Merge branch 'master' into ref/tuner

.github/mergify.yml

@@ -36,15 +36,15 @@ pull_request_rules:
       label:
         remove: [ "has conflicts" ]
 
-  - name: update PR
-    conditions:
-      - -conflict
-      - -draft # filter-out GH draft PRs
-      - base=master  # apply only on master
-      - -title~=(?i)wip # skip all PR that title contains “WIP” (ignoring case)
-      - "#approved-reviews-by>=3" # number of review approvals
-    actions:
-      update: {}
+  #- name: update PR
+  #  conditions:
+  #    - -conflict
+  #    - -draft # filter-out GH draft PRs
+  #    - base=master  # apply only on master
+  #    - -title~=(?i)wip # skip all PR that title contains “WIP” (ignoring case)
+  #    - "#approved-reviews-by>=3" # number of review approvals
+  #  actions:
+  #    update: {}
 
   - name: add core reviewer
     conditions:

.github/workflows/ci_dockers.yml

@@ -75,21 +75,21 @@ jobs:
       matrix:
         include:
           # todo: see notes in Dockerfile
-          #- python_version: 3.7
-          #  pytorch_version: 1.8
-          - python_version: 3.8
-            pytorch_version: 1.7
-          - python_version: 3.7
-            pytorch_version: 1.6
           - python_version: 3.6
             pytorch_version: 1.4
+          - python_version: 3.7
+            pytorch_version: 1.6
+          - python_version: 3.8
+            pytorch_version: 1.7
+          #- python_version: 3.9
+          #  pytorch_version: 1.8
     steps:
       - name: Checkout
         uses: actions/checkout@v2
 
-      # for PT 1.3 and 1.4 we need to use CUDA 10.1
+      # for PT 1.4 we need to use CUDA 10.1
       - run: |
-          cuda=$(python -c "print(10.2 if float(${{matrix.pytorch_version}}) > 1.4 else 10.1)" 2>&1)
+          cuda=$(python -c "print(10.2 if float(${{matrix.pytorch_version}}) >= 1.5 else 10.1)" 2>&1)
           echo "::set-output name=CUDA::$cuda"
         id: extend
 
@@ -114,8 +114,6 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - python_version: 3.8
-            pytorch_version: 1.6
           - python_version: 3.6
             pytorch_version: 1.4
           - python_version: 3.7

.github/workflows/events-nightly.yml

@@ -14,8 +14,6 @@ jobs:
     steps:
       # does nightly releases from feature branch
       - uses: actions/checkout@v2
-        with:
-          ref: release/1.2-dev
       - uses: actions/setup-python@v2
         with:
           python-version: 3.7

.gitignore

@@ -153,4 +153,5 @@ wandb
 cifar-10-batches-py
 *.pt
 # ctags
-tags
+tags
+data

.yapfignore

@@ -1,5 +1 @@
 .git/*
-
-
-# TODO
-pytorch_lightning/plugins/legacy/*

CHANGELOG.md

@@ -193,6 +193,10 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Update `lr_finder` to check for attribute if not running `fast_dev_run` ([#5990](https://github.com/PyTorchLightning/pytorch-lightning/pull/5990))
 
 
+- LightningOptimizer manual optimizer is more flexible and expose `toggle_model` ([#5771](https://github.com/PyTorchLightning/pytorch-lightning/pull/5771))
+
+
+
 ### Deprecated
 
 - Function `stat_scores_multiple_classes` is deprecated in favor of `stat_scores` ([#4839](https://github.com/PyTorchLightning/pytorch-lightning/pull/4839))

README.md

@@ -115,41 +115,51 @@ Simple installation from PyPI
 pip install pytorch-lightning
 ```
 
+<!-- following section will be skipped from PyPI description -->
 <details>
   <summary>Other installation options</summary>
     <!-- following section will be skipped from PyPI description -->
-
-  #### Install with optional dependencies (CPU)
-
-  ```bash
-  pip install pytorch-lightning['cpu-extra']
-  ```
 
-  #### Install with optional dependencies (GPU, TPU)
+  #### Install with optional dependencies
 
   ```bash
   pip install pytorch-lightning['extra']
   ```
 
   #### Conda
+
   ```bash
   conda install pytorch-lightning -c conda-forge
   ```
 
+  #### Install stable - future 1.1.x
+
+  the actual status of 1.1 [stable] is following:
+
+  ![CI base testing](https://github.com/PyTorchLightning/pytorch-lightning/workflows/CI%20base%20testing/badge.svg?branch=release%2F1.1.x&event=push)
+  ![CI complete testing](https://github.com/PyTorchLightning/pytorch-lightning/workflows/CI%20complete%20testing/badge.svg?branch=release%2F1.1.x&event=push)
+  ![PyTorch & Conda](https://github.com/PyTorchLightning/pytorch-lightning/workflows/PyTorch%20&%20Conda/badge.svg?branch=release%2F1.1.x&event=push)
+  ![TPU tests](https://github.com/PyTorchLightning/pytorch-lightning/workflows/TPU%20tests/badge.svg?branch=release%2F1.1.x&event=push)
+  ![Docs check](https://github.com/PyTorchLightning/pytorch-lightning/workflows/Docs%20check/badge.svg?branch=release%2F1.1.x&event=push)
+
+  Install future release from the source
+  ```bash
+  pip install git+https://github.com/PytorchLightning/pytorch-lightning.git@release/1.1.x --upgrade
+  ```
+
   #### Install bleeding-edge - future 1.2
 
-  Install future release from the source (no guarantees)
+  Install nightly from the source (no guarantees)
   ```bash
-  pip install git+https://github.com/PytorchLightning/pytorch-lightning.git@release/1.2-dev --upgrade
+  pip install https://github.com/PyTorchLightning/pytorch-lightning/archive/master.zip
   ```
-  or nightly from testing PyPI
+
+  or from testing PyPI
   ```bash
   pip install -iU https://test.pypi.org/simple/ pytorch-lightning
   ```
-
-  <!-- end skipping PyPI description -->
 </details>
-
+<!-- end skipping PyPI description -->
 
 ### Step 1: Add these imports
 
@@ -369,8 +379,8 @@ class LitAutoEncoder(pl.LightningModule):
 ## Community
 
 The lightning community is maintained by
-- [16 core contributors](https://pytorch-lightning.readthedocs.io/en/latest/governance.html) who are all a mix of professional engineers, Research Scientists, Ph.D. students from top AI labs.
-- 280+ community contributors.
+- [16 core contributors](https://pytorch-lightning.readthedocs.io/en/latest/governance.html) who are all a mix of professional engineers, Research Scientists, and Ph.D. students from top AI labs.
+- 400+ community contributors.
 
 Lightning is also part of the [PyTorch ecosystem](https://pytorch.org/ecosystem/) which requires projects to have solid testing, documentation and support.
 

dockers/base-xla/Dockerfile

@@ -115,4 +115,4 @@ RUN \
     conda info && \
     pip list && \
     python -c "import sys; assert sys.version[:3] == '$PYTHON_VERSION', sys.version" && \
-    python -c "import torch; ver = '$XLA_VERSION' ; ver = dict(nightly='1.8').get(ver, ver) ; assert torch.__version__[:3] == ver, torch.__version__"
+    python -c "import torch; ver = '$XLA_VERSION' ; ver = dict(nightly='1.9').get(ver, ver) ; assert torch.__version__[:3] == ver, torch.__version__"

docs/source/advanced/multi_gpu.rst

@@ -580,9 +580,9 @@ Below are the possible configurations we support.
 
 Implement Your Own Distributed (DDP) training
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-If you need your own way to init PyTorch DDP you can override :meth:`pytorch_lightning.plugins.legacy.ddp_plugin.DDPPlugin.init_ddp_connection`.
+If you need your own way to init PyTorch DDP you can override :meth:`pytorch_lightning.plugins.training_type.ddp.DDPPlugin.init_ddp_connection`.
 
-If you also need to use your own DDP implementation, override :meth:`pytorch_lightning.plugins.legacy.ddp_plugin.DDPPlugin.configure_ddp`.
+If you also need to use your own DDP implementation, override :meth:`pytorch_lightning.plugins.training_type.ddp.DDPPlugin.configure_ddp`.
 
 
 ----------
@@ -679,20 +679,20 @@ In addition, we use Gradient Checkpointing to reduce GPU memory requirements fur
 
 Reference: https://arxiv.org/abs/1811.06965
 
-.. note:: DDPSequentialPlugin is currently supported only for Pytorch 1.6.
+.. note:: RPCSequentialPlugin is currently supported only for Pytorch 1.6.
 
 To get started, install FairScale using the command below. We install a specific branch which contains PyTorch related fixes for Sequential Parallelism.
 
 .. code-block:: bash
 
-     pip install https://github.com/PyTorchLightning/fairscale/archive/pl_1.1.0.zip
+     pip install https://github.com/PyTorchLightning/fairscale/archive/pl_1.2.0.zip
 
 To use Sequential Model Parallelism, you must define a  :class:`nn.Sequential <torch.nn.Sequential>` module that defines the layers you wish to parallelize across GPUs.
 This should be kept within the ``sequential_module`` variable within your ``LightningModule`` like below.
 
 .. code-block:: python
 
-    from pytorch_lightning.plugins.legacy.ddp_sequential_plugin import DDPSequentialPlugin
+    from pytorch_lightning.plugins.training_type.rpc_sequential import RPCSequentialPlugin
     from pytorch_lightning import LightningModule
 
     class MyModel(LightningModule):
@@ -702,7 +702,7 @@ This should be kept within the ``sequential_module`` variable within your ``Ligh
 
     # Split my module across 4 gpus, one layer each
     model = MyModel()
-    plugin = DDPSequentialPlugin(balance=[1, 1, 1, 1])
+    plugin = RPCSequentialPlugin(balance=[1, 1, 1, 1])
     trainer = Trainer(accelerator='ddp', gpus=4, plugins=[plugin])
     trainer.fit(model)