mirror of https://github.com/meta-llama/llama-stack.git synced 2025-10-18 15:27:16 +00:00

chore!: remove telemetry API usage (#3815 )

# What does this PR do?

remove telemetry as a providable API from the codebase. This includes
removing it from generated distributions but also the provider registry,
the router, etc

since `setup_logger` is tied pretty strictly to `Api.telemetry` being in
impls we still need an "instantiated provider" in our implementations.
However it should not be auto-routed or provided. So in
validate_and_prepare_providers (called from resolve_impls) I made it so
that if run_config.telemetry.enabled, we set up the meta-reference
"provider" internally to be used so that log_event will work when
called.

This is the neatest way I think we can remove telemetry from the
provider configs but also not need to rip apart the whole "telemetry is
a provider" logic just yet, but we can do it internally later without
disrupting users.

so telemetry is removed from the registry such that if a user puts
`telemetry:` as an API in their build/run config it will err out, but
can still be used by us internally as we go through this transition.


relates to #3806

Signed-off-by: Charlie Doern <cdoern@redhat.com>

2025-10-16 10:39:32 -07:00

3.2 KiB

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Meta Reference GPU Distribution

:maxdepth: 2
:hidden:

self

The llamastack/distribution-meta-reference-gpu distribution consists of the following provider configurations:

API	Provider(s)
agents	`inline::meta-reference`
datasetio	`remote::huggingface`, `inline::localfs`
eval	`inline::meta-reference`
inference	`inline::meta-reference`
safety	`inline::llama-guard`
scoring	`inline::basic`, `inline::llm-as-judge`, `inline::braintrust`
tool_runtime	`remote::brave-search`, `remote::tavily-search`, `inline::rag-runtime`, `remote::model-context-protocol`
vector_io	`inline::faiss`, `remote::chromadb`, `remote::pgvector`

Note that you need access to nvidia GPUs to run this distribution. This distribution is not compatible with CPU-only machines or machines with AMD GPUs.

Environment Variables

The following environment variables can be configured:

LLAMA_STACK_PORT: Port for the Llama Stack distribution server (default: 8321)
INFERENCE_MODEL: Inference model loaded into the Meta Reference server (default: meta-llama/Llama-3.2-3B-Instruct)
INFERENCE_CHECKPOINT_DIR: Directory containing the Meta Reference model checkpoint (default: null)
SAFETY_MODEL: Name of the safety (Llama-Guard) model to use (default: meta-llama/Llama-Guard-3-1B)
SAFETY_CHECKPOINT_DIR: Directory containing the Llama-Guard model checkpoint (default: null)

Prerequisite: Downloading Models

Please check that you have llama model checkpoints downloaded in ~/.llama before proceeding. See installation guide here to download the models using the Hugging Face CLI.


## Running the Distribution

You can do this via venv or Docker which has a pre-built image.

### Via Docker

This method allows you to get started quickly without having to build the distribution code.

```bash
LLAMA_STACK_PORT=8321
docker run \
  -it \
  --pull always \
  --gpu all \
  -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
  -v ~/.llama:/root/.llama \
  -e INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct \
  llamastack/distribution-meta-reference-gpu \
  --port $LLAMA_STACK_PORT

If you are using Llama Stack Safety / Shield APIs, use:

docker run \
  -it \
  --pull always \
  --gpu all \
  -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
  -v ~/.llama:/root/.llama \
  -e INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct \
  -e SAFETY_MODEL=meta-llama/Llama-Guard-3-1B \
  llamastack/distribution-meta-reference-gpu \
  --port $LLAMA_STACK_PORT

Via venv

Make sure you have done uv pip install llama-stack and have the Llama Stack CLI available.

llama stack build --distro meta-reference-gpu --image-type venv
INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct \
llama stack run distributions/meta-reference-gpu/run.yaml \
  --port 8321