Update PyG notebooks and gradient links

gnn/cluster_gcn/pytorch_geometric/README.md

@@ -1,7 +1,11 @@
-# Cluster GCN on IPU: Node classification task on a large graph using sampling
+# Node Classification on IPU using Cluster-GCN - Training with PyTorch Geometric
 
 In this notebook cluster graph convolutional networks are used for node classification, using cluster sampling. This model has been optimised for Graphcore's IPU.
 
+Run our Cluster GCN training on Paperspace.
+<br>
+[![Gradient](../../../gradient-badge.svg)](https://ipu.dev/PmAtSw)
+
 | Framework | Domain | Model | Datasets | Tasks | Training | Inference | Reference |
 |-----------|--------|-------|----------|-------|----------|-----------|-----------|
 | PyTorch | GNNs | CGCN | Reddit | Node Classification | <div style="text-align: center;">✅ <br>Min. 4 IPUs (POD4) required | <p style="text-align: center;">❌ | [Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks](https://arxiv.org/pdf/1905.07953.pdf) |

gnn/cluster_gcn/pytorch_geometric/node_classification_with_cluster_gcn.ipynb

@@ -15,11 +15,15 @@
  "id": "361b1074",
  "metadata": {},
  "source": [
- "Cluster GCN on IPU: Node classification task on a large graph using sampling\n",
+ "Node Classification on IPU using Cluster-GCN - Training with PyTorch Geometric\n",
  "==================\n",
  "\n",
  "This notebook demonstrates training a Cluster GCN model presented in [Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks](https://arxiv.org/pdf/1905.07953.pdf) with PyTorch Geometric on the Graphcore IPU. We will use the Reddit dataset from [Inductive Representation Learning on Large Graphs](https://arxiv.org/abs/1706.02216) and train the model to predict the community a post belongs to.\n",
  "\n",
+ "| Domain | Tasks | Model | Datasets | Workflow | Number of IPUs | Execution time |\n",
+ "|---------|-------|-------|----------|----------|--------------------|----------------|\n",
+ "| GNNs | Node Classification | CGCN | Reddit | Training, evaluation | recommended: 4 | 20mn |\n",
+ "\n",
  "This notebook assumes some familiarity with PopTorch as well as PyTorch Geometric (PyG). For additional resources please consult:\n",
  "\n",
  "* [PopTorch Documentation](https://docs.graphcore.ai/projects/poptorch-user-guide/en/latest/index.html),\n",
@@ -726,7 +730,7 @@
  ],
  "metadata": {
  "kernelspec": {
- "display_name": "3.2.0+1262_poptorch",
+ "display_name": "3.2.0+1277_poptorch",
  "language": "python",
  "name": "python3"
  },
@@ -740,7 +744,7 @@
  "name": "python",
  "nbconvert_exporter": "python",
  "pygments_lexer": "ipython3",
- "version": "3.8.10 (default, Nov 14 2022, 12:59:47) \n[GCC 9.4.0]"
+ "version": "3.8.10 (default, Mar 13 2023, 10:26:41) \n[GCC 9.4.0]"
  },
  "traceability": {
  "sdk_version": "3.2.0-EA.1+1249",
@@ -750,7 +754,7 @@
  },
  "vscode": {
  "interpreter": {
- "hash": "4b7133ac454a965c7b25bf17fb22ff5a4b1e3a6d812b3e17a861b05f76606f09"
+ "hash": "14ed74787bcb3a85d33b99e2a461605961fba8ba6d9ddfcf06c9973f1378dba0"
  }
  }
  },

gnn/message_passing/pytorch_geometric/README.md

@@ -1,7 +1,11 @@
-# Molecular property prediction with GIN
+# Molecular property prediction on IPU using GIN - Training with PyTorch Geometric
 
 In this notebook a Graph Isomorphism Network (GIN) is used to perform graph classification for molecular property prediction. The model has been optimised for Graphcore's IPU.
 
+Run our GIN training on Paperspace.
+<br>
+[![Gradient](../../../gradient-badge.svg)](https://ipu.dev/1vOXXi)
+
 | Framework | Domain | Model | Datasets | Tasks | Training | Inference | Reference |
 |-----------|--------|-------|----------|-------|----------|-----------|-----------|
 | PyTorch | GNNs | GIN | NCI1 | Graph Classification | <div style="text-align: center;">✅ <br>Min. 4 IPUs (POD4) required | <p style="text-align: center;">❌ | [Graph Isomorphism Network](https://arxiv.org/pdf/1810.00826.pdf) |

gnn/message_passing/pytorch_geometric/molecular_property_prediction_with_gin.ipynb

@@ -15,10 +15,14 @@
  "id": "d5193627",
  "metadata": {},
  "source": [
- "## Graph Classification with Graph Isomorphism Networks\n",
+ "## Molecular property prediction on IPU using GIN - Training with PyTorch Geometric\n",
  "\n",
  "In this tutorial we show an example of how to perform the task of graph classification using the [Graph Isomorphism Network](https://arxiv.org/pdf/1810.00826.pdf) architecture on the Graphcore IPU. This tutorial builds on the [Graph Classification](https://colab.research.google.com/drive/1I8a0DfQ3fI7Njc62__mVXUlcAleUclnb?usp=sharing#scrollTo=1tBMhOrq4JKw) introduction notebook provided by PyTorch Geometric with modifications required in order to use Poptorch. \n",
  "\n",
+ "| Domain | Tasks | Model | Datasets | Workflow | Number of IPUs | Execution time |\n",
+ "|---------|-------|-------|----------|----------|--------------------|----------------|\n",
+ "| GNNs | Graph Classification | GIN | NCI1 | Training, evaluation | recommended: 4 | 20mn |\n",
+ "\n",
  "This notebook assumes some familiarity with PopTorch as well as PyTorch Geometric (PyG). For additional resources please consult:\n",
  "\n",
  "* [PopTorch Documentation](https://docs.graphcore.ai/projects/poptorch-user-guide/en/latest/index.html)\n",
@@ -767,7 +771,7 @@
  ],
  "metadata": {
  "kernelspec": {
- "display_name": "3.2.0+1262_poptorch",
+ "display_name": "3.2.0+1277_poptorch",
  "language": "python",
  "name": "python3"
  },
@@ -781,7 +785,7 @@
  "name": "python",
  "nbconvert_exporter": "python",
  "pygments_lexer": "ipython3",
- "version": "3.8.10 (default, Nov 14 2022, 12:59:47) \n[GCC 9.4.0]"
+ "version": "3.8.10 (default, Mar 13 2023, 10:26:41) \n[GCC 9.4.0]"
  },
  "traceability": {
  "sdk_version": "3.2.0-EA.1+1249",
@@ -791,7 +795,7 @@
  },
  "vscode": {
  "interpreter": {
- "hash": "4b7133ac454a965c7b25bf17fb22ff5a4b1e3a6d812b3e17a861b05f76606f09"
+ "hash": "14ed74787bcb3a85d33b99e2a461605961fba8ba6d9ddfcf06c9973f1378dba0"
  }
  }
  },

gnn/nbfnet/pytorch_geometric/NBFNet_training.ipynb

@@ -14,14 +14,18 @@
  "id": "73b4dca6-3257-4709-aac0-9ae6441216aa",
  "metadata": {},
  "source": [
- "# Training Neural Bellman-Ford networks (NBFnet) for inductive knowledge graph link prediction on IPUs\n",
+ "# Training Neural Bellman-Ford networks (NBFnet) for inductive knowledge graph link prediction on IPUs \n",
  "\n",
  "<a href=\"https://arxiv.org/abs/2106.06935\" target=\"_blank\">Neural Bellman-Ford networks (NBFNet)</a> is a model that generalises path-based reasoning models for predicting links in homogeneous and heterogeneous graphs. \n",
  "\n",
  "In this notebook we use NBFNet for link prediction in the FB15k-237 knowledge graph with 14541 entities, 237 relation types and 272115 triples. However in practice we explicitly insert reverse edges, which brings us to a total of 474 relation types and 544230 triples.\n",
  "\n",
  "Unlike many other knowledge graph completion models, NBFNet can be *inductive*, in other words it can generalise to entities that do not appear in the training data. To demonstrate this inductive behaviour we train the model on a small subset of the graph (4707 entities, 54406 triples) and perform inference on the complete FB15k-237 graph.\n",
  "\n",
+ "| Domain | Tasks | Model | Datasets | Workflow | Number of IPUs | Execution time |\n",
+ "|---------|-------|-------|----------|----------|--------------------|----------------|\n",
+ "| GNNs | Link Prediction | NBFNet | FB15k-237 | Training, evaluation | recommended: 16 (min: 4) | 20mn |\n",
+ "\n",
  "This notebook assumes some familiarity with PopTorch as well as PyTorch Geometric (PyG). For additional resources please consult:\n",
  "* [PopTorch Documentation](https://docs.graphcore.ai/projects/poptorch-user-guide/en/latest/index.html),\n",
  "* [PopTorch Examples and Tutorials](https://docs.graphcore.ai/en/latest/examples.html#pytorch),\n",
@@ -617,7 +621,7 @@
  "name": "python",
  "nbconvert_exporter": "python",
  "pygments_lexer": "ipython3",
- "version": "3.8.10 (default, Nov 14 2022, 12:59:47) \n[GCC 9.4.0]"
+ "version": "3.8.10 (default, Mar 13 2023, 10:26:41) \n[GCC 9.4.0]"
  },
  "pycharm": {
  "stem_cell": {

gnn/nbfnet/pytorch_geometric/README.md

@@ -1,11 +1,11 @@
-# NBFNet: Neural Bellman-Ford Networks
+# Training Neural Bellman-Ford networks (NBFnet) for inductive knowledge graph link prediction on IPUs
 
 This directory contains a PyTorch Geometric implementation of [NBFNet](https://arxiv.org/abs/2106.06935) used for link
 prediction in homogeneous and heterogeneous graphs. The model has been optimised for Graphcore's IPU.
 
 Run inductive training on the FB15k-237 knowledge graph on Paperspace.
 <br>
-[![Gradient](../../../gradient-badge.svg)](TODO)
+[![Gradient](../../../gradient-badge.svg)](https://ipu.dev/EzpMQD)
 
 | Framework | Domain | Model | Datasets | Tasks | Training | Inference | Reference |
 |-----------|--------|-------|----------|-------|----------|-----------|-----------|

gnn/schnet/pytorch_geometric/README.md

@@ -1,7 +1,11 @@
-# Molecular property prediction with SchNet
+# Molecular property prediction on IPU using SchNet - Training with PyTorch Geometric
 
 In this notebook an SchNet GNN is used to perform graph classification for molecular property prediction. The model has been optimised for Graphcore's IPU.
 
+Run our SchNet training on Paperspace.
+<br>
+[![Gradient](../../../gradient-badge.svg)](https://ipu.dev/zUl8uW)
+
 | Framework | Domain | Model | Datasets | Tasks | Training | Inference | Reference |
 |-----------|--------|-------|----------|-------|----------|-----------|-----------|
 | PyTorch | GNNs | SchNet | QM9 | Graph Classification | <div style="text-align: center;">✅ <br>Min. 4 IPUs (POD4) required | <p style="text-align: center;">❌ | [SchNet graph neural network](https://arxiv.org/abs/1712.06113) |

gnn/schnet/pytorch_geometric/molecular_property_prediction_with_schnet.ipynb

@@ -14,12 +14,16 @@
  "id": "3e1fbc52",
  "metadata": {},
  "source": [
- "Prediction of Molecular Properties using SchNet on Graphcore IPUs\n",
+ "Molecular property prediction on IPU using SchNet - Training with PyTorch Geometric\n",
  "=================================================================\n",
  "\n",
  "This notebook demonstrates training a [SchNet graph neural network](https://arxiv.org/abs/1712.06113) with PyTorch Geometric on the Graphcore IPU. We will use the QM9 dataset from the [MoleculeNet: A Benchmark for Molecular\n",
  " Machine Learning](https://arxiv.org/abs/1703.00564) paper and train the SchNet model to predict the HOMO-LUMO energy gap.\n",
  "\n",
+ "| Domain | Tasks | Model | Datasets | Workflow | Number of IPUs | Execution time |\n",
+ "|---------|-------|-------|----------|----------|--------------------|----------------|\n",
+ "| GNNs | Graph Classification | SchNet | QM9 | Training, evaluation | recommended: 16 (min: 4) | 20mn |\n",
+ "\n",
  "This notebook assumes some familiarity with PopTorch as well as PyTorch Geometric (PyG). For additional resources please consult:\n",
  "\n",
  "* [PopTorch Documentation](https://docs.graphcore.ai/projects/poptorch-user-guide/en/latest/index.html)\n",
@@ -943,7 +947,7 @@
  ],
  "metadata": {
  "kernelspec": {
- "display_name": "3.2.0+1262_poptorch",
+ "display_name": "3.2.0+1277_poptorch",
  "language": "python",
  "name": "python3"
  },
@@ -957,7 +961,7 @@
  "name": "python",
  "nbconvert_exporter": "python",
  "pygments_lexer": "ipython3",
- "version": "3.8.10 (default, Nov 14 2022, 12:59:47) \n[GCC 9.4.0]"
+ "version": "3.8.10 (default, Mar 13 2023, 10:26:41) \n[GCC 9.4.0]"
  },
  "traceability": {
  "sdk_version": "3.2.0-EA.1+1249",
@@ -967,7 +971,7 @@
  },
  "vscode": {
  "interpreter": {
- "hash": "4b7133ac454a965c7b25bf17fb22ff5a4b1e3a6d812b3e17a861b05f76606f09"
+ "hash": "14ed74787bcb3a85d33b99e2a461605961fba8ba6d9ddfcf06c9973f1378dba0"
  }
  }
  },

gnn/tgn/pytorch/README.md

@@ -1,10 +1,10 @@
-# Temporal Graph Networks
+# Training dynamic graphs on IPU using Temporal Graph Networks (TGN)
 
 Temporal graph networks for link prediction in dynamic graphs, based on [`examples/tgn.py`](https://github.com/rusty1s/pytorch_geometric/blob/master/examples/tgn.py) from PyTorch-Geometric, optimised for Graphcore's IPU.
 
 Run our TGN on paperspace.
 <br>
-[![Gradient](../../../gradient-badge.svg)](https://ipu.dev/3CG1WqL)
+[![Gradient](../../../gradient-badge.svg)](https://ipu.dev/jffnhe)
 
 | Framework | Domain | Model | Datasets | Tasks | Training | Inference | Reference |
 |-----------|--------|-------|----------|-------|----------|-----------|-----------|

gnn/tgn/pytorch/Train_TGN.ipynb

@@ -9,18 +9,24 @@
  ]
  },
  {
+ "attachments": {},
  "cell_type": "markdown",
  "id": "c820e409-be83-40ba-b93e-83f7d82d1227",
  "metadata": {
  "tags": []
  },
  "source": [
- "# Temporal Graph Networks training\n",
+ "# Training dynamic graphs on IPU using Temporal Graph Networks (TGN)\n",
+ "\n",
  "This notebook demonstrates how to train [Temporal Graph Networks](https://arxiv.org/abs/2006.10637)\n",
  "(TGNs) on the IPU. See our [blog post](https://www.graphcore.ai/posts/accelerating-and-scaling-temporal-graph-networks-on-the-graphcore-ipu) for details on\n",
  "performance and scaling.\n",
  "TGN can be used to predict connections in a dynamically evolving graph. This application looks at graphs that gain edges over time. A typical use case is a social network where users form new connections over time, or a recommendation system where new edges represent an interaction of a user with a product or content.\n",
  "\n",
+ "| Domain | Tasks | Model | Datasets | Workflow | Number of IPUs | Execution time |\n",
+ "|---------|-------|-------|----------|----------|--------------------|----------------|\n",
+ "| GNNs | Link Prediction | TGN | JODIE | Training, evaluation | recommended: 4 | 20mn |\n",
+ "\n",
  "![dynamic_graph.png](static/dynamic_graph.png)\n",
  "\n",
  "In this notebook we apply TGN to the [JODIE Wikipedia dataset](https://snap.stanford.edu/jodie/), a dynamic graph of 1,000 Wikipedia articles and 8,227 Wikipedia users. 157,474 time-stamped edges describe the interactions of users with articles."

tutorials/tutorials/README.md

@@ -63,7 +63,7 @@ Running a Hugging Face model on the IPU:
 
 - [Fine-tuning a HuggingFace Vision Transformer (ViT) on the IPU Using a Local Dataset](pytorch/vit_model_training)
 
-## PyTorch Geometric [![Gradient](../../gradient-badge.svg)](TODO)
+## PyTorch Geometric [![Gradient](../../gradient-badge.svg)](https://ipu.dev/RfTOyI)
 
 Getting started: