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270 lines
11 KiB
Markdown
270 lines
11 KiB
Markdown
# Building a Llama Stack Distribution
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This guide will walk you through the steps to get started with building a Llama Stack distributiom from scratch with your choice of API providers. Please see the [Getting Started Guide](./getting_started.md) if you just want the basic steps to start a Llama Stack distribution.
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## Step 1. Build
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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:
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- `name`: the name for our distribution (e.g. `8b-instruct`)
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- `image_type`: our build image type (`conda | docker`)
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- `distribution_spec`: our distribution specs for specifying API providers
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- `description`: a short description of the configurations for the distribution
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- `providers`: specifies the underlying implementation for serving each API endpoint
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- `image_type`: `conda` | `docker` to specify whether to build the distribution in the form of Docker image or Conda environment.
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At the end of build command, we will generate `<name>-build.yaml` file storing the build configurations.
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After this step is complete, a file named `<name>-build.yaml` will be generated and saved at the output file path specified at the end of the command.
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#### Building from scratch
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- 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.
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```
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llama stack build
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```
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Running the command above will allow you to fill in the configuration to build your Llama Stack distribution, you will see the following outputs.
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```
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> Enter an unique name for identifying your Llama Stack build distribution (e.g. my-local-stack): 8b-instruct
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> Enter the image type you want your distribution to be built with (docker or conda): conda
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Llama Stack is composed of several APIs working together. Let's configure the providers (implementations) you want to use for these APIs.
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> Enter the API provider for the inference API: (default=meta-reference): meta-reference
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> Enter the API provider for the safety API: (default=meta-reference): meta-reference
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> Enter the API provider for the agents API: (default=meta-reference): meta-reference
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> Enter the API provider for the memory API: (default=meta-reference): meta-reference
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> Enter the API provider for the telemetry API: (default=meta-reference): meta-reference
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> (Optional) Enter a short description for your Llama Stack distribution:
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Build spec configuration saved at ~/.conda/envs/llamastack-my-local-llama-stack/8b-instruct-build.yaml
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```
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**Ollama (optional)**
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If you plan to use Ollama for inference, you'll need to install the server [via these instructions](https://ollama.com/download).
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#### Building from templates
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- To build from alternative API providers, we provide distribution templates for users to get started building a distribution backed by different providers.
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The following command will allow you to see the available templates and their corresponding providers.
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```
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llama stack build --list-templates
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```
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You may then pick a template to build your distribution with providers fitted to your liking.
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```
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llama stack build --template tgi
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```
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```
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$ llama stack build --template tgi
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...
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...
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Build spec configuration saved at ~/.conda/envs/llamastack-tgi/tgi-build.yaml
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You may now run `llama stack configure tgi` or `llama stack configure ~/.conda/envs/llamastack-tgi/tgi-build.yaml`
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```
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#### Building from config file
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- 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.
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- The config file will be of contents like the ones in `llama_stack/distributions/templates/`.
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```
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$ cat llama_stack/templates/ollama/build.yaml
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name: ollama
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distribution_spec:
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description: Like local, but use ollama for running LLM inference
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providers:
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inference: remote::ollama
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memory: meta-reference
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safety: meta-reference
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agents: meta-reference
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telemetry: meta-reference
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image_type: conda
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```
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```
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llama stack build --config llama_stack/templates/ollama/build.yaml
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```
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#### How to build distribution with Docker image
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> [!TIP]
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> Podman is supported as an alternative to Docker. Set `DOCKER_BINARY` to `podman` in your environment to use Podman.
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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.
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```
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llama stack build --template local --image-type docker
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```
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Alternatively, you may use a config file and set `image_type` to `docker` in our `<name>-build.yaml` file, and run `llama stack build <name>-build.yaml`. The `<name>-build.yaml` will be of contents like:
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```
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name: local-docker-example
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distribution_spec:
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description: Use code from `llama_stack` itself to serve all llama stack APIs
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docker_image: null
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providers:
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inference: meta-reference
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memory: meta-reference-faiss
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safety: meta-reference
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agentic_system: meta-reference
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telemetry: console
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image_type: docker
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```
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The following command allows you to build a Docker image with the name `<name>`
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```
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llama stack build --config <name>-build.yaml
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Dockerfile created successfully in /tmp/tmp.I0ifS2c46A/DockerfileFROM python:3.10-slim
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WORKDIR /app
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...
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...
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You can run it with: podman run -p 8000:8000 llamastack-docker-local
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Build spec configuration saved at ~/.llama/distributions/docker/docker-local-build.yaml
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```
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## Step 2. Configure
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After our distribution is built (either in form of docker or conda environment), we will run the following command to
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```
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llama stack configure [ <docker-image-name> | <path/to/name.build.yaml>]
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```
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- For `conda` environments: <path/to/name.build.yaml> would be the generated build spec saved from Step 1.
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- For `docker` images downloaded from Dockerhub, you could also use <docker-image-name> as the argument.
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- Run `docker images` to check list of available images on your machine.
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```
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$ llama stack configure tgi
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Configuring API: inference (meta-reference)
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Enter value for model (existing: Meta-Llama3.1-8B-Instruct) (required):
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Enter value for quantization (optional):
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Enter value for torch_seed (optional):
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Enter value for max_seq_len (existing: 4096) (required):
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Enter value for max_batch_size (existing: 1) (required):
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Configuring API: memory (meta-reference-faiss)
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Configuring API: safety (meta-reference)
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Do you want to configure llama_guard_shield? (y/n): y
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Entering sub-configuration for llama_guard_shield:
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Enter value for model (default: Llama-Guard-3-1B) (required):
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Enter value for excluded_categories (default: []) (required):
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Enter value for disable_input_check (default: False) (required):
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Enter value for disable_output_check (default: False) (required):
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Do you want to configure prompt_guard_shield? (y/n): y
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Entering sub-configuration for prompt_guard_shield:
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Enter value for model (default: Prompt-Guard-86M) (required):
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Configuring API: agentic_system (meta-reference)
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Enter value for brave_search_api_key (optional):
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Enter value for bing_search_api_key (optional):
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Enter value for wolfram_api_key (optional):
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Configuring API: telemetry (console)
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YAML configuration has been written to ~/.llama/builds/conda/tgi-run.yaml
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```
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After this step is successful, you should be able to find a run configuration spec in `~/.llama/builds/conda/tgi-run.yaml` with the following contents. You may edit this file to change the settings.
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As you can see, we did basic configuration above and configured:
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- inference to run on model `Meta-Llama3.1-8B-Instruct` (obtained from `llama model list`)
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- Llama Guard safety shield with model `Llama-Guard-3-1B`
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- Prompt Guard safety shield with model `Prompt-Guard-86M`
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For how these configurations are stored as yaml, checkout the file printed at the end of the configuration.
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Note that all configurations as well as models are stored in `~/.llama`
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## Step 3. Run
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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.
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```
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llama stack run 8b-instruct
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```
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You should see the Llama Stack server start and print the APIs that it is supporting
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```
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$ llama stack run 8b-instruct
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> initializing model parallel with size 1
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> initializing ddp with size 1
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> initializing pipeline with size 1
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Loaded in 19.28 seconds
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NCCL version 2.20.5+cuda12.4
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Finished model load YES READY
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Serving POST /inference/batch_chat_completion
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Serving POST /inference/batch_completion
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Serving POST /inference/chat_completion
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Serving POST /inference/completion
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Serving POST /safety/run_shield
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Serving POST /agentic_system/memory_bank/attach
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Serving POST /agentic_system/create
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Serving POST /agentic_system/session/create
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Serving POST /agentic_system/turn/create
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Serving POST /agentic_system/delete
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Serving POST /agentic_system/session/delete
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Serving POST /agentic_system/memory_bank/detach
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Serving POST /agentic_system/session/get
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Serving POST /agentic_system/step/get
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Serving POST /agentic_system/turn/get
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Listening on :::5000
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INFO: Started server process [453333]
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INFO: Waiting for application startup.
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INFO: Application startup complete.
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INFO: Uvicorn running on http://[::]:5000 (Press CTRL+C to quit)
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```
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> [!NOTE]
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> Configuration is in `~/.llama/builds/local/conda/tgi-run.yaml`. Feel free to increase `max_seq_len`.
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> [!IMPORTANT]
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> The "local" distribution inference server currently only supports CUDA. It will not work on Apple Silicon machines.
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> [!TIP]
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> You might need to use the flag `--disable-ipv6` to Disable IPv6 support
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This server is running a Llama model locally.
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## Step 4. Test with Client
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Once the server is setup, we can test it with a client to see the example outputs.
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```
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cd /path/to/llama-stack
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conda activate <env> # any environment containing the llama-stack pip package will work
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python -m llama_stack.apis.inference.client localhost 5000
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```
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This will run the chat completion client and query the distribution’s /inference/chat_completion API.
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Here is an example output:
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```
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User>hello world, write me a 2 sentence poem about the moon
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Assistant> Here's a 2-sentence poem about the moon:
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The moon glows softly in the midnight sky,
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A beacon of wonder, as it passes by.
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```
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Similarly you can test safety (if you configured llama-guard and/or prompt-guard shields) by:
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```
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python -m llama_stack.apis.safety.client localhost 5000
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```
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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.
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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.
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