Cloud accelerators with a single decorator
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
# pi.py
import skyward as sky
@sky.app(
provider=sky.Salad(priority="high"),
accelerator=sky.accelerators.RTX_3090(),
image=sky.Image(pip=["torch", "numpy"]),
)
def estimate_pi(n: int = 100_000_000) -> dict[str, str | float]:
"""Estimate pi from N random points, on a GPU."""
import torch
points = torch.rand(2, n, device="cuda")
inside = (points[0] ** 2 + points[1] ** 2 <= 1).sum().item()
return {"gpu": torch.cuda.get_device_name(), "pi": 4 * inside / n}
sky server start
sky run pi.py --n 500000000
sky.app declares the machines a function runs on. sky run provisions them, parses the command line against the function's signature, runs the function there, prints what it returned as JSON, and tears the machines down.
Inside a program
sky.Compute is the same compute as a context manager, for a program that dispatches functions itself:
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[all]"
The base package is what a node installs. The SDK, the daemon, the sky command and each provider SDK are extras; getting started lists them.
Requirements
- Python 3.12+
- Cloud provider credentials (setup guide)
Documentation
Full documentation at gabfssilva.github.io/skyward.
License
MIT
1 maintainer
Metadata
1 maintainer
Release files for skyward 0.12.0
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| skyward-0.12.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 31.2 MB
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