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---------

Co-authored-by: dltn <6599399+dltn@users.noreply.github.com>
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
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# Meta Reference Distribution
The `llamastack/distribution-meta-reference-gpu` distribution consists of the following provider configurations.
| **API** | **Inference** | **Agents** | **Memory** | **Safety** | **Telemetry** |
|----------------- |--------------- |---------------- |-------------------------------------------------- |---------------- |---------------- |
| **Provider(s)** | meta-reference | meta-reference | meta-reference, remote::pgvector, remote::chroma | meta-reference | meta-reference |
### Start the Distribution (Single Node GPU)
```
$ cd distributions/meta-reference-gpu
$ ls
build.yaml compose.yaml README.md run.yaml
$ docker compose up
```
> [!NOTE]
> This assumes you have access to GPU to start a local server with access to your GPU.
> [!NOTE]
> `~/.llama` should 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
```
### Alternative (Build and start distribution locally via conda)
- You may checkout the [Getting Started](../../docs/getting_started.md) for more details on building locally via conda and starting up a meta-reference distribution.
### Start Distribution With pgvector/chromadb Memory Provider
##### pgvector
1. Start running the pgvector server:
```
docker run --network host --name mypostgres -it -p 5432:5432 -e POSTGRES_PASSWORD=mysecretpassword -e POSTGRES_USER=postgres -e POSTGRES_DB=postgres pgvector/pgvector:pg16
```
2. Edit the `run.yaml` file to point to the pgvector server.
```
memory:
- provider_id: pgvector
provider_type: remote::pgvector
config:
host: 127.0.0.1
port: 5432
db: postgres
user: postgres
password: mysecretpassword
```
> [!NOTE]
> If you get a `RuntimeError: Vector extension is not installed.`. You will need to run `CREATE EXTENSION IF NOT EXISTS vector;` to include the vector extension. E.g.
```
docker exec -it mypostgres ./bin/psql -U postgres
postgres=# CREATE EXTENSION IF NOT EXISTS vector;
postgres=# SELECT extname from pg_extension;
extname
```
3. Run `docker compose up` with the updated `run.yaml` file.
##### chromadb
1. Start running chromadb server
```
docker run -it --network host --name chromadb -p 6000:6000 -v ./chroma_vdb:/chroma/chroma -e IS_PERSISTENT=TRUE chromadb/chroma:latest
```
2. Edit the `run.yaml` file to point to the chromadb server.
```
memory:
- provider_id: remote::chromadb
provider_type: remote::chromadb
config:
host: localhost
port: 6000
```
3. Run `docker compose up` with the updated `run.yaml` file.
### Serving a new model
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`.
```
inference:
- provider_id: meta0
provider_type: meta-reference
config:
model: Llama3.2-11B-Vision-Instruct
quantization: null
torch_seed: null
max_seq_len: 4096
max_batch_size: 1
```
Run `llama model list` to see the available models to download, and `llama model download` to download the checkpoints.