Tutorials

1. Introduction To Deepchem

1. The Basic Tools of the Deep Life Sciences

2. Working With Datasets

3. An Introduction To MoleculeNet

4. Molecular Fingerprints

5. Creating Models with TensorFlow and PyTorch

6. Introduction to Graph Convolutions

7. Going Deeper on Molecular Featurizations

8. Working With Splitters

9. Advanced Model Training

10. Creating a high fidelity model from experimental data

11. Putting Multitask Learning to Work

12. Modeling Protein Ligand Interactions

13. Modeling Protein Ligand Interactions With Atomic Convolutions

14. Conditional Generative Adversarial Networks

15. Training a Generative Adversarial Network on MNIST

16. Advanced model training using hyperopt

17. Introduction to Gaussian Processes

18. PytorchLightning Integration

19. Compiling DeepChem Torch Models

2. Molecular Machine Learning

1. Molecular Fingerprints

2. Going Deeper on Molecular Featurizations

3. Learning Unsupervised Embeddings for Molecules

4. Synthetic Feasibility Scoring

5. Atomic Contributions for Molecules

6. Interactive Model Evaluation with Trident Chemwidgets

7. Transfer Learning With ChemBERTa Transformers

8. Training a Normalizing Flow on QM9

9. Large Scale Chemical Screens

10. Introduction to Molecular Attention Transformer

11. Generating molecules with MolGAN

12. Introduction to GROVER

13. Introduction to PROTACs

14. Druggablity Assessment with Fpocket and Machine Learning

3. Modeling Proteins

1. Protein Deep Learning

2. DeepChem AntibodyTutorial Simplified

3. Protein Structure Prediction with ESMFold

4. Introduction to ProtBERT

5. ProteinLM Tutorial0

4. Protein Ligand Modeling

1. Introduction to Binding Sites

2. Modeling Protein Ligand Interactions

3. Modeling Protein Ligand Interactions With Atomic Convolutions

4. DeepChemXAlphafold

5. UniProt Data Preprocessing for Binding Sites

5. Quantum Chemistry

1. Exploring Quantum Chemistry with GDB1k

2. DeepQMC tutorial

3. Training an Exchange Correlation Functional using Deepchem

6. Bioinformatics

1. Introduction to Bioinformatics

2. Multisequence Alignments

3. Scanpy

4. Deep probabilistic analysis of single-cell omics data

5. Cell Counting Tutorial

7. Material Science

1. Introduction To Material Science

8. Machine Learning Methods

1. Using Reinforcement Learning to Play Pong

2. Introduction to Model Interpretability

3. Uncertainty In Deep Learning

9. Deep Differential Equations

1. Physics Informed Neural Networks

2. Introducing JaxModel and PINNModel

3. About nODE Using Torchdiffeq in Deepchem

4. Differentiation Infrastructure in Deepchem

5. Ordinary Differential Equation Solving using deepchem

10. Equivariance

1. Introduction to Equivariance

11. Olfaction

1. Predict Multi Label Odor Descriptors using OpenPOM

12. Polymer Science

1. An Introduction to the Polymers and Their Representation

2. crystallization tendency regression

3. Introduction to Polymer SMILES

4. Understanding Weighted Directed Graphs for Polymer Implimentations

5. PolyBERT Introduction to Chemical Language Models for Fingerprint Generation

Pytorch-Lightning Integration for DeepChem Models ¶

In this tutorial we will go through how to setup a deepchem model inside the pytorch-lightning framework. Lightning is a pytorch framework which simplifies the process of experimenting with pytorch models easier. A few key functionalities offered by pytorch lightning which deepchem users can find useful are:

Multi-gpu training functionalities: pytorch-lightning provides easy multi-gpu, multi-node training. It also simplifies the process of launching multi-gpu, multi-node jobs across different cluster infrastructure, e.g. AWS, slurm based clusters.
Reducing boilerplate pytorch code: lightning takes care of details like, optimizer.zero_grad(), model.train(), model.eval() . Lightning also provides experiment logging functionality, for e.g. irrespective of training on CPU, GPU, multi-nodes the user can use the method self.log inside the trainer and it will appropriately log the metrics.
Features that can speed up training: half-precision training, gradient checkpointing, code profiling.

Setup ¶

This notebook assumes that you have already installed deepchem, if you have not follow the instructions at the deepchem installation page: https://deepchem.readthedocs.io/en/latest/get_started/installation.html .
Install pytorch lightning following the instructions on lightning's home page: https://www.pytorchlightning.ai/

In [1]:

!pip install --pre deepchem
!pip install pytorch_lightning

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Import the relevant packages.

In [2]:

import deepchem as dc
from deepchem.models import GCNModel
import pytorch_lightning as pl
import torch
from torch.nn import functional as F
from torch import nn
import pytorch_lightning as pl
from pytorch_lightning.core.lightning import LightningModule
from torch.optim import Adam
import numpy as np
import torch

Deepchem Example ¶

Below we show an example of a Graph Convolution Network (GCN). Note that this is a simple example which uses a GCNModel to predict the label from an input sequence. We do not showcase the complete functionality of deepchem in this example as we want to restructure the deepchem code and adapt it so that it can be easily plugged into pytorch-lightning. This example was inspired from the GCNModel documentation present here .

Prepare the dataset : for training our deepchem models we need a dataset that we can use to train the model. Below we prepare a sample dataset for the purposes of this tutorial. Below we also directly use the featurized to encode examples for the dataset.

In [3]:

smiles = ["C1CCC1", "CCC"]
labels = [0., 1.]
featurizer = dc.feat.MolGraphConvFeaturizer()
X = featurizer.featurize(smiles)
dataset = dc.data.NumpyDataset(X=X, y=labels)

Setup the model : now we initialize the Graph Convolutional Network model that we will use in our training.

In [4]:

model = GCNModel(
    mode='classification',
    n_tasks=1,
    batch_size=2,
    learning_rate=0.001
)

[16:00:37] /Users/princychahal/Documents/github/dgl/src/runtime/tensordispatch.cc:43: TensorDispatcher: dlopen failed: Using backend: pytorch
dlopen(/Users/princychahal/mambaforge/envs/keras_try_5/lib/python3.8/site-packages/dgl-0.8-py3.8-macosx-11.0-arm64.egg/dgl/tensoradapter/pytorch/libtensoradapter_pytorch_1.10.2.dylib, 1): image not found

Train the model : fit the model on our training dataset, also specify the number of epochs to run.

In [5]:

loss = model.fit(dataset, nb_epoch=5)
print(loss)

0.18830760717391967

/Users/princychahal/mambaforge/envs/keras_try_5/lib/python3.8/site-packages/torch/autocast_mode.py:141: UserWarning: User provided device_type of 'cuda', but CUDA is not available. Disabling
  warnings.warn('User provided device_type of \'cuda\', but CUDA is not available. Disabling')

Pytorch-Lightning + Deepchem example ¶

Now we will look at an example of the GCN model adapt for Pytorch-Lightning. For using Pytorch-Lightning there are two important components:

LightningDataModule : This module defines who the data is prepared and fed into the model so that the model can use it for training. The module defines the train dataloader function which are directly used by the trainer to generate data for the LightningModule . To learn more about the LightningDataModule refer to the datamodules documentation .
LightningModule : This module defines the training, validation steps for our model. We can use this module to initialize our model based on the hyperparameters. There are a number of boilerplate functions which we use directly to track our experiments, for example we can save all the hyperparameters that we used for training using the self.save_hyperparameters() method. For more details on how to use this module refer to the lightningmodules documentation .

Setup the torch dataset : Note that here we need to create a custome SmilesDataset so that we can easily interface with the deepchem featurizers. For this interface we need to define a collate method so that we can create batches for the dataset.

In [6]:

# prepare LightningDataModule
class SmilesDataset(torch.utils.data.Dataset):
    def __init__(self, smiles, labels):
        assert len(smiles) == len(labels)
        featurizer = dc.feat.MolGraphConvFeaturizer()
        X = featurizer.featurize(smiles)
        self._samples = dc.data.NumpyDataset(X=X, y=labels)
        
    def __len__(self):
        return len(self._samples)
        
    def __getitem__(self, index):
        return (
            self._samples.X[index],
            self._samples.y[index],
            self._samples.w[index],
        )
    
    
class SmilesDatasetBatch:
    def __init__(self, batch):
        X = [np.array([b[0] for b in batch])]
        y = [np.array([b[1] for b in batch])]
        w = [np.array([b[2] for b in batch])]
        self.batch_list = [X, y, w]
        
        
def collate_smiles_dataset_wrapper(batch):
    return SmilesDatasetBatch(batch)

Create the GCN specific lightning module : in this part we use an object of the SmilesDataset created above to create the SmilesDatasetModule

In [7]:

class SmilesDatasetModule(pl.LightningDataModule):
    def __init__(self, train_smiles, train_labels, batch_size):
        super().__init__()
        self._train_smiles = train_smiles
        self._train_labels = train_labels
        self._batch_size = batch_size
        
    def setup(self, stage):
        self.train_dataset = SmilesDataset(
            self._train_smiles,
            self._train_labels,
        )
        
    def train_dataloader(self):
        return torch.utils.data.DataLoader(
            self.train_dataset,
            batch_size=self._batch_size,
            collate_fn=collate_smiles_dataset_wrapper,
            shuffle=True,  
        )

Create the lightning module : in this part we create the GCN specific lightning module. This class specifies the logic flow for the training step. We also create the required models, optimizers and losses for the training flow.

In [8]:

# prepare the LightningModule
class GCNModule(pl.LightningModule):
    def __init__(self, mode, n_tasks, learning_rate):
        super().__init__()
        self.save_hyperparameters(
            "mode",
            "n_tasks",
            "learning_rate",
        )
        self.gcn_model = GCNModel(
            mode=self.hparams.mode,
            n_tasks=self.hparams.n_tasks,
            learning_rate=self.hparams.learning_rate,
        )
        self.pt_model = self.gcn_model.model
        self.loss = self.gcn_model._loss_fn
        
    def configure_optimizers(self):
        return self.gcn_model.optimizer._create_pytorch_optimizer(
            self.pt_model.parameters(),
        )
    
    def training_step(self, batch, batch_idx):
        batch = batch.batch_list
        inputs, labels, weights = self.gcn_model._prepare_batch(batch)
        outputs = self.pt_model(inputs)
        
        if isinstance(outputs, torch.Tensor):
            outputs = [outputs]
    
        if self.gcn_model._loss_outputs is not None:
            outputs = [outputs[i] for i in self.gcn_model._loss_outputs]
    
        loss_outputs = self.loss(outputs, labels, weights)
        
        self.log(
            "train_loss",
            loss_outputs,
            on_epoch=True,
            sync_dist=True,
            reduce_fx="mean",
            prog_bar=True,
        )
        
        return loss_outputs

Create the relevant objects

In [9]:

# create module objects
smiles_datasetmodule = SmilesDatasetModule(
    train_smiles=["C1CCC1", "CCC", "C1CCC1", "CCC", "C1CCC1", "CCC", "C1CCC1", "CCC", "C1CCC1", "CCC"],
    train_labels=[0., 1., 0., 1., 0., 1., 0., 1., 0., 1.],
    batch_size=2,
)

gcnmodule = GCNModule(
    mode="classification",
    n_tasks=1,
    learning_rate=1e-3,
)

Lightning Trainer ¶

Trainer is the wrapper which builds on top of the LightningDataModule and LightningModule . When constructing the lightning trainer you can also specify the number of epochs, max-steps to run, number of GPUs, number of nodes to be used for trainer. Lightning trainer acts as a wrapper over your distributed training setup and this way you are able to build your models in a way you would build them in a simple way for your local runs.

In [10]:

trainer = pl.Trainer(
    max_epochs=5,
)

GPU available: False, used: False
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs

Call the fit function to run model training

In [11]:

# train
trainer.fit(
    model=gcnmodule,
    datamodule=smiles_datasetmodule,
)

  | Name     | Type | Params
----------------------------------
0 | pt_model | GCN  | 29.4 K
----------------------------------
29.4 K    Trainable params
0         Non-trainable params
29.4 K    Total params
0.118     Total estimated model params size (MB)
/Users/princychahal/mambaforge/envs/keras_try_5/lib/python3.8/site-packages/pytorch_lightning/trainer/data_loading.py:132: UserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.
  rank_zero_warn(
/Users/princychahal/mambaforge/envs/keras_try_5/lib/python3.8/site-packages/pytorch_lightning/trainer/data_loading.py:428: UserWarning: The number of training samples (5) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.
  rank_zero_warn(

Training: 0it [00:00, ?it/s]

In [ ]: