colab-cli

name	colab-cli
description	Manage Google Colab sessions and execute code on remote Colab VMs via the colab-cli.

Colab Session Operator

This skill allows an agent to provision, manage, and execute code on Google Colab environments using the colab-cli. It is the preferred way for agents to interact with Colab, supporting both simple scripts and complex ML engineering workflows.

Key Capabilities

• Provisioning: Create new Colab sessions with specific hardware (CPU, GPU - A100/L4/T4, or TPU - v6e).
• Execution: Run local Python scripts or shell commands on the remote Colab VM.
• Automation: Handles authentication (browser-based or token), Google Drive mounting, and package installation.
• Artifacts: Captures session history as Jupyter notebooks and intercepts image outputs (e.g., from matplotlib).
• Management: Monitor session status, list active sessions, and stop VMs to conserve compute units.

Tooling

The colab-cli provides a command-line interface that you can invoke via exec or other shell tools.

Core Commands

• colab init: Initialize the CLI and authenticate.
• colab start: Provision a new session.
• colab exec -f <script.py>: Execute a local script on the remote VM.
• colab shell "<command>": Run a shell command on the remote VM.
• colab list: List active sessions.
• colab stop <session_id>: Stop and delete a session.

Usage Guidelines

• Authentication: In a local terminal, the user may need to perform a one-time OAuth flow. For autonomous agents, ensure COLAB_API_KEY or similar is set if required, or assume the environment is already initialized.
• Non-interactive: Avoid commands that require interactive input (like colab repl). Always use -f for scripts or direct strings for shell commands.
• Mounting Drive: Use colab shell "python -c 'from google.colab import drive; drive.mount(\"/content/drive\")'" if you need Google Drive access.
• Artifact Extraction: After running a script that generates images, colab-cli may provide paths to extracted artifacts. Check the command output.
• Cleanup: Always run colab stop when the task is complete to avoid wasting the user's Colab compute units.

TPU provisioning (JAX / Pallas)

For MaxEvolve kernel profiling on Google TPUs:

1. colab start --tpu tpuv6e (or tpuv5e when available in your account)
2. Install JAX on the remote VM (example shell one-liner): colab shell "pip install -U 'jax[tpu]' -f https://storage.googleapis.com/jax-releases/libtpu_releases.html"
3. Upload or sync profile_script.py and run: colab exec -f profile_script.py
4. Capture RESULT_LATENCY_MS= / RESULT_TFLOPS= lines for kernel_db_insert
5. colab stop when done

For GPU profiling (Hopper/L4/A100), use colab start --gpu and install CUDA-enabled jax[cuda12].

Configuration

Local configuration and session state are stored in ~/.config/colab-cli/. You can inspect sessions.json there to see persistent session metadata.