7.2 KiB
Getting Started
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distributions/index
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developer_cookbook
At the end of the guide, you will have learnt how to:
- get a Llama Stack server up and running
- get a agent (with tool-calling, vector stores) which works with the above server
To see more example apps built using Llama Stack, see llama-stack-apps.
Step 1. Starting Up Llama Stack Server
Decide Your Build Type
There are two ways to start a Llama Stack:
- Docker: we provide a number of pre-built Docker containers allowing you to get started instantly. If you are focused on application development, we recommend this option.
- Conda: the
llamaCLI provides a simple set of commands to build, configure and run a Llama Stack server containing the exact combination of providers you wish. We have provided various templates to make getting started easier.
Both of these provide options to run model inference using our reference implementations, Ollama, TGI, vLLM or even remote providers like Fireworks, Together, Bedrock, etc.
Decide Your Inference Provider
Running inference of the underlying Llama model is one of the most critical requirements. Depending on what hardware you have available, you have various options. Note that each option have different necessary prerequisites.
-
Do you have access to a machine with powerful GPUs? If so, we suggest:
-
Are you running on a "regular" desktop machine? If so, we suggest:
-
Do you have access to a remote inference provider like Fireworks, Togther, etc.? If so, we suggest:
Quick Start Commands
The following quick starts commands. Please visit each distribution page on detailed setup.
1.0 Prerequisite
::::{tab-set}
:::{tab-item} meta-reference-gpu
Downloading Models
Please make sure you have llama model checkpoints downloaded in ~/.llama before proceeding. See installation guide here to download the models.
$ ls ~/.llama/checkpoints
Llama3.1-8B Llama3.2-11B-Vision-Instruct Llama3.2-1B-Instruct Llama3.2-90B-Vision-Instruct Llama-Guard-3-8B
Llama3.1-8B-Instruct Llama3.2-1B Llama3.2-3B-Instruct Llama-Guard-3-1B Prompt-Guard-86M
:::
:::{tab-item} tgi This assumes you have access to GPU to start a TGI server with access to your GPU. :::
::::
1.1. Start the distribution
Via Docker ::::{tab-set}
:::{tab-item} meta-reference-gpu
$ cd distributions/meta-reference-gpu && docker compose up
Note
This assumes you have access to GPU to start a local server with access to your GPU.
Note
~/.llamashould be the path containing downloaded weights of Llama models.
This will download and start running a pre-built docker container. Alternatively, you may use the following commands:
docker run -it -p 5000:5000 -v ~/.llama:/root/.llama -v ./run.yaml:/root/my-run.yaml --gpus=all distribution-meta-reference-gpu --yaml_config /root/my-run.yaml
:::
:::{tab-item} tgi
$ cd distributions/tgi/gpu && docker compose up
The script will first start up TGI server, then start up Llama Stack distribution server hooking up to the remote TGI provider for inference. You should be able to see the following outputs --
[text-generation-inference] | 2024-10-15T18:56:33.810397Z INFO text_generation_router::server: router/src/server.rs:1813: Using config Some(Llama)
[text-generation-inference] | 2024-10-15T18:56:33.810448Z WARN text_generation_router::server: router/src/server.rs:1960: Invalid hostname, defaulting to 0.0.0.0
[text-generation-inference] | 2024-10-15T18:56:33.864143Z INFO text_generation_router::server: router/src/server.rs:2353: Connected
INFO: Started server process [1]
INFO: Waiting for application startup.
INFO: Application startup complete.
INFO: Uvicorn running on http://[::]:5000 (Press CTRL+C to quit)
To kill the server
docker compose down
:::
::::
Via Conda ::::{tab-set}
:::{tab-item} meta-reference-gpu
-
Install the
llamaCLI. See CLI Reference -
Build the
meta-reference-gpudistribution
$ llama stack build --template meta-reference-gpu --image-type conda
- Start running distribution
$ cd distributions/meta-reference-gpu
$ llama stack run ./run.yaml
:::
:::{tab-item} tgi
llama stack build --template tgi --image-type conda
# -- start a TGI server endpoint
llama stack run ./gpu/run.yaml
:::
::::
1.2 (Optional) Serving Model
Step 2. Build Your Llama Stack App
chat_completion sanity test
Once the server is setup, we can test it with a client to see the example outputs by . This will run the chat completion client and query the distribution’s /inference/chat_completion API. Send a POST request to the server:
$ curl http://localhost:5000/inference/chat_completion \
-H "Content-Type: application/json" \
-d '{
"model": "Llama3.1-8B-Instruct",
"messages": [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Write me a 2 sentence poem about the moon"}
],
"sampling_params": {"temperature": 0.7, "seed": 42, "max_tokens": 512}
}'
Output:
{'completion_message': {'role': 'assistant',
'content': 'The moon glows softly in the midnight sky, \nA beacon of wonder, as it catches the eye.',
'stop_reason': 'out_of_tokens',
'tool_calls': []},
'logprobs': null}
Run Agent App
To run an agent app, check out examples demo scripts with client SDKs to talk with the Llama Stack server in our llama-stack-apps repo. To run a simple agent app:
$ git clone git@github.com:meta-llama/llama-stack-apps.git
$ cd llama-stack-apps
$ pip install -r requirements.txt
$ python -m examples.agents.client <host> <port>
You will see outputs of the form --
User> I am planning a trip to Switzerland, what are the top 3 places to visit?
inference> Switzerland is a beautiful country with a rich history, stunning landscapes, and vibrant culture. Here are three must-visit places to add to your itinerary:
...
User> What is so special about #1?
inference> Jungfraujoch, also known as the "Top of Europe," is a unique and special place for several reasons:
...
User> What other countries should I consider to club?
inference> Considering your interest in Switzerland, here are some neighboring countries that you may want to consider visiting: