The code interpreter is the stateful execution layer for Python and other notebook-style kernels. Use template="code-interpreter" for the standard Python environment.

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Quick start

from nullspace import Machine

with Machine.create(template="code-interpreter") as machine:
    execution = machine.run_code("x = 40 + 2\nx")
    print(execution.results[0].text)

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Contexts

ctx = machine.code_interpreter.create_code_context(
    cwd="/workspace",
    language="python",
)
machine.code_interpreter.run_code("value = 10", context=ctx)
result = machine.code_interpreter.run_code("value * 2", context=ctx)
print(result.results[0].text)

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Stream callbacks

def on_stdout(message):
    print(message.line, end="")

machine.run_code("print('streamed')", on_stdout=on_stdout)
Use machine.code_interpreter.run_code() directly when you need the full callback surface: 
machine.code_interpreter.run_code(
    "open('/workspace/out.txt', 'w').write('done')",
    language="python",
    envs={"RUN_ID": "docs"},
    timeout=30,
    on_artifact=lambda artifact: print(artifact.id, artifact.kind),
)
Queued async run helpers (create_run, get_run, cancel_run, wait_for_run, and list_runs) are supported. Artifact helpers (list_artifacts, get_artifact, download_artifact, preview_artifact, and delete_artifact) are supported for hosted beta run_code executions. Concept: Exec. API reference: executeCode, createCodeContext, createCodeRun, and listCodeArtifacts.