SageMaker
less than a minute
W&B integrates with Amazon SageMaker, automatically reading hyperparameters, grouping distributed runs, and resuming runs from checkpoints.
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!
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 tutorial on deploying a sentiment analyzer using SageMaker and W&B.
The W&B sweep agent will only behave as expected in a SageMaker job if our SageMaker integration is turned off. You can turn off 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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