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--- a +++ b/README.md @@ -0,0 +1,185 @@ +[](https://torchdrug.ai/) +<h1 align="center"> + with + <a href="https://torchprotein.ai/"> + <img src="asset/torchprotein_logo_tight.svg" alt="TorchProtein" style="height:26px" /> + </a> +</h1> + +[](https://colab.research.google.com/drive/1Tbnr1Fog_YjkqU1MOhcVLuxqZ4DC-c8-#forceEdit=true&sandboxMode=true) +[](https://github.com/DeepGraphLearning/torchdrug/blob/master/CONTRIBUTING.md) +[](https://github.com/DeepGraphLearning/torchdrug/blob/master/LICENSE) +[](https://pypi.org/project/torchdrug/) +[](https://twitter.com/DrugTorch) + +[Docs] | [Tutorials] | [Benchmarks] | [Papers Implemented] + +[Docs]: https://deepgraphlearning.github.io/torchdrug-site/docs +[Tutorials]: https://deepgraphlearning.github.io/torchdrug-site/docs/tutorials +[Benchmarks]: https://deepgraphlearning.github.io/torchdrug-site/docs/benchmark +[Papers Implemented]: https://deepgraphlearning.github.io/torchdrug-site/docs/paper + +TorchDrug is a [PyTorch]-based machine learning toolbox designed for several purposes. + +- Easy implementation of graph operations in a PyTorchic style with GPU support +- Being friendly to practitioners with minimal knowledge about drug discovery +- Rapid prototyping of machine learning research + +[PyTorch]: https://pytorch.org/ + +Installation +------------ + +TorchDrug can be installed on either Linux, Windows or macOS. It is compatible with +3.7 <= Python <= 3.10 and PyTorch >= 1.8.0. + +### From Conda ### + +```bash +conda install torchdrug -c milagraph -c conda-forge -c pytorch -c pyg +``` + +### From Pip ### + +```bash +pip install torch==1.9.0 +pip install torch-scatter torch-cluster -f https://pytorch-geometric.com/whl/torch-1.9.0+cu102.html +pip install torchdrug +``` + +To install `torch-scatter` for other PyTorch or CUDA versions, please see the +instructions in https://github.com/rusty1s/pytorch_scatter + +### From Source ### + +```bash +git clone https://github.com/DeepGraphLearning/torchdrug +cd torchdrug +pip install -r requirements.txt +python setup.py install +``` + +### Windows (PowerShell) ### + +We need to first install the build tools for Visual Studio. We then install the +following modules in PowerShell. + +```powershell +Install-Module Pscx -AllowClobber +Install-Module VSSetup +``` + +Initialize Visual Studio in PowerShell with the following commands. We may setup +this for all PowerShell sessions by writing it to the PowerShell profile. Change +the library path according to your own case. + +```powershell +Import-VisualStudioVars -Architecture x64 +$env:LIB += ";C:\Program Files\Python37\libs" +``` + +### Apple Silicon (M1/M2 Chips) ### + +We need PyTorch >= 1.13 to run TorchDrug on Apple silicon. For `torch-scatter` and +`torch-cluster`, they can be compiled from their sources. Note TorchDrug doesn't +support `mps` devices. + +```bash +pip install torch==1.13.0 +pip install git+https://github.com/rusty1s/pytorch_scatter.git +pip install git+https://github.com/rusty1s/pytorch_cluster.git +pip install torchdrug +``` + +Quick Start +----------- + +TorchDrug is designed for humans and focused on graph structured data. +It enables easy implementation of graph operations in machine learning models. +All the operations in TorchDrug are backed by [PyTorch] framework, and support GPU +acceleration and auto differentiation. + +```python +from torchdrug import data + +edge_list = [[0, 1], [1, 2], [2, 3], [3, 4], [4, 5], [5, 0]] +graph = data.Graph(edge_list, num_node=6) +graph = graph.cuda() +# the subgraph induced by nodes 2, 3 & 4 +subgraph = graph.subgraph([2, 3, 4]) +``` + +Molecules are also supported in TorchDrug. You can get the desired molecule +properties without any domain knowledge. + +```python +mol = data.Molecule.from_smiles("CCOC(=O)N", atom_feature="default", bond_feature="default") +print(mol.node_feature) +print(mol.atom_type) +print(mol.to_scaffold()) +``` + +You may also register custom node, edge or graph attributes. They will be +automatically processed during indexing operations. + +```python +with mol.edge(): + mol.is_CC_bond = (mol.edge_list[:, :2] == td.CARBON).all(dim=-1) +sub_mol = mol.subgraph(mol.atom_type != td.NITROGEN) +print(sub_mol.is_CC_bond) +``` + +TorchDrug provides a wide range of common datasets and building blocks for drug +discovery. With minimal code, you can apply standard models to solve your own +problem. + +```python +import torch +from torchdrug import datasets + +dataset = datasets.Tox21() +dataset[0].visualize() +lengths = [int(0.8 * len(dataset)), int(0.1 * len(dataset))] +lengths += [len(dataset) - sum(lengths)] +train_set, valid_set, test_set = torch.utils.data.random_split(dataset, lengths) +``` + +```python +from torchdrug import models, tasks + +model = models.GIN(dataset.node_feature_dim, hidden_dims=[256, 256, 256, 256]) +task = tasks.PropertyPrediction(model, task=dataset.tasks) +``` + +Training and inference are accelerated by multiple CPUs or GPUs. +This can be seamlessly switched in TorchDrug by just a line of code. +```python +from torchdrug import core + +# Single CPU / Multiple CPUs / Distributed CPUs +solver = core.Engine(task, train_set, valid_set, test_set, optimizer) +# Single GPU +solver = core.Engine(task, train_set, valid_set, test_set, optimizer, gpus=[0]) +# Multiple GPUs +solver = core.Engine(task, train_set, valid_set, test_set, optimizer, gpus=[0, 1, 2, 3]) +# Distributed GPUs +solver = core.Engine(task, train_set, valid_set, test_set, optimizer, gpus=[0, 1, 2, 3, 0, 1, 2, 3]) +``` + +Experiments can be easily tracked and managed through [Weights & Biases platform]. +```python +solver = core.Engine(task, train_set, valid_set, test_set, optimizer, logger="wandb") +``` + +[Weights & Biases platform]: https://wandb.ai/ + +Contributing +------------ + +Everyone is welcome to contribute to the development of TorchDrug. +Please refer to [contributing guidelines](CONTRIBUTING.md) for more details. + +License +------- + +TorchDrug is released under [Apache-2.0 License](LICENSE).