DeepChem

The DeepChem library provides open source tools that democratize the use of deep-learning in drug discovery, materials science, chemistry, and biology. This W&B integration adds simple and easy-to-use experiment tracking and model checkpointing while training models using DeepChem.

DeepChem logging in 3 lines of code

logger = WandbLogger(…)
model = TorchModel(…, wandb_logger=logger)
model.fit(…)

Report and Google Colab

Explore the Using W&B with DeepChem: Molecular Graph Convolutional Networks article for an example charts generated using the W&B DeepChem integration.

If you’d rather dive straight into working code, check out this Google Colab.

Track experiments

Setup Weights & Biases for DeepChem models of type KerasModel or TorchModel.

1. Install the wandb library and log in

   • Command Line
   • Notebook

   ```
   pip install wandb
   wandb login
   ```
   

   ```python
   !pip install wandb
   
   import wandb
   wandb.login()
   ```
   

2. Initialize and configure WandbLogger

   from deepchem.models import WandbLogger
   
   logger = WandbLogger(entity="my_entity", project="my_project")
   

3. Log your training and evaluation data to W&B

   Training loss and evaluation metrics can be automatically logged to Weights & Biases. Optional evaluation can be enabled using the DeepChem ValidationCallback, the WandbLogger will detect ValidationCallback callback and log the metrics generated.

   • TorchModel
   • KerasModel

   ```python
   from deepchem.models import TorchModel, ValidationCallback
   
   vc = ValidationCallback(…)  # optional
   model = TorchModel(…, wandb_logger=logger)
   model.fit(…, callbacks=[vc])
   logger.finish()
   ```
   

   ```python
   from deepchem.models import KerasModel, ValidationCallback
   
   vc = ValidationCallback(…)  # optional
   model = KerasModel(…, wandb_logger=logger)
   model.fit(…, callbacks=[vc])
   logger.finish()
   ```