> ## Documentation Index
> Fetch the complete documentation index at: https://wb-21fd5541-docs-1917.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

> Integrate W&B with the DeepChem library for experiment tracking and visualization of molecular ML models.

# DeepChem

The [DeepChem library](https://github.com/deepchem/deepchem) 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

```python theme={null}
logger = WandbLogger(…)
model = TorchModel(…, wandb_logger=logger)
model.fit(…)
```

<Frame>
  <img src="https://mintcdn.com/wb-21fd5541-docs-1917/pKzhFl-6PjFOaAdA/images/integrations/cd.png?fit=max&auto=format&n=pKzhFl-6PjFOaAdA&q=85&s=361caaa20714b87dabd316e096ff34e1" alt="DeepChem molecular analysis" width="1513" height="842" data-path="images/integrations/cd.png" />
</Frame>

## Report and Google Colab

Explore the Using [W\&B with DeepChem: Molecular Graph Convolutional Networks](https://wandb.ai/kshen/deepchem_graphconv/reports/Using-W-B-with-DeepChem-Molecular-Graph-Convolutional-Networks--Vmlldzo4MzU5MDc?galleryTag=) article for an example charts generated using the W\&B DeepChem integration.

To dive straight into working code, check out this [Google Colab](https://colab.research.google.com/github/wandb/examples/blob/master/colabs/deepchem/W%26B_x_DeepChem.ipynb).

## Track experiments

Set up W\&B for DeepChem models of type [KerasModel](https://deepchem.readthedocs.io/en/latest/api_reference/models.html#keras-models) or [TorchModel](https://deepchem.readthedocs.io/en/latest/api_reference/models.html#pytorch-models).

### 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.

<Note>
  For a more streamlined approach, create an API key by going directly to [User Settings](https://wandb.ai/settings). Copy the newly created API key immediately and save it in a secure location such as a password manager.
</Note>

1. Click your user profile icon in the upper right corner.
2. Select **User Settings**, then scroll to the **API Keys** section.

### Install the `wandb` library and log in

To install the `wandb` library locally and log in:

<Tabs>
  <Tab title="Command Line">
    1. Set the `WANDB_API_KEY` [environment variable](/models/track/environment-variables/) to your API key.

       ```bash theme={null}
       export WANDB_API_KEY=<your_api_key>
       ```

    2. Install the `wandb` library and log in.

       ```shell theme={null}
       pip install wandb

       wandb login
       ```
  </Tab>

  <Tab title="Python">
    ```bash theme={null}
    pip install wandb
    ```

    ```python theme={null}
    import wandb
    wandb.login()
    ```
  </Tab>

  <Tab title="Python notebook">
    ```notebook theme={null}
    !pip install wandb

    import wandb
    wandb.login()
    ```
  </Tab>
</Tabs>

### 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](https://github.com/deepchem/deepchem/blob/master/deepchem/models/callbacks.py), the `WandbLogger` will detect ValidationCallback callback and log the metrics generated.

<Tabs>
  <Tab title="TorchModel">
    ```python theme={null}
    from deepchem.models import TorchModel, ValidationCallback

    vc = ValidationCallback(…)  # optional
    model = TorchModel(…, wandb_logger=logger)
    model.fit(…, callbacks=[vc])
    logger.finish()
    ```
  </Tab>

  <Tab title="KerasModel">
    ```python theme={null}
    from deepchem.models import KerasModel, ValidationCallback

    vc = ValidationCallback(…)  # optional
    model = KerasModel(…, wandb_logger=logger)
    model.fit(…, callbacks=[vc])
    logger.finish()
    ```
  </Tab>
</Tabs>