# Skyward

> Distributed compute orchestration for ML/AI. Decorate a Python function with `@sky.function`, and Skyward provisions cloud GPUs, ships your code over SSH, runs it remotely, and returns the result - behind a synchronous, operator-based API.

Skyward turns ordinary functions into remote GPU jobs: define lazily with `@sky.function`, provision with `with sky.Compute(...) as pool:`, dispatch with an operator (`>>` one node, `@` broadcast, `&` parallel, `>` async). Works across AWS, GCP, RunPod, VastAI, Lambda, and more, with multi-provider fallback, autoscaling, distributed collections, and framework plugins (PyTorch, JAX, Keras, Accelerate).

## Complete reference

- [Skyward for LLMs (full, single file)](https://gabfssilva.github.io/skyward/llms-full.txt): the entire usage surface - mental model, operators, specs, accelerators, runtime API, distributed collections, plugins, storage, options, observability, config, offers, CLI, recipes, gotchas, and a quick API index. Start here.

## Docs

- [Getting started](https://gabfssilva.github.io/skyward/getting-started/): install and first job.
- [Core concepts](https://gabfssilva.github.io/skyward/concepts/): lazy functions, operators, pools, sessions.
- [Providers](https://gabfssilva.github.io/skyward/providers/): supported clouds and their config classes.
- [Accelerators](https://gabfssilva.github.io/skyward/accelerators/): full GPU/accelerator catalog.
- [Distributed training](https://gabfssilva.github.io/skyward/distributed-training/): multi-node PyTorch/JAX/Keras.
- [Distributed collections](https://gabfssilva.github.io/skyward/distributed-collections/): dict, set, counter, queue, barrier, lock.
- [Plugins](https://gabfssilva.github.io/skyward/plugins/): torch, jax, keras, cuml, accelerate, mig, mps, and custom plugins.
- [Volumes](https://gabfssilva.github.io/skyward/volumes/): mount S3/GCS buckets as a filesystem.
- [CLI](https://gabfssilva.github.io/skyward/cli/): the `sky` command-line client and long-lived server.

## Guides (runnable)

- [Hello, Skyward!](https://gabfssilva.github.io/skyward/guides/hello-skyward/): minimal end-to-end example.
- [Parallel execution](https://gabfssilva.github.io/skyward/guides/parallel-execution/): the `&` and `gather` patterns.
- [Broadcast](https://gabfssilva.github.io/skyward/guides/broadcast/): run on every node with `@`.
- [Data sharding](https://gabfssilva.github.io/skyward/guides/data-sharding/): `sky.shard` for data parallelism.
- [PyTorch distributed](https://gabfssilva.github.io/skyward/guides/pytorch-distributed/): DDP across nodes.
- [Multi-provider selection](https://gabfssilva.github.io/skyward/guides/multi-provider/): cheapest across clouds.

## API reference

- [Pool & compute](https://gabfssilva.github.io/skyward/reference/pool/): `Compute`, `Pool`, `Session`.
- [Runtime](https://gabfssilva.github.io/skyward/reference/runtime/): `instance_info`, `shard`, output control.
- [Distributed collections](https://gabfssilva.github.io/skyward/reference/distributed/)
- [Events](https://gabfssilva.github.io/skyward/reference/events/)
- [Plugins](https://gabfssilva.github.io/skyward/reference/plugins/)

## Optional

- [Configuration](https://gabfssilva.github.io/skyward/reference/config/): `skyward.toml` named pools.
- [Choosing a provider](https://gabfssilva.github.io/skyward/choosing-a-provider/): opinionated guide.
- [Architecture](https://gabfssilva.github.io/skyward/architecture/): actor system internals.
- [Repository](https://github.com/gabfssilva/skyward): source and issues.