name: colab description: Execute code on Google Colab GPU runtimes (T4/L4/A100/H100) and manage persistent storage via Google Drive. Use when tasks need GPU compute (ML training, inference, TTS), when running experiments that exceed local hardware, or when the user mentions Colab, GPU training, or remote compute. Also use for voice synthesis via F5-TTS on Colab. NOT for local-only code, simple scripts that don't need GPU, or tasks better suited for the host machine.
Execute Python on Google Colab GPU runtimes via headless API. No browser needed.
colab-mcp OAuth flow to create ~/.colab-mcp-auth-token.json. See https://github.com/googlecolab/colab-mcpscripts/reauth_with_drive.pycolab_run.py auto-creates a .colab-venv/ venv via uv and installs deps. Requires uv (install: pip install uv). Deps: google-auth-oauthlib, google-auth, jupyter-kernel-client, requests, google-api-python-clientAll scripts live in scripts/ and use a .colab-venv/ sibling directory for dependencies.
colab_run.py — Execute code on Colab# Inline code on CPU
python3 scripts/colab_run.py exec "print('hello')"
# Script on T4 GPU
python3 scripts/colab_run.py exec --gpu T4 --file script.py
# Keep runtime alive between calls
python3 scripts/colab_run.py exec --gpu T4 --keep "x = 42"
# GPU options: T4 (default), L4, A100, H100
python3 scripts/colab_run.py exec --gpu A100 --file heavy_training.py
# Runtime management
python3 scripts/colab_run.py list # Active runtimes
python3 scripts/colab_run.py stop <ep> # Stop runtime
python3 scripts/colab_run.py info # CU balance + eligible GPUs
colab_drive.py — Google Drive file transferpython3 scripts/colab_drive.py upload file.pt --folder colab-workspace
python3 scripts/colab_drive.py download checkpoint.pt --output ./checkpoint.pt
python3 scripts/colab_drive.py list --folder colab-workspace
Requires drive.file OAuth scope (run scripts/reauth_with_drive.py once).
inject_and_run.sh — Run scripts with Drive access inside ColabInjects the local OAuth token into a script before sending it to Colab. Place __COLAB_TOKEN_PLACEHOLDER__ where the base64 token should go:
bash scripts/inject_and_run.sh my_script.py --gpu T4
Inside the script:
import json, base64
from google.oauth2.credentials import Credentials
from googleapiclient.discovery import build
token_data = json.loads(base64.b64decode("__COLAB_TOKEN_PLACEHOLDER__"))
with open('/tmp/token.json', 'w') as f:
json.dump(token_data, f)
creds = Credentials.from_authorized_user_file('/tmp/token.json')
service = build('drive', 'v3', credentials=creds)
colab_tts.py — Voice synthesis via F5-TTS# One-time: fetch voice samples from ElevenLabs
python3 scripts/colab_tts.py fetch-voice --voice-id <id> --api-key <key>
# Generate speech with cloned voice
python3 scripts/colab_tts.py speak "Hello world" --output hello.wav --gpu T4
reauth_with_drive.py — Add Drive scope to OAuth tokenpython3 scripts/reauth_with_drive.py
Opens browser for Google sign-in. One-time setup.
| GPU | CU/hr | $/hr | VRAM | Use for |
|---|---|---|---|---|
| T4 | ~2 | $0.20 | 15GB | Default. Inference, small training, TTS |
| L4 | ~3.5 | $0.35 | 24GB | Medium models, need bf16 or >15GB |
| A100 | ~5-15 | $0.50-1.50 | 40/80GB | Large models, serious training |
| H100 | ~20+ | $2.00+ | 80GB | Maximum throughput, time-critical |
Start with T4. Escalate only when VRAM or speed demands it.
Assign → execute → get output → unassign. Don't use --keep.
Use --keep. State persists in kernel between exec calls. Unassign when done.
Use inject_and_run.sh. Script checkpoints to Drive every N epochs. If runtime dies, re-run — script loads latest checkpoint from Drive. See references/examples.md for template.
Embed parseable markers in script stdout:
print(f"__RESULT__{json.dumps(metrics)}") # Structured data
print(f"__AUDIO__{base64_audio}") # Binary as base64
/tmp/ — use Drive for anything you need to keep7w4.net提供免費和付費技能下載。
GPU assignment requires split GET/POST: GET fetches XSRF token (no GPU params), POST creates assignment with variant=GPU&accelerator=T4. Already handled by colab_run.py.
這是一個功能較完整的 Colab 執行工具,可以幫助使用者在 Google Colab GPU 上執行 Python 程式碼。文件清晰、示例豐富是明顯優勢,但依賴外部認證配置,多指令碼協同使用有一定學習成本。整體質量中上,推薦有 Google Colab 使用經驗的使用者嘗試。