Attaching and Sessions

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Attaching to an existing sandbox

Use Sandbox.from_id or Sandbox.from_name to reconnect to a sandbox from another process, instead of creating a second one:

from chalkcompute import Sandbox

sandbox = Sandbox.from_id("sandbox-id")
# or, if it was created with a name=:
sandbox = Sandbox.from_name("hello-sandbox")

Sandbox.list_all() returns SandboxInfo for every sandbox in the current environment, useful for finding an id or name to attach to.

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Running work across sessions

These examples use the multi-day-training sandbox from Long-running sandboxes.

Sandbox.exec blocks until the command finishes, so start work measured in hours or days as a detached process:

from chalkcompute import Sandbox

sandbox = Sandbox.from_name("multi-day-training")
sandbox.exec(
    "sh",
    "-c",
    "nohup python /app/train.py --checkpoint-dir /checkpoints > /checkpoints/train.log 2>&1 &",
)

Reattach by name from another process, or on another day, to check on it:

from chalkcompute import Sandbox

sandbox = Sandbox.from_name("multi-day-training")
print(sandbox.refresh().status)
print(sandbox.exec("tail", "-n", "20", "/checkpoints/train.log").stdout_text)

Within a single exec call, auto_reattach (enabled by default) reconnects and replays buffered output after a transient transport disconnect, so brief network interruptions need no handling of their own.

Terminate the sandbox once the work is done. A sandbox reserves the CPU, memory, and any GPU you requested for as long as it exists, and those resources are billed as provisioned rather than as used, so an idle sandbox costs the same as a busy one. RestartPolicy.ALWAYS keeps bringing it back until you terminate it:

sandbox.terminate()