jarvislabs 0.2.0b9

CLI and SDK for JarvisLabs.ai GPU cloud

jarvislabs

CLI and Python SDK for managing GPU instances on JarvisLabs.ai.

Beta release. The v0.2 rewrite is in pre-release. Install with --pre to get it.

Installation

As a CLI tool (recommended)

uv tool install --pre jarvislabs

To upgrade:

uv tool upgrade --pre jarvislabs

As a library

pip install --pre jarvislabs

Or with uv:

uv pip install --pre jarvislabs

Requires Python 3.11+.

Authentication

Get your API key at jarvislabs.ai/settings/api-keys.

jl setup

Or set an environment variable:

export JL_API_KEY="your_api_key"

CLI Quick Start

# See available GPUs and pricing
jl gpus

# Create an instance
jl instance create --gpu A100 --name "my-instance"

# Create an instance and expose a custom HTTP port
jl instance create --gpu RTX5000 --http-ports 7860

# SSH into it
jl instance ssh <machine_id>

# Pause when done (stops compute billing, data persists)
jl instance pause <machine_id>

# Resume later — optionally with different hardware
jl instance resume <machine_id> --gpu H100

# Destroy when no longer needed
jl instance destroy <machine_id>

Managed Runs

Run scripts on GPU instances without manual setup. Code is uploaded, a virtual environment is created, and logs are tracked automatically.

# Run a training script on a fresh GPU (instance auto-pauses when done)
jl run train.py --gpu RTX5000

# Start a long-running web app on a fresh GPU and expose port 8000
jl run app.py --gpu RTX5000 --http-ports 8000 --keep --no-follow

# Pass script arguments
jl run train.py --gpu RTX5000 -- --epochs 50 --lr 0.001

# Sync a project directory and run a script inside it
jl run . --script train.py --gpu A100 --requirements requirements.txt

# Run on an existing instance
jl run train.py --on <machine_id>

# Check on a run
jl run logs <run_id> --follow
jl run status <run_id>
jl run stop <run_id>

More Commands

jl status                   # Account info and balance
jl templates                # Available framework templates
jl instance list            # List all instances
jl instance exec <id> -- nvidia-smi   # Run a command remotely
jl instance upload <id> ./data        # Upload files
jl instance download <id> /home/results.csv  # Download files
jl ssh-key add ~/.ssh/id_ed25519.pub --name "my-key"
jl scripts add ./setup.sh --name "install-deps"
jl filesystem create --name "datasets" --storage 200
jl instance get <id>                  # Shows Jupyter + exposed port URLs

Every command supports --help, --json (machine-readable output), and --yes (skip confirmations).

Python SDK

from jarvislabs import Client

with Client() as client:
    # Create a GPU instance (blocks until running)
    inst = client.instances.create(gpu_type="A100", name="my-run")
    print(f"SSH: {inst.ssh_command}")
    print(f"URL: {inst.url}")

    # When done
    client.instances.pause(inst.machine_id)

from jarvislabs import Client

with Client() as client:
    # List and filter instances
    running = [i for i in client.instances.list() if i.status == "Running"]

    # Check GPU availability and pricing
    for gpu in client.account.gpu_availability():
        print(f"{gpu.gpu_type}: {gpu.num_free_devices} free, ${gpu.price_per_hour}/hr")

    # Manage filesystems
    fs_id = client.filesystems.create(fs_name="data", storage=100)

    # Manage startup scripts
    client.scripts.add(script="#!/bin/bash\npip install wandb", name="setup")

Development

uv pip install -e ".[dev]"
uv run ruff format . && uv run ruff check --fix .
uv run pytest

License

MIT