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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.

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DeepChem logging in 3 lines of code

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

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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. To dive straight into working code, check out this Google Colab.

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Track experiments

Set up W&B for DeepChem models of type KerasModel or TorchModel.

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Sign up and create an API key

An API key authenticates your machine to W&B. You can generate an API key from your user profile.
For a more streamlined approach, create an API key by going directly to User Settings. Copy the newly created API key immediately and save it in a secure location such as a password manager.
  1. Click your user profile icon in the upper right corner.
  2. Select User Settings, then scroll to the API Keys section.

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Install the wandb library and log in

To install the wandb library locally and log in:
  1. Set the WANDB_API_KEY environment variable to your API key. 
    export WANDB_API_KEY=<your_api_key>
  2. Install the wandb library and log in. 
    pip install wandb

wandb login

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Log your training and evaluation data to W&B

Training loss and evaluation metrics can be automatically logged to W&B. 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)
model.fit(…, callbacks=[vc])
logger.finish()