{ } 📦 Code Sandboxes

Code Sandboxes (code_sandboxes) is a Python package for running code in isolated sandbox variants through a unified API.

Canonical variant names:

jupyter
docker
eval
monty
kaggle
colab
modal
datalayer

Documentation

The full documentation is the single source of truth:

Docs home: https://code-sandboxes.datalayer.tech
Sandboxes and variant setup: https://code-sandboxes.datalayer.tech/sandboxes
Installation: https://code-sandboxes.datalayer.tech/installation
CLI usage: https://code-sandboxes.datalayer.tech/cli
API reference: https://code-sandboxes.datalayer.tech/api-reference
Examples: https://code-sandboxes.datalayer.tech/examples
Comparison: https://code-sandboxes.datalayer.tech/comparison

Published site:

https://code-sandboxes.datalayer.tech

Install

pip install code-sandboxes

For backend-specific extras and credentials, see https://code-sandboxes.datalayer.tech/installation and https://code-sandboxes.datalayer.tech/sandboxes.

Quick Examples

Python: launch a `jupyter` sandbox

from code_sandboxes import Sandbox

# Option 1: manage a local Jupyter server automatically
with Sandbox.create(variant="jupyter") as sandbox:
  print(sandbox.run_code("1 + 1").text)  # 2

# Option 2: connect to an existing Jupyter server
with Sandbox.create(
  variant="jupyter",
  server_url="http://localhost:8888",
  token="MY_TOKEN",
) as sandbox:
  sandbox.run_code("x = 40")
  print(sandbox.run_code("x + 2").text)  # 42

CLI REPL: `kaggle` variant

Kaggle REPL supports both interactive runtime mode and credential-based batch mode.

Required credentials for batch mode:

~/.kaggle/kaggle.json, or
KAGGLE_API_KEY

# Install Kaggle support
pip install code-sandboxes[kaggle]

# Optional: env-based credentials (if not using ~/.kaggle/kaggle.json)
export KAGGLE_API_KEY="<your-kaggle-api-key>"

# Launch the REPL
sandbox repl --variant kaggle

Kaggle

Kaggle supports both batch execution and interactive connections through the kaggle sandbox. Install its optional dependency first:

pip install "code-sandboxes[kaggle]"

For batch execution, configure Kaggle credentials and create the sandbox without a runtime URL:

from code_sandboxes import Sandbox

with Sandbox.create(variant="kaggle") as sandbox:
    result = sandbox.run_code("print('hello from kaggle')")
    print(result.stdout)

The lower-level batch API is also available directly:

from code_sandboxes import KaggleKernelExecutor

executor = KaggleKernelExecutor()
result = executor.execute(
    "print('hello from kaggle')",
    title="code-sandboxes-demo",
    accelerator="NvidiaTeslaT4",
    wait=True,
)
print(result.status, result.stdout)
print(result.to_kernel_reply())

For interactive execution, copy the WebSocket channels URL from an active Kaggle notebook session and pass it to the sandbox or client:

from code_sandboxes import KaggleKernelClient

with KaggleKernelClient.from_channels_url(channels_url, token=None) as kernel:
    print(kernel.execute("x = 1 + 1; print(x)"))

See the complete Kaggle guide for authentication, accelerators, channels URL retrieval, and execution options.

Google Colab

Google Colab exposes an already-running kernel through an authenticating proxy. Copy its WebSocket channels URL from the browser's Network tools, then pass it directly to the sandbox:

from code_sandboxes import Sandbox

with Sandbox.create(variant="colab", channels_url=channels_url) as sandbox:
    print(sandbox.run_code("x = 1 + 1; print(x)").stdout)

The lower-level client and parser are owned by Code Sandboxes as well:

from code_sandboxes import ColabKernelClient, parse_colab_channels_url

server_url, kernel_id, proxy_token = parse_colab_channels_url(channels_url)
with ColabKernelClient.from_channels_url(channels_url) as kernel:
    print(kernel.execute("print('hello from colab')"))

See the complete Google Colab guide for proxy authentication, explicit connection values, and channels URL retrieval.

For full setup and parameters for all variants, see:

License

BSD 3-Clause License

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