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111 lines
3.7 KiB
Markdown
111 lines
3.7 KiB
Markdown
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# Getting Started with Llama Stack
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This guide will walk you through the steps to set up an end-to-end workflow with Llama Stack. It focuses on building a Llama Stack distribution and starting up a Llama Stack server. See our [documentation](../README.md) for more on Llama Stack's capabilities, or visit [llama-stack-apps](https://github.com/meta-llama/llama-stack-apps/tree/main) for example apps.
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## Installation
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The `llama` CLI tool helps you manage the Llama toolchain & agentic systems. After installing the `llama-stack` package, the `llama` command should be available in your path.
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You can install this repository in two ways:
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1. **Install as a package**:
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Install directly from [PyPI](https://pypi.org/project/llama-stack/) with:
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```bash
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pip install llama-stack
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```
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2. **Install from source**:
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Follow these steps to install from the source code:
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```bash
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mkdir -p ~/local
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cd ~/local
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git clone git@github.com:meta-llama/llama-stack.git
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conda create -n stack python=3.10
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conda activate stack
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cd llama-stack
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$CONDA_PREFIX/bin/pip install -e .
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```
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Refer to the [CLI Reference](./cli_reference.md) for details on Llama CLI commands.
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## Starting Up Llama Stack Server
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There are two ways to start the Llama Stack server:
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1. **Using Docker**:
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We provide a pre-built Docker image of Llama Stack, available in the [distributions](../distributions/) folder.
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> **Note:** For GPU inference, set environment variables to specify the local directory with your model checkpoints and enable GPU inference.
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```bash
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export LLAMA_CHECKPOINT_DIR=~/.llama
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```
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Download Llama models with:
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```
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llama download --model-id Llama3.1-8B-Instruct
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```
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Start a Docker container with:
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```bash
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cd llama-stack/distributions/meta-reference-gpu
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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
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```
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**Tip:** For remote providers, use `docker compose up` with scripts in the [distributions folder](../distributions/).
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2. **Build->Configure->Run via Conda**:
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For development, build a LlamaStack distribution from scratch.
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**`llama stack build`**
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Enter build information interactively:
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```bash
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llama stack build
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```
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**`llama stack configure`**
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Run `llama stack configure <name>` using the name from the build step.
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```bash
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llama stack configure my-local-stack
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```
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**`llama stack run`**
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Start the server with:
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```bash
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llama stack run my-local-stack
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```
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## Testing with Client
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After setup, test the server with a client:
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```bash
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cd /path/to/llama-stack
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conda activate <env>
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python -m llama_stack.apis.inference.client localhost 5000
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```
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You can also send a POST request:
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```bash
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curl http://localhost:5000/inference/chat_completion \
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-H "Content-Type: application/json" \
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-d '{
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"model": "Llama3.1-8B-Instruct",
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"messages": [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": "Write me a 2-sentence poem about the moon"}
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],
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"sampling_params": {"temperature": 0.7, "seed": 42, "max_tokens": 512}
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}'
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```
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For testing safety, run:
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```bash
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python -m llama_stack.apis.safety.client localhost 5000
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```
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Check our client SDKs for various languages: [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).
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## Advanced Guides
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For more on custom Llama Stack distributions, refer to our [Building a Llama Stack Distribution](./building_distro.md) guide.
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