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docs/source/distribution_dev/building_distro.md
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# Developer Guide: Assemble a Llama Stack Distribution # Developer Guide: Assemble a Llama Stack Distribution
> NOTE: This doc may be out-of-date.
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. 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](https://llama-stack.readthedocs.io/en/latest/getting_started/index.html) if you just want the basic steps to start a Llama Stack distribution.
## Step 1. Build ## 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`) ```
llama stack build -h
usage: llama stack build [-h] [--config CONFIG] [--template TEMPLATE] [--list-templates | --no-list-templates] [--image-type {conda,docker}]
Build a Llama stack container
options:
-h, --help show this help message and exit
--config CONFIG Path to a config file to use for the build. You can find example configs in llama_stack/distribution/example_configs. If this argument is not provided, you will be prompted to enter information interactively
--template TEMPLATE Name of the example template config to use for build. You may use `llama stack build --list-templates` to check out the available templates
--list-templates, --no-list-templates
Show the available templates for building a Llama Stack distribution
--image-type {conda,docker}
Image Type to use for the build. This can be either conda or docker. If not specified, will use the image type from the template 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. `my-stack`)
- `image_type`: our build image type (`conda | docker`) - `image_type`: our build image type (`conda | docker`)
- `distribution_spec`: our distribution specs for specifying API providers - `distribution_spec`: our distribution specs for specifying API providers
- `description`: a short description of the configurations for the distribution - `description`: a short description of the configurations for the distribution
- `providers`: specifies the underlying implementation for serving each API endpoint - `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. - `image_type`: `conda` | `docker` to specify whether to build the distribution in the form of Docker image or Conda environment.
After this step is complete, a file named `<name>-build.yaml` and template file `<name>-run.yaml` will be generated and saved at the output file path specified at the end of the command.
At the end of build command, we will generate `<name>-build.yaml` file storing the build configurations.
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. You have 3 options for building your distribution:
1.1 Building from scratch
1.2. Building from a template
1.3. Building from a pre-existing build config file
#### Building from scratch
### 1.1 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. - 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 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 (docker 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 <TAB> to see options for the providers.
> Enter provider for API inference: meta-reference
> Enter provider for API safety: meta-reference
> Enter provider for API agents: meta-reference
> Enter provider for API memory: meta-reference
> Enter provider for API datasetio: meta-reference
> Enter provider for API scoring: meta-reference
> Enter provider for API eval: meta-reference
> Enter provider for API telemetry: 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`
``` ```
Running the command above will allow you to fill in the configuration to build your Llama Stack distribution, you will see the following outputs. ### 1.2 Building from a template
```
> 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. - 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. The following command will allow you to see the available templates and their corresponding providers.
@ -59,18 +78,21 @@ llama stack build --list-templates
+------------------------------+--------------------------------------------+----------------------------------------------------------------------------------+ +------------------------------+--------------------------------------------+----------------------------------------------------------------------------------+
| Template Name | Providers | Description | | Template Name | Providers | Description |
+------------------------------+--------------------------------------------+----------------------------------------------------------------------------------+ +------------------------------+--------------------------------------------+----------------------------------------------------------------------------------+
| bedrock | { | Use Amazon Bedrock APIs. | | hf-serverless | { | Like local, but use Hugging Face Inference API (serverless) for running LLM |
| | "inference": "remote::bedrock", | | | | "inference": "remote::hf::serverless", | inference. |
| | "memory": "meta-reference", | | | | "memory": "meta-reference", | See https://hf.co/docs/api-inference. |
| | "safety": "meta-reference", | | | | "safety": "meta-reference", | |
| | "agents": "meta-reference", | | | | "agents": "meta-reference", | |
| | "telemetry": "meta-reference" | | | | "telemetry": "meta-reference" | |
| | } | | | | } | |
+------------------------------+--------------------------------------------+----------------------------------------------------------------------------------+ +------------------------------+--------------------------------------------+----------------------------------------------------------------------------------+
| databricks | { | Use Databricks for running LLM inference | | together | { | Use Together.ai for running LLM inference |
| | "inference": "remote::databricks", | | | | "inference": "remote::together", | |
| | "memory": "meta-reference", | | | | "memory": [ | |
| | "safety": "meta-reference", | | | | "meta-reference", | |
| | "remote::weaviate" | |
| | ], | |
| | "safety": "remote::together", | |
| | "agents": "meta-reference", | | | | "agents": "meta-reference", | |
| | "telemetry": "meta-reference" | | | | "telemetry": "meta-reference" | |
| | } | | | | } | |
@ -88,17 +110,37 @@ llama stack build --list-templates
| | "telemetry": "meta-reference" | | | | "telemetry": "meta-reference" | |
| | } | | | | } | |
+------------------------------+--------------------------------------------+----------------------------------------------------------------------------------+ +------------------------------+--------------------------------------------+----------------------------------------------------------------------------------+
| hf-endpoint | { | Like local, but use Hugging Face Inference Endpoints for running LLM inference. | | databricks | { | Use Databricks for running LLM inference |
| | "inference": "remote::hf::endpoint", | See https://hf.co/docs/api-endpoints. | | | "inference": "remote::databricks", | |
| | "memory": "meta-reference", | | | | "memory": "meta-reference", | |
| | "safety": "meta-reference", | | | | "safety": "meta-reference", | |
| | "agents": "meta-reference", | | | | "agents": "meta-reference", | |
| | "telemetry": "meta-reference" | | | | "telemetry": "meta-reference" | |
| | } | | | | } | |
+------------------------------+--------------------------------------------+----------------------------------------------------------------------------------+ +------------------------------+--------------------------------------------+----------------------------------------------------------------------------------+
| hf-serverless | { | Like local, but use Hugging Face Inference API (serverless) for running LLM | | vllm | { | Like local, but use vLLM for running LLM inference |
| | "inference": "remote::hf::serverless", | inference. | | | "inference": "vllm", | |
| | "memory": "meta-reference", | See https://hf.co/docs/api-inference. | | | "memory": "meta-reference", | |
| | "safety": "meta-reference", | |
| | "agents": "meta-reference", | |
| | "telemetry": "meta-reference" | |
| | } | |
+------------------------------+--------------------------------------------+----------------------------------------------------------------------------------+
| tgi | { | Use TGI for running LLM inference |
| | "inference": "remote::tgi", | |
| | "memory": [ | |
| | "meta-reference", | |
| | "remote::chromadb", | |
| | "remote::pgvector" | |
| | ], | |
| | "safety": "meta-reference", | |
| | "agents": "meta-reference", | |
| | "telemetry": "meta-reference" | |
| | } | |
+------------------------------+--------------------------------------------+----------------------------------------------------------------------------------+
| bedrock | { | Use Amazon Bedrock APIs. |
| | "inference": "remote::bedrock", | |
| | "memory": "meta-reference", | |
| | "safety": "meta-reference", | | | | "safety": "meta-reference", | |
| | "agents": "meta-reference", | | | | "agents": "meta-reference", | |
| | "telemetry": "meta-reference" | | | | "telemetry": "meta-reference" | |
@ -140,31 +182,8 @@ llama stack build --list-templates
| | "telemetry": "meta-reference" | | | | "telemetry": "meta-reference" | |
| | } | | | | } | |
+------------------------------+--------------------------------------------+----------------------------------------------------------------------------------+ +------------------------------+--------------------------------------------+----------------------------------------------------------------------------------+
| tgi | { | Use TGI for running LLM inference | | hf-endpoint | { | Like local, but use Hugging Face Inference Endpoints for running LLM inference. |
| | "inference": "remote::tgi", | | | | "inference": "remote::hf::endpoint", | See https://hf.co/docs/api-endpoints. |
| | "memory": [ | |
| | "meta-reference", | |
| | "remote::chromadb", | |
| | "remote::pgvector" | |
| | ], | |
| | "safety": "meta-reference", | |
| | "agents": "meta-reference", | |
| | "telemetry": "meta-reference" | |
| | } | |
+------------------------------+--------------------------------------------+----------------------------------------------------------------------------------+
| together | { | Use Together.ai for running LLM inference |
| | "inference": "remote::together", | |
| | "memory": [ | |
| | "meta-reference", | |
| | "remote::weaviate" | |
| | ], | |
| | "safety": "remote::together", | |
| | "agents": "meta-reference", | |
| | "telemetry": "meta-reference" | |
| | } | |
+------------------------------+--------------------------------------------+----------------------------------------------------------------------------------+
| vllm | { | Like local, but use vLLM for running LLM inference |
| | "inference": "vllm", | |
| | "memory": "meta-reference", | | | | "memory": "meta-reference", | |
| | "safety": "meta-reference", | | | | "safety": "meta-reference", | |
| | "agents": "meta-reference", | | | | "agents": "meta-reference", | |
@ -175,6 +194,7 @@ llama stack build --list-templates
You may then pick a template to build your distribution with providers fitted to your liking. 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
``` ```
@ -182,14 +202,13 @@ llama stack build --template tgi
``` ```
$ 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`
You can now edit ~/meta-llama/llama-stack/tmp/configs/tgi-run.yaml and run `llama stack run ~/meta-llama/llama-stack/tmp/configs/tgi-run.yaml`
``` ```
#### Building from config file ### 1.3 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. - 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/`. - The config file will be of contents like the ones in `llama_stack/templates/*build.yaml`.
``` ```
$ cat llama_stack/templates/ollama/build.yaml $ cat llama_stack/templates/ollama/build.yaml
@ -210,104 +229,106 @@ image_type: conda
llama stack build --config llama_stack/templates/ollama/build.yaml llama stack build --config llama_stack/templates/ollama/build.yaml
``` ```
#### How to build distribution with Docker image ### How to build distribution with Docker image
> [!TIP] > [!TIP]
> Podman is supported as an alternative to Docker. Set `DOCKER_BINARY` to `podman` in your environment to use Podman. > 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. 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 llama stack build --template ollama --image-type docker
``` ```
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:
``` ```
name: local-docker-example $ llama stack build --template ollama --image-type docker
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 `<name>`
```
llama stack build --config <name>-build.yaml
Dockerfile created successfully in /tmp/tmp.I0ifS2c46A/DockerfileFROM python:3.10-slim
WORKDIR /app
... ...
Dockerfile created successfully in /tmp/tmp.viA3a3Rdsg/DockerfileFROM python:3.10-slim
... ...
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 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 a run configuration spec in `~/.llama/builds/conda/tgi-run.yaml` with the following contents. You may edit this file to change the settings. After this step is successful, you should be able to find the built docker image and test it with `llama stack run <path/to/run.yaml>`.
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 2. Run ## Step 2. 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 build` step. 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 8b-instruct llama stack run ~/.llama/distributions/llamastack-my-local-stack/my-local-stack-run.yaml
``` ```
You should see the Llama Stack server start and print the APIs that it is supporting
``` ```
$ llama stack run 8b-instruct $ llama stack run ~/.llama/distributions/llamastack-my-local-stack/my-local-stack-run.yaml
> initializing model parallel with size 1 Loaded model...
> initializing ddp with size 1 Serving API datasets
> initializing pipeline with size 1 GET /datasets/get
Loaded in 19.28 seconds GET /datasets/list
NCCL version 2.20.5+cuda12.4 POST /datasets/register
Finished model load YES READY Serving API inspect
Serving POST /inference/batch_chat_completion GET /health
Serving POST /inference/batch_completion GET /providers/list
Serving POST /inference/chat_completion GET /routes/list
Serving POST /inference/completion Serving API inference
Serving POST /safety/run_shield POST /inference/chat_completion
Serving POST /agentic_system/memory_bank/attach POST /inference/completion
Serving POST /agentic_system/create POST /inference/embeddings
Serving POST /agentic_system/session/create Serving API scoring_functions
Serving POST /agentic_system/turn/create GET /scoring_functions/get
Serving POST /agentic_system/delete GET /scoring_functions/list
Serving POST /agentic_system/session/delete POST /scoring_functions/register
Serving POST /agentic_system/memory_bank/detach Serving API scoring
Serving POST /agentic_system/session/get POST /scoring/score
Serving POST /agentic_system/step/get POST /scoring/score_batch
Serving POST /agentic_system/turn/get Serving API memory_banks
Listening on :::5000 GET /memory_banks/get
INFO: Started server process [453333] GET /memory_banks/list
POST /memory_banks/register
Serving API memory
POST /memory/insert
POST /memory/query
Serving API safety
POST /safety/run_shield
Serving API eval
POST /eval/evaluate
POST /eval/evaluate_batch
POST /eval/job/cancel
GET /eval/job/result
GET /eval/job/status
Serving API shields
GET /shields/get
GET /shields/list
POST /shields/register
Serving API datasetio
GET /datasetio/get_rows_paginated
Serving API telemetry
GET /telemetry/get_trace
POST /telemetry/log_event
Serving API models
GET /models/get
GET /models/list
POST /models/register
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']:5000
INFO: Started server process [2935911]
INFO: Waiting for application startup. INFO: Waiting for application startup.
INFO: Application startup complete. INFO: Application startup complete.
INFO: Uvicorn running on http://[::]:5000 (Press CTRL+C to quit) INFO: Uvicorn running on http://['::', '0.0.0.0']:5000 (Press CTRL+C to quit)
INFO: 2401:db00:35c:2d2b:face:0:c9:0:54678 - "GET /models/list HTTP/1.1" 200 OK
``` ```
> [!NOTE]
> Configuration is in `~/.llama/builds/local/conda/tgi-run.yaml`. Feel free to increase `max_seq_len`.
> [!IMPORTANT] > [!IMPORTANT]
> The "local" distribution inference server currently only supports CUDA. It will not work on Apple Silicon machines. > The "local" distribution inference server currently only supports CUDA. It will not work on Apple Silicon machines.
> [!TIP] > [!TIP]
> You might need to use the flag `--disable-ipv6` to Disable IPv6 support > You might need to use the flag `--disable-ipv6` to Disable IPv6 support
This server is running a Llama model locally.