forked from phoenix-oss/llama-stack-mirror
582 lines
21 KiB
Markdown
582 lines
21 KiB
Markdown
# Getting Started
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```{toctree}
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:maxdepth: 2
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:hidden:
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distributions/self_hosted_distro/index
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distributions/remote_hosted_distro/index
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distributions/ondevice_distro/index
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```
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At the end of the guide, you will have learned how to:
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- get a Llama Stack server up and running
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- set up an agent (with tool-calling and vector stores) that works with the above server
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To see more example apps built using Llama Stack, see [llama-stack-apps](https://github.com/meta-llama/llama-stack-apps/tree/main).
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## Step 1. Starting Up Llama Stack Server
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### Decide Your Build Type
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There are two ways to start a Llama Stack:
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- **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.
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- **Conda**: the `llama` CLI 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.
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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.
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### Decide Your Inference Provider
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Running inference on 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.
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- **Do you have access to a machine with powerful GPUs?**
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If so, we suggest:
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- [distribution-meta-reference-gpu](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/self_hosted_distro/meta-reference-gpu.html)
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- [distribution-tgi](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/tgi.html)
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- **Are you running on a "regular" desktop machine?**
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If so, we suggest:
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- [distribution-ollama](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/self_hosted_distro/ollama.html)
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- **Do you have an API key for a remote inference provider like Fireworks, Together, etc.?** If so, we suggest:
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- [distribution-together](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/remote_hosted_distro/together.html)
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- [distribution-fireworks](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/remote_hosted_distro/fireworks.html)
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- **Do you want to run Llama Stack inference on your iOS / Android device** If so, we suggest:
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- [iOS](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/ondevice_distro/ios_sdk.html)
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- [Android](https://github.com/meta-llama/llama-stack-client-kotlin) (coming soon)
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Please see our pages in detail for the types of distributions we offer:
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1. [Self-Hosted Distribution](./distributions/self_hosted_distro/index.md): If you want to run Llama Stack inference on your local machine.
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2. [Remote-Hosted Distribution](./distributions/remote_hosted_distro/index.md): If you want to connect to a remote hosted inference provider.
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3. [On-device Distribution](./distributions/ondevice_distro/index.md): If you want to run Llama Stack inference on your iOS / Android device.
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### Quick Start Commands
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Once you have decided on the inference provider and distribution to use, use the following quick start commands to get started.
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##### 1.0 Prerequisite
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```
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$ git clone git@github.com:meta-llama/llama-stack.git
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```
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::::{tab-set}
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:::{tab-item} meta-reference-gpu
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##### System Requirements
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Access to Single-Node GPU to start a local server.
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##### Downloading Models
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Please make sure you have Llama model checkpoints downloaded in `~/.llama` before proceeding. See [installation guide](https://llama-stack.readthedocs.io/en/latest/cli_reference/download_models.html) here to download the models.
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```
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$ ls ~/.llama/checkpoints
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Llama3.1-8B Llama3.2-11B-Vision-Instruct Llama3.2-1B-Instruct Llama3.2-90B-Vision-Instruct Llama-Guard-3-8B
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Llama3.1-8B-Instruct Llama3.2-1B Llama3.2-3B-Instruct Llama-Guard-3-1B Prompt-Guard-86M
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```
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:::
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:::{tab-item} vLLM
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##### System Requirements
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Access to Single-Node GPU to start a vLLM server.
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:::
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:::{tab-item} tgi
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##### System Requirements
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Access to Single-Node GPU to start a TGI server.
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:::
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:::{tab-item} ollama
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##### System Requirements
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Access to Single-Node CPU/GPU able to run ollama.
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:::
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:::{tab-item} together
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##### System Requirements
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Access to Single-Node CPU with Together hosted endpoint via API_KEY from [together.ai](https://api.together.xyz/signin).
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:::
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:::{tab-item} fireworks
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##### System Requirements
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Access to Single-Node CPU with Fireworks hosted endpoint via API_KEY from [fireworks.ai](https://fireworks.ai/).
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:::
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::::
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##### 1.1. Start the distribution
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**(Option 1) Via Docker**
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::::{tab-set}
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:::{tab-item} meta-reference-gpu
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```
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$ cd llama-stack/distributions/meta-reference-gpu && docker compose up
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```
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This will download and start running a pre-built Docker container. Alternatively, you may use the following commands:
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```
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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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:::
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:::{tab-item} vLLM
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```
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$ cd llama-stack/distributions/remote-vllm && docker compose up
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```
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The script will first start up vLLM server on port 8000, then start up Llama Stack distribution server hooking up to it for inference. You should see the following outputs --
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```
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<TO BE FILLED>
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```
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To kill the server
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```
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docker compose down
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```
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:::
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:::{tab-item} tgi
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```
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$ cd llama-stack/distributions/tgi && docker compose up
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```
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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 see the following outputs --
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```
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[text-generation-inference] | 2024-10-15T18:56:33.810397Z INFO text_generation_router::server: router/src/server.rs:1813: Using config Some(Llama)
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[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
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[text-generation-inference] | 2024-10-15T18:56:33.864143Z INFO text_generation_router::server: router/src/server.rs:2353: Connected
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INFO: Started server process [1]
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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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To kill the server
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```
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docker compose down
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```
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:::
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:::{tab-item} ollama
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```
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$ cd llama-stack/distributions/ollama && docker compose up
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# OR
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$ cd llama-stack/distributions/ollama-gpu && docker compose up
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```
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You will see outputs similar to following ---
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```
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[ollama] | [GIN] 2024/10/18 - 21:19:41 | 200 | 226.841µs | ::1 | GET "/api/ps"
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[ollama] | [GIN] 2024/10/18 - 21:19:42 | 200 | 60.908µs | ::1 | GET "/api/ps"
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INFO: Started server process [1]
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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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[llamastack] | Resolved 12 providers
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[llamastack] | inner-inference => ollama0
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[llamastack] | models => __routing_table__
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[llamastack] | inference => __autorouted__
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```
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To kill the server
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```
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docker compose down
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```
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:::
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:::{tab-item} fireworks
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```
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$ cd llama-stack/distributions/fireworks && docker compose up
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```
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Make sure your `run.yaml` file has the inference provider pointing to the correct Fireworks URL server endpoint. E.g.
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```
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inference:
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- provider_id: fireworks
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provider_type: remote::fireworks
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config:
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url: https://api.fireworks.ai/inference
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api_key: <optional api key>
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```
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:::
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:::{tab-item} together
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```
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$ cd distributions/together && docker compose up
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```
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Make sure your `run.yaml` file has the inference provider pointing to the correct Together URL server endpoint. E.g.
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```
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inference:
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- provider_id: together
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provider_type: remote::together
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config:
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url: https://api.together.xyz/v1
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api_key: <optional api key>
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```
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:::
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::::
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**(Option 2) Via Conda**
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::::{tab-set}
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:::{tab-item} meta-reference-gpu
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1. Install the `llama` CLI. See [CLI Reference](https://llama-stack.readthedocs.io/en/latest/cli_reference/index.html)
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2. Build the `meta-reference-gpu` distribution
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```
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$ llama stack build --template meta-reference-gpu --image-type conda
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```
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3. Start running distribution
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```
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$ llama stack run ~/.llama/distributions/llamastack-meta-reference-gpu/meta-reference-gpu-run.yaml
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```
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Note: If you wish to use pgvector or chromadb as memory provider. You may need to update generated `run.yaml` file to point to the desired memory provider. See [Memory Providers](https://llama-stack.readthedocs.io/en/latest/api_providers/memory_api.html) for more details. Or comment out the pgvector or chromadb memory provider in `run.yaml` file to use the default inline memory provider, keeping only the following section:
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```
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memory:
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- provider_id: faiss-0
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provider_type: faiss
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config:
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kvstore:
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namespace: null
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type: sqlite
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db_path: ~/.llama/runtime/faiss_store.db
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```
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:::
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:::{tab-item} tgi
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1. Install the `llama` CLI. See [CLI Reference](https://llama-stack.readthedocs.io/en/latest/cli_reference/index.html)
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2. Build the `tgi` distribution
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```bash
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llama stack build --template tgi --image-type conda
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```
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3. Start a TGI server endpoint
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4. Make sure in your `run.yaml` file, your `conda_env` is pointing to the conda environment and inference provider is pointing to the correct TGI server endpoint. E.g.
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```
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conda_env: llamastack-tgi
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...
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inference:
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- provider_id: tgi0
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provider_type: remote::tgi
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config:
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url: http://127.0.0.1:5009
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```
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5. Start Llama Stack server
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```bash
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$ llama stack run ~/.llama/distributions/llamastack-tgi/tgi-run.yaml
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```
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Note: If you wish to use pgvector or chromadb as memory provider. You may need to update generated `run.yaml` file to point to the desired memory provider. See [Memory Providers](https://llama-stack.readthedocs.io/en/latest/api_providers/memory_api.html) for more details. Or comment out the pgvector or chromadb memory provider in `run.yaml` file to use the default inline memory provider, keeping only the following section:
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```
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memory:
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- provider_id: faiss-0
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provider_type: faiss
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config:
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kvstore:
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namespace: null
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type: sqlite
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db_path: ~/.llama/runtime/faiss_store.db
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```
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:::
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:::{tab-item} ollama
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If you wish to separately spin up a Ollama server, and connect with Llama Stack, you may use the following commands.
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#### Start Ollama server.
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- Please check the [Ollama Documentations](https://github.com/ollama/ollama) for more details.
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**Via Docker**
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```
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docker run -d -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama
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```
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**Via CLI**
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```
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ollama run <model_id>
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```
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#### Start Llama Stack server pointing to Ollama server
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Make sure your `run.yaml` file has the inference provider pointing to the correct Ollama endpoint. E.g.
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```
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conda_env: llamastack-ollama
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...
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inference:
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- provider_id: ollama0
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provider_type: remote::ollama
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config:
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url: http://127.0.0.1:11434
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```
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```
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llama stack build --template ollama --image-type conda
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llama stack run ~/.llama/distributions/llamastack-ollama/ollama-run.yaml
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```
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Note: If you wish to use pgvector or chromadb as memory provider. You may need to update generated `run.yaml` file to point to the desired memory provider. See [Memory Providers](https://llama-stack.readthedocs.io/en/latest/api_providers/memory_api.html) for more details. Or comment out the pgvector or chromadb memory provider in `run.yaml` file to use the default inline memory provider, keeping only the following section:
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```
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memory:
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- provider_id: faiss-0
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provider_type: faiss
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config:
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kvstore:
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namespace: null
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type: sqlite
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db_path: ~/.llama/runtime/faiss_store.db
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```
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:::
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:::{tab-item} fireworks
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```bash
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llama stack build --template fireworks --image-type conda
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# -- modify run.yaml to a valid Fireworks server endpoint
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llama stack run ./run.yaml
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```
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Make sure your `run.yaml` file has the inference provider pointing to the correct Fireworks URL server endpoint. E.g.
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```
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conda_env: llamastack-fireworks
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...
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inference:
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- provider_id: fireworks
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provider_type: remote::fireworks
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config:
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url: https://api.fireworks.ai/inference
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api_key: <optional api key>
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```
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:::
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:::{tab-item} together
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```bash
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llama stack build --template together --image-type conda
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# -- modify run.yaml to a valid Together server endpoint
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llama stack run ~/.llama/distributions/llamastack-together/together-run.yaml
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```
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Make sure your `run.yaml` file has the inference provider pointing to the correct Together URL server endpoint. E.g.
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```
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conda_env: llamastack-together
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...
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inference:
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- provider_id: together
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provider_type: remote::together
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config:
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url: https://api.together.xyz/v1
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api_key: <optional api key>
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```
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:::
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::::
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##### 1.2 (Optional) Update Model Serving Configuration
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::::{tab-set}
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:::{tab-item} meta-reference-gpu
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You may change the `config.model` in `run.yaml` to update the model currently being served by the distribution. Make sure you have the model checkpoint downloaded in your `~/.llama`.
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```
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inference:
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- provider_id: meta0
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provider_type: inline::meta-reference
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config:
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model: Llama3.2-11B-Vision-Instruct
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quantization: null
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torch_seed: null
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max_seq_len: 4096
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max_batch_size: 1
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```
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Run `llama model list` to see the available models to download, and `llama model download` to download the checkpoints.
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:::
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:::{tab-item} tgi
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To serve a new model with `tgi`, change the docker command flag `--model-id <model-to-serve>`.
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This can be done by edit the `command` args in `compose.yaml`. E.g. Replace "Llama-3.2-1B-Instruct" with the model you want to serve.
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```
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command: ["--dtype", "bfloat16", "--usage-stats", "on", "--sharded", "false", "--model-id", "meta-llama/Llama-3.2-1B-Instruct", "--port", "5009", "--cuda-memory-fraction", "0.3"]
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```
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or by changing the docker run command's `--model-id` flag
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```
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docker run --rm -it -v $HOME/.cache/huggingface:/data -p 5009:5009 --gpus all ghcr.io/huggingface/text-generation-inference:latest --dtype bfloat16 --usage-stats on --sharded false --model-id meta-llama/Llama-3.2-1B-Instruct --port 5009
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```
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Make sure your `run.yaml` file has the inference provider pointing to the TGI server endpoint serving your model.
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```
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inference:
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- provider_id: tgi0
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provider_type: remote::tgi
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config:
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url: http://127.0.0.1:5009
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```
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```
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Run `llama model list` to see the available models to download, and `llama model download` to download the checkpoints.
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:::
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:::{tab-item} ollama
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You can use ollama for managing model downloads.
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```
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ollama pull llama3.1:8b-instruct-fp16
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ollama pull llama3.1:70b-instruct-fp16
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```
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> Please check the [OLLAMA_SUPPORTED_MODELS](https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers.remote/inference/ollama/ollama.py) for the supported Ollama models.
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To serve a new model with `ollama`
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```
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ollama run <model_name>
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```
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To make sure that the model is being served correctly, run `ollama ps` to get a list of models being served by ollama.
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```
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$ ollama ps
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NAME ID SIZE PROCESSOR UNTIL
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llama3.1:8b-instruct-fp16 4aacac419454 17 GB 100% GPU 4 minutes from now
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```
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To verify that the model served by ollama is correctly connected to Llama Stack server
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```
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$ llama-stack-client models list
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+----------------------+----------------------+---------------+-----------------------------------------------+
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| identifier | llama_model | provider_id | metadata |
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+======================+======================+===============+===============================================+
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| Llama3.1-8B-Instruct | Llama3.1-8B-Instruct | ollama0 | {'ollama_model': 'llama3.1:8b-instruct-fp16'} |
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+----------------------+----------------------+---------------+-----------------------------------------------+
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```
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:::
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:::{tab-item} together
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Use `llama-stack-client models list` to check the available models served by together.
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```
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$ llama-stack-client models list
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+------------------------------+------------------------------+---------------+------------+
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| identifier | llama_model | provider_id | metadata |
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+==============================+==============================+===============+============+
|
|
| Llama3.1-8B-Instruct | Llama3.1-8B-Instruct | together0 | {} |
|
|
+------------------------------+------------------------------+---------------+------------+
|
|
| Llama3.1-70B-Instruct | Llama3.1-70B-Instruct | together0 | {} |
|
|
+------------------------------+------------------------------+---------------+------------+
|
|
| Llama3.1-405B-Instruct | Llama3.1-405B-Instruct | together0 | {} |
|
|
+------------------------------+------------------------------+---------------+------------+
|
|
| Llama3.2-3B-Instruct | Llama3.2-3B-Instruct | together0 | {} |
|
|
+------------------------------+------------------------------+---------------+------------+
|
|
| Llama3.2-11B-Vision-Instruct | Llama3.2-11B-Vision-Instruct | together0 | {} |
|
|
+------------------------------+------------------------------+---------------+------------+
|
|
| Llama3.2-90B-Vision-Instruct | Llama3.2-90B-Vision-Instruct | together0 | {} |
|
|
+------------------------------+------------------------------+---------------+------------+
|
|
```
|
|
:::
|
|
|
|
:::{tab-item} fireworks
|
|
Use `llama-stack-client models list` to check the available models served by Fireworks.
|
|
```
|
|
$ llama-stack-client models list
|
|
+------------------------------+------------------------------+---------------+------------+
|
|
| identifier | llama_model | provider_id | metadata |
|
|
+==============================+==============================+===============+============+
|
|
| Llama3.1-8B-Instruct | Llama3.1-8B-Instruct | fireworks0 | {} |
|
|
+------------------------------+------------------------------+---------------+------------+
|
|
| Llama3.1-70B-Instruct | Llama3.1-70B-Instruct | fireworks0 | {} |
|
|
+------------------------------+------------------------------+---------------+------------+
|
|
| Llama3.1-405B-Instruct | Llama3.1-405B-Instruct | fireworks0 | {} |
|
|
+------------------------------+------------------------------+---------------+------------+
|
|
| Llama3.2-1B-Instruct | Llama3.2-1B-Instruct | fireworks0 | {} |
|
|
+------------------------------+------------------------------+---------------+------------+
|
|
| Llama3.2-3B-Instruct | Llama3.2-3B-Instruct | fireworks0 | {} |
|
|
+------------------------------+------------------------------+---------------+------------+
|
|
| Llama3.2-11B-Vision-Instruct | Llama3.2-11B-Vision-Instruct | fireworks0 | {} |
|
|
+------------------------------+------------------------------+---------------+------------+
|
|
| Llama3.2-90B-Vision-Instruct | Llama3.2-90B-Vision-Instruct | fireworks0 | {} |
|
|
+------------------------------+------------------------------+---------------+------------+
|
|
```
|
|
:::
|
|
|
|
::::
|
|
|
|
|
|
##### Troubleshooting
|
|
- If you encounter any issues, search through our [GitHub Issues](https://github.com/meta-llama/llama-stack/issues), or file an new issue.
|
|
- Use `--port <PORT>` flag to use a different port number. For docker run, update the `-p <PORT>:<PORT>` flag.
|
|
|
|
|
|
## Step 2. Run Llama Stack App
|
|
|
|
### Chat Completion Test
|
|
Once the server is set up, we can test it with a client to verify it's working correctly. The following command will send a chat completion request to the server's `/inference/chat_completion` API:
|
|
|
|
```bash
|
|
$ curl http://localhost:5000/alpha/inference/chat-completion \
|
|
-H "Content-Type: application/json" \
|
|
-d '{
|
|
"model_id": "meta-llama/Llama-3.1-8B-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](https://github.com/meta-llama/llama-stack-apps/tree/main/examples) repo. To run a simple agent app:
|
|
|
|
```bash
|
|
$ 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:
|
|
```
|