diff --git a/pl_examples/pytorch_ecosystem/pytorch_geometric/README.md b/pl_examples/pytorch_ecosystem/pytorch_geometric/README.md
deleted file mode 100644
index 5c9a42d5a8942..0000000000000
--- a/pl_examples/pytorch_ecosystem/pytorch_geometric/README.md
+++ /dev/null
@@ -1,38 +0,0 @@
-#  [Pytorch Geometric](https://github.com/rusty1s/pytorch_geometric) examples with Lighting
-
-### Introduction
-
-PyTorch Geometric (PyG) is a geometric deep learning extension library for PyTorch. It relies on lower level libraries such as
-
-* PyTorch Cluster: A package consists of a small extension library of highly optimized graph cluster algorithms in Pytorch
-* PyTorch Sparse: A package consists of a small extension library of optimized sparse matrix operations with autograd support in Pytorch
-* PyTorch Scatter: A package consists of a small extension library of highly optimized sparse update (scatter and segment) operations for the use in PyTorch
-
-## Setup
-
-```
-pyenv install 3.7.8
-pyenv local 3.7.8
-python -m venv
-source .venv/bin/activate
-poetry install
-```
-
-Run example
-
-```
-python cora_dna.py
-```
-
-## Current example lists
-
-| `DATASET` | `MODEL` | `TASK` | DATASET DESCRIPTION | MODEL DESCRIPTION                                                                                                                                                                   |                                                                                                                                                                     |
-| :---: | :---: | :---: | :---: | :---: | :---: |
-| Cora | DNA | Node Classification | The citation network datasets "Cora", "CiteSeer" and "PubMed" from the "Revisiting Semi-Supervised Learning with Graph Embeddings" <https://arxiv.org/abs/1603.08861> | The dynamic neighborhood aggregation operator from the "Just Jump: Towards Dynamic Neighborhood Aggregation in Graph Neural Networks"
-
-
-## DATASET SIZES
-
-```
- 16M    ./cora
-```
diff --git a/pl_examples/pytorch_ecosystem/pytorch_geometric/__init__.py b/pl_examples/pytorch_ecosystem/pytorch_geometric/__init__.py
deleted file mode 100644
index e69de29bb2d1d..0000000000000
diff --git a/pl_examples/pytorch_ecosystem/pytorch_geometric/cora_dna.py b/pl_examples/pytorch_ecosystem/pytorch_geometric/cora_dna.py
deleted file mode 100644
index e4e040ff7072e..0000000000000
--- a/pl_examples/pytorch_ecosystem/pytorch_geometric/cora_dna.py
+++ /dev/null
@@ -1,375 +0,0 @@
-"""Graph Convolution Example using Pytorch Geometric
-
-This example illustrates how one could train a graph convolution model with DNA Conv
-on Cora Dataset using pytorch-lightning. This example will also demonstrate how this
-model can be easily torch-scripted, thanks to Pytorch Geometric.
-"""
-# python imports
-import os
-import os.path as osp
-import sys
-from functools import partial
-from collections import namedtuple
-from argparse import ArgumentParser
-from typing import List, Optional, NamedTuple
-
-# thrid parties libraries
-import numpy as np
-from torch import nn
-import torch
-from torch import Tensor
-from torch.optim import Adam
-import torch.nn.functional as F
-
-# Lightning imports
-from pytorch_lightning import (
-    Trainer,
-    LightningDataModule,
-    LightningModule
-)
-from pytorch_lightning.metrics import Accuracy
-
-try:
-    # Pytorch Geometric imports
-    from torch_geometric.nn import DNAConv, MessagePassing
-    from torch_geometric.data import DataLoader
-    from torch_geometric.datasets import Planetoid
-    import torch_geometric.transforms as T
-    from torch_geometric.data import NeighborSampler
-    from lightning import lightning_logo, nice_print
-except Exception:
-    HAS_PYTORCH_GEOMETRIC = False
-else:
-    HAS_PYTORCH_GEOMETRIC = True
-
-
-# use to make model jittable
-OptTensor = Optional[Tensor]
-ListTensor = List[Tensor]
-
-
-class TensorBatch(NamedTuple):
-    x: Tensor
-    edge_index: ListTensor
-    edge_attr: OptTensor
-    batch: OptTensor
-
-###################################
-#       LightningDataModule       #
-###################################
-
-
-class CoraDataset(LightningDataModule):
-
-    r"""The citation network datasets "Cora", "CiteSeer" and "PubMed" from the
-    `"Revisiting Semi-Supervised Learning with Graph Embeddings"
-    <https://arxiv.org/abs/1603.08861>`_ paper.
-    Nodes represent documents and edges represent citation links.
-    Training, validation and test splits are given by binary masks.
-    c.f https://github.com/rusty1s/pytorch_geometric/blob/master/torch_geometric/datasets/planetoid.py
-    """
-
-    NAME = "cora"
-
-    def __init__(self,
-                 num_workers: int = 1,
-                 batch_size: int = 8,
-                 drop_last: bool = True,
-                 pin_memory: bool = True,
-                 num_layers: int = None):
-        super().__init__()
-
-        assert num_layers is not None
-
-        self._num_workers = num_workers
-        self._batch_size = batch_size
-        self._drop_last = drop_last
-        self._pin_memory = pin_memory
-        self._num_layers = num_layers
-
-        self._transform = T.NormalizeFeatures()
-
-    @property
-    def num_features(self):
-        return 1433
-
-    @property
-    def num_classes(self):
-        return 7
-
-    @property
-    def hyper_parameters(self):
-        # used to inform the model the dataset specifications
-        return {"num_features": self.num_features, "num_classes": self.num_classes}
-
-    def prepare_data(self):
-        path = osp.join(
-            osp.dirname(osp.realpath(__file__)), "..", "..", "data", self.NAME
-        )
-        self.dataset = Planetoid(path, self.NAME, transform=self._transform)
-        self.data = self.dataset[0]
-
-    def create_neighbor_sampler(self, batch_size=2, stage=None):
-        # https://github.com/rusty1s/pytorch_geometric/tree/master/torch_geometric/data/sampler.py#L18
-        return NeighborSampler(
-            self.data.edge_index,
-            # the nodes that should be considered for sampling.
-            node_idx=getattr(self.data, f"{stage}_mask"),
-            # -1 indicates all neighbors will be selected
-            sizes=[self._num_layers, -1],
-            num_workers=self._num_workers,
-            drop_last=self._drop_last,
-            pin_memory=self._pin_memory,
-        )
-
-    def train_dataloader(self):
-        return self.create_neighbor_sampler(stage="train")
-
-    def validation_dataloader(self):
-        return self.create_neighbor_sampler(stage="val")
-
-    def test_dataloader(self):
-        return self.create_neighbor_sampler(stage="test")
-
-    def gather_data_and_convert_to_namedtuple(self, batch, batch_nb):
-        """
-        This function will select features using node_idx
-        and create a NamedTuple Object.
-        """
-
-        usual_keys = ["x", "edge_index", "edge_attr", "batch"]
-        Batch: TensorBatch = namedtuple("Batch", usual_keys)
-        return (
-            Batch(
-                self.data.x[batch[1]],
-                [e.edge_index for e in batch[2]],
-                None,
-                None,
-            ),
-            self.data.y[batch[1]],
-        )
-
-    @staticmethod
-    def add_argparse_args(parser):
-        parser.add_argument("--num_workers", type=int, default=1)
-        parser.add_argument("--batch_size", type=int, default=2)
-        parser.add_argument("--drop_last", default=True)
-        parser.add_argument("--pin_memory", default=True)
-        return parser
-
-
-###############################
-#       LightningModule       #
-###############################
-
-
-class DNAConvNet(LightningModule):
-
-    r"""The dynamic neighborhood aggregation operator from the `"Just Jump:
-    Towards Dynamic Neighborhood Aggregation in Graph Neural Networks"
-    <https://arxiv.org/abs/1904.04849>`_ paper
-    c.f https://github.com/rusty1s/pytorch_geometric/blob/master/torch_geometric/nn/conv/dna_conv.py#L172
-    """
-
-    def __init__(self,
-                 num_layers: int = 2,
-                 hidden_channels: int = 128,
-                 heads: int = 8,
-                 groups: int = 16,
-                 dropout: float = 0.8,
-                 cached: bool = False,
-                 num_features: int = None,
-                 num_classes: int = None,
-                 ):
-        super().__init__()
-
-        assert num_features is not None
-        assert num_classes is not None
-
-        # utils from Lightning to save __init__ arguments
-        self.save_hyperparameters()
-        hparams = self.hparams
-
-        # Instantiate metrics
-        self.val_acc = Accuracy(hparams["num_classes"])
-        self.test_acc = Accuracy(hparams["num_classes"])
-
-        # Define DNA graph convolution model
-        self.hidden_channels = hparams["hidden_channels"]
-        self.lin1 = nn.Linear(hparams["num_features"], hparams["hidden_channels"])
-
-        # Create ModuleList to hold all convolutions
-        self.convs = nn.ModuleList()
-
-        # Iterate through the number of layers
-        for _ in range(hparams["num_layers"]):
-
-            # Create a DNA Convolution - This graph convolution relies on MultiHead Attention mechanism
-            # to route information similar to Transformers.
-            # https://github.com/rusty1s/pytorch_geometric/blob/master/torch_geometric/nn/conv/dna_conv.py#L172
-            self.convs.append(
-                DNAConv(
-                    hparams["hidden_channels"],
-                    hparams["heads"],
-                    hparams["groups"],
-                    dropout=hparams["dropout"],
-                    cached=False,
-                )
-            )
-        # classification MLP
-        self.lin2 = nn.Linear(hparams["hidden_channels"], hparams["num_classes"], bias=False)
-
-    def forward(self, batch: TensorBatch):
-        # batch needs to be typed for making this model jittable.
-        x = batch.x
-        x = F.relu(self.lin1(x))
-        x = F.dropout(x, p=0.5, training=self.training)
-        x_all = x.view(-1, 1, self.hidden_channels)
-
-        # iterate over all convolutions
-        for idx, conv in enumerate(self.convs):
-            # perform convolution using previously concatenated embedding
-            # through edge_index
-            x = F.relu(conv(x_all, batch.edge_index[idx]))
-            x = x.view(-1, 1, self.hidden_channels)
-
-            # concatenate with previously computed embedding
-            x_all = torch.cat([x_all, x], dim=1)
-
-        # extra latest layer embedding
-        x = x_all[:, -1]
-
-        x = F.dropout(x, p=0.5, training=self.training)
-
-        # return logits per nodes
-        return F.log_softmax(self.lin2(x), -1)
-
-    def step(self, batch, batch_nb):
-        typed_batch, targets = self.gather_data_and_convert_to_namedtuple(batch, batch_nb)
-        logits = self(typed_batch)
-        return logits, targets
-
-    def training_step(self, batch, batch_nb):
-        logits, targets = self.step(batch, batch_nb)
-        train_loss = F.nll_loss(logits, targets)
-        self.log("train_loss", train_loss, on_step=True, on_epoch=True, prog_bar=True)
-        return train_loss
-
-    def validation_step(self, batch, batch_nb):
-        logits, targets = self.step(batch, batch_nb)
-        val_loss = F.nll_loss(logits, targets)
-        self.log("val_loss", val_loss, on_step=False, on_epoch=True, prog_bar=True)
-        self.log("val_acc", self.val_acc(logits, targets), on_step=False, on_epoch=True, prog_bar=True)
-
-    def test_step(self, batch, batch_nb):
-        logits, targets = self.step(batch, batch_nb)
-        test_loss = F.nll_loss(logits, targets)
-        self.log("test_loss", test_loss, on_step=False, on_epoch=True, prog_bar=True)
-        self.log("test_acc", self.test_acc(logits, targets), on_step=False, on_epoch=True, prog_bar=True)
-
-    # Use for jittable demonstration.
-
-    def _convert_to_jittable(self, module):
-        for key, m in module._modules.items():
-            if isinstance(m, MessagePassing) and m.jittable is not None:
-                # Pytorch Geometric MessagePassing implements a `.jittable` function
-                # which converts the current module into its jittable version.
-                module._modules[key] = m.jittable()
-            else:
-                self._convert_to_jittable(m)
-        return module
-
-    def jittable(self):
-        for key, m in self._modules.items():
-            self._modules[key] = self._convert_to_jittable(m)
-
-    def configure_optimizers(self):
-        return Adam(self.parameters(), lr=1e-3)
-
-    @staticmethod
-    def add_argparse_args(parser):
-        parser.add_argument("--num_layers", type=int, default=2)
-        parser.add_argument("--hidden_channels", type=int, default=128)
-        parser.add_argument("--heads", type=int, default=8)
-        parser.add_argument("--groups", type=int, default=16)
-        parser.add_argument("--dropout", type=float, default=0.8)
-        parser.add_argument("--cached", type=int, default=0)
-        parser.add_argument("--jit", default=True)
-        return parser
-
-#################################
-#     Instantiate Functions     #
-#################################
-
-
-def instantiate_datamodule(args):
-    datamodule = CoraDataset(
-        num_workers=args.num_workers,
-        batch_size=args.batch_size,
-        drop_last=args.drop_last,
-        pin_memory=args.pin_memory,
-        num_layers=args.num_layers,
-    )
-    return datamodule
-
-
-def instantiate_model(args, datamodule):
-    model = DNAConvNet(
-        num_layers=args.num_layers,
-        hidden_channels=args.hidden_channels,
-        heads=args.heads,
-        groups=args.groups,
-        dropout=args.dropout,
-        # provide dataset specific arguments
-        **datamodule.hyper_parameters,
-    )
-    if args.jit:
-        model.jittable()
-
-    # Attached datamodule function to model
-    model.gather_data_and_convert_to_namedtuple = datamodule.gather_data_and_convert_to_namedtuple
-    return model
-
-
-def get_single_batch(datamodule):
-    for batch in datamodule.test_dataloader():
-        return datamodule.gather_data_and_convert_to_namedtuple(batch, 0)
-
-#######################
-#     Trainer Run     #
-#######################
-
-
-def run(args):
-
-    nice_print("You are about to train a TorchScripted Pytorch Geometric Lightning model !")
-    nice_print(lightning_logo)
-
-    datamodule: LightningDataModule = instantiate_datamodule(args)
-    model: LightningModule = instantiate_model(args, datamodule)
-    trainer = Trainer.from_argparse_args(args)
-    trainer.fit(model, datamodule)
-    trainer.test()
-
-    batch = get_single_batch(datamodule)
-    model.to_torchscript(file_path="model_trace.pt",
-                         method='script',
-                         example_inputs=batch)
-
-    nice_print("Congratulations !")
-    nice_print("You trained your first TorchScripted Pytorch Geometric Lightning model !", last=True)
-
-
-if __name__ == "__main__":
-    if not HAS_PYTORCH_GEOMETRIC:
-        print("Skip training. Pytorch Geometric isn't installed. Please, check README.md !")
-
-    else:
-        parser = ArgumentParser(description="Pytorch Geometric Example")
-        parser = Trainer.add_argparse_args(parser)
-        parser = CoraDataset.add_argparse_args(parser)
-        parser = DNAConvNet.add_argparse_args(parser)
-
-        cmd_line = '--max_epochs 1'.split(' ')
-
-        run(parser.parse_args(cmd_line))
diff --git a/pl_examples/pytorch_ecosystem/pytorch_geometric/lightning.py b/pl_examples/pytorch_ecosystem/pytorch_geometric/lightning.py
deleted file mode 100644
index 2c765d1449c57..0000000000000
--- a/pl_examples/pytorch_ecosystem/pytorch_geometric/lightning.py
+++ /dev/null
@@ -1,31 +0,0 @@
-def nice_print(msg, last=False):
-    print()
-    print("\033[0;35m" + msg + "\033[0m")
-    if last:
-        print()
-
-
-lightning_logo = """
-                    ####
-                ###########
-             ####################
-         ############################
-    #####################################
-##############################################
-#########################  ###################
-#######################    ###################
-####################      ####################
-##################       #####################
-################        ######################
-#####################        #################
-######################     ###################
-#####################    #####################
-####################   #######################
-###################  #########################
-##############################################
-    #####################################
-         ############################
-             ####################
-                  ##########
-                     ####
-"""
diff --git a/pl_examples/pytorch_ecosystem/pytorch_geometric/pyproject.toml b/pl_examples/pytorch_ecosystem/pytorch_geometric/pyproject.toml
deleted file mode 100644
index 99f516323e976..0000000000000
--- a/pl_examples/pytorch_ecosystem/pytorch_geometric/pyproject.toml
+++ /dev/null
@@ -1,25 +0,0 @@
-[tool.poetry]
-name = "lightning-geometric"
-version = "0.1.0"
-description = "TorchScripted Pytorch Geometric Examples with Pytorch Lightning"
-authors = ["Thomas Chaton <thomas.ai@grid.com>"]
-
-[tool.poetry.dependencies]
-python = "3.7.8"
-torch = "^1.6.0"
-torch-cluster = "^1.5.7"
-torch-sparse = "^0.6.7"
-torch-scatter = "^2.0.5"
-torch-geometric = "^1.6.1"
-pytorch-lightning = "^ 1.0.5"
-openmesh = "^1.1.4"
-torch-spline-conv = "^1.2.0"
-tqdm = "^4.50.0"
-pytest = "^6.1.0"
-
-[tool.poetry.dev-dependencies]
-black = {version = "^20.8b1", allow-prereleases = true}
-
-[build-system]
-requires = ["poetry>=0.12"]
-build-backend = "poetry.masonry.api"