base on SkyPilot: Run AI and batch jobs on any infra (Kubernetes or 12+ clouds). Get unified execution, cost savings, and high GPU availability via a simple interface. <p align="center"> <picture> <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/skypilot-org/skypilot/master/docs/source/images/skypilot-wide-dark-1k.png"> <img alt="SkyPilot" src="https://raw.githubusercontent.com/skypilot-org/skypilot/master/docs/source/images/skypilot-wide-light-1k.png" width=55%> </picture> </p> <p align="center"> <a href="https://docs.skypilot.co/"> <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://docs.skypilot.co/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://docs.skypilot.co/en/latest/examples/interactive-development.html), [jobs](https://docs.skypilot.co/en/latest/examples/managed-jobs.html), and [serving](https://docs.skypilot.co/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://docs.skypilot.co/en/latest/examples/auto-failover.html) of GPUs, TPUs, CPUs, with auto-retry SkyPilot **cuts your cloud costs & maximizes GPU availability**: * [Autostop](https://docs.skypilot.co/en/latest/reference/auto-stop.html): automatic cleanup of idle resources * [Managed Spot](https://docs.skypilot.co/en/latest/examples/managed-jobs.html): 3-6x cost savings using spot instances, with preemption auto-recovery * [Optimizer](https://docs.skypilot.co/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://docs.skypilot.co/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://docs.skypilot.co/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"> <picture> <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/skypilot-org/skypilot/master/docs/source/images/cloud-logos-dark.png"> <img alt="SkyPilot" src="https://raw.githubusercontent.com/skypilot-org/skypilot/master/docs/source/images/cloud-logos-light.png" width=85%> </picture> </p> ## Getting Started You can find our documentation [here](https://docs.skypilot.co/). - [Installation](https://docs.skypilot.co/en/latest/getting-started/installation.html) - [Quickstart](https://docs.skypilot.co/en/latest/getting-started/quickstart.html) - [CLI reference](https://docs.skypilot.co/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://docs.skypilot.co/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://docs.skypilot.co/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://docs.skypilot.co/en/latest/getting-started/quickstart.html) to get started with SkyPilot. ## More Information To learn more, see [Concept: Sky Computing](https://docs.skypilot.co/en/latest/sky-computing.html), [SkyPilot docs](https://docs.skypilot.co/en/latest/), and [SkyPilot blog](https://blog.skypilot.co/). <!-- Keep this section in sync with index.rst in SkyPilot Docs --> Runnable examples: - [**AI Gallery**](https://docs.skypilot.co/en/latest/gallery/index.html) - 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/), [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. ", Assign "at most 3 tags" to the expected json: {"id":"2721","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"