Create a launch job

Launch jobs are blueprints for reproducing W&B runs. Jobs are W&B Artifacts that capture the source code, dependencies, and inputs required to execute a workload.

Create and run jobs with the wandb launch command.

Git jobs

You can create a Git-based job where code and other tracked assets are cloned from a certain commit, branch, or tag in a remote git repository with W&B Launch. Use the --uri or -u flag to specify the URI containing the code, along with optionally a --build-context flag to specify a subdirectory.

Run a “hello world” job from a git repository with the following command:

wandb launch --uri "https://github.com/wandb/launch-jobs.git" --build-context jobs/hello_world --dockerfile Dockerfile.wandb --project "hello-world" --job-name "hello-world" --entry-point "python job.py"

The command does the following:

1. Clones the W&B Launch jobs repository to a temporary directory.
2. Creates a job named hello-world-git in the hello project. The job is associated with the commit at the head of the default branch of the repository.
3. Builds a container image from the jobs/hello_world directory and the Dockerfile.wandb.
4. Starts the container and runs python job.py.

To build a job from a specific branch or commit hash, append the -g, --git-hash argument. For a full list of arguments, run wandb launch --help.

Remote URL format

The git remote associated with a Launch job can be either an HTTPS or an SSH URL. The URL type determines the protocol used to fetch job source code.

Note that the exact URL format varies by hosting provider. Jobs created with wandb launch --uri will use the transfer protocol specified in the provided --uri.

Code artifact jobs

Jobs can be created from any source code stored in a W&B Artifact. Use a local directory with the --uri or -u argument to create a new code artifact and job.

To get started, create an empty directory and add a Python script named main.py with the following content:

import wandb

with wandb.init() as run:
    run.log({"metric": 0.5})

Add a file requirements.txt with the following content:

wandb>=0.17.1

Log the directory as a code artifact and launch a job with the following command:

wandb launch --uri . --job-name hello-world-code --project launch-quickstart --entry-point "python main.py"

The preceding command does the following:

1. Logs the current directory as a code artifact named hello-world-code.
2. Creates a job named hello-world-code in the launch-quickstart project.
3. Builds a container image from the current directory and Launch’s default Dockerfile. The default Dockerfile will install the requirements.txt file and set the entry point to python main.py.

Image jobs

Alternatively, you can build jobs off of pre-made Docker images. This is useful when you already have an established build system for your ML code, or when you don’t expect to adjust the code or requirements for the job but do want to experiment with hyperparameters or different infrastructure scales.

The image is pulled from a Docker registry and run with the specified entry point, or the default entry point if none is specified. Pass a full image tag to the --docker-image option to create and run a job from a Docker image.

To run a simple job from a pre-made image, use the following command:

wandb launch --docker-image "wandb/job_hello_world:main" --project "hello-world"           

Automatic job creation

W&B will automatically create and track a job for any run with tracked source code, even if that run was not created with Launch. Runs are considered to have tracked source code if any of the three following conditions are met:

• The run has an associated git remote and commit hash
• The run logged a code artifact (see Run.log_code for more information)
• The run was executed in a Docker container with the WANDB_DOCKER environment variable set to an image tag

The Git remote URL is inferred from the local git repository if your Launch job is created automatically by a W&B run.

Launch job names

By default, W&B automatically generates a job name for you. The name is generated depending on how the job is created (GitHub, code artifact, or Docker image). Alternatively, you can define a Launch job’s name with environment variables or with the W&B Python SDK.

The following table describes the job naming convention used by default based on job source:

Name your job with a W&B environment variable or with the W&B Python SDK

• Environment variable
• W&B Python SDK

WANDB_JOB_NAME=awesome-job-name

settings = wandb.Settings(job_name="my-job-name")
wandb.init(settings=settings)

Containerization

Jobs are executed in a container. Image jobs use a pre-built Docker image, while Git and code artifact jobs require a container build step.

Job containerization can be customized with arguments to wandb launch and files within the job source code.

Build context

The term build context refers to the tree of files and directories that are sent to the Docker daemon to build a container image. By default, Launch uses the root of the job source code as the build context. To specify a subdirectory as the build context, use the --build-context argument of wandb launch when creating and launching a job.

Dockerfile

The Dockerfile is a text file that contains instructions for building a Docker image. By default, Launch uses a default Dockerfile that installs the requirements.txt file. To use a custom Dockerfile, specify the path to the file with the --dockerfile argument of wandb launch.

The Dockerfile path is specified relative to the build context. For example, if the build context is jobs/hello_world, and the Dockerfile is located in the jobs/hello_world directory, the --dockerfile argument should be set to Dockerfile.wandb. See the example above for a demonstration of how to use the --dockerfile argument with the official W&B Launch jobs repository.

Requirements file

If no custom Dockerfile is provided, Launch will look in the build context for Python dependencies to install. If a requirements.txt file is found at the root of the build context, Launch will install the dependencies listed in the file. Otherwise, if a pyproject.toml file is found, Launch will install dependencies from the project.dependencies section.