{ } 📦 Code Sandboxes
Code Sandboxes (code_sandboxes) is a Python package for running code in isolated sandbox variants through a unified API.
Canonical variant names:
cloudflarecoreweavedatalayerdaytonadockere2bevalgoogle-colabjupyter-serverkagglemodalmonty
Documentation
The full documentation is the single source of truth:
- Docs home: https://code-sandboxes.datalayer.tech
- Providers and variant setup: https://code-sandboxes.datalayer.tech/providers
- Installation: https://code-sandboxes.datalayer.tech/install
- CLI usage: https://code-sandboxes.datalayer.tech/cli
- API reference: https://code-sandboxes.datalayer.tech/api-reference
- Examples: https://code-sandboxes.datalayer.tech/examples
- Guide: https://code-sandboxes.datalayer.tech/guide
Published site:
Install
pip install code-sandboxes
For backend-specific extras and credentials, see https://code-sandboxes.datalayer.tech/install and https://code-sandboxes.datalayer.tech/providers.
Jupyter Server Sandbox
from code_sandboxes import Sandbox
# Option 1: manage a local Jupyter server automatically
with Sandbox.create(variant="jupyter-server") as sandbox:
print(sandbox.run_code("1 + 1").text) # 2
# Option 2: connect to an existing Jupyter server
with Sandbox.create(
variant="jupyter-server",
server_url="http://localhost:8888",
token="MY_TOKEN",
) as sandbox:
sandbox.run_code("x = 40")
print(sandbox.run_code("x + 2").text) # 42
Kaggle Sandbox
Kaggle supports both batch execution and interactive connections through the
kaggle sandbox. Install its optional dependency first:
pip install "code-sandboxes[kaggle]"
Required credentials for batch mode:
~/.kaggle/kaggle.json, orKAGGLE_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
code-sandboxes repl --variant 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="google-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 GoogleColabKernelClient, parse_google_colab_channels_url
server_url, kernel_id, proxy_token = parse_google_colab_channels_url(channels_url)
with GoogleColabKernelClient.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.
Manage Sandboxes (CRUD)
Every variant answers the same verbs — create, list, get, update, delete — from Python or from the CLI, rendered as rich tables:
code-sandboxes list # every variant that answers, one table
code-sandboxes list -v kaggle # one variant
code-sandboxes get <id> -v modal # one sandbox, live status
code-sandboxes create -v modal # create detached, leave it running
code-sandboxes update <id> -v modal --tag team=ai # tags (modal), --name
# (docker), --capability (datalayer),
# --code (kaggle: a new version)
code-sandboxes delete <id> -v modal --yes
code-sandboxes environments # what sandboxes can be created in
from code_sandboxes import get_manager
manager = get_manager("modal")
for info in manager.list():
print(info.id, info.status)
manager.delete("sb-...")
See the management guide for what each variant maps to and its connection settings.
License
BSD 3-Clause License
1 maintainer
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