Merge branch 'master' into k8s_pod_dns

.github/workflows/pytest.yml

@@ -53,8 +53,8 @@ jobs:
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
-          pip install ".[all]"
+          pip install -e ".[all]"
           pip install pytest pytest-xdist pytest-env>=0.6 memory-profiler==0.61.0
 
       - name: Run tests with pytest
-        run: SKYPILOT_DISABLE_USAGE_COLLECTION=1 SKYPILOT_SKIP_CLOUD_IDENTITY_CHECK=1 pytest -n 1 --dist no ${{ matrix.test-path }}
+        run: SKYPILOT_DISABLE_USAGE_COLLECTION=1 SKYPILOT_SKIP_CLOUD_IDENTITY_CHECK=1 pytest -n 0 --dist no ${{ matrix.test-path }}

README.md

@@ -26,29 +26,32 @@
 
 ----
 :fire: *News* :fire:
-- [Sep, 2024] Run and deploy [Pixtral](./llm/pixtral), the first open-source multimodal model from Mistral AI.
-- [Jul, 2024] [Finetune](./llm/llama-3_1-finetuning/) and [serve](./llm/llama-3_1/) **Llama 3.1** on your infra
-- [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 and finetune [**Llama 3**](https://skypilot.readthedocs.io/en/latest/gallery/llms/llama-3.html) on any cloud or Kubernetes: [**example**](./llm/llama-3/)
-- [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/)
-- [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/)
-- [Aug, 2023] **Finetuning Cookbook**: Finetuning Llama 2 in your own cloud environment, privately: [**example**](./llm/vicuna-llama-2/), [**blog post**](https://blog.skypilot.co/finetuning-llama2-operational-guide/)
+- [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>
 
-- [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)
-- [July, 2023] Self-Hosted **Llama-2 Chatbot** on Any Cloud: [**example**](./llm/llama-2/)
-- [June, 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/)
-- [April, 2023] [SkyPilot YAMLs](./llm/vicuna/) for finetuning & serving the [Vicuna LLM](https://lmsys.org/blog/2023-03-30-vicuna/) with a single command!
+- [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>
 
@@ -107,7 +110,7 @@ Paste the following into a file `my_task.yaml`:
 
 ```yaml
 resources:
-  accelerators: V100:1  # 1x NVIDIA V100 GPU
+  accelerators: A100:8  # 8x NVIDIA A100 GPU
 
 num_nodes: 1  # Number of VMs to launch
 
@@ -152,11 +155,12 @@ SkyPilot then performs the heavy-lifting for you, including:
 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/) and [Tutorials](https://github.com/skypilot-org/skypilot-tutorial).
+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/)
@@ -179,7 +183,7 @@ Runnable examples:
   - [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) and [many more (`examples/`)](./examples).
+- 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/)
 
@@ -202,4 +206,4 @@ We are excited to hear your feedback!
 For general discussions, join us on the [SkyPilot Slack](http://slack.skypilot.co).
 
 ## Contributing
-We welcome and value all contributions to the project! Please refer to [CONTRIBUTING](CONTRIBUTING.md) for how to get involved.
+We welcome all contributions to the project! See [CONTRIBUTING](CONTRIBUTING.md) for how to get involved.

docs/requirements-docs.txt

@@ -12,6 +12,7 @@ sphinx-book-theme==1.1.0
 sphinx-togglebutton==0.3.2
 sphinxcontrib-applehelp==1.0.7
 sphinxcontrib-devhelp==1.0.5
+sphinxcontrib-googleanalytics==0.4
 sphinxcontrib-htmlhelp==2.0.4
 sphinxcontrib-jsmath==1.0.1
 sphinxcontrib-qthelp==1.0.6

docs/source/_gallery_original/index.rst

@@ -34,16 +34,17 @@ Contents
    :maxdepth: 1
    :caption: LLM Models
 
+   Vision Llama 3.2 (Meta) <llms/llama-3_2>
+   Llama 3.1 (Meta) <llms/llama-3_1>
+   Llama 3 (Meta) <llms/llama-3>
+   Llama 2 (Meta) <llms/llama-2>
+   CodeLlama (Meta) <llms/codellama>
    Pixtral (Mistral AI) <llms/pixtral>
    Mixtral (Mistral AI) <llms/mixtral>
    Mistral 7B (Mistral AI) <https://docs.mistral.ai/self-deployment/skypilot/>
-   DBRX (Databricks) <llms/dbrx>
-   Llama-2 (Meta) <llms/llama-2>
-   Llama-3 (Meta) <llms/llama-3>
-   Llama-3.1 (Meta) <llms/llama-3_1>
-   Qwen (Alibaba) <llms/qwen>
-   CodeLlama (Meta) <llms/codellama>
+   Qwen 2.5 (Alibaba) <llms/qwen>
    Gemma (Google) <llms/gemma>
+   DBRX (Databricks) <llms/dbrx>
 
 .. toctree::
    :maxdepth: 1

docs/source/_gallery_original/llms/llama-3_2.md

@@ -0,0 +1 @@
+../../../../llm/llama-3_2/README.md

docs/source/_static/custom.js

@@ -27,10 +27,12 @@ document.addEventListener('DOMContentLoaded', () => {
     const newItems = [
         { selector: '.caption-text', text: 'SkyServe: Model Serving' },
         { selector: '.toctree-l1 > a', text: 'Managed Jobs' },
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         { selector: '.toctree-l1 > a', text: 'Pixtral (Mistral AI)' },
         { selector: '.toctree-l1 > a', text: 'Many Parallel Jobs' },
         { selector: '.toctree-l1 > a', text: 'Reserved, Capacity Blocks, DWS' },
+        { selector: '.toctree-l1 > a', text: 'Llama 3.2 (Meta)' },
+        { selector: '.toctree-l1 > a', text: 'Admin Policy Enforcement' },
+        { selector: '.toctree-l1 > a', text: 'Using Existing Machines' },
     ];
     newItems.forEach(({ selector, text }) => {
         document.querySelectorAll(selector).forEach((el) => {