Launch

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Easily scale training runs from your desktop to a compute resource like Amazon SageMaker, Kubernetes and more with W&B Launch. Once W&B Launch is configured, you can quickly run training scripts, model evaluation suites, prepare models for production inference, and more with a few clicks and commands.

How it works

Launch is composed of three fundamental components: launch jobs, queues, and agents.

A launch job is a blueprint for configuring and running tasks in your ML workflow. Once you have a launch job, you can add it to a launch queue. A launch queue is a first-in, first-out (FIFO) queue where you can configure and submit your jobs to a particular compute target resource, such as Amazon SageMaker or a Kubernetes cluster.

As jobs are added to the queue, one or more launch agents will poll that queue and execute the job on the system targeted by the queue.

Based on your use case, you (or someone on your team) will configure the launch queue according to your chosen compute resource target (for example Amazon SageMaker) and deploy a launch agent on your own infrastructure.

See the Terms and concepts page for more information on launch jobs, how queues work, launch agents, and additional information on how W&B Launch works.

How to get started

Depending on your use case, explore the following resources to get started with W&B Launch:

• If this is your first time using W&B Launch, we recommend you go through the Walkthrough guide.
• Learn how to set up W&B Launch.
• Create a launch job.
• Check out the W&B Launch public jobs GitHub repository for templates of common tasks like deploying to Triton, evaluating an LLM, or more.
  • View launch jobs created from this repository in this public wandb/jobs project W&B project.