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z)hOUcvPTv#136BTe*mXR&3GDn7;~DN|F3EFjA8I^Iu=j^>8iy_9dZi7vyNJi1q^NP zp{I&(Pfs_y(*V8L=#_()rG&aWCqT*Jn;Re&_yD@5FX9U%RvqL@)Z?GY40CFEg=+rpWbg;$vVX<*8UCwH=Ksd$^p|mD z_$OMI{_-3Du3LtGlJ@xTdR~7zE`~p975sm}HTbvV%<$iZO#d66JHvl-p8B_A_pf}; h@P8^${5SX?8UFwJ_Mf(|{wuGyKeF%tx7W9+{{T>STm1k4 diff --git a/docs/stable/cli/_category_.json b/docs/stable/cli/_category_.json index 569373a..5cfc7b0 100644 --- a/docs/stable/cli/_category_.json +++ b/docs/stable/cli/_category_.json @@ -1,8 +1,4 @@ { "label": "ServerlessLLM CLI", - "position": 4, - "link": { - "type": "generated-index", - "description": "TODO" - } + "position": 4 } diff --git a/docs/stable/getting_started/_category_.json b/docs/stable/getting_started/_category_.json index 4ff56d9..877a378 100644 --- a/docs/stable/getting_started/_category_.json +++ b/docs/stable/getting_started/_category_.json @@ -1,8 +1,4 @@ { "label": "Getting Started", - "position": 2, - "link": { - "type": "generated-index", - "description": "TODO" - } + "position": 2 } diff --git a/docs/stable/getting_started/docker_quickstart.md b/docs/stable/getting_started/docker_quickstart.md index fe5c034..99e9821 100644 --- a/docs/stable/getting_started/docker_quickstart.md +++ b/docs/stable/getting_started/docker_quickstart.md @@ -13,9 +13,9 @@ Ensure you have the following pre-requirements installed: 1. **GPUs**: Ensure you have at least 2 GPUs available. If more GPUs are provided, you can adjust the number of workers and the number of devices assigned to each worker. 2. **NVIDIA Docker Toolkit**: This allows Docker to use NVIDIA GPUs. You can find the installation guide [here](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html). -## Local Test Using Docker +## Run ServerlessLLM using Docker -First, let's start a local Docker-based ray cluster to test the ServerlessLLM. +First, let's start a local Docker-based ray cluster to run ServerlessLLM. ### Step 1: Build Docker Images diff --git a/docs/stable/getting_started/quickstart.md b/docs/stable/getting_started/quickstart.md index 3b85bd0..4283da6 100644 --- a/docs/stable/getting_started/quickstart.md +++ b/docs/stable/getting_started/quickstart.md @@ -6,8 +6,8 @@ sidebar_position: 1 This guide will help you get started with the basics of using ServerlessLLM. Please make sure you have installed the ServerlessLLM following the [installation guide](./installation.md). -## Local test -First, let's start a local ray cluster to test the ServerlessLLM. You can start a local ray cluster by running the following command: +## Run ServerlessLLM on your local machine +First, let's start a local ray cluster to run ServerlessLLM. You can start a local ray cluster by running the following command: Start a local ray cluster with 1 head node and 1 worker node: ```bash diff --git a/docs/stable/intro.md b/docs/stable/intro.md index c56def2..a14db4e 100644 --- a/docs/stable/intro.md +++ b/docs/stable/intro.md @@ -4,24 +4,28 @@ sidebar_position: 1 # Serverless LLM - -![ServerlessLLM](../images/serverlessllm.jpg) + +ServerlessLLM -ServerlessLLM is a fast, affordable, and easy-to-use library designed for multi-LLM serving, also known as [Serverless Inference](https://docs.aws.amazon.com/sagemaker/latest/dg/serverless-endpoints.html), [Inference Endpoint](https://huggingface.co/inference-endpoints/dedicated), or [Model Endpoints](https://learn.microsoft.com/en-us/azure/machine-learning/concept-endpoints?view=azureml-api-2). This library is ideal for environments with limited GPU resources, as it allows efficient dynamic loading of models onto GPUs. By supporting high levels of GPU multiplexing, it maximizes GPU utilization without the need to dedicate GPUs to individual models. +ServerlessLLM is a **fast** and **easy-to-use** serving system designed for **affordable** multi-LLM serving, also known as LLM-as-a-Service. ServerlessLLM is ideal for environments with multiple LLMs that need to be served on limited GPU resources, as it enables efficient dynamic loading of LLMs onto GPUs. By elastically scaling model instances and multiplexing GPUs, ServerlessLLM can significantly reduce costs compared to traditional GPU-dedicated serving systems while still providing low-latency (Time-to-First-Token, TTFT) LLM completions. ## Documentation ### Getting Started - [Install ServerlessLLM](./getting_started/installation.md) -- [Deploy a ServerlessLLM cluster on your local machine](./getting_started/quickstart.md) -- [Deploy ServerlessLLM using Docker (Recommended)](./getting_started/docker_quickstart.md) -- [Deploy ServerlessLLM on a multi-machine cluster](./getting_started/multi_machine_setup.md) +- [Quickstart](./getting_started/quickstart.md) +- [Quickstart with Docker](./getting_started/docker_quickstart.md) +- [Multi-machine Quickstart](./getting_started/multi_machine_setup.md) + +### ServerlessLLM Serve + +- [Storage-Aware Scheduler](./serve/storage_aware_scheduling.md) ### ServerlessLLM Store -- [Use ServerlessLLM Store in your own code](./store/quickstart.md) +- [Quickstart](./store/quickstart.md) ### ServerlessLLM CLI -- [ServerlessLLM CLI Documentation](./cli/cli_api.md) +- [ServerlessLLM CLI API](./cli/cli_api.md) diff --git a/docs/stable/serve/_category_.json b/docs/stable/serve/_category_.json index 3e65d64..51db74b 100644 --- a/docs/stable/serve/_category_.json +++ b/docs/stable/serve/_category_.json @@ -1,8 +1,4 @@ { "label": "ServerlessLLM Serve", - "position": 3, - "link": { - "type": "generated-index", - "description": "TODO" - } + "position": 3 } \ No newline at end of file diff --git a/docs/stable/store/_category_.json b/docs/stable/store/_category_.json index 8710859..78b547f 100644 --- a/docs/stable/store/_category_.json +++ b/docs/stable/store/_category_.json @@ -1,8 +1,4 @@ { "label": "ServerlessLLM Store", - "position": 5, - "link": { - "type": "generated-index", - "description": "`sllm-store` is an internal library of ServerlessLLM that provides high-performance model loading from local storage into GPU memory. You can also install and use this library in your own projects." - } + "position": 5 }