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Using AWS Batch

Here are some useful tips and tricks related to running Metaflow on AWS Batch. See our engineering resources for additional information about setting up and operating AWS Batch for Metaflow.

What value of @timeout should I set?

Metaflow sets a default timeout of 5 days so that you tasks don't get stuck infinitely while running on AWS Batch. For more details on how to use @timeout please read this.

How much @resources can I request?

Here are the current defaults for different resource types:

  • cpu: 1
  • memory: 4000 (4GB)

When setting @resources, keep in mind the configuration of your AWS Batch Compute Environment. Your job will be stuck in a RUNNABLE state if AWS is unable to provision the requested resources. Additionally, as a good measure, don't request more resources than what your workflow actually needs. On the other hand, never optimize resources prematurely.

You can place your AWS Batch task in a specific queue by using the queue argument. By default, all tasks execute on a vanilla python docker image corresponding to the version of Python interpreter used to launch the flow and can be overridden using the image argument.

You can also specify the resource requirements on command line as well:

$ python run --with batch:cpu=4,memory=10000,queue=default,image=ubuntu:latest

Using GPUs and Trainium instances with AWS Batch

To use GPUs in Metaflow tasks that run on AWS Batch, you need to run the flow in a Job Queue that is attached to a Compute Environment with GPU/Trainium instances.

To set this up, you can either modify the allowable instances in a Metaflow AWS deployment template or manually add such a compute environment from the AWS console. The steps are:

  1. Create a compute environment with GPU-enabled EC2 instances or Trainium instances.
  2. Attach the compute environment to a new Job Queue - for example named my-gpu-queue.
  3. Run a flow with a GPU task in the my-gpu-queue job queue by
    • setting the METAFLOW_BATCH_JOB_QUEUE environment variable, or
    • setting the METAFLOW_BATCH_JOB_QUEUE value in your Metaflow config, or
    • (most explicit) setting the queue parameter in the @batch decorator.

It is a good practice to separate the job queues that you run GPU tasks on from those that do not require GPUs (or Trainium instances). This makes it easier to track hardware-accelerated workflows, which can be costly, independent of other workflows. Just add a line like

@batch(gpu=1, queue='my-gpu-queue')

in steps that require GPUs.

My job is stuck in RUNNABLE state. What should I do?

Does the Batch job queue you are trying to run the Metaflow task in have a compute environment with EC2 instances with the resources requested? For example, if your job queue is connected to a single compute environment that only has p3.2xlarge as a GPU instance, and a user requests 2 GPUs, that job will never get scheduled because p3.2xlarge only have 1 GPU per instance.

For more information, see this article.

My job is stuck in STARTING state. What should I do?

Are the resources requested in your Metaflow code/command sufficient? Especially when using custom GPU images, you might need to increase the requested memory to pull the container image into your compute environment.

Listing and killing AWS Batch tasks

If you interrupt a Metaflow run, any AWS Batch tasks launched by the run get killed by Metaflow automatically. Even if something went wrong during the final cleanup, the tasks will finish and die eventually, at the latest when they hit the maximum time allowed for an AWS Batch task.

If you want to make sure you have no AWS Batch tasks running, or you want to manage them manually, you can use the batch list and batch kill commands.

You can easily see what AWS Batch tasks were launched by your latest run with

$ python batch list

You can kill the tasks started by the latest run with

$ python batch kill

If you have started multiple runs, you can make sure there are no orphaned tasks still running with

$ python batch list --my-runs

You can kill the tasks started by the latest run with

$ python batch kill --my-runs

If you see multiple runs running, you can cherry-pick a specific job, e.g. 456, to be killed as follows

$ python batch kill --run-id 456

If you are working with another person, you can see and kill their tasks related to this flow with

$ python batch kill --user willsmith

Note that all the above commands only affect the flow defined in your script. You can work on many flows in parallel and be confident that kill kills tasks only related to the flow you called kill with.

Accessing AWS Batch logs

As a convenience feature, you can also see the logs of any past step as follows:

$ python logs 15/end

Disk space

You can request higher disk space on AWS Batch instances by using an unmanaged Compute Environment with a custom AMI.

How to configure AWS Batch for distributed computing?

See these instructions if you want to use AWS Batch for distributed computing.