Skyward

Cloud accelerators with a single decorator

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Skyward Demo

Skyward is a Python library for ephemeral accelerator compute. Spin up cloud accelerators, run your code, and tear them down automatically. No infrastructure to manage, no idle costs.

Quick Example

import skyward as sky

@sky.function
def train(epochs: int) -> dict:
    import torch

    model = torch.nn.Linear(100, 10).cuda()
    optimizer = torch.optim.Adam(model.parameters())

    for epoch in range(epochs):
        loss = model(torch.randn(32, 100, device="cuda")).sum()
        loss.backward()
        optimizer.step()

    return { "final_loss": loss.item() }


with sky.Compute(
    provider=sky.AWS(), 
    accelerator=sky.accelerators.T4(), 
    image=sky.Image(pip=["torch"])
) as compute:
    result = train(epochs=100) >> compute
    print(result)

Features

  • A single API, any cloud — A unified declarative API to run functions on AWS, GCP, Hyperstack, RunPod, TensorDock, VastAI, Verda, and more.
  • Operators, not boilerplate — >> executes on one node, @ broadcasts to all, & runs in parallel. No job configs, no YAML.
  • Ephemeral by default — Instances provision on demand and terminate automatically. Context managers guarantee cleanup.
  • Multi-provider support — AWS, GCP, Hyperstack, RunPod, TensorDock, VastAI, Verda with automatic fallback and cost optimization.
  • Distributed training — PyTorch DDP, Keras 3, JAX, TensorFlow, and HuggingFace integration decorators.
  • Distributed collections — Dict, set, counter, queue, barrier, and lock replicated across the cluster.
  • Spot-aware — Automatic spot instance selection, preemption detection, and replacement. Save 60-90% on compute costs.

Install

uv add skyward

Requirements

  • Python 3.12+
  • Cloud provider credentials (setup guide)

Documentation

Full documentation at gabfssilva.github.io/skyward.

License

MIT

Key dates

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  • Released:
Latest release

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for gabfssilva from gravatar.com gabfssilva

Credits

Author: Gabriel Francisco

License

MIT License (MIT)

Requires

Python >=3.12

Provides Extra

all aws cli dev gcp huggingface jarvis notebook openai pandas providers pytorch server

Tags

cloud compute distributed gpu serverless

Classifiers

Report project as malware

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