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W&B integrates with Amazon SageMaker, automatically reading hyperparameters, grouping distributed runs, and resuming runs from checkpoints.

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Authentication

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!

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Existing estimators

If you’re using one of SageMakers preconfigured estimators you need to add a requirements.txt to your source directory that includes wandb 
wandb
If you’re using an estimator that’s running Python 2, you’ll need to install psutil directly from this wheel before installing wandb: 
https://wheels.galaxyproject.org/packages/psutil-5.4.8-cp27-cp27mu-manylinux1_x86_64.whl
wandb
Review a complete example on GitHub, and read more on our blog. You can also read the Deploy Sentiment Analyzer Using SageMaker and W&B tutorial on deploying a sentiment analyzer using SageMaker and W&B.
The W&B sweep agent behaves as expected in a SageMaker job only if your SageMaker integration is turned off. Turn off the SageMaker integration by modifying your invocation of wandb.init:
wandb.init(..., settings=wandb.Settings(sagemaker_disable=True))