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 & 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
- Command Line
- Notebook
pip install wandb
wandb login
!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
from deepchem.models import TorchModel, ValidationCallback
vc = ValidationCallback(…) # optional
model = TorchModel(…, wandb_logger=logger)
model.fit(…, callbacks=[vc])
logger.finish()
from deepchem.models import KerasModel, ValidationCallback
vc = ValidationCallback(…) # optional
model = KerasModel(…, wandb_logger=logger)
model.fit(…, callbacks=[vc])
logger.finish()