Metadata-Version: 2.1
Name: trainy-skypilot-nightly
Version: 1.0.0.dev20241104
Summary: SkyPilot: An intercloud broker for the clouds
Author: SkyPilot Team
License: Apache 2.0
Project-URL: Homepage, https://github.com/skypilot-org/skypilot
Project-URL: Issues, https://github.com/skypilot-org/skypilot/issues
Project-URL: Discussion, https://github.com/skypilot-org/skypilot/discussions
Project-URL: Documentation, https://skypilot.readthedocs.io/en/latest/
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: System :: Distributed Computing
Description-Content-Type: text/markdown
License-File: LICENSE
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<p align="center">
  <picture>
    <img alt="Trainy Logo" src="https://raw.githubusercontent.com/Trainy-ai/konduktor/main/docs/source/images/konduktor-logo-white-no-background.png" width="353" height="64" style="max-width: 100%;">
  </picture>
  <br/>
  <br/>
</p>

This repository is a fork of the [original Skypilot](https://github.com/skypilot-org/skypilot) and maintained by [Trainy](https://trainy.ai/) in order to support running jobs Trainy's our managed Kubernetes cluster platform as a service, Konduktor ([Github](https://github.com/Trainy-ai/konduktor) and [Documentation](https://konduktor.readthedocs.io/en/latest/)). You can see some our contributions to the mainline project [here](https://github.com/skypilot-org/skypilot/pulls?q=is%3Apr+author%3Aasaiacai+). If there are features in this fork you feel like make sense to contribute back to upstream, please let us know and we are happy to make a pull request. We are planning on keeping this fork the same license as the original project (Apache 2.0), as we have also greatly benefit from the open nature of the project and believe that sharing our work reduces redundant work streams for maintainers, contributors and users alike.

----

<p align="center">
  <img alt="SkyPilot" src="https://raw.githubusercontent.com/skypilot-org/skypilot/master/docs/source/images/skypilot-wide-light-1k.png" width=55%>
</p>

<p align="center">
  <a href="https://skypilot.readthedocs.io/en/latest/">
    <img alt="Documentation" src="https://readthedocs.org/projects/skypilot/badge/?version=latest">
  </a>

  <a href="https://github.com/skypilot-org/skypilot/releases">
    <img alt="GitHub Release" src="https://img.shields.io/github/release/skypilot-org/skypilot.svg">
  </a>

  <a href="http://slack.skypilot.co">
    <img alt="Join Slack" src="https://img.shields.io/badge/SkyPilot-Join%20Slack-blue?logo=slack">
  </a>

</p>

<h3 align="center">
    Run AI on Any Infra — Unified, Faster, Cheaper
</h3>

----
:fire: *News* :fire:
- [Oct 2024] :tada: **SkyPilot crossed 1M+ downloads** :tada:: Thank you to our community! [**Twitter/X**](https://x.com/skypilot_org/status/1844770841718067638)
- [Sep 2024] Point, Launch and Serve **Llama 3.2** on Kubernetes or Any Cloud: [**example**](./llm/llama-3_2/)
- [Sep 2024] Run and deploy [**Pixtral**](./llm/pixtral), the first open-source multimodal model from Mistral AI.
- [Jun 2024] Reproduce **GPT** with [llm.c](https://github.com/karpathy/llm.c/discussions/481) on any cloud: [**guide**](./llm/gpt-2/)
- [Apr 2024] Serve [**Qwen-110B**](https://qwenlm.github.io/blog/qwen1.5-110b/) on your infra: [**example**](./llm/qwen/)
- [Apr 2024] Using [**Ollama**](https://github.com/ollama/ollama) to deploy quantized LLMs on CPUs and GPUs: [**example**](./llm/ollama/)
- [Feb 2024] Deploying and scaling [**Gemma**](https://blog.google/technology/developers/gemma-open-models/) with SkyServe: [**example**](./llm/gemma/)
- [Feb 2024] Serving [**Code Llama 70B**](https://ai.meta.com/blog/code-llama-large-language-model-coding/) with vLLM and SkyServe: [**example**](./llm/codellama/)
- [Dec 2023] [**Mixtral 8x7B**](https://mistral.ai/news/mixtral-of-experts/), a high quality sparse mixture-of-experts model, was released by Mistral AI! Deploy via SkyPilot on any cloud: [**example**](./llm/mixtral/)
- [Nov 2023] Using [**Axolotl**](https://github.com/OpenAccess-AI-Collective/axolotl) to finetune Mistral 7B on the cloud (on-demand and spot): [**example**](./llm/axolotl/)

**LLM Finetuning Cookbooks**: Finetuning Llama 2 / Llama 3.1 in your own cloud environment, privately: Llama 2 [**example**](./llm/vicuna-llama-2/) and [**blog**](https://blog.skypilot.co/finetuning-llama2-operational-guide/); Llama 3.1 [**example**](./llm/llama-3_1-finetuning/) and [**blog**](https://blog.skypilot.co/finetune-llama-3_1-on-your-infra/)

<details>
  <summary>Archived</summary>

- [Jul 2024] [**Finetune**](./llm/llama-3_1-finetuning/) and [**serve**](./llm/llama-3_1/) **Llama 3.1** on your infra
- [Apr 2024] Serve and finetune [**Llama 3**](https://skypilot.readthedocs.io/en/latest/gallery/llms/llama-3.html) on any cloud or Kubernetes: [**example**](./llm/llama-3/)
- [Mar 2024] Serve and deploy [**Databricks DBRX**](https://www.databricks.com/blog/introducing-dbrx-new-state-art-open-llm) on your infra: [**example**](./llm/dbrx/)
- [Feb 2024] Speed up your LLM deployments with [**SGLang**](https://github.com/sgl-project/sglang) for 5x throughput on SkyServe: [**example**](./llm/sglang/)
- [Dec 2023] Using [**LoRAX**](https://github.com/predibase/lorax) to serve 1000s of finetuned LLMs on a single instance in the cloud: [**example**](./llm/lorax/)
- [Sep 2023] [**Mistral 7B**](https://mistral.ai/news/announcing-mistral-7b/), a high-quality open LLM, was released! Deploy via SkyPilot on any cloud: [**Mistral docs**](https://docs.mistral.ai/self-deployment/skypilot)
- [Sep 2023] Case study: [**Covariant**](https://covariant.ai/) transformed AI development on the cloud using SkyPilot, delivering models 4x faster cost-effectively: [**read the case study**](https://blog.skypilot.co/covariant/)
- [Jul 2023] Self-Hosted **Llama-2 Chatbot** on Any Cloud: [**example**](./llm/llama-2/)
- [Jun 2023] Serving LLM 24x Faster On the Cloud [**with vLLM**](https://vllm.ai/) and SkyPilot: [**example**](./llm/vllm/), [**blog post**](https://blog.skypilot.co/serving-llm-24x-faster-on-the-cloud-with-vllm-and-skypilot/)
- [Apr 2023] [SkyPilot YAMLs](./llm/vicuna/) for finetuning & serving the [Vicuna LLM](https://lmsys.org/blog/2023-03-30-vicuna/) with a single command!

</details>

----

SkyPilot is a framework for running AI and batch workloads on any infra, offering unified execution, high cost savings, and high GPU availability.

SkyPilot **abstracts away infra burdens**:
- Launch [dev clusters](https://skypilot.readthedocs.io/en/latest/examples/interactive-development.html), [jobs](https://skypilot.readthedocs.io/en/latest/examples/managed-jobs.html), and [serving](https://skypilot.readthedocs.io/en/latest/serving/sky-serve.html) on any infra
- Easy job management: queue, run, and auto-recover many jobs

SkyPilot **supports multiple clusters, clouds, and hardware** ([the Sky](https://arxiv.org/abs/2205.07147)):
- Bring your reserved GPUs, Kubernetes clusters, or 12+ clouds
- [Flexible provisioning](https://skypilot.readthedocs.io/en/latest/examples/auto-failover.html) of GPUs, TPUs, CPUs, with auto-retry

SkyPilot **cuts your cloud costs & maximizes GPU availability**:
* [Autostop](https://skypilot.readthedocs.io/en/latest/reference/auto-stop.html): automatic cleanup of idle resources
* [Managed Spot](https://skypilot.readthedocs.io/en/latest/examples/managed-jobs.html): 3-6x cost savings using spot instances, with preemption auto-recovery
* [Optimizer](https://skypilot.readthedocs.io/en/latest/examples/auto-failover.html): 2x cost savings by auto-picking the cheapest & most available infra

SkyPilot supports your existing GPU, TPU, and CPU workloads, with no code changes.

Install with pip:
```bash
# Choose your clouds:
pip install -U "skypilot[kubernetes,aws,gcp,azure,oci,lambda,runpod,fluidstack,paperspace,cudo,ibm,scp]"
```
To get the latest features and fixes, use the nightly build or [install from source](https://skypilot.readthedocs.io/en/latest/getting-started/installation.html):
```bash
# Choose your clouds:
pip install "skypilot-nightly[kubernetes,aws,gcp,azure,oci,lambda,runpod,fluidstack,paperspace,cudo,ibm,scp]"
```

[Current supported infra](https://skypilot.readthedocs.io/en/latest/getting-started/installation.html) (Kubernetes; AWS, GCP, Azure, OCI, Lambda Cloud, Fluidstack, RunPod, Cudo, Paperspace, Cloudflare, Samsung, IBM, VMware vSphere):
<p align="center">
  <img alt="SkyPilot" src="https://raw.githubusercontent.com/skypilot-org/skypilot/master/docs/source/images/cloud-logos-light.png" width=85%>
</p>


## Getting Started
You can find our documentation [here](https://skypilot.readthedocs.io/en/latest/).
- [Installation](https://skypilot.readthedocs.io/en/latest/getting-started/installation.html)
- [Quickstart](https://skypilot.readthedocs.io/en/latest/getting-started/quickstart.html)
- [CLI reference](https://skypilot.readthedocs.io/en/latest/reference/cli.html)

## SkyPilot in 1 Minute

A SkyPilot task specifies: resource requirements, data to be synced, setup commands, and the task commands.

Once written in this [**unified interface**](https://skypilot.readthedocs.io/en/latest/reference/yaml-spec.html) (YAML or Python API), the task can be launched on any available cloud.  This avoids vendor lock-in, and allows easily moving jobs to a different provider.

Paste the following into a file `my_task.yaml`:

```yaml
resources:
  accelerators: A100:8  # 8x NVIDIA A100 GPU

num_nodes: 1  # Number of VMs to launch

# Working directory (optional) containing the project codebase.
# Its contents are synced to ~/sky_workdir/ on the cluster.
workdir: ~/torch_examples

# Commands to be run before executing the job.
# Typical use: pip install -r requirements.txt, git clone, etc.
setup: |
  pip install "torch<2.2" torchvision --index-url https://download.pytorch.org/whl/cu121

# Commands to run as a job.
# Typical use: launch the main program.
run: |
  cd mnist
  python main.py --epochs 1
```

Prepare the workdir by cloning:
```bash
git clone https://github.com/pytorch/examples.git ~/torch_examples
```

Launch with `sky launch` (note: [access to GPU instances](https://skypilot.readthedocs.io/en/latest/cloud-setup/quota.html) is needed for this example):
```bash
sky launch my_task.yaml
```

SkyPilot then performs the heavy-lifting for you, including:
1. Find the lowest priced VM instance type across different clouds
2. Provision the VM, with auto-failover if the cloud returned capacity errors
3. Sync the local `workdir` to the VM
4. Run the task's `setup` commands to prepare the VM for running the task
5. Run the task's `run` commands

<p align="center">
  <img src="https://i.imgur.com/TgamzZ2.gif" alt="SkyPilot Demo"/>
</p>


Refer to [Quickstart](https://skypilot.readthedocs.io/en/latest/getting-started/quickstart.html) to get started with SkyPilot.

## More Information
To learn more, see our [documentation](https://skypilot.readthedocs.io/en/latest/), [blog](https://blog.skypilot.co/), and [community integrations](https://blog.skypilot.co/community/).

<!-- Keep this section in sync with index.rst in SkyPilot Docs -->
Runnable examples:
- LLMs on SkyPilot
  - [Llama 3.2: lightweight and vision models](./llm/llama-3_2/)
  - [Pixtral](./llm/pixtral/)
  - [Llama 3.1 finetuning](./llm/llama-3_1-finetuning/) and [serving](./llm/llama-3_1/)
  - [GPT-2 via `llm.c`](./llm/gpt-2/)
  - [Llama 3](./llm/llama-3/)
  - [Qwen](./llm/qwen/)
  - [Databricks DBRX](./llm/dbrx/)
  - [Gemma](./llm/gemma/)
  - [Mixtral 8x7B](./llm/mixtral/); [Mistral 7B](https://docs.mistral.ai/self-deployment/skypilot/) (from official Mistral team)
  - [Code Llama](./llm/codellama/)
  - [vLLM: Serving LLM 24x Faster On the Cloud](./llm/vllm/) (from official vLLM team)
  - [SGLang: Fast and Expressive LLM Serving On the Cloud](./llm/sglang/) (from official SGLang team)
  - [Vicuna chatbots: Training & Serving](./llm/vicuna/) (from official Vicuna team)
  - [Train your own Vicuna on Llama-2](./llm/vicuna-llama-2/)
  - [Self-Hosted Llama-2 Chatbot](./llm/llama-2/)
  - [Ollama: Quantized LLMs on CPUs](./llm/ollama/)
  - [LoRAX](./llm/lorax/)
  - [QLoRA](https://github.com/artidoro/qlora/pull/132)
  - [LLaMA-LoRA-Tuner](https://github.com/zetavg/LLaMA-LoRA-Tuner#run-on-a-cloud-service-via-skypilot)
  - [Tabby: Self-hosted AI coding assistant](https://github.com/TabbyML/tabby/blob/bed723fcedb44a6b867ce22a7b1f03d2f3531c1e/experimental/eval/skypilot.yaml)
  - [LocalGPT](./llm/localgpt)
  - [Falcon](./llm/falcon)
  - Add yours here & see more in [`llm/`](./llm)!
- Framework examples: [PyTorch DDP](https://github.com/skypilot-org/skypilot/blob/master/examples/resnet_distributed_torch.yaml), [DeepSpeed](./examples/deepspeed-multinode/sky.yaml), [JAX/Flax on TPU](https://github.com/skypilot-org/skypilot/blob/master/examples/tpu/tpuvm_mnist.yaml), [Stable Diffusion](https://github.com/skypilot-org/skypilot/tree/master/examples/stable_diffusion), [Detectron2](https://github.com/skypilot-org/skypilot/blob/master/examples/detectron2_docker.yaml), [Distributed](https://github.com/skypilot-org/skypilot/blob/master/examples/resnet_distributed_tf_app.py) [TensorFlow](https://github.com/skypilot-org/skypilot/blob/master/examples/resnet_app_storage.yaml), [Ray Train](examples/distributed_ray_train/ray_train.yaml), [NeMo](https://github.com/skypilot-org/skypilot/blob/master/examples/nemo/nemo.yaml), [programmatic grid search](https://github.com/skypilot-org/skypilot/blob/master/examples/huggingface_glue_imdb_grid_search_app.py), [Docker](https://github.com/skypilot-org/skypilot/blob/master/examples/docker/echo_app.yaml), [Cog](https://github.com/skypilot-org/skypilot/blob/master/examples/cog/), [Unsloth](https://github.com/skypilot-org/skypilot/blob/master/examples/unsloth/unsloth.yaml), [Ollama](https://github.com/skypilot-org/skypilot/blob/master/llm/ollama), [llm.c](https://github.com/skypilot-org/skypilot/tree/master/llm/gpt-2), [Airflow](./examples/airflow/training_workflow) and [many more (`examples/`)](./examples).

Case Studies and Integrations: [Community Spotlights](https://blog.skypilot.co/community/)

Follow updates:
- [Twitter](https://twitter.com/skypilot_org)
- [Slack](http://slack.skypilot.co)
- [SkyPilot Blog](https://blog.skypilot.co/) ([Introductory blog post](https://blog.skypilot.co/introducing-skypilot/))

Read the research:
- [SkyPilot paper](https://www.usenix.org/system/files/nsdi23-yang-zongheng.pdf) and [talk](https://www.usenix.org/conference/nsdi23/presentation/yang-zongheng) (NSDI 2023)
- [Sky Computing whitepaper](https://arxiv.org/abs/2205.07147)
- [Sky Computing vision paper](https://sigops.org/s/conferences/hotos/2021/papers/hotos21-s02-stoica.pdf) (HotOS 2021)
- [Policy for Managed Spot Jobs](https://www.usenix.org/conference/nsdi24/presentation/wu-zhanghao)  (NSDI 2024)

## Support and Questions
We are excited to hear your feedback!
* For issues and feature requests, please [open a GitHub issue](https://github.com/skypilot-org/skypilot/issues/new).
* For questions, please use [GitHub Discussions](https://github.com/skypilot-org/skypilot/discussions).

For general discussions, join us on the [SkyPilot Slack](http://slack.skypilot.co).

## Contributing
We welcome all contributions to the project! See [CONTRIBUTING](CONTRIBUTING.md) for how to get involved.
