Datalayer

Cloud-based execution with full isolation, GPU support, snapshots, and persistence.

• Requirements: code-sandboxes[datalayer] (installs agent_runtimes).
• Parameters: token (defaults to DATALAYER_API_KEY), run_url, snapshot_name, plus creation options like environment, gpu, cpu, memory.

How To Obtain Datalayer Credentials

1. Create an account at datalayer.ai.
2. Generate an API token from your account settings (IAM → Tokens / API Keys).
3. Export DATALAYER_API_KEY (or pass it as the token parameter). Set DATALAYER_RUN_URL only for a self-hosted / custom deployment.

Usage

import os
from code_sandboxes import Sandbox

os.environ["DATALAYER_API_KEY"] = "your-datalayer-token"

with Sandbox.create(
    variant="datalayer",
    gpu="A100",
    environment="python-gpu-env",
) as sandbox:
    sandbox.run_code("import torch; print(torch.cuda.is_available())")

The concrete implementation is available from a top-level module:

from code_sandboxes.datalayer_sandbox import DatalayerSandbox

Snapshots

Save and restore sandbox state (datalayer only):

with Sandbox.create(variant="datalayer") as sandbox:
    # Set up environment
    sandbox.run_code("import pandas as pd")
    sandbox.run_code("df = pd.DataFrame({'a': [1,2,3]})")

    # Create snapshot
    snapshot = sandbox.create_snapshot("my-setup")
    print(f"Snapshot: {snapshot.id}")

# Later: restore from snapshot
with Sandbox.create(
    variant="datalayer",
    snapshot_name="my-setup"
) as sandbox:
    result = sandbox.run_code("print(df)")  # State restored