# Getting Started This guide will walk you though the steps to get started on end-to-end flow for LlamaStack. This guide mainly focuses on getting started with building a LlamaStack distribution, and starting up a LlamaStack server. Please see our [documentations](https://github.com/meta-llama/llama-stack/README.md) on what you can do with Llama Stack, and [llama-stack-apps](https://github.com/meta-llama/llama-stack-apps/tree/main) on examples apps built with Llama Stack. ## Installation The `llama` CLI tool helps you setup and use the Llama toolchain & agentic systems. It should be available on your path after installing the `llama-stack` package. You can install this repository as a [package](https://pypi.org/project/llama-stack/) with `pip install llama-stack` If you want to install from source: ```bash mkdir -p ~/local cd ~/local git clone git@github.com:meta-llama/llama-stack.git conda create -n stack python=3.10 conda activate stack cd llama-stack $CONDA_PREFIX/bin/pip install -e . ``` For what you can do with the Llama CLI, please refer to [CLI Reference](./cli_reference.md). ## Quick Starting Llama Stack Server ### Starting up server via docker We provide 2 pre-built Docker image of Llama Stack distribution, which can be found in the following links. - [llamastack-local-gpu](https://hub.docker.com/repository/docker/llamastack/llamastack-local-gpu/general) - This is a packaged version with our local meta-reference implementations, where you will be running inference locally with downloaded Llama model checkpoints. - [llamastack-local-cpu](https://hub.docker.com/repository/docker/llamastack/llamastack-local-cpu/general) - This is a lite version with remote inference where you can hook up to your favourite remote inference framework (e.g. ollama, fireworks, together, tgi) for running inference without GPU. > [!NOTE] > For GPU inference, you need to set these environment variables for specifying local directory containing your model checkpoints, and enable GPU inference to start running docker container. ``` export LLAMA_CHECKPOINT_DIR=~/.llama ``` > [!NOTE] > `~/.llama` should be the path containing downloaded weights of Llama models. To download and start running a pre-built docker container, you may use the following commands: ``` docker run -it -p 5000:5000 -v ~/.llama:/root/.llama --gpus=all llamastack/llamastack-local-gpu ``` > [!TIP] > Pro Tip: We may use `docker compose up` for starting up a distribution with remote providers (e.g. TGI) using [llamastack-local-cpu](https://hub.docker.com/repository/docker/llamastack/llamastack-local-cpu/general). You can checkout [these scripts](https://github.com/meta-llama/llama-stack/llama_stack/distribution/docker/README.md) to help you get started. ### Build->Configure->Run Llama Stack server via conda You may also build a LlamaStack distribution from scratch, configure it, and start running the distribution. This is useful for developing on LlamaStack. **`llama stack build`** - You'll be prompted to enter build information interactively. ``` llama stack build > Enter an unique name for identifying your Llama Stack build distribution (e.g. my-local-stack): my-local-stack > Enter the image type you want your distribution to be built with (docker or conda): conda Llama Stack is composed of several APIs working together. Let's configure the providers (implementations) you want to use for these APIs. > Enter the API provider for the inference API: (default=meta-reference): meta-reference > Enter the API provider for the safety API: (default=meta-reference): meta-reference > Enter the API provider for the agents API: (default=meta-reference): meta-reference > Enter the API provider for the memory API: (default=meta-reference): meta-reference > Enter the API provider for the telemetry API: (default=meta-reference): meta-reference > (Optional) Enter a short description for your Llama Stack distribution: Build spec configuration saved at ~/.conda/envs/llamastack-my-local-stack/my-local-stack-build.yaml You can now run `llama stack configure my-local-stack` ``` **`llama stack configure`** - Run `llama stack configure ` with the name you have previously defined in `build` step. ``` llama stack configure ``` - You will be prompted to enter configurations for your Llama Stack ``` $ llama stack configure my-local-stack Could not find my-local-stack. Trying conda build name instead... Configuring API `inference`... === Configuring provider `meta-reference` for API inference... Enter value for model (default: Llama3.1-8B-Instruct) (required): Do you want to configure quantization? (y/n): n Enter value for torch_seed (optional): Enter value for max_seq_len (default: 4096) (required): Enter value for max_batch_size (default: 1) (required): Configuring API `safety`... === Configuring provider `meta-reference` for API safety... Do you want to configure llama_guard_shield? (y/n): n Do you want to configure prompt_guard_shield? (y/n): n Configuring API `agents`... === Configuring provider `meta-reference` for API agents... Enter `type` for persistence_store (options: redis, sqlite, postgres) (default: sqlite): Configuring SqliteKVStoreConfig: Enter value for namespace (optional): Enter value for db_path (default: /home/xiyan/.llama/runtime/kvstore.db) (required): Configuring API `memory`... === Configuring provider `meta-reference` for API memory... > Please enter the supported memory bank type your provider has for memory: vector Configuring API `telemetry`... === Configuring provider `meta-reference` for API telemetry... > YAML configuration has been written to ~/.llama/builds/conda/my-local-stack-run.yaml. You can now run `llama stack run my-local-stack --port PORT` ``` **`llama stack run`** - Run `llama stack run ` with the name you have previously defined. ``` llama stack run my-local-stack ... > initializing model parallel with size 1 > initializing ddp with size 1 > initializing pipeline with size 1 ... Finished model load YES READY Serving POST /inference/chat_completion Serving POST /inference/completion Serving POST /inference/embeddings Serving POST /memory_banks/create Serving DELETE /memory_bank/documents/delete Serving DELETE /memory_banks/drop Serving GET /memory_bank/documents/get Serving GET /memory_banks/get Serving POST /memory_bank/insert Serving GET /memory_banks/list Serving POST /memory_bank/query Serving POST /memory_bank/update Serving POST /safety/run_shield Serving POST /agentic_system/create Serving POST /agentic_system/session/create Serving POST /agentic_system/turn/create Serving POST /agentic_system/delete Serving POST /agentic_system/session/delete Serving POST /agentic_system/session/get Serving POST /agentic_system/step/get Serving POST /agentic_system/turn/get Serving GET /telemetry/get_trace Serving POST /telemetry/log_event Listening on :::5000 INFO: Started server process [587053] INFO: Waiting for application startup. INFO: Application startup complete. INFO: Uvicorn running on http://[::]:5000 (Press CTRL+C to quit) ``` ### End-to-end flow of building, configuring, running, and testing a Distribution #### Step 1. Build In the following steps, imagine we'll be working with a `Meta-Llama3.1-8B-Instruct` model. We will name our build `8b-instruct` to help us remember the config. We will start build our distribution (in the form of a Conda environment, or Docker image). In this step, we will specify: - `name`: the name for our distribution (e.g. `8b-instruct`) - `image_type`: our build image type (`conda | docker`) - `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` | `docker` to specify whether to build the distribution in the form of Docker image or Conda environment. At the end of build command, we will generate `-build.yaml` file storing the build configurations. After this step is complete, a file named `-build.yaml` will be generated and saved at the output file path specified at the end of the command. #### 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 ``` Running the command above will allow you to fill in the configuration to build your Llama Stack distribution, you will see the following outputs. ``` > Enter an unique name for identifying your Llama Stack build distribution (e.g. my-local-stack): 8b-instruct > Enter the image type you want your distribution to be built with (docker or conda): conda Llama Stack is composed of several APIs working together. Let's configure the providers (implementations) you want to use for these APIs. > Enter the API provider for the inference API: (default=meta-reference): meta-reference > Enter the API provider for the safety API: (default=meta-reference): meta-reference > Enter the API provider for the agents API: (default=meta-reference): meta-reference > Enter the API provider for the memory API: (default=meta-reference): meta-reference > Enter the API provider for the telemetry API: (default=meta-reference): meta-reference > (Optional) Enter a short description for your Llama Stack distribution: Build spec configuration saved at ~/.conda/envs/llamastack-my-local-llama-stack/8b-instruct-build.yaml ``` **Ollama (optional)** If you plan to use Ollama for inference, you'll need to install the server [via these instructions](https://ollama.com/download). #### Building from templates - 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 ``` ![alt text](https://github.com/meta-llama/llama-stack/docs/resources/list-templates.png) You may then pick a template to build your distribution with providers fitted to your liking. ``` llama stack build --template local-tgi --name my-tgi-stack ``` ``` $ llama stack build --template local-tgi --name my-tgi-stack ... ... Build spec configuration saved at ~/.conda/envs/llamastack-my-tgi-stack/my-tgi-stack-build.yaml You may now run `llama stack configure my-tgi-stack` or `llama stack configure ~/.conda/envs/llamastack-my-tgi-stack/my-tgi-stack-build.yaml` ``` #### Building from 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/distributions/templates/`. ``` $ cat llama_stack/distribution/templates/local-ollama-build.yaml name: local-ollama distribution_spec: description: Like local, but use ollama for running LLM inference providers: inference: remote::ollama memory: meta-reference safety: meta-reference agents: meta-reference telemetry: meta-reference image_type: conda ``` ``` llama stack build --config llama_stack/distribution/templates/local-ollama-build.yaml ``` #### How to build distribution with Docker image > [!TIP] > Podman is supported as an alternative to Docker. Set `DOCKER_BINARY` to `podman` in your environment to use Podman. To build a docker image, you may start off from a template and use the `--image-type docker` flag to specify `docker` as the build image type. ``` llama stack build --template local --image-type docker --name docker-0 ``` Alternatively, you may use a config file and set `image_type` to `docker` in our `-build.yaml` file, and run `llama stack build -build.yaml`. The `-build.yaml` will be of contents like: ``` name: local-docker-example distribution_spec: description: Use code from `llama_stack` itself to serve all llama stack APIs docker_image: null providers: inference: meta-reference memory: meta-reference-faiss safety: meta-reference agentic_system: meta-reference telemetry: console image_type: docker ``` The following command allows you to build a Docker image with the name `` ``` llama stack build --config -build.yaml Dockerfile created successfully in /tmp/tmp.I0ifS2c46A/DockerfileFROM python:3.10-slim WORKDIR /app ... ... You can run it with: podman run -p 8000:8000 llamastack-docker-local Build spec configuration saved at ~/.llama/distributions/docker/docker-local-build.yaml ``` ### Step 2. Configure After our distribution is built (either in form of docker or conda environment), we will run the following command to ``` llama stack configure [ | | ] ``` - For `conda` environments: would be the generated build spec saved from Step 1. - For `docker` images downloaded from Dockerhub, you could also use as the argument. - Run `docker images` to check list of available images on your machine. ``` $ llama stack configure 8b-instruct Configuring API: inference (meta-reference) Enter value for model (existing: Meta-Llama3.1-8B-Instruct) (required): Enter value for quantization (optional): Enter value for torch_seed (optional): Enter value for max_seq_len (existing: 4096) (required): Enter value for max_batch_size (existing: 1) (required): Configuring API: memory (meta-reference-faiss) Configuring API: safety (meta-reference) Do you want to configure llama_guard_shield? (y/n): y Entering sub-configuration for llama_guard_shield: Enter value for model (default: Llama-Guard-3-1B) (required): Enter value for excluded_categories (default: []) (required): Enter value for disable_input_check (default: False) (required): Enter value for disable_output_check (default: False) (required): Do you want to configure prompt_guard_shield? (y/n): y Entering sub-configuration for prompt_guard_shield: Enter value for model (default: Prompt-Guard-86M) (required): Configuring API: agentic_system (meta-reference) Enter value for brave_search_api_key (optional): Enter value for bing_search_api_key (optional): Enter value for wolfram_api_key (optional): Configuring API: telemetry (console) YAML configuration has been written to ~/.llama/builds/conda/8b-instruct-run.yaml ``` After this step is successful, you should be able to find a run configuration spec in `~/.llama/builds/conda/8b-instruct-run.yaml` with the following contents. You may edit this file to change the settings. As you can see, we did basic configuration above and configured: - inference to run on model `Meta-Llama3.1-8B-Instruct` (obtained from `llama model list`) - Llama Guard safety shield with model `Llama-Guard-3-1B` - Prompt Guard safety shield with model `Prompt-Guard-86M` For how these configurations are stored as yaml, checkout the file printed at the end of the configuration. Note that all configurations as well as models are stored in `~/.llama` ### Step 3. Run 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 configure` step. ``` llama stack run 8b-instruct ``` You should see the Llama Stack server start and print the APIs that it is supporting ``` $ llama stack run 8b-instruct > initializing model parallel with size 1 > initializing ddp with size 1 > initializing pipeline with size 1 Loaded in 19.28 seconds NCCL version 2.20.5+cuda12.4 Finished model load YES READY Serving POST /inference/batch_chat_completion Serving POST /inference/batch_completion Serving POST /inference/chat_completion Serving POST /inference/completion Serving POST /safety/run_shield Serving POST /agentic_system/memory_bank/attach Serving POST /agentic_system/create Serving POST /agentic_system/session/create Serving POST /agentic_system/turn/create Serving POST /agentic_system/delete Serving POST /agentic_system/session/delete Serving POST /agentic_system/memory_bank/detach Serving POST /agentic_system/session/get Serving POST /agentic_system/step/get Serving POST /agentic_system/turn/get Listening on :::5000 INFO: Started server process [453333] INFO: Waiting for application startup. INFO: Application startup complete. INFO: Uvicorn running on http://[::]:5000 (Press CTRL+C to quit) ``` > [!NOTE] > Configuration is in `~/.llama/builds/local/conda/8b-instruct-run.yaml`. Feel free to increase `max_seq_len`. > [!IMPORTANT] > The "local" distribution inference server currently only supports CUDA. It will not work on Apple Silicon machines. > [!TIP] > You might need to use the flag `--disable-ipv6` to Disable IPv6 support This server is running a Llama model locally. ### Step 4. Test with Client Once the server is setup, we can test it with a client to see the example outputs. ``` cd /path/to/llama-stack conda activate # any environment containing the llama-stack pip package will work python -m llama_stack.apis.inference.client localhost 5000 ``` This will run the chat completion client and query the distribution’s /inference/chat_completion API. Here is an example output: ``` User>hello world, write me a 2 sentence poem about the moon Assistant> Here's a 2-sentence poem about the moon: The moon glows softly in the midnight sky, A beacon of wonder, as it passes by. ``` Similarly you can test safety (if you configured llama-guard and/or prompt-guard shields) by: ``` python -m llama_stack.apis.safety.client localhost 5000 ``` Check out our client SDKs for connecting to Llama Stack server in your preferred language, you can choose from [python](https://github.com/meta-llama/llama-stack-client-python), [node](https://github.com/meta-llama/llama-stack-client-node), [swift](https://github.com/meta-llama/llama-stack-client-swift), and [kotlin](https://github.com/meta-llama/llama-stack-client-kotlin) programming languages to quickly build your applications. You can find more example scripts with client SDKs to talk with the Llama Stack server in our [llama-stack-apps](https://github.com/meta-llama/llama-stack-apps/tree/main/examples) repo.