W&B integrates with Amazon SageMaker by automatically reading hyperparameters, grouping distributed runs, and resuming runs from checkpoints.
W&B looks for a file named
secrets.env relative to the training script and loads them into the environment when
wandb.init() is called. You can generate a
secrets.env file by calling
wandb.sagemaker_auth(path="source_dir") in the script you use to launch your experiments. Be sure to add this file to your
If you're using one of SageMakers preconfigured estimators you need to add a
requirements.txt to your source directory that includes wandb
If you're using an estimator that's running Python 2, you'll need to install psutil directly from a wheel before installing wandb:
https://wheels.galaxyproject /packages/psutil-5.4.8-cp27-cp27mu-manylinux1_x86_64.whl wandb
A complete example is available on GitHub.