nni?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) in NNI.
-* [File an issue](https://github.com/microsoft/nni/issues/new/choose) on GitHub.
-* Ask a question with NNI tags on [Stack Overflow](https://stackoverflow.com/questions/tagged/nni?sort=Newest&edited=true).
-
-## Related Projects
-Targeting at openness and advancing state-of-art technology, [Microsoft Research (MSR)](https://www.microsoft.com/en-us/research/group/systems-research-group-asia/) had also released few other open source projects.
-
-* [OpenPAI](https://github.com/Microsoft/pai) : an open source platform that provides complete AI model training and resource management capabilities, it is easy to extend and supports on-premise, cloud and hybrid environments in various scale.
-* [FrameworkController](https://github.com/Microsoft/frameworkcontroller) : an open source general-purpose Kubernetes Pod Controller that orchestrate all kinds of applications on Kubernetes by a single controller.
-* [MMdnn](https://github.com/Microsoft/MMdnn) : A comprehensive, cross-framework solution to convert, visualize and diagnose deep neural network models. The "MM" in MMdnn stands for model management and "dnn" is an acronym for deep neural network.
-* [SPTAG](https://github.com/Microsoft/SPTAG) : Space Partition Tree And Graph (SPTAG) is an open source library for large scale vector approximate nearest neighbor search scenario.
-
-We encourage researchers and students leverage these projects to accelerate the AI development and research.
-
-## **License**
-
-The entire codebase is under [MIT license](LICENSE)
-
+<p align="center">
+<img src="docs/img/nni_logo.png" width="300"/>
+</p>
+
+-----------
+
+[![MIT licensed](https://img.shields.io/badge/license-MIT-brightgreen.svg)](LICENSE)
+[![Build Status](https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/integration-test-local?branchName=master)](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=17&branchName=master)
+[![Issues](https://img.shields.io/github/issues-raw/Microsoft/nni.svg)](https://github.com/Microsoft/nni/issues?q=is%3Aissue+is%3Aopen)
+[![Bugs](https://img.shields.io/github/issues/Microsoft/nni/bug.svg)](https://github.com/Microsoft/nni/issues?q=is%3Aissue+is%3Aopen+label%3Abug)
+[![Pull Requests](https://img.shields.io/github/issues-pr-raw/Microsoft/nni.svg)](https://github.com/Microsoft/nni/pulls?q=is%3Apr+is%3Aopen)
+[![Version](https://img.shields.io/github/release/Microsoft/nni.svg)](https://github.com/Microsoft/nni/releases) [![Join the chat at https://gitter.im/Microsoft/nni](https://badges.gitter.im/Microsoft/nni.svg)](https://gitter.im/Microsoft/nni?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
+[![Documentation Status](https://readthedocs.org/projects/nni/badge/?version=latest)](https://nni.readthedocs.io/en/latest/?badge=latest)
+
+[简体中文](README_zh_CN.md)
+
+**NNI (Neural Network Intelligence)** is a lightweight but powerful toolkit to help users **automate** <a href="docs/en_US/FeatureEngineering/Overview.md">Feature Engineering</a>, <a href="docs/en_US/NAS/Overview.md">Neural Architecture Search</a>, <a href="docs/en_US/Tuner/BuiltinTuner.md">Hyperparameter Tuning</a> and <a href="docs/en_US/Compressor/Overview.md">Model Compression</a>.
+
+The tool manages automated machine learning (AutoML) experiments, **dispatches and runs** experiments' trial jobs generated by tuning algorithms to search the best neural architecture and/or hyper-parameters in **different training environments** like <a href="docs/en_US/TrainingService/LocalMode.md">Local Machine</a>, <a href="docs/en_US/TrainingService/RemoteMachineMode.md">Remote Servers</a>, <a href="docs/en_US/TrainingService/PaiMode.md">OpenPAI</a>, <a href="docs/en_US/TrainingService/KubeflowMode.md">Kubeflow</a>, <a href="docs/en_US/TrainingService/FrameworkControllerMode.md">FrameworkController on K8S (AKS etc.)</a> and other cloud options.
+
+## **Who should consider using NNI**
+
+* Those who want to **try different AutoML algorithms** in their training code/model.
+* Those who want to run AutoML trial jobs **in different environments** to speed up search.
+* Researchers and data scientists who want to easily **implement and experiement new AutoML algorithms**, may it be: hyperparameter tuning algorithm, neural architect search algorithm or model compression algorithm.
+* ML Platform owners who want to **support AutoML in their platform**.
+
+### **NNI v1.3 has been released! &nbsp;<a href="#nni-released-reminder"><img width="48" src="docs/img/release_icon.png"></a>**
+
+## **NNI capabilities in a glance**
+NNI provides CommandLine Tool as well as an user friendly WebUI to manage training experiements. With the extensible API, you can customize your own AutoML algorithms and training services. To make it easy for new users, NNI also provides a set of build-in stat-of-the-art AutoML algorithms and out of box support for popular training platforms. 
+
+Within the following table, we summarized the current NNI capabilities, we are gradually adding new capabilities and we'd love to have your contribution.
+
+<p align="center">
+  <a href="#nni-has-been-released"><img src="docs/img/overview.svg" /></a>
+</p>
+
+<table>
+  <tbody>
+    <tr align="center" valign="bottom">
+    <td>
+      </td>
+      <td>
+        <b>Frameworks & Libraries</b>
+        <img src="docs/img/bar.png"/>
+      </td>
+      <td>
+        <b>Algorithms</b>
+        <img src="docs/img/bar.png"/>
+      </td>
+      <td>
+        <b>Training Services</b>
+        <img src="docs/img/bar.png"/>
+      </td>
+    </tr>
+    </tr>
+    <tr valign="top">
+    <td align="center" valign="middle">
+    <b>Built-in</b>
+      </td>
+      <td>
+      <ul><li><b>Supported Frameworks</b></li>
+        <ul>
+          <li>PyTorch</li>
+          <li>Keras</li>
+          <li>TensorFlow</li>
+          <li>MXNet</li>
+          <li>Caffe2</li>
+          <a href="docs/en_US/SupportedFramework_Library.md">More...</a><br/>
+        </ul>
+        </ul>
+      <ul>
+        <li><b>Supported Libraries</b></li>
+          <ul>
+           <li>Scikit-learn</li>
+           <li>XGBoost</li>
+           <li>LightGBM</li>
+           <a href="docs/en_US/SupportedFramework_Library.md">More...</a><br/>
+          </ul>
+      </ul>
+        <ul>
+        <li><b>Examples</b></li>
+         <ul>
+           <li><a href="examples/trials/mnist-pytorch">MNIST-pytorch</li></a>
+           <li><a href="examples/trials/mnist-tfv1">MNIST-tensorflow</li></a>
+           <li><a href="examples/trials/mnist-keras">MNIST-keras</li></a>
+           <li><a href="docs/en_US/TrialExample/GbdtExample.md">Auto-gbdt</a></li>
+           <li><a href="docs/en_US/TrialExample/Cifar10Examples.md">Cifar10-pytorch</li></a>
+           <li><a href="docs/en_US/TrialExample/SklearnExamples.md">Scikit-learn</a></li>
+              <a href="docs/en_US/SupportedFramework_Library.md">More...</a><br/>
+          </ul>
+        </ul>
+      </td>
+      <td align="left" >
+        <a href="docs/en_US/Tuner/BuiltinTuner.md">Hyperparameter Tuning</a>
+        <ul>
+          <b>Exhaustive search</b>
+          <ul>
+            <li><a href="docs/en_US/Tuner/BuiltinTuner.md#Random">Random Search</a></li>
+            <li><a href="docs/en_US/Tuner/BuiltinTuner.md#GridSearch">Grid Search</a></li>
+            <li><a href="docs/en_US/Tuner/BuiltinTuner.md#Batch">Batch</a></li>
+            </ul>
+          <b>Heuristic search</b>
+          <ul>
+            <li><a href="docs/en_US/Tuner/BuiltinTuner.md#Evolution">Naïve Evolution</a></li>
+            <li><a href="docs/en_US/Tuner/BuiltinTuner.md#Anneal">Anneal</a></li>  
+            <li><a href="docs/en_US/Tuner/BuiltinTuner.md#Hyperband">Hyperband</a></li>
+          </ul>
+          <b>Bayesian optimization</b>
+            <ul>
+              <li><a href="docs/en_US/Tuner/BuiltinTuner.md#BOHB">BOHB</a></li>  
+              <li><a href="docs/en_US/Tuner/BuiltinTuner.md#TPE">TPE</a></li>
+            <li><a href="docs/en_US/Tuner/BuiltinTuner.md#SMAC">SMAC</a></li> 
+            <li><a href="docs/en_US/Tuner/BuiltinTuner.md#MetisTuner">Metis Tuner</a></li>
+            <li><a href="docs/en_US/Tuner/BuiltinTuner.md#GPTuner">GP Tuner</a> </li>
+            </ul>  
+          <b>RL Based</b>
+          <ul>
+            <li><a href="docs/en_US/Tuner/BuiltinTuner.md#PPOTuner">PPO Tuner</a> </li>
+          </ul>
+        </ul>
+          <a href="docs/en_US/NAS/Overview.md">Neural Architecture Search</a>
+          <ul>                        
+            <ul>
+              <li><a href="docs/en_US/NAS/Overview.md#enas">ENAS</a></li>
+              <li><a href="docs/en_US/NAS/Overview.md#darts">DARTS</a></li>
+              <li><a href="docs/en_US/NAS/Overview.md#p-darts">P-DARTS</a></li>
+              <li><a href="docs/en_US/NAS/Overview.md#cdarts">CDARTS</a></li>
+              <li><a href="docs/en_US/Tuner/BuiltinTuner.md#NetworkMorphism">Network Morphism</a> </li>
+            </ul>    
+          </ul>
+          <a href="docs/en_US/Compressor/Overview.md">Model Compression</a>
+          <ul>
+            <b>Pruning</b>
+            <ul>
+              <li><a href="docs/en_US/Compressor/Pruner.md#agp-pruner">AGP Pruner</a></li>
+              <li><a href="docs/en_US/Compressor/Pruner.md#slim-pruner">Slim Pruner</a></li>
+              <li><a href="docs/en_US/Compressor/Pruner.md#fpgm-pruner">FPGM Pruner</a></li>
+            </ul>
+            <b>Quantization</b>
+            <ul>
+              <li><a href="docs/en_US/Compressor/Quantizer.md#qat-quantizer">QAT Quantizer</a></li>
+              <li><a href="docs/en_US/Compressor/Quantizer.md#dorefa-quantizer">DoReFa Quantizer</a></li>
+            </ul>
+          </ul>
+          <a href="docs/en_US/FeatureEngineering/Overview.md">Feature Engineering (Beta)</a>
+          <ul>
+          <li><a href="docs/en_US/FeatureEngineering/GradientFeatureSelector.md">GradientFeatureSelector</a></li>
+          <li><a href="docs/en_US/FeatureEngineering/GBDTSelector.md">GBDTSelector</a></li>
+          </ul>
+          <a href="docs/en_US/Assessor/BuiltinAssessor.md">Early Stop Algorithms</a>
+          <ul>
+          <li><a href="docs/en_US/Assessor/BuiltinAssessor.md#Medianstop">Median Stop</a></li>
+          <li><a href="docs/en_US/Assessor/BuiltinAssessor.md#Curvefitting">Curve Fitting</a></li>   
+          </ul>
+      </td>
+      <td>
+      <ul>
+        <li><a href="docs/en_US/TrainingService/LocalMode.md">Local Machine</a></li>
+        <li><a href="docs/en_US/TrainingService/RemoteMachineMode.md">Remote Servers</a></li>
+        <li><b>Kubernetes based services</b></li>
+            <ul><li><a href="docs/en_US/TrainingService/PaiMode.md">OpenPAI</a></li>
+            <li><a href="docs/en_US/TrainingService/KubeflowMode.md">Kubeflow</a></li>
+            <li><a href="docs/en_US/TrainingService/FrameworkControllerMode.md">FrameworkController on K8S (AKS etc.)</a></li>
+            </ul>
+      </ul>
+      </td>
+    </tr> 
+      <tr align="center" valign="bottom">
+      </td>
+      </tr>
+      <tr valign="top">
+       <td valign="middle">
+    <b>References</b>
+      </td>
+     <td style="border-top:#FF0000 solid 0px;">
+      <ul>
+        <li><a href="docs/en_US/sdk_reference.rst">Python API</a></li>
+        <li><a href="docs/en_US/Tutorial/AnnotationSpec.md">NNI Annotation</a></li>
+         <li><a href="docs/en_US/Tutorial/Installation.md">Supported OS</a></li>
+      </ul>
+      </td>
+       <td style="border-top:#FF0000 solid 0px;">
+      <ul>
+        <li><a href="docs/en_US/Tuner/CustomizeTuner.md">CustomizeTuner</a></li>
+        <li><a href="docs/en_US/Assessor/CustomizeAssessor.md">CustomizeAssessor</a></li>
+      </ul>
+      </td>
+        <td style="border-top:#FF0000 solid 0px;">
+      <ul>
+        <li><a href="docs/en_US/TrainingService/SupportTrainingService.md">Support TrainingService</li>
+        <li><a href="docs/en_US/TrainingService/HowToImplementTrainingService.md">Implement TrainingService</a></li>
+      </ul>
+      </td>     
+    </tr> 
+  </tbody>
+</table>
+
+## **Install & Verify**
+
+**Install through pip**
+
+* We support Linux, MacOS and Windows (local, remote and pai mode) in current stage, Ubuntu 16.04 or higher, MacOS 10.14.1 along with Windows 10.1809 are tested and supported. Simply run the following `pip install` in an environment that has `python >= 3.5`.
+
+Linux and MacOS
+
+```bash
+python3 -m pip install --upgrade nni
+```
+
+Windows
+
+```bash
+python -m pip install --upgrade nni
+```
+
+Note:
+
+* `--user` can be added if you want to install NNI in your home directory, which does not require any special privileges.
+* Currently NNI on Windows support local, remote and pai mode. Anaconda or Miniconda is highly recommended to install NNI on Windows.
+* If there is any error like `Segmentation fault`, please refer to [FAQ](docs/en_US/Tutorial/FAQ.md)
+
+**Install through source code**
+
+* We support Linux (Ubuntu 16.04 or higher), MacOS (10.14.1) and Windows (10.1809) in our current stage.
+
+Linux and MacOS
+
+* Run the following commands in an environment that has `python >= 3.5`, `git` and `wget`.
+
+```bash
+    git clone -b v1.3 https://github.com/Microsoft/nni.git
+    cd nni
+    source install.sh
+```
+
+Windows
+
+* Run the following commands in an environment that has `python >=3.5`, `git` and `PowerShell`
+
+```bash
+  git clone -b v1.3 https://github.com/Microsoft/nni.git
+  cd nni
+  powershell -ExecutionPolicy Bypass -file install.ps1
+```
+
+For the system requirements of NNI, please refer to [Install NNI](docs/en_US/Tutorial/Installation.md)
+
+For NNI on Windows, please refer to [NNI on Windows](docs/en_US/Tutorial/NniOnWindows.md)
+
+**Verify install**
+
+The following example is an experiment built on TensorFlow. Make sure you have **TensorFlow 1.x installed** before running it. Note that **currently Tensorflow 2.0 is NOT supported**.
+
+* Download the examples via clone the source code.
+
+```bash
+    git clone -b v1.3 https://github.com/Microsoft/nni.git
+```
+
+Linux and MacOS
+
+* Run the MNIST example.
+
+```bash
+    nnictl create --config nni/examples/trials/mnist-tfv1/config.yml
+```
+
+Windows
+
+* Run the MNIST example.
+
+```bash
+    nnictl create --config nni\examples\trials\mnist-tfv1\config_windows.yml
+```
+
+* Wait for the message `INFO: Successfully started experiment!` in the command line. This message indicates that your experiment has been successfully started. You can explore the experiment using the `Web UI url`.
+
+```text
+INFO: Starting restful server...
+INFO: Successfully started Restful server!
+INFO: Setting local config...
+INFO: Successfully set local config!
+INFO: Starting experiment...
+INFO: Successfully started experiment!
+-----------------------------------------------------------------------
+The experiment id is egchD4qy
+The Web UI urls are: http://223.255.255.1:8080   http://127.0.0.1:8080
+-----------------------------------------------------------------------
+
+You can use these commands to get more information about the experiment
+-----------------------------------------------------------------------
+         commands                       description
+1. nnictl experiment show        show the information of experiments
+2. nnictl trial ls               list all of trial jobs
+3. nnictl top                    monitor the status of running experiments
+4. nnictl log stderr             show stderr log content
+5. nnictl log stdout             show stdout log content
+6. nnictl stop                   stop an experiment
+7. nnictl trial kill             kill a trial job by id
+8. nnictl --help                 get help information about nnictl
+-----------------------------------------------------------------------
+```
+
+* Open the `Web UI url` in your browser, you can view detail information of the experiment and all the submitted trial jobs as shown below. [Here](docs/en_US/Tutorial/WebUI.md) are more Web UI pages.
+
+<table style="border: none">
+    <th><img src="./docs/img/webui_overview_page.png" alt="drawing" width="395"/></th>
+    <th><img src="./docs/img/webui_trialdetail_page.png" alt="drawing" width="410"/></th>
+</table>
+
+## **Documentation**
+* To learn about what's NNI, read the [NNI Overview](https://nni.readthedocs.io/en/latest/Overview.html). 
+* To get yourself familiar with how to use NNI, read the [documentation](https://nni.readthedocs.io/en/latest/index.html). 
+* To get started and install NNI on your system, please refer to [Install NNI](docs/en_US/Tutorial/Installation.md).
+
+## **Contributing**
+This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.
+
+When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.
+
+This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). For more information see the Code of [Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or contact opencode@microsoft.com with any additional questions or comments.
+
+After getting familiar with contribution agreements, you are ready to create your first PR =), follow the NNI developer tutorials to get start:
+* We recommend new contributors to start with ['good first issue'](https://github.com/Microsoft/nni/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) or ['help-wanted'](https://github.com/microsoft/nni/issues?q=is%3Aopen+is%3Aissue+label%3A%22help+wanted%22), these issues are simple and easy to start.
+* [NNI developer environment installation tutorial](docs/en_US/Tutorial/SetupNniDeveloperEnvironment.md)
+* [How to debug](docs/en_US/Tutorial/HowToDebug.md)
+* [Customize your own Tuner](docs/en_US/Tuner/CustomizeTuner.md)
+* [Implement customized TrainingService](docs/en_US/TrainingService/HowToImplementTrainingService.md)
+* [Implement a new NAS trainer on NNI](https://github.com/microsoft/nni/blob/master/docs/en_US/NAS/NasInterface.md#implement-a-new-nas-trainer-on-nni)
+* [Customize your own Advisor](docs/en_US/Tuner/CustomizeAdvisor.md)
+
+## **External Repositories and References**
+With authors' permission, we listed a set of NNI usage examples and relevant articles.
+* ### **External Repositories** ###
+   * Run [ENAS](examples/tuners/enas_nni/README.md) with NNI
+   * Run [Neural Network Architecture Search](examples/trials/nas_cifar10/README.md) with NNI 
+   * [Automatic Feature Engineering](examples/feature_engineering/auto-feature-engineering/README.md) with NNI 
+   * [Hyperparameter Tuning for Matrix Factorization](https://github.com/microsoft/recommenders/blob/master/notebooks/04_model_select_and_optimize/nni_surprise_svd.ipynb) with NNI
+   * [scikit-nni](https://github.com/ksachdeva/scikit-nni) Hyper-parameter search for scikit-learn pipelines using NNI
+
+* ### **Relevant Articles** ###
+  
+  * [Hyper Parameter Optimization Comparison](docs/en_US/CommunitySharings/HpoComparision.md)
+  * [Neural Architecture Search Comparison](docs/en_US/CommunitySharings/NasComparision.md)
+  * [Parallelizing a Sequential Algorithm TPE](docs/en_US/CommunitySharings/ParallelizingTpeSearch.md)
+  * [Automatically tuning SVD with NNI](docs/en_US/CommunitySharings/RecommendersSvd.md)
+  * [Automatically tuning SPTAG with NNI](docs/en_US/CommunitySharings/SptagAutoTune.md)
+  * [Find thy hyper-parameters for scikit-learn pipelines using Microsoft NNI](https://towardsdatascience.com/find-thy-hyper-parameters-for-scikit-learn-pipelines-using-microsoft-nni-f1015b1224c1)
+  * **Blog (in Chinese)** - [AutoML tools (Advisor, NNI and Google Vizier) comparison](http://gaocegege.com/Blog/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/katib-new#%E6%80%BB%E7%BB%93%E4%B8%8E%E5%88%86%E6%9E%90) by [@gaocegege](https://github.com/gaocegege) - 总结与分析 section of design and implementation of kubeflow/katib
+  * **Blog (in Chinese)** - [A summary of NNI new capabilities in 2019](https://mp.weixin.qq.com/s/7_KRT-rRojQbNuJzkjFMuA) by @squirrelsc
+
+## **Feedback**
+* Discuss on the NNI [Gitter](https://gitter.im/Microsoft/nni?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) in NNI.
+* [File an issue](https://github.com/microsoft/nni/issues/new/choose) on GitHub.
+* Ask a question with NNI tags on [Stack Overflow](https://stackoverflow.com/questions/tagged/nni?sort=Newest&edited=true).
+
+## Related Projects
+Targeting at openness and advancing state-of-art technology, [Microsoft Research (MSR)](https://www.microsoft.com/en-us/research/group/systems-research-group-asia/) had also released few other open source projects.
+
+* [OpenPAI](https://github.com/Microsoft/pai) : an open source platform that provides complete AI model training and resource management capabilities, it is easy to extend and supports on-premise, cloud and hybrid environments in various scale.
+* [FrameworkController](https://github.com/Microsoft/frameworkcontroller) : an open source general-purpose Kubernetes Pod Controller that orchestrate all kinds of applications on Kubernetes by a single controller.
+* [MMdnn](https://github.com/Microsoft/MMdnn) : A comprehensive, cross-framework solution to convert, visualize and diagnose deep neural network models. The "MM" in MMdnn stands for model management and "dnn" is an acronym for deep neural network.
+* [SPTAG](https://github.com/Microsoft/SPTAG) : Space Partition Tree And Graph (SPTAG) is an open source library for large scale vector approximate nearest neighbor search scenario.
+
+We encourage researchers and students leverage these projects to accelerate the AI development and research.
+
+## **License**
+
+The entire codebase is under [MIT license](LICENSE)
+
diff --git a/docs/en_US/NAS/Overview.md b/docs/en_US/NAS/Overview.md
index 589ea4972b..1cd27c877d 100644
--- a/docs/en_US/NAS/Overview.md
+++ b/docs/en_US/NAS/Overview.md
@@ -22,7 +22,7 @@ NNI supports below NAS algorithms now and is adding more. User can reproduce an
 | [DARTS](DARTS.md) | [DARTS: Differentiable Architecture Search](https://arxiv.org/abs/1806.09055) introduces a novel algorithm for differentiable network architecture search on bilevel optimization. |
 | [P-DARTS](PDARTS.md) | [Progressive Differentiable Architecture Search: Bridging the Depth Gap between Search and Evaluation](https://arxiv.org/abs/1904.12760) is based on DARTS. It introduces an efficient algorithm which allows the depth of searched architectures to grow gradually during the training procedure. |
 | [SPOS](SPOS.md) | [Single Path One-Shot Neural Architecture Search with Uniform Sampling](https://arxiv.org/abs/1904.00420) constructs a simplified supernet trained with an uniform path sampling method, and applies an evolutionary algorithm to efficiently search for the best-performing architectures. |
-| [CDARTS](PDARTS.md) | [Cyclic Differentiable Architecture Search](https://arxiv.org/abs/****) builds a cyclic feedback mechanism between the search and evaluation networks. It introduces a cyclic differentiable architecture search framework which integrates the two networks into a unified architecture.|
+| [CDARTS](CDARTS.md) | [Cyclic Differentiable Architecture Search](https://arxiv.org/abs/****) builds a cyclic feedback mechanism between the search and evaluation networks. It introduces a cyclic differentiable architecture search framework which integrates the two networks into a unified architecture.|
 
 One-shot algorithms run **standalone without nnictl**. Only PyTorch version has been implemented. Tensorflow 2.x will be supported in future release.
 

From 28b579c77701554f9b676745ce88e15a8f5375d0 Mon Sep 17 00:00:00 2001
From: "penghouwen@icloud.com" <penghouwen@icloud.com>
Date: Wed, 15 Jan 2020 17:06:30 +0800
Subject: [PATCH 04/21] new revisions

---
 .../pynni/nni/nas/pytorch/cdarts/trainer.py   | 82 ++-----------------
 src/sdk/pynni/nni/nas/pytorch/cdarts/utils.py | 72 ++++++++++++++++
 2 files changed, 79 insertions(+), 75 deletions(-)
 create mode 100644 src/sdk/pynni/nni/nas/pytorch/cdarts/utils.py

diff --git a/src/sdk/pynni/nni/nas/pytorch/cdarts/trainer.py b/src/sdk/pynni/nni/nas/pytorch/cdarts/trainer.py
index 878294cfa2..109cc35ca0 100644
--- a/src/sdk/pynni/nni/nas/pytorch/cdarts/trainer.py
+++ b/src/sdk/pynni/nni/nas/pytorch/cdarts/trainer.py
@@ -2,13 +2,13 @@
 import os
 import torch
 import torch.nn as nn
-import torch.distributed as dist
 import torch.nn.functional as F
 import apex # pylint: disable=import-error
 
 from apex.parallel import DistributedDataParallel # pylint: disable=import-error
 from nni.nas.pytorch.cdarts import RegularizedDartsMutator, RegularizedMutatorParallel, DartsDiscreteMutator
 from nni.nas.pytorch.utils import AverageMeterGroup
+from .utils import CyclicIterator, TorchTensorEncoder, accuracy, reduce_metrics
 
 PHASE_SMALL = "small"
 PHASE_LARGE = "large"
@@ -152,7 +152,7 @@ def __init__(self, model_small, model_large, criterion, loaders, samplers, logge
                 self.model_large.callback_queued = True
             # mutator large never gets optimized, so do not need parallelized
 
-    def warmup(self, phase, epoch):
+    def _warmup(self, phase, epoch):
         assert phase in [PHASE_SMALL, PHASE_LARGE]
         if phase == PHASE_SMALL:
             model, optimizer = self.model_small, self.optimizer_small
@@ -192,7 +192,7 @@ def _reset_nan(self, parameters):
                     if p != p:  # equivalent to `isnan(p)`
                         param[i] = float("-inf")
 
-    def joint_train(self, epoch):
+    def _joint_train(self, epoch):
         self.model_large.train()
         self.model_small.train()
         meters = AverageMeterGroup()
@@ -246,12 +246,12 @@ def train(self):
             if epoch < self.warmup_epochs:
                 with torch.no_grad():  # otherwise grads will be retained on the architecture params
                     self.mutator_small.reset_with_loss()
-                self.warmup(PHASE_SMALL, epoch)
+                self._warmup(PHASE_SMALL, epoch)
             else:
                 with torch.no_grad():
                     self.mutator_large.reset()
-                self.warmup(PHASE_LARGE, epoch)
-                self.joint_train(epoch)
+                self._warmup(PHASE_LARGE, epoch)
+                self._joint_train(epoch)
 
             self.export(os.path.join(self.checkpoint_dir, "epoch_{:02d}.json".format(epoch)),
                         os.path.join(self.checkpoint_dir, "epoch_{:02d}.genotypes".format(epoch)))
@@ -262,72 +262,4 @@ def export(self, file, genotype_file):
             with open(file, "w") as f:
                 json.dump(mutator_export, f, indent=2, sort_keys=True, cls=TorchTensorEncoder)
             with open(genotype_file, "w") as f:
-                f.write(str(genotypes))
-
-class CyclicIterator:
-    def __init__(self, loader, sampler, distributed):
-        self.loader = loader
-        self.sampler = sampler
-        self.epoch = 0
-        self.distributed = distributed
-        self._next_epoch()
-
-    def _next_epoch(self):
-        if self.distributed:
-            self.sampler.set_epoch(self.epoch)
-        self.iterator = iter(self.loader)
-        self.epoch += 1
-
-    def __len__(self):
-        return len(self.loader)
-
-    def __iter__(self):
-        return self
-
-    def __next__(self):
-        try:
-            return next(self.iterator)
-        except StopIteration:
-            self._next_epoch()
-            return next(self.iterator)
-
-class TorchTensorEncoder(json.JSONEncoder):
-    def default(self, o):  # pylint: disable=method-hidden
-        if isinstance(o, torch.Tensor):
-            olist = o.tolist()
-            if "bool" not in o.type().lower() and all(map(lambda d: d == 0 or d == 1, olist)):
-                _logger.warning("Every element in %s is either 0 or 1. "
-                                "You might consider convert it into bool.", olist)
-            return olist
-        return super().default(o)
-
-def accuracy(output, target, topk=(1,)):
-    """ Computes the precision@k for the specified values of k """
-    maxk = max(topk)
-    batch_size = target.size(0)
-
-    _, pred = output.topk(maxk, 1, True, True)
-    pred = pred.t()
-    # one-hot case
-    if target.ndimension() > 1:
-        target = target.max(1)[1]
-
-    correct = pred.eq(target.view(1, -1).expand_as(pred))
-
-    res = []
-    for k in topk:
-        correct_k = correct[:k].view(-1).float().sum(0)
-        res.append(correct_k.mul_(1.0 / batch_size))
-    return res
-
-def reduce_tensor(tensor):
-    rt = tensor.clone()
-    dist.all_reduce(rt, op=dist.ReduceOp.SUM)
-    rt /= float(os.environ["WORLD_SIZE"])
-    return rt
-
-
-def reduce_metrics(metrics, distributed=False):
-    if distributed:
-        return {k: reduce_tensor(v).item() for k, v in metrics.items()}
-    return {k: v.item() for k, v in metrics.items()}
+                f.write(str(genotypes))
\ No newline at end of file
diff --git a/src/sdk/pynni/nni/nas/pytorch/cdarts/utils.py b/src/sdk/pynni/nni/nas/pytorch/cdarts/utils.py
new file mode 100644
index 0000000000..eb31bf4945
--- /dev/null
+++ b/src/sdk/pynni/nni/nas/pytorch/cdarts/utils.py
@@ -0,0 +1,72 @@
+import os
+import json
+import torch
+import torch.distributed as dist
+
+class CyclicIterator:
+    def __init__(self, loader, sampler, distributed):
+        self.loader = loader
+        self.sampler = sampler
+        self.epoch = 0
+        self.distributed = distributed
+        self._next_epoch()
+
+    def _next_epoch(self):
+        if self.distributed:
+            self.sampler.set_epoch(self.epoch)
+        self.iterator = iter(self.loader)
+        self.epoch += 1
+
+    def __len__(self):
+        return len(self.loader)
+
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        try:
+            return next(self.iterator)
+        except StopIteration:
+            self._next_epoch()
+            return next(self.iterator)
+
+class TorchTensorEncoder(json.JSONEncoder):
+    def default(self, o):  # pylint: disable=method-hidden
+        if isinstance(o, torch.Tensor):
+            olist = o.tolist()
+            if "bool" not in o.type().lower() and all(map(lambda d: d == 0 or d == 1, olist)):
+                _logger.warning("Every element in %s is either 0 or 1. "
+                                "You might consider convert it into bool.", olist)
+            return olist
+        return super().default(o)
+
+def accuracy(output, target, topk=(1,)):
+    """ Computes the precision@k for the specified values of k """
+    maxk = max(topk)
+    batch_size = target.size(0)
+
+    _, pred = output.topk(maxk, 1, True, True)
+    pred = pred.t()
+    # one-hot case
+    if target.ndimension() > 1:
+        target = target.max(1)[1]
+
+    correct = pred.eq(target.view(1, -1).expand_as(pred))
+
+    res = []
+    for k in topk:
+        correct_k = correct[:k].view(-1).float().sum(0)
+        res.append(correct_k.mul_(1.0 / batch_size))
+    return res
+
+def reduce_tensor(tensor):
+    rt = tensor.clone()
+    dist.all_reduce(rt, op=dist.ReduceOp.SUM)
+    rt /= float(os.environ["WORLD_SIZE"])
+    return rt
+
+
+def reduce_metrics(metrics, distributed=False):
+    if distributed:
+        return {k: reduce_tensor(v).item() for k, v in metrics.items()}
+    return {k: v.item() for k, v in metrics.items()}

From 022faa161d793d1e19cbb5be80cdaabc0bafc07c Mon Sep 17 00:00:00 2001
From: zhangyuge <v-yugzh@microsoft.com>
Date: Wed, 15 Jan 2020 17:22:06 +0800
Subject: [PATCH 05/21] fix code style

---
 src/sdk/pynni/nni/nas/pytorch/cdarts/mutator.py |  5 +++--
 src/sdk/pynni/nni/nas/pytorch/cdarts/trainer.py | 14 ++++++++------
 src/sdk/pynni/nni/nas/pytorch/cdarts/utils.py   | 13 +++++++------
 3 files changed, 18 insertions(+), 14 deletions(-)

diff --git a/src/sdk/pynni/nni/nas/pytorch/cdarts/mutator.py b/src/sdk/pynni/nni/nas/pytorch/cdarts/mutator.py
index dd76fa5632..a9a5ce0c7c 100644
--- a/src/sdk/pynni/nni/nas/pytorch/cdarts/mutator.py
+++ b/src/sdk/pynni/nni/nas/pytorch/cdarts/mutator.py
@@ -3,11 +3,12 @@
 
 import torch
 
-from apex.parallel import DistributedDataParallel # pylint: disable=import-error
+from apex.parallel import DistributedDataParallel  # pylint: disable=import-error
 from nni.nas.pytorch.darts import DartsMutator
 from nni.nas.pytorch.mutables import LayerChoice
 from nni.nas.pytorch.mutator import Mutator
 
+
 class RegularizedDartsMutator(DartsMutator):
     def reset(self):
         raise ValueError("You should probably call `reset_with_loss`.")
@@ -69,4 +70,4 @@ def __init__(self, model, parent_mutator):
         self.__dict__["parent_mutator"] = parent_mutator  # avoid parameters to be included
 
     def sample_search(self):
-        return self.parent_mutator.sample_final()
\ No newline at end of file
+        return self.parent_mutator.sample_final()
diff --git a/src/sdk/pynni/nni/nas/pytorch/cdarts/trainer.py b/src/sdk/pynni/nni/nas/pytorch/cdarts/trainer.py
index 109cc35ca0..58c1e45a84 100644
--- a/src/sdk/pynni/nni/nas/pytorch/cdarts/trainer.py
+++ b/src/sdk/pynni/nni/nas/pytorch/cdarts/trainer.py
@@ -1,18 +1,20 @@
 import json
 import os
+
+import apex  # pylint: disable=import-error
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-import apex # pylint: disable=import-error
-
-from apex.parallel import DistributedDataParallel # pylint: disable=import-error
+from apex.parallel import DistributedDataParallel  # pylint: disable=import-error
 from nni.nas.pytorch.cdarts import RegularizedDartsMutator, RegularizedMutatorParallel, DartsDiscreteMutator
 from nni.nas.pytorch.utils import AverageMeterGroup
+
 from .utils import CyclicIterator, TorchTensorEncoder, accuracy, reduce_metrics
 
 PHASE_SMALL = "small"
 PHASE_LARGE = "large"
 
+
 class InteractiveKLLoss(nn.Module):
     def __init__(self, temperature):
         super().__init__()
@@ -55,7 +57,7 @@ def __init__(self, model_small, model_large, criterion, loaders, samplers, logge
         warmup_epochs : int
             The epochs to warmup the search network
         fix_head : bool
-            ``True`` if fixing the paramters of auxiliary heads, else unfix the paramters of auxiliary heads. 
+            ``True`` if fixing the paramters of auxiliary heads, else unfix the paramters of auxiliary heads.
         epochs : int
             Number of epochs planned for training.
         steps_per_epoch : int
@@ -206,7 +208,7 @@ def _joint_train(self, epoch):
             self.optimizer_alpha.zero_grad()
             self.optimizer_large.zero_grad()
             reg_decay = max(self.regular_coeff * (1 - float(epoch - self.warmup_epochs) / (
-                (self.epochs - self.warmup_epochs) * self.regular_ratio)), 0)
+                 (self.epochs - self.warmup_epochs) * self.regular_ratio)), 0)
             loss_regular = self.mutator_small.reset_with_loss()
             if loss_regular:
                 loss_regular *= reg_decay
@@ -262,4 +264,4 @@ def export(self, file, genotype_file):
             with open(file, "w") as f:
                 json.dump(mutator_export, f, indent=2, sort_keys=True, cls=TorchTensorEncoder)
             with open(genotype_file, "w") as f:
-                f.write(str(genotypes))
\ No newline at end of file
+                f.write(str(genotypes))
diff --git a/src/sdk/pynni/nni/nas/pytorch/cdarts/utils.py b/src/sdk/pynni/nni/nas/pytorch/cdarts/utils.py
index eb31bf4945..b00f0744f0 100644
--- a/src/sdk/pynni/nni/nas/pytorch/cdarts/utils.py
+++ b/src/sdk/pynni/nni/nas/pytorch/cdarts/utils.py
@@ -1,8 +1,10 @@
-import os
 import json
+import os
+
 import torch
 import torch.distributed as dist
 
+
 class CyclicIterator:
     def __init__(self, loader, sampler, distributed):
         self.loader = loader
@@ -30,16 +32,14 @@ def __next__(self):
             self._next_epoch()
             return next(self.iterator)
 
+
 class TorchTensorEncoder(json.JSONEncoder):
     def default(self, o):  # pylint: disable=method-hidden
         if isinstance(o, torch.Tensor):
-            olist = o.tolist()
-            if "bool" not in o.type().lower() and all(map(lambda d: d == 0 or d == 1, olist)):
-                _logger.warning("Every element in %s is either 0 or 1. "
-                                "You might consider convert it into bool.", olist)
-            return olist
+            return o.tolist()
         return super().default(o)
 
+
 def accuracy(output, target, topk=(1,)):
     """ Computes the precision@k for the specified values of k """
     maxk = max(topk)
@@ -59,6 +59,7 @@ def accuracy(output, target, topk=(1,)):
         res.append(correct_k.mul_(1.0 / batch_size))
     return res
 
+
 def reduce_tensor(tensor):
     rt = tensor.clone()
     dist.all_reduce(rt, op=dist.ReduceOp.SUM)

From 25778f550aab3d8d7e70742865a99d490448a7b8 Mon Sep 17 00:00:00 2001
From: zhangyuge <v-yugzh@microsoft.com>
Date: Wed, 15 Jan 2020 17:52:55 +0800
Subject: [PATCH 06/21] fix code style

---
 src/sdk/pynni/nni/nas/pytorch/cdarts/trainer.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/sdk/pynni/nni/nas/pytorch/cdarts/trainer.py b/src/sdk/pynni/nni/nas/pytorch/cdarts/trainer.py
index 58c1e45a84..e23c332ab2 100644
--- a/src/sdk/pynni/nni/nas/pytorch/cdarts/trainer.py
+++ b/src/sdk/pynni/nni/nas/pytorch/cdarts/trainer.py
@@ -1,10 +1,10 @@
 import json
 import os
 
-import apex  # pylint: disable=import-error
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
+import apex  # pylint: disable=import-error
 from apex.parallel import DistributedDataParallel  # pylint: disable=import-error
 from nni.nas.pytorch.cdarts import RegularizedDartsMutator, RegularizedMutatorParallel, DartsDiscreteMutator
 from nni.nas.pytorch.utils import AverageMeterGroup
@@ -208,7 +208,7 @@ def _joint_train(self, epoch):
             self.optimizer_alpha.zero_grad()
             self.optimizer_large.zero_grad()
             reg_decay = max(self.regular_coeff * (1 - float(epoch - self.warmup_epochs) / (
-                 (self.epochs - self.warmup_epochs) * self.regular_ratio)), 0)
+                (self.epochs - self.warmup_epochs) * self.regular_ratio)), 0)
             loss_regular = self.mutator_small.reset_with_loss()
             if loss_regular:
                 loss_regular *= reg_decay

From f9590506ddf1dc0a5a1a43740b5ca1718262a733 Mon Sep 17 00:00:00 2001
From: zhangyuge <v-yugzh@microsoft.com>
Date: Wed, 15 Jan 2020 18:20:21 +0800
Subject: [PATCH 07/21] disable wrong import order

---
 src/sdk/pynni/nni/nas/pytorch/cdarts/mutator.py | 6 +++---
 src/sdk/pynni/nni/nas/pytorch/cdarts/trainer.py | 4 ++--
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/src/sdk/pynni/nni/nas/pytorch/cdarts/mutator.py b/src/sdk/pynni/nni/nas/pytorch/cdarts/mutator.py
index a9a5ce0c7c..35460fa3d8 100644
--- a/src/sdk/pynni/nni/nas/pytorch/cdarts/mutator.py
+++ b/src/sdk/pynni/nni/nas/pytorch/cdarts/mutator.py
@@ -4,9 +4,9 @@
 import torch
 
 from apex.parallel import DistributedDataParallel  # pylint: disable=import-error
-from nni.nas.pytorch.darts import DartsMutator
-from nni.nas.pytorch.mutables import LayerChoice
-from nni.nas.pytorch.mutator import Mutator
+from nni.nas.pytorch.darts import DartsMutator  # pylint: disable=wrong-import-order
+from nni.nas.pytorch.mutables import LayerChoice  # pylint: disable=wrong-import-order
+from nni.nas.pytorch.mutator import Mutator  # pylint: disable=wrong-import-order
 
 
 class RegularizedDartsMutator(DartsMutator):
diff --git a/src/sdk/pynni/nni/nas/pytorch/cdarts/trainer.py b/src/sdk/pynni/nni/nas/pytorch/cdarts/trainer.py
index e23c332ab2..6dc5a31559 100644
--- a/src/sdk/pynni/nni/nas/pytorch/cdarts/trainer.py
+++ b/src/sdk/pynni/nni/nas/pytorch/cdarts/trainer.py
@@ -6,8 +6,8 @@
 import torch.nn.functional as F
 import apex  # pylint: disable=import-error
 from apex.parallel import DistributedDataParallel  # pylint: disable=import-error
-from nni.nas.pytorch.cdarts import RegularizedDartsMutator, RegularizedMutatorParallel, DartsDiscreteMutator
-from nni.nas.pytorch.utils import AverageMeterGroup
+from nni.nas.pytorch.cdarts import RegularizedDartsMutator, RegularizedMutatorParallel, DartsDiscreteMutator  # pylint: disable=wrong-import-order
+from nni.nas.pytorch.utils import AverageMeterGroup  # pylint: disable=wrong-import-order
 
 from .utils import CyclicIterator, TorchTensorEncoder, accuracy, reduce_metrics
 

From 2e96bdfff5bb8aca836842ab8a94f5d7749e7ac7 Mon Sep 17 00:00:00 2001
From: Yuge Zhang <scottyugochang@gmail.com>
Date: Wed, 15 Jan 2020 19:36:15 +0800
Subject: [PATCH 08/21] fix syntax issues

---
 examples/nas/cdarts/datasets/imagenet.py |  2 ++
 examples/nas/cdarts/ops.py               |  1 -
 examples/nas/cdarts/retrain.py           |  4 +--
 examples/nas/cdarts/run_retrain_cifar.sh |  0
 examples/nas/cdarts/run_search_cifar.sh  |  0
 examples/nas/cdarts/utils.py             | 46 +-----------------------
 6 files changed, 5 insertions(+), 48 deletions(-)
 mode change 100644 => 100755 examples/nas/cdarts/run_retrain_cifar.sh
 mode change 100644 => 100755 examples/nas/cdarts/run_search_cifar.sh

diff --git a/examples/nas/cdarts/datasets/imagenet.py b/examples/nas/cdarts/datasets/imagenet.py
index c803a76755..29b880a36b 100644
--- a/examples/nas/cdarts/datasets/imagenet.py
+++ b/examples/nas/cdarts/datasets/imagenet.py
@@ -1,3 +1,5 @@
+import os
+
 import numpy as np
 import torch
 import torchvision.datasets as dset
diff --git a/examples/nas/cdarts/ops.py b/examples/nas/cdarts/ops.py
index 403a2b6b8a..9d2f6b20ad 100644
--- a/examples/nas/cdarts/ops.py
+++ b/examples/nas/cdarts/ops.py
@@ -5,7 +5,6 @@
 import torch.nn as nn
 
 OPS = {
-    'none': lambda C, stride, affine: Zero(stride),
     'avg_pool_3x3': lambda C, stride, affine: PoolWithoutBN('avg', C, 3, stride, 1, affine=affine),
     'max_pool_3x3': lambda C, stride, affine: PoolWithoutBN('max', C, 3, stride, 1, affine=affine),
     'skip_connect': lambda C, stride, affine: nn.Identity() if stride == 1 else FactorizedReduce(C, C, affine=affine),
diff --git a/examples/nas/cdarts/retrain.py b/examples/nas/cdarts/retrain.py
index 453c3f8ae9..4b4eb1b780 100644
--- a/examples/nas/cdarts/retrain.py
+++ b/examples/nas/cdarts/retrain.py
@@ -7,10 +7,10 @@
 import torch
 import torch.nn as nn
 
+import apex  # pylint: disable=import-error
 import datasets
 import utils
-import apex # pylint: disable=import-error
-from apex.parallel import DistributedDataParallel # pylint: disable=import-error
+from apex.parallel import DistributedDataParallel  # pylint: disable=import-error
 from config import RetrainConfig
 from datasets.cifar import get_augment_datasets
 from model import Model
diff --git a/examples/nas/cdarts/run_retrain_cifar.sh b/examples/nas/cdarts/run_retrain_cifar.sh
old mode 100644
new mode 100755
diff --git a/examples/nas/cdarts/run_search_cifar.sh b/examples/nas/cdarts/run_search_cifar.sh
old mode 100644
new mode 100755
diff --git a/examples/nas/cdarts/utils.py b/examples/nas/cdarts/utils.py
index d0b9d48a80..4ed223c060 100644
--- a/examples/nas/cdarts/utils.py
+++ b/examples/nas/cdarts/utils.py
@@ -11,6 +11,7 @@
 
 from genotypes import Genotype
 from ops import PRIMITIVES
+from nni.nas.pytorch.cdarts.utils import *
 
 
 def get_logger(file_path):
@@ -73,51 +74,6 @@ def forward(self, inputs, targets):
         loss = (-targets * log_probs).mean(0).sum()
         return loss
 
-
-class TorchTensorEncoder(json.JSONEncoder):
-    def default(self, o):  # pylint: disable=method-hidden
-        if isinstance(o, torch.Tensor):
-            olist = o.tolist()
-            if "bool" not in o.type().lower() and all(map(lambda d: d == 0 or d == 1, olist)):
-                _logger.warning("Every element in %s is either 0 or 1. "
-                                "You might consider convert it into bool.", olist)
-            return olist
-        return super().default(o)
-
-
-def accuracy(output, target, topk=(1,)):
-    """ Computes the precision@k for the specified values of k """
-    maxk = max(topk)
-    batch_size = target.size(0)
-
-    _, pred = output.topk(maxk, 1, True, True)
-    pred = pred.t()
-    # one-hot case
-    if target.ndimension() > 1:
-        target = target.max(1)[1]
-
-    correct = pred.eq(target.view(1, -1).expand_as(pred))
-
-    res = []
-    for k in topk:
-        correct_k = correct[:k].view(-1).float().sum(0)
-        res.append(correct_k.mul_(1.0 / batch_size))
-    return res
-
-
-def reduce_tensor(tensor):
-    rt = tensor.clone()
-    dist.all_reduce(rt, op=dist.ReduceOp.SUM)
-    rt /= float(os.environ["WORLD_SIZE"])
-    return rt
-
-
-def reduce_metrics(metrics, distributed=False):
-    if distributed:
-        return {k: reduce_tensor(v).item() for k, v in metrics.items()}
-    return {k: v.item() for k, v in metrics.items()}
-
-
 def parse_results(results, n_nodes):
     concat = range(2, 2 + n_nodes)
     normal_gene = []

From 386a4a98b3e6e9d4fd40f79dc5255bbdeed2fe7e Mon Sep 17 00:00:00 2001
From: Yuge Zhang <scottyugochang@gmail.com>
Date: Wed, 15 Jan 2020 19:56:59 +0800
Subject: [PATCH 09/21] update nas docs

---
 docs/en_US/NAS/CDARTS.md                      | 20 +++++++++++++
 docs/en_US/nas.rst                            |  1 +
 .../pynni/nni/nas/pytorch/cdarts/mutator.py   | 28 +++++++++++++++++++
 .../pynni/nni/nas/pytorch/cdarts/trainer.py   |  1 +
 4 files changed, 50 insertions(+)

diff --git a/docs/en_US/NAS/CDARTS.md b/docs/en_US/NAS/CDARTS.md
index 9c88d13b81..1c2a758f1e 100644
--- a/docs/en_US/NAS/CDARTS.md
+++ b/docs/en_US/NAS/CDARTS.md
@@ -5,6 +5,7 @@ CDARTS builds a cyclic feedback mechanism between the search and evaluation netw
 
 ## Reproduction Results
 This is CDARTS based on the NNI platform, which currently supports CIFAR10 search and retrain. ImageNet search and retrain should also be supported, and we provide corresponding interfaces. Our reproduced results on NNI are slightly lower than the paper, but much higher than the original DARTS. Here we show the results of three independent experiments on CIFAR10.
+
 | Runs | Paper | NNI | 
 | ---- |:-------------:| :-----:|
 | 1 | 97.52 | 97.44 |
@@ -32,3 +33,22 @@ bash run_search_cifar.sh
 # train the best architecture.
 bash run_retrain_cifar.sh
 ```
+
+## Reference
+
+### PyTorch
+
+```eval_rst
+..  autoclass:: nni.nas.pytorch.cdarts.CdartsTrainer
+    :members:
+
+    .. automethod:: __init__
+
+..  autoclass:: nni.nas.pytorch.cdarts.RegularizedDartsMutator
+    :members:
+
+..  autoclass:: nni.nas.pytorch.cdarts.DartsDiscreteMutator
+    :members:
+
+    .. automethod:: __init__
+```
diff --git a/docs/en_US/nas.rst b/docs/en_US/nas.rst
index 32c235b3bb..a5bd8f6b8f 100644
--- a/docs/en_US/nas.rst
+++ b/docs/en_US/nas.rst
@@ -24,3 +24,4 @@ For details, please refer to the following tutorials:
     DARTS <NAS/DARTS>
     P-DARTS <NAS/PDARTS>
     SPOS <NAS/SPOS>
+    CDARTS <NAS/CDARTS>
diff --git a/src/sdk/pynni/nni/nas/pytorch/cdarts/mutator.py b/src/sdk/pynni/nni/nas/pytorch/cdarts/mutator.py
index 35460fa3d8..2a669d06ba 100644
--- a/src/sdk/pynni/nni/nas/pytorch/cdarts/mutator.py
+++ b/src/sdk/pynni/nni/nas/pytorch/cdarts/mutator.py
@@ -10,10 +10,24 @@
 
 
 class RegularizedDartsMutator(DartsMutator):
+    """
+    DartsMutator with choice cut and regularization on some of the choices.
+    """
+
     def reset(self):
         raise ValueError("You should probably call `reset_with_loss`.")
 
     def cut_choices(self, cut_num=2):
+        """
+        Cut the choices with the smallest weights.
+        ``cut_num`` should be the accumulative number of cutting, e.g., if first time cutting
+        is 2, the second time should be 4 to cut another two.
+
+        Parameters
+        ----------
+        cut_num : int
+            Number of choices to cut, so far.
+        """
         # `cut_choices` is implemented but not used
         for mutable in self.mutables:
             if isinstance(mutable, LayerChoice):
@@ -23,6 +37,9 @@ def cut_choices(self, cut_num=2):
                         self.choices[mutable.key][i] = -float("inf")
 
     def reset_with_loss(self):
+        """
+        Resample and return loss. If loss is 0, to avoid device issue, it will return ``None``.
+        """
         self._cache, reg_loss = self.sample_search()
         return reg_loss
 
@@ -66,6 +83,17 @@ def export(self, logger):
 class DartsDiscreteMutator(Mutator):
 
     def __init__(self, model, parent_mutator):
+        """
+        Initialization.
+
+        Parameters
+        ----------
+        model : nn.Module
+            The model to apply the mutator.
+        parent_mutator : Mutator
+            The mutator that is used to call ``sample_final()`` method to get the architecture
+            for training.
+        """
         super().__init__(model)
         self.__dict__["parent_mutator"] = parent_mutator  # avoid parameters to be included
 
diff --git a/src/sdk/pynni/nni/nas/pytorch/cdarts/trainer.py b/src/sdk/pynni/nni/nas/pytorch/cdarts/trainer.py
index 6dc5a31559..8859da824b 100644
--- a/src/sdk/pynni/nni/nas/pytorch/cdarts/trainer.py
+++ b/src/sdk/pynni/nni/nas/pytorch/cdarts/trainer.py
@@ -36,6 +36,7 @@ def __init__(self, model_small, model_large, criterion, loaders, samplers, logge
                  nasnet_lr=0.2, local_rank=0, share_module=True):
         """
         Initialize a CdartsTrainer.
+        
         Parameters
         ----------
         model_small : nn.Module

From 457be736aed1f628bfc7bc8c8b3c6e930d57c9a1 Mon Sep 17 00:00:00 2001
From: Yuge Zhang <scottyugochang@gmail.com>
Date: Thu, 16 Jan 2020 09:13:54 +0800
Subject: [PATCH 10/21] remove trailing whitespace in trainer

---
 src/sdk/pynni/nni/nas/pytorch/cdarts/trainer.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/sdk/pynni/nni/nas/pytorch/cdarts/trainer.py b/src/sdk/pynni/nni/nas/pytorch/cdarts/trainer.py
index 8859da824b..e23e19e255 100644
--- a/src/sdk/pynni/nni/nas/pytorch/cdarts/trainer.py
+++ b/src/sdk/pynni/nni/nas/pytorch/cdarts/trainer.py
@@ -36,7 +36,7 @@ def __init__(self, model_small, model_large, criterion, loaders, samplers, logge
                  nasnet_lr=0.2, local_rank=0, share_module=True):
         """
         Initialize a CdartsTrainer.
-        
+
         Parameters
         ----------
         model_small : nn.Module

From 2320cdd1842c6d99cf07b12ef7147815fb962cb4 Mon Sep 17 00:00:00 2001
From: Yuge Zhang <scottyugochang@gmail.com>
Date: Thu, 16 Jan 2020 09:44:52 +0800
Subject: [PATCH 11/21] mock apex import

---
 docs/en_US/conf.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/docs/en_US/conf.py b/docs/en_US/conf.py
index 60b2afe782..a8f06f5fc1 100644
--- a/docs/en_US/conf.py
+++ b/docs/en_US/conf.py
@@ -47,6 +47,9 @@
     'sphinx.ext.napoleon',
 ]
 
+# Add mock modules
+autodoc_mock_imports = ['apex']
+
 # Add any paths that contain templates here, relative to this directory.
 templates_path = ['_templates']
 

From 2dcab31b5fbe8cc272ef456389d1e40864a950bf Mon Sep 17 00:00:00 2001
From: Yuge Zhang <scottyugochang@gmail.com>
Date: Thu, 16 Jan 2020 11:32:06 +0800
Subject: [PATCH 12/21] elaborate documentation

---
 docs/en_US/NAS/CDARTS.md                      |  7 ++++
 .../pynni/nni/nas/pytorch/cdarts/mutator.py   | 41 +++++++++++++++++--
 .../pynni/nni/nas/pytorch/cdarts/trainer.py   |  3 +-
 3 files changed, 46 insertions(+), 5 deletions(-)

diff --git a/docs/en_US/NAS/CDARTS.md b/docs/en_US/NAS/CDARTS.md
index 1c2a758f1e..46f8cff015 100644
--- a/docs/en_US/NAS/CDARTS.md
+++ b/docs/en_US/NAS/CDARTS.md
@@ -1,9 +1,13 @@
 # CDARTS
 
 ## Introduction
+
 CDARTS builds a cyclic feedback mechanism between the search and evaluation networks. First, the search network generates an initial topology for evaluation, so that the weights of the evaluation network can be optimized. Second, the architecture topology in the search network is further optimized by the label supervision in classification, as well as the regularization from the evaluation network through feature distillation. Repeating the above cycle results in a joint optimization of the search and evaluation networks, and thus enables the evolution of the topology to fit the final evaluation network.
 
+In implementation of `CdartsTrainer`, it first instantiates two models and two mutators (one for each). The first model is the so-called "search network", which is mutated with a `RegularizedDartsMutator` -- a mutator with subtle differences with `DartsMutator`. The second model is the "evaluation network", which is mutated with a discrete mutator that leverages the previous search network mutator, to sample a single path each time. Trainers train models and mutators alternatively. Users to refer to [References](#Reference) if they are interested in more details on these trainers and mutators.
+
 ## Reproduction Results
+
 This is CDARTS based on the NNI platform, which currently supports CIFAR10 search and retrain. ImageNet search and retrain should also be supported, and we provide corresponding interfaces. Our reproduced results on NNI are slightly lower than the paper, but much higher than the original DARTS. Here we show the results of three independent experiments on CIFAR10.
 
 | Runs | Paper | NNI | 
@@ -51,4 +55,7 @@ bash run_retrain_cifar.sh
     :members:
 
     .. automethod:: __init__
+
+..  autoclass:: nni.nas.pytorch.cdarts.RegularizedMutatorParallel
+    :members:
 ```
diff --git a/src/sdk/pynni/nni/nas/pytorch/cdarts/mutator.py b/src/sdk/pynni/nni/nas/pytorch/cdarts/mutator.py
index 2a669d06ba..8cb6e82747 100644
--- a/src/sdk/pynni/nni/nas/pytorch/cdarts/mutator.py
+++ b/src/sdk/pynni/nni/nas/pytorch/cdarts/mutator.py
@@ -11,10 +11,20 @@
 
 class RegularizedDartsMutator(DartsMutator):
     """
-    DartsMutator with choice cut and regularization on some of the choices.
+    This is :class:`~nni.nas.pytorch.darts.DartsMutator` basically, with two differences.
+
+    1. Choices can be cut (bypassed). This is done by ``cut_choices``. Cutted choices will not be used in
+    forward pass and thus consumes no memory.
+
+    2. Regularization on choices, to prevent the mutator from overfitting on some choices.
     """
 
     def reset(self):
+        """
+        Warnings
+        --------
+        Renamed :func:`~reset_with_loss` to return regularization loss on reset.
+        """
         raise ValueError("You should probably call `reset_with_loss`.")
 
     def cut_choices(self, cut_num=2):
@@ -27,8 +37,13 @@ def cut_choices(self, cut_num=2):
         ----------
         cut_num : int
             Number of choices to cut, so far.
+
+        Warnings
+        --------
+        Though the parameters are set to :math:`-\infty` to be bypassed, they will still receive gradient of 0,
+        which introduced ``nan`` problem when calling ``optimizer.step()``. To solve this issue, 
         """
-        # `cut_choices` is implemented but not used
+        # `cut_choices` is implemented but not used in current implementation of CdartsTrainer
         for mutable in self.mutables:
             if isinstance(mutable, LayerChoice):
                 _, idx = torch.topk(-self.choices[mutable.key], cut_num)
@@ -39,6 +54,10 @@ def cut_choices(self, cut_num=2):
     def reset_with_loss(self):
         """
         Resample and return loss. If loss is 0, to avoid device issue, it will return ``None``.
+
+        Currently loss penalty are proportional to the L1-norm of parameters corresponding
+        to modules if their type name contains certain substrings. These substrings include: ``poolwithoutbn``,
+        ``identity``, ``dilconv``.
         """
         self._cache, reg_loss = self.sample_search()
         return reg_loss
@@ -60,14 +79,28 @@ def need_reg(choice):
             return result, None
         return result, sum(loss)
 
-    def export(self, logger):
+    def export(self, logger=None):
+        """
+        Export an architecture with logger. Genotype will be printed with logger.
+
+        Returns
+        -------
+        dict
+            A mapping from mutable keys to decisions.
+        """
         result = self.sample_final()
-        if hasattr(self.model, "plot_genotype"):
+        if hasattr(self.model, "plot_genotype") and logger is not None:
             genotypes = self.model.plot_genotype(result, logger)
         return result, genotypes
 
 
 class RegularizedMutatorParallel(DistributedDataParallel):
+    """
+    Parallelize :class:`~RegularizedDartsMutator`.
+
+    This makes ``reset_with_loss`` method parallelized, also allowing ``cut_choies`` and
+    ``export`` to be easily accessible (instead of using ``.module.``).
+    """
     def reset_with_loss(self):
         result = self.module.reset_with_loss()
         self.callback_queued = False
diff --git a/src/sdk/pynni/nni/nas/pytorch/cdarts/trainer.py b/src/sdk/pynni/nni/nas/pytorch/cdarts/trainer.py
index e23e19e255..00fe8cf808 100644
--- a/src/sdk/pynni/nni/nas/pytorch/cdarts/trainer.py
+++ b/src/sdk/pynni/nni/nas/pytorch/cdarts/trainer.py
@@ -1,4 +1,5 @@
 import json
+import logging
 import os
 
 import torch
@@ -49,7 +50,7 @@ def __init__(self, model_small, model_large, criterion, loaders, samplers, logge
             List of training dataset and test dataset. Will be split for training weights and architecture weights.
         samplers : list of Dateset Samplers
             List of training dataset and test dataset samplers.
-        logger : callable
+        logger : logging.Logger
             The logging object. It receives the contents for logging.
         regular_coeff : float
             The coefficient of regular loss.

From 36882f06eb03e0b6042fdda7fd7aff543470fb7f Mon Sep 17 00:00:00 2001
From: Yuge Zhang <scottyugochang@gmail.com>
Date: Thu, 16 Jan 2020 11:32:53 +0800
Subject: [PATCH 13/21] update

---
 docs/en_US/NAS/CDARTS.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/en_US/NAS/CDARTS.md b/docs/en_US/NAS/CDARTS.md
index 46f8cff015..574eca01fc 100644
--- a/docs/en_US/NAS/CDARTS.md
+++ b/docs/en_US/NAS/CDARTS.md
@@ -4,7 +4,7 @@
 
 CDARTS builds a cyclic feedback mechanism between the search and evaluation networks. First, the search network generates an initial topology for evaluation, so that the weights of the evaluation network can be optimized. Second, the architecture topology in the search network is further optimized by the label supervision in classification, as well as the regularization from the evaluation network through feature distillation. Repeating the above cycle results in a joint optimization of the search and evaluation networks, and thus enables the evolution of the topology to fit the final evaluation network.
 
-In implementation of `CdartsTrainer`, it first instantiates two models and two mutators (one for each). The first model is the so-called "search network", which is mutated with a `RegularizedDartsMutator` -- a mutator with subtle differences with `DartsMutator`. The second model is the "evaluation network", which is mutated with a discrete mutator that leverages the previous search network mutator, to sample a single path each time. Trainers train models and mutators alternatively. Users to refer to [References](#Reference) if they are interested in more details on these trainers and mutators.
+In implementation of `CdartsTrainer`, it first instantiates two models and two mutators (one for each). The first model is the so-called "search network", which is mutated with a `RegularizedDartsMutator` -- a mutator with subtle differences with `DartsMutator`. The second model is the "evaluation network", which is mutated with a discrete mutator that leverages the previous search network mutator, to sample a single path each time. Trainers train models and mutators alternatively. Users to refer to [references](#reference) if they are interested in more details on these trainers and mutators.
 
 ## Reproduction Results
 

From 19eb69e9a96c0cb67aa1e8796c6f33f7073852dc Mon Sep 17 00:00:00 2001
From: Yuge Zhang <scottyugochang@gmail.com>
Date: Thu, 16 Jan 2020 11:35:59 +0800
Subject: [PATCH 14/21] update docs

---
 src/sdk/pynni/nni/nas/pytorch/cdarts/mutator.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/src/sdk/pynni/nni/nas/pytorch/cdarts/mutator.py b/src/sdk/pynni/nni/nas/pytorch/cdarts/mutator.py
index 8cb6e82747..fb7e12e03e 100644
--- a/src/sdk/pynni/nni/nas/pytorch/cdarts/mutator.py
+++ b/src/sdk/pynni/nni/nas/pytorch/cdarts/mutator.py
@@ -124,8 +124,7 @@ def __init__(self, model, parent_mutator):
         model : nn.Module
             The model to apply the mutator.
         parent_mutator : Mutator
-            The mutator that is used to call ``sample_final()`` method to get the architecture
-            for training.
+            The mutator that provides ``sample_final`` method, that will be called to get the architecture.
         """
         super().__init__(model)
         self.__dict__["parent_mutator"] = parent_mutator  # avoid parameters to be included

From 46c229dfd147e3d0502df3e3b08a37fe78b8450a Mon Sep 17 00:00:00 2001
From: Yuge Zhang <scottyugochang@gmail.com>
Date: Thu, 16 Jan 2020 11:44:16 +0800
Subject: [PATCH 15/21] fix crossref

---
 src/sdk/pynni/nni/nas/pytorch/cdarts/mutator.py | 14 ++++++++++++--
 1 file changed, 12 insertions(+), 2 deletions(-)

diff --git a/src/sdk/pynni/nni/nas/pytorch/cdarts/mutator.py b/src/sdk/pynni/nni/nas/pytorch/cdarts/mutator.py
index fb7e12e03e..9a33d27fca 100644
--- a/src/sdk/pynni/nni/nas/pytorch/cdarts/mutator.py
+++ b/src/sdk/pynni/nni/nas/pytorch/cdarts/mutator.py
@@ -98,18 +98,28 @@ class RegularizedMutatorParallel(DistributedDataParallel):
     """
     Parallelize :class:`~RegularizedDartsMutator`.
 
-    This makes ``reset_with_loss`` method parallelized, also allowing ``cut_choies`` and
-    ``export`` to be easily accessible (instead of using ``.module.``).
+    This makes :func:`~RegularizedDartsMutator.reset_with_loss` method parallelized,
+    also allowing :func:`~RegularizedDartsMutator.cut_choices` and :func:`~RegularizedDartsMutator.export`
+    to be easily accessible.
     """
     def reset_with_loss(self):
+        """
+        Parallelized :func:`~RegularizedDartsMutator.reset_with_loss`.
+        """
         result = self.module.reset_with_loss()
         self.callback_queued = False
         return result
 
     def cut_choices(self, *args, **kwargs):
+        """
+        Parallelized :func:`~RegularizedDartsMutator.cut_choices`.
+        """
         self.module.cut_choices(*args, **kwargs)
 
     def export(self, logger):
+        """
+        Parallelized :func:`~RegularizedDartsMutator.export`.
+        """
         return self.module.export(logger)
 
 

From bbb8873267af1ce163491aac1c0a7c26077c7026 Mon Sep 17 00:00:00 2001
From: Yuge Zhang <scottyugochang@gmail.com>
Date: Thu, 16 Jan 2020 11:45:00 +0800
Subject: [PATCH 16/21] fix typo

---
 docs/en_US/NAS/CDARTS.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/en_US/NAS/CDARTS.md b/docs/en_US/NAS/CDARTS.md
index 574eca01fc..4242040f08 100644
--- a/docs/en_US/NAS/CDARTS.md
+++ b/docs/en_US/NAS/CDARTS.md
@@ -4,7 +4,7 @@
 
 CDARTS builds a cyclic feedback mechanism between the search and evaluation networks. First, the search network generates an initial topology for evaluation, so that the weights of the evaluation network can be optimized. Second, the architecture topology in the search network is further optimized by the label supervision in classification, as well as the regularization from the evaluation network through feature distillation. Repeating the above cycle results in a joint optimization of the search and evaluation networks, and thus enables the evolution of the topology to fit the final evaluation network.
 
-In implementation of `CdartsTrainer`, it first instantiates two models and two mutators (one for each). The first model is the so-called "search network", which is mutated with a `RegularizedDartsMutator` -- a mutator with subtle differences with `DartsMutator`. The second model is the "evaluation network", which is mutated with a discrete mutator that leverages the previous search network mutator, to sample a single path each time. Trainers train models and mutators alternatively. Users to refer to [references](#reference) if they are interested in more details on these trainers and mutators.
+In implementation of `CdartsTrainer`, it first instantiates two models and two mutators (one for each). The first model is the so-called "search network", which is mutated with a `RegularizedDartsMutator` -- a mutator with subtle differences with `DartsMutator`. The second model is the "evaluation network", which is mutated with a discrete mutator that leverages the previous search network mutator, to sample a single path each time. Trainers train models and mutators alternatively. Users can refer to [references](#reference) if they are interested in more details on these trainers and mutators.
 
 ## Reproduction Results
 

From 386110956571afba19d5f0b30f9a2a75e64d63e4 Mon Sep 17 00:00:00 2001
From: Yuge Zhang <scottyugochang@gmail.com>
Date: Thu, 16 Jan 2020 11:59:06 +0800
Subject: [PATCH 17/21] update mutator and trainer

---
 src/sdk/pynni/nni/nas/pytorch/cdarts/mutator.py |  4 +++-
 src/sdk/pynni/nni/nas/pytorch/cdarts/trainer.py | 17 ++++++++++-------
 2 files changed, 13 insertions(+), 8 deletions(-)

diff --git a/src/sdk/pynni/nni/nas/pytorch/cdarts/mutator.py b/src/sdk/pynni/nni/nas/pytorch/cdarts/mutator.py
index 9a33d27fca..a83d3d24da 100644
--- a/src/sdk/pynni/nni/nas/pytorch/cdarts/mutator.py
+++ b/src/sdk/pynni/nni/nas/pytorch/cdarts/mutator.py
@@ -124,7 +124,9 @@ def export(self, logger):
 
 
 class DartsDiscreteMutator(Mutator):
-
+    """
+    A mutator that applies the final sampling result of a parent mutator on another model to train.
+    """
     def __init__(self, model, parent_mutator):
         """
         Initialization.
diff --git a/src/sdk/pynni/nni/nas/pytorch/cdarts/trainer.py b/src/sdk/pynni/nni/nas/pytorch/cdarts/trainer.py
index 00fe8cf808..43d3cee5d3 100644
--- a/src/sdk/pynni/nni/nas/pytorch/cdarts/trainer.py
+++ b/src/sdk/pynni/nni/nas/pytorch/cdarts/trainer.py
@@ -29,7 +29,7 @@ def forward(self, student, teacher):
 
 
 class CdartsTrainer(object):
-    def __init__(self, model_small, model_large, criterion, loaders, samplers, logger,
+    def __init__(self, model_small, model_large, criterion, loaders, samplers, logger=None,
                  regular_coeff=5, regular_ratio=0.2, warmup_epochs=2, fix_head=True,
                  epochs=32, steps_per_epoch=None, loss_alpha=2, loss_T=2, distributed=True,
                  log_frequency=10, grad_clip=5.0, interactive_type='kl', output_path='./outputs',
@@ -45,13 +45,14 @@ def __init__(self, model_small, model_large, criterion, loaders, samplers, logge
         model_large : nn.Module
             PyTorch model to be trained. This is the evaluation network of CDARTS.
         criterion : callable
-            Receives logits and ground truth label, return a loss tensor.
-        loaders : list
-            List of training dataset and test dataset. Will be split for training weights and architecture weights.
-        samplers : list of Dateset Samplers
-            List of training dataset and test dataset samplers.
+            Receives logits and ground truth label, return a loss tensor, e.g., ``nn.CrossEntropyLoss()``.
+        loaders : list of torch.utils.data.DataLoader
+            List of train data and valid data loaders, for training weights and architecture weights respectively.
+        samplers : list of torch.utils.data.Sampler
+            List of train data and valid data samplers. This can be PyTorch standard samplers if not distributed.
+            In distributed mode, sampler needs to have ``set_epoch`` method. Refer to data utils in CDARTS example for details.
         logger : logging.Logger
-            The logging object. It receives the contents for logging.
+            The logger for logging. Will use nni logger by default (if logger is ``None``).
         regular_coeff : float
             The coefficient of regular loss.
         regular_ratio : float
@@ -95,6 +96,8 @@ def __init__(self, model_small, model_large, criterion, loaders, samplers, logge
         share_module : bool
             ``True`` if sharing the stem and auxiliary heads, else not sharing these modules.
         """
+        if logger is None:
+            logger = logging.getLogger(__name__)
         train_loader, valid_loader = loaders
         train_sampler, valid_sampler = samplers
         self.train_loader = CyclicIterator(train_loader, train_sampler, distributed)

From cf0471f58de0e51e75f1f11147eb00e7dc5652a9 Mon Sep 17 00:00:00 2001
From: Yuge Zhang <scottyugochang@gmail.com>
Date: Thu, 16 Jan 2020 12:24:13 +0800
Subject: [PATCH 18/21] remove trailing whitespace

---
 src/sdk/pynni/nni/nas/pytorch/cdarts/mutator.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/sdk/pynni/nni/nas/pytorch/cdarts/mutator.py b/src/sdk/pynni/nni/nas/pytorch/cdarts/mutator.py
index a83d3d24da..6010057828 100644
--- a/src/sdk/pynni/nni/nas/pytorch/cdarts/mutator.py
+++ b/src/sdk/pynni/nni/nas/pytorch/cdarts/mutator.py
@@ -41,7 +41,8 @@ def cut_choices(self, cut_num=2):
         Warnings
         --------
         Though the parameters are set to :math:`-\infty` to be bypassed, they will still receive gradient of 0,
-        which introduced ``nan`` problem when calling ``optimizer.step()``. To solve this issue, 
+        which introduced ``nan`` problem when calling ``optimizer.step()``. To solve this issue, a simple way is to
+        reset nan to :math:`-\infty` each time after the parameters are updated.
         """
         # `cut_choices` is implemented but not used in current implementation of CdartsTrainer
         for mutable in self.mutables:

From 7c87a7f268f6c83072166ed4a12008c05cf4d9fe Mon Sep 17 00:00:00 2001
From: Yuge Zhang <scottyugochang@gmail.com>
Date: Thu, 16 Jan 2020 13:26:24 +0800
Subject: [PATCH 19/21] fix dosctring format

---
 examples/nas/cdarts/datasets/data_utils.py | 35 +++++++++++-----------
 1 file changed, 17 insertions(+), 18 deletions(-)

diff --git a/examples/nas/cdarts/datasets/data_utils.py b/examples/nas/cdarts/datasets/data_utils.py
index 5989d274f9..f567d61a68 100644
--- a/examples/nas/cdarts/datasets/data_utils.py
+++ b/examples/nas/cdarts/datasets/data_utils.py
@@ -9,25 +9,32 @@
 
 
 class SubsetDistributedSampler(Sampler):
-    """Sampler that restricts data loading to a subset of the dataset.
+    """
+    Sampler that restricts data loading to a subset of the dataset.
 
     It is especially useful in conjunction with
     :class:`torch.nn.parallel.DistributedDataParallel`. In such case, each
     process can pass a DistributedSampler instance as a DataLoader sampler,
     and load a subset of the original dataset that is exclusive to it.
 
-    .. note::
-        Dataset is assumed to be of constant size.
-
-    Arguments:
-        dataset: Dataset used for sampling.
-        num_replicas (optional): Number of processes participating in
-            distributed training.
-        rank (optional): Rank of the current process within num_replicas.
-        shuffle (optional): If true (default), sampler will shuffle the indices
+    Dataset is assumed to be of constant size.
     """
 
     def __init__(self, dataset, indices, num_replicas=None, rank=None, shuffle=True):
+        """
+        Initialization.
+
+        Parameters
+        ----------
+        dataset : torch.utils.data.Dataset
+            Dataset used for sampling.
+        num_replicas : int
+            Number of processes participating in distributed training. Default: World size.
+        rank : int
+            Rank of the current process within num_replicas. Default: Current rank.
+        shuffle : bool
+            If true (default), sampler will shuffle the indices.
+        """
         if num_replicas is None:
             if not dist.is_available():
                 raise RuntimeError("Requires distributed package to be available")
@@ -79,10 +86,6 @@ def __init__(self, loader):
         self.stream = torch.cuda.Stream()
         self.mean = torch.tensor([0.485 * 255, 0.456 * 255, 0.406 * 255]).cuda().view(1, 3, 1, 1)
         self.std = torch.tensor([0.229 * 255, 0.224 * 255, 0.225 * 255]).cuda().view(1, 3, 1, 1)
-        # With Amp, it isn't necessary to manually convert data to half.
-        # if args.fp16:
-        #     self.mean = self.mean.half()
-        #     self.std = self.std.half()
         self.preload()
 
     def preload(self):
@@ -95,10 +98,6 @@ def preload(self):
         with torch.cuda.stream(self.stream):
             self.next_input = self.next_input.cuda(non_blocking=True)
             self.next_target = self.next_target.cuda(non_blocking=True)
-            # With Amp, it isn't necessary to manually convert data to half.
-            # if args.fp16:
-            #     self.next_input = self.next_input.half()
-            # else:
             self.next_input = self.next_input.float()
             self.next_input = self.next_input.sub_(self.mean).div_(self.std)
 

From 1a167ba0357688fa7e64b9d15dc4c6e55541155e Mon Sep 17 00:00:00 2001
From: Yuge Zhang <scottyugochang@gmail.com>
Date: Thu, 16 Jan 2020 13:30:23 +0800
Subject: [PATCH 20/21] add license

---
 examples/nas/cdarts/aux_head.py                 |  3 +++
 examples/nas/cdarts/config.py                   |  4 +++-
 examples/nas/cdarts/datasets/cifar.py           |  3 +++
 examples/nas/cdarts/datasets/data_utils.py      |  3 +++
 examples/nas/cdarts/datasets/imagenet.py        |  3 +++
 examples/nas/cdarts/genotypes.py                | 11 +++++++----
 examples/nas/cdarts/retrain.py                  |  3 +++
 examples/nas/cdarts/utils.py                    |  3 +++
 src/sdk/pynni/nni/nas/pytorch/cdarts/trainer.py |  3 +++
 src/sdk/pynni/nni/nas/pytorch/cdarts/utils.py   |  3 +++
 10 files changed, 34 insertions(+), 5 deletions(-)

diff --git a/examples/nas/cdarts/aux_head.py b/examples/nas/cdarts/aux_head.py
index 352db6a7df..9a67d09fec 100644
--- a/examples/nas/cdarts/aux_head.py
+++ b/examples/nas/cdarts/aux_head.py
@@ -1,3 +1,6 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 import torch.nn as nn
 
 
diff --git a/examples/nas/cdarts/config.py b/examples/nas/cdarts/config.py
index 2894edf7a8..f0200f39cd 100644
--- a/examples/nas/cdarts/config.py
+++ b/examples/nas/cdarts/config.py
@@ -1,4 +1,6 @@
-""" Config class for search/retrain """
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 import argparse
 from functools import partial
 
diff --git a/examples/nas/cdarts/datasets/cifar.py b/examples/nas/cdarts/datasets/cifar.py
index ea9a03520c..493335f151 100644
--- a/examples/nas/cdarts/datasets/cifar.py
+++ b/examples/nas/cdarts/datasets/cifar.py
@@ -1,3 +1,6 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 import numpy as np
 import torch
 import torchvision.datasets as dset
diff --git a/examples/nas/cdarts/datasets/data_utils.py b/examples/nas/cdarts/datasets/data_utils.py
index f567d61a68..096b5a1fa7 100644
--- a/examples/nas/cdarts/datasets/data_utils.py
+++ b/examples/nas/cdarts/datasets/data_utils.py
@@ -1,3 +1,6 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 import math
 import random
 
diff --git a/examples/nas/cdarts/datasets/imagenet.py b/examples/nas/cdarts/datasets/imagenet.py
index 29b880a36b..3bba3d552e 100644
--- a/examples/nas/cdarts/datasets/imagenet.py
+++ b/examples/nas/cdarts/datasets/imagenet.py
@@ -1,3 +1,6 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 import os
 
 import numpy as np
diff --git a/examples/nas/cdarts/genotypes.py b/examples/nas/cdarts/genotypes.py
index 7619cfb791..0cc4d3fa63 100644
--- a/examples/nas/cdarts/genotypes.py
+++ b/examples/nas/cdarts/genotypes.py
@@ -1,7 +1,10 @@
-""" Genotypes
-    - Genotype: normal/reduce gene + normal/reduce cell output connection (concat)
-    - gene: discrete ops information (w/o output connection)
-    - dag: real ops (can be mixed or discrete, but Genotype has only discrete information itself)
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+"""
+- Genotype: normal/reduce gene + normal/reduce cell output connection (concat)
+- gene: discrete ops information (w/o output connection)
+- dag: real ops (can be mixed or discrete, but Genotype has only discrete information itself)
 """
 from collections import namedtuple
 
diff --git a/examples/nas/cdarts/retrain.py b/examples/nas/cdarts/retrain.py
index 4b4eb1b780..4cd320d58c 100644
--- a/examples/nas/cdarts/retrain.py
+++ b/examples/nas/cdarts/retrain.py
@@ -1,3 +1,6 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 import json
 import logging
 import os
diff --git a/examples/nas/cdarts/utils.py b/examples/nas/cdarts/utils.py
index 4ed223c060..11febc0beb 100644
--- a/examples/nas/cdarts/utils.py
+++ b/examples/nas/cdarts/utils.py
@@ -1,3 +1,6 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 import json
 import logging
 import os
diff --git a/src/sdk/pynni/nni/nas/pytorch/cdarts/trainer.py b/src/sdk/pynni/nni/nas/pytorch/cdarts/trainer.py
index 43d3cee5d3..e050986b4c 100644
--- a/src/sdk/pynni/nni/nas/pytorch/cdarts/trainer.py
+++ b/src/sdk/pynni/nni/nas/pytorch/cdarts/trainer.py
@@ -1,3 +1,6 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 import json
 import logging
 import os
diff --git a/src/sdk/pynni/nni/nas/pytorch/cdarts/utils.py b/src/sdk/pynni/nni/nas/pytorch/cdarts/utils.py
index b00f0744f0..780f6fdc0e 100644
--- a/src/sdk/pynni/nni/nas/pytorch/cdarts/utils.py
+++ b/src/sdk/pynni/nni/nas/pytorch/cdarts/utils.py
@@ -1,3 +1,6 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 import json
 import os
 

From 5d3970572621553bdbd95c5cebe1527e8fdea9ce Mon Sep 17 00:00:00 2001
From: Yuge Zhang <scottyugochang@gmail.com>
Date: Thu, 16 Jan 2020 13:33:05 +0800
Subject: [PATCH 21/21] resolve comments in ops.py

---
 examples/nas/cdarts/ops.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/examples/nas/cdarts/ops.py b/examples/nas/cdarts/ops.py
index 9d2f6b20ad..285dc2998b 100644
--- a/examples/nas/cdarts/ops.py
+++ b/examples/nas/cdarts/ops.py
@@ -31,6 +31,7 @@ class DropPath(nn.Module):
     def __init__(self, p=0.):
         """
         Drop path with probability.
+
         Parameters
         ----------
         p : float
@@ -61,7 +62,7 @@ def __init__(self, pool_type, C, kernel_size, stride, padding, affine=True):
         elif pool_type.lower() == 'avg':
             self.pool = nn.AvgPool2d(kernel_size, stride, padding, count_include_pad=False)
         else:
-            raise ValueError()
+            raise NotImplementedError("Pool doesn't support pooling type other than max and avg.")
 
     def forward(self, x):
         out = self.pool(x)