# Build your own Distribution This guide will walk you through the steps to get started with building a Llama Stack distribution from scratch with your choice of API providers. ## Llama Stack Build In order to build your own distribution, we recommend you clone the `llama-stack` repository. ``` git clone git@github.com:meta-llama/llama-stack.git cd llama-stack pip install -e . llama stack build -h ``` We will start build our distribution (in the form of a Conda environment, or Container image). In this step, we will specify: - `name`: the name for our distribution (e.g. `my-stack`) - `image_type`: our build image type (`conda | container`) - `distribution_spec`: our distribution specs for specifying API providers - `description`: a short description of the configurations for the distribution - `providers`: specifies the underlying implementation for serving each API endpoint - `image_type`: `conda` | `container` to specify whether to build the distribution in the form of Container image or Conda environment. After this step is complete, a file named `-build.yaml` and template file `-run.yaml` will be generated and saved at the output file path specified at the end of the command. ::::{tab-set} :::{tab-item} Building from Scratch - For a new user, we could start off with running `llama stack build` which will allow you to a interactively enter wizard where you will be prompted to enter build configurations. ``` llama stack build > Enter a name for your Llama Stack (e.g. my-local-stack): my-stack > Enter the image type you want your Llama Stack to be built as (container or conda): conda Llama Stack is composed of several APIs working together. Let's select the provider types (implementations) you want to use for these APIs. Tip: use to see options for the providers. > Enter provider for API inference: inline::meta-reference > Enter provider for API safety: inline::llama-guard > Enter provider for API agents: inline::meta-reference > Enter provider for API memory: inline::faiss > Enter provider for API datasetio: inline::meta-reference > Enter provider for API scoring: inline::meta-reference > Enter provider for API eval: inline::meta-reference > Enter provider for API telemetry: inline::meta-reference > (Optional) Enter a short description for your Llama Stack: You can now edit ~/.llama/distributions/llamastack-my-local-stack/my-local-stack-run.yaml and run `llama stack run ~/.llama/distributions/llamastack-my-local-stack/my-local-stack-run.yaml` ``` ::: :::{tab-item} Building from a template - To build from alternative API providers, we provide distribution templates for users to get started building a distribution backed by different providers. The following command will allow you to see the available templates and their corresponding providers. ``` llama stack build --list-templates ``` ``` +------------------------------+----------------------------------------+-----------------------------------------------------------------------------+ | Template Name | Providers | Description | +------------------------------+----------------------------------------+-----------------------------------------------------------------------------+ | tgi | { | Use (an external) TGI server for running LLM inference | | | "inference": [ | | | | "remote::tgi" | | | | ], | | | | "memory": [ | | | | "inline::faiss", | | | | "remote::chromadb", | | | | "remote::pgvector" | | | | ], | | | | "safety": [ | | | | "inline::llama-guard" | | | | ], | | | | "agents": [ | | | | "inline::meta-reference" | | | | ], | | | | "telemetry": [ | | | | "inline::meta-reference" | | | | ] | | | | } | | +------------------------------+----------------------------------------+-----------------------------------------------------------------------------+ | remote-vllm | { | Use (an external) vLLM server for running LLM inference | | | "inference": [ | | | | "remote::vllm" | | | | ], | | | | "memory": [ | | | | "inline::faiss", | | | | "remote::chromadb", | | | | "remote::pgvector" | | | | ], | | | | "safety": [ | | | | "inline::llama-guard" | | | | ], | | | | "agents": [ | | | | "inline::meta-reference" | | | | ], | | | | "telemetry": [ | | | | "inline::meta-reference" | | | | ] | | | | } | | +------------------------------+----------------------------------------+-----------------------------------------------------------------------------+ | vllm-gpu | { | Use a built-in vLLM engine for running LLM inference | | | "inference": [ | | | | "inline::vllm" | | | | ], | | | | "memory": [ | | | | "inline::faiss", | | | | "remote::chromadb", | | | | "remote::pgvector" | | | | ], | | | | "safety": [ | | | | "inline::llama-guard" | | | | ], | | | | "agents": [ | | | | "inline::meta-reference" | | | | ], | | | | "telemetry": [ | | | | "inline::meta-reference" | | | | ] | | | | } | | +------------------------------+----------------------------------------+-----------------------------------------------------------------------------+ | meta-reference-quantized-gpu | { | Use Meta Reference with fp8, int4 quantization for running LLM inference | | | "inference": [ | | | | "inline::meta-reference-quantized" | | | | ], | | | | "memory": [ | | | | "inline::faiss", | | | | "remote::chromadb", | | | | "remote::pgvector" | | | | ], | | | | "safety": [ | | | | "inline::llama-guard" | | | | ], | | | | "agents": [ | | | | "inline::meta-reference" | | | | ], | | | | "telemetry": [ | | | | "inline::meta-reference" | | | | ] | | | | } | | +------------------------------+----------------------------------------+-----------------------------------------------------------------------------+ | meta-reference-gpu | { | Use Meta Reference for running LLM inference | | | "inference": [ | | | | "inline::meta-reference" | | | | ], | | | | "memory": [ | | | | "inline::faiss", | | | | "remote::chromadb", | | | | "remote::pgvector" | | | | ], | | | | "safety": [ | | | | "inline::llama-guard" | | | | ], | | | | "agents": [ | | | | "inline::meta-reference" | | | | ], | | | | "telemetry": [ | | | | "inline::meta-reference" | | | | ] | | | | } | | +------------------------------+----------------------------------------+-----------------------------------------------------------------------------+ | hf-serverless | { | Use (an external) Hugging Face Inference Endpoint for running LLM inference | | | "inference": [ | | | | "remote::hf::serverless" | | | | ], | | | | "memory": [ | | | | "inline::faiss", | | | | "remote::chromadb", | | | | "remote::pgvector" | | | | ], | | | | "safety": [ | | | | "inline::llama-guard" | | | | ], | | | | "agents": [ | | | | "inline::meta-reference" | | | | ], | | | | "telemetry": [ | | | | "inline::meta-reference" | | | | ] | | | | } | | +------------------------------+----------------------------------------+-----------------------------------------------------------------------------+ | together | { | Use Together.AI for running LLM inference | | | "inference": [ | | | | "remote::together" | | | | ], | | | | "memory": [ | | | | "inline::faiss", | | | | "remote::chromadb", | | | | "remote::pgvector" | | | | ], | | | | "safety": [ | | | | "inline::llama-guard" | | | | ], | | | | "agents": [ | | | | "inline::meta-reference" | | | | ], | | | | "telemetry": [ | | | | "inline::meta-reference" | | | | ] | | | | } | | +------------------------------+----------------------------------------+-----------------------------------------------------------------------------+ | ollama | { | Use (an external) Ollama server for running LLM inference | | | "inference": [ | | | | "remote::ollama" | | | | ], | | | | "memory": [ | | | | "inline::faiss", | | | | "remote::chromadb", | | | | "remote::pgvector" | | | | ], | | | | "safety": [ | | | | "inline::llama-guard" | | | | ], | | | | "agents": [ | | | | "inline::meta-reference" | | | | ], | | | | "telemetry": [ | | | | "inline::meta-reference" | | | | ] | | | | } | | +------------------------------+----------------------------------------+-----------------------------------------------------------------------------+ | bedrock | { | Use AWS Bedrock for running LLM inference and safety | | | "inference": [ | | | | "remote::bedrock" | | | | ], | | | | "memory": [ | | | | "inline::faiss", | | | | "remote::chromadb", | | | | "remote::pgvector" | | | | ], | | | | "safety": [ | | | | "remote::bedrock" | | | | ], | | | | "agents": [ | | | | "inline::meta-reference" | | | | ], | | | | "telemetry": [ | | | | "inline::meta-reference" | | | | ] | | | | } | | +------------------------------+----------------------------------------+-----------------------------------------------------------------------------+ | hf-endpoint | { | Use (an external) Hugging Face Inference Endpoint for running LLM inference | | | "inference": [ | | | | "remote::hf::endpoint" | | | | ], | | | | "memory": [ | | | | "inline::faiss", | | | | "remote::chromadb", | | | | "remote::pgvector" | | | | ], | | | | "safety": [ | | | | "inline::llama-guard" | | | | ], | | | | "agents": [ | | | | "inline::meta-reference" | | | | ], | | | | "telemetry": [ | | | | "inline::meta-reference" | | | | ] | | | | } | | +------------------------------+----------------------------------------+-----------------------------------------------------------------------------+ | fireworks | { | Use Fireworks.AI for running LLM inference | | | "inference": [ | | | | "remote::fireworks" | | | | ], | | | | "memory": [ | | | | "inline::faiss", | | | | "remote::chromadb", | | | | "remote::pgvector" | | | | ], | | | | "safety": [ | | | | "inline::llama-guard" | | | | ], | | | | "agents": [ | | | | "inline::meta-reference" | | | | ], | | | | "telemetry": [ | | | | "inline::meta-reference" | | | | ] | | | | } | | +------------------------------+----------------------------------------+-----------------------------------------------------------------------------+ | cerebras | { | Use Cerebras for running LLM inference | | | "inference": [ | | | | "remote::cerebras" | | | | ], | | | | "safety": [ | | | | "inline::llama-guard" | | | | ], | | | | "memory": [ | | | | "inline::meta-reference" | | | | ], | | | | "agents": [ | | | | "inline::meta-reference" | | | | ], | | | | "telemetry": [ | | | | "inline::meta-reference" | | | | ] | | | | } | | +------------------------------+----------------------------------------+-----------------------------------------------------------------------------+ ``` You may then pick a template to build your distribution with providers fitted to your liking. For example, to build a distribution with TGI as the inference provider, you can run: ``` llama stack build --template tgi ``` ``` $ llama stack build --template tgi ... You can now edit ~/.llama/distributions/llamastack-tgi/tgi-run.yaml and run `llama stack run ~/.llama/distributions/llamastack-tgi/tgi-run.yaml` ``` ::: :::{tab-item} Building from a pre-existing build config file - In addition to templates, you may customize the build to your liking through editing config files and build from config files with the following command. - The config file will be of contents like the ones in `llama_stack/templates/*build.yaml`. ``` $ cat llama_stack/templates/ollama/build.yaml name: ollama distribution_spec: description: Like local, but use ollama for running LLM inference providers: inference: remote::ollama memory: inline::faiss safety: inline::llama-guard agents: inline::meta-reference telemetry: inline::meta-reference image_type: conda ``` ``` llama stack build --config llama_stack/templates/ollama/build.yaml ``` ::: :::{tab-item} Building Container > [!TIP] > Podman is supported as an alternative to Docker. Set `CONTAINER_BINARY` to `podman` in your environment to use Podman. To build a container image, you may start off from a template and use the `--image-type container` flag to specify `container` as the build image type. ``` llama stack build --template ollama --image-type container ``` ``` $ llama stack build --template ollama --image-type container ... Containerfile created successfully in /tmp/tmp.viA3a3Rdsg/ContainerfileFROM python:3.10-slim ... You can now edit ~/meta-llama/llama-stack/tmp/configs/ollama-run.yaml and run `llama stack run ~/meta-llama/llama-stack/tmp/configs/ollama-run.yaml` ``` After this step is successful, you should be able to find the built container image and test it with `llama stack run `. ::: :::: ## Running your Stack server Now, let's start the Llama Stack Distribution Server. You will need the YAML configuration file which was written out at the end by the `llama stack build` step. ``` llama stack run ~/.llama/distributions/llamastack-my-local-stack/my-local-stack-run.yaml ``` ``` $ llama stack run ~/.llama/distributions/llamastack-my-local-stack/my-local-stack-run.yaml Serving API inspect GET /health GET /providers/list GET /routes/list Serving API inference POST /inference/chat_completion POST /inference/completion POST /inference/embeddings ... Serving API agents POST /agents/create POST /agents/session/create POST /agents/turn/create POST /agents/delete POST /agents/session/delete POST /agents/session/get POST /agents/step/get POST /agents/turn/get Listening on ['::', '0.0.0.0']:8321 INFO: Started server process [2935911] INFO: Waiting for application startup. INFO: Application startup complete. INFO: Uvicorn running on http://['::', '0.0.0.0']:8321 (Press CTRL+C to quit) INFO: 2401:db00:35c:2d2b:face:0:c9:0:54678 - "GET /models/list HTTP/1.1" 200 OK ``` ### Troubleshooting If you encounter any issues, search through our [GitHub Issues](https://github.com/meta-llama/llama-stack/issues), or file an new issue.