> ## Documentation Index
> Fetch the complete documentation index at: https://docs.wandb.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# W&B Sandboxes

<Warning>
  W\&B Sandboxes is in private preview, available by invitation only. To request enrollment, contact [support](mailto:support@wandb.com) or your AISE.
</Warning>

W\&B Sandboxes gives you on-demand, isolated compute environments that you can create, use, and discard using Python.

W\&B Sandboxes is built using the CoreWeave Sandbox library. For the underlying API reference and library docs, see the [CoreWeave Sandbox documentation](https://docs.coreweave.com/products/coreweave-sandbox).

## How it works

A *sandbox* is a single isolated compute environment. You [create](/sandboxes/create-sandbox) it, [run commands inside it](/sandboxes/run-commands), and stop it when it is done. Each sandbox runs in its own container with its own filesystem, network, and process space.

W\&B authenticates your identity when you create and manage sandboxes. To use W\&B or Weave inside a sandbox, pass your API key using the [W\&B Secrets Manager](/platform/secrets#manage-access-to-secrets) or environment variables. See [Secrets](/sandboxes/secrets) for more information about using secrets in sandboxes.

A sandbox goes through [several states in its lifecycle](/sandboxes/lifecycle). When a container is running, you can [execute commands](/sandboxes/run-commands) inside it.

[Read, write, and mount read-only](/sandboxes/file-access) files to and from the sandbox. Common examples include reading in a Python script to execute, writing out logs or results, or mounting a directory of data read-only for the sandbox to access.

Use a *session* to manage multiple sandboxes that share configuration. When a session closes, all of its sandboxes are stopped automatically. See [Manage multiple sandboxes](/sandboxes/create-sandbox#create-multiple-sandboxes-with-a-session) for more details.

## Basic usage

Follow these steps to create a sandbox and run a command inside it:

1. Install the W\&B Python SDK (`wandb`) from the source provided by your support team or AISE contact.

2. Log in to W\&B with the [`wandb login`](/models/ref/cli/wandb-login) CLI command. When prompted, provide your API key to authenticate your identity and access your W\&B account:

   ```bash theme={null}
   wandb login
   ```

3. Copy and paste the following code snippet into a Python file and run it. The code snippet accomplishes the following:

   1. Create a sandbox with [`Sandbox.run()`](https://docs.coreweave.com/products/coreweave-sandbox/client/ref/core/sandbox#run).
   2. Run the command `echo "Hello from W&B Sandboxes!"` inside the sandbox using the [`Sandbox.exec()`](https://docs.coreweave.com/products/coreweave-sandbox/client/ref/core/sandbox#exec) method.
   3. Print the output to the console using the `Process` object returned by `Sandbox.exec()`.

   ```python show lines title="hello_sandbox.py" theme={null}
   from wandb.sandbox import Sandbox

   with Sandbox.run() as sandbox:
       process = sandbox.exec(["echo", "Hello from W&B Sandboxes!"]).result()
       print(process.stdout)
   ```

   You should see the output `Hello from W&B Sandboxes!` printed to the console.

   The sandbox automatically stops when the context manager (the `with` block) exits. For more information on sandbox lifecycle and states, see [Lifecycle of sandboxes](/sandboxes/lifecycle).

## W\&B Sandboxes tutorials

For more in-depth examples, see:

* [Invoke an agent in a W\&B Sandbox tutorial](/sandboxes/invoke-agent-sandbox-tutorial)
* [Train a PyTorch model in a W\&B Sandbox tutorial](/sandboxes/mltrain-in-sandbox-tutorial)