Google Colab GPU Runtime

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.

Colab Skill

Execute Python on Google Colab GPU runtimes via headless API. No browser needed.

Setup

1. Authenticate with Colab (one-time): Run the colab-mcp OAuth flow to create ~/.colab-mcp-auth-token.json. See https://github.com/googlecolab/colab-mcp
2. Add Drive scope (optional, for persistence): scripts/reauth_with_drive.py
3. Enable Drive API (if using Drive): https://console.developers.google.com/apis/api/drive.googleapis.com — enable for your GCP project
4. Python deps: On first run, colab_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-client

Scripts

All scripts live in scripts/ and use a .colab-venv/ sibling directory for dependencies.

colab_run.py — Execute code on Colab

bash

# 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 transfer

bash

python3 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 Colab

Injects the local OAuth token into a script before sending it to Colab. Place __COLAB_TOKEN_PLACEHOLDER__ where the base64 token should go:

bash

bash scripts/inject_and_run.sh my_script.py --gpu T4

Inside the script:

python

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

bash

# 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 token

bash

python3 scripts/reauth_with_drive.py

Opens browser for Google sign-in. One-time setup.

GPU Selection Guide

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.

Patterns

Short task (inference, TTS, quick experiment)

Assign → execute → get output → unassign. Don't use --keep.

Multi-step session

Use --keep. State persists in kernel between exec calls. Unassign when done.

Long training with Drive checkpoints

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.

Structured output

Embed parseable markers in script stdout:

python

print(f"__RESULT__{json.dumps(metrics)}")    # Structured data
print(f"__AUDIO__{base64_audio}")             # Binary as base64

Timeouts

• Idle timeout (Pro): ~90 min without active execution
• Max session (Pro): ~24 hours continuous
• Active code execution prevents idle disconnect
• Runtime death loses /tmp/ — use Drive for anything you need to keep

API Quirk

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.