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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)

Report & 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.

Getting started: track experiments

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

1) Install the wandb library and log in

pip install 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.

from deepchem.models import TorchModel, ValidationCallback

vc = ValidationCallback() # optional
model = TorchModel(, wandb_logger=logger), callbacks=[vc])
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