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SageMaker Integrationโ€‹

W&B integrates with Amazon SageMaker, 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 .gitignore!

Existing Estimatorsโ€‹

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:

A complete example is available on GitHub and you can read more on our blog.\ You can also read the tutorial on deploying a sentiment analyzer using SageMaker and W&B.


The W&B sweep agent will not behave as expected in a SageMaker job unless our SageMaker integration is disabled. You can disable the SageMaker integration in your runs by modifying your invocation of wandb.init as follows:

wandb.init(..., settings=wandb.Settings(sagemaker_disable=True))
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