mirror of https://github.com/meta-llama/llama-stack.git synced 2025-06-28 19:04:19 +00:00

rename LLAMASTACK_PORT to LLAMA_STACK_PORT for consistency with other env vars (#744 )

# What does this PR do?

Rename environment var for consistency

## Test Plan

No regressions

## Sources

## Before submitting

- [X] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
- [X] Ran pre-commit to handle lint / formatting issues.
- [X] Read the [contributor
guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
Pull Request section?
- [X] Updated relevant documentation.
- [ ] Wrote necessary unit or integration tests.

---------

Signed-off-by: Yuan Tang <terrytangyuan@gmail.com>
Co-authored-by: Yuan Tang <terrytangyuan@gmail.com>

2025-01-10 11:09:49 -08:00

2.1 KiB

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Bedrock Distribution

:maxdepth: 2
:hidden:

self

The llamastack/distribution-bedrock distribution consists of the following provider configurations:

API	Provider(s)
agents	`inline::meta-reference`
datasetio	`remote::huggingface`, `inline::localfs`
eval	`inline::meta-reference`
inference	`remote::bedrock`
memory	`inline::faiss`, `remote::chromadb`, `remote::pgvector`
safety	`remote::bedrock`
scoring	`inline::basic`, `inline::llm-as-judge`, `inline::braintrust`
telemetry	`inline::meta-reference`
tool_runtime	`remote::brave-search`, `remote::tavily-search`, `inline::code-interpreter`, `inline::memory-runtime`

Environment Variables

The following environment variables can be configured:

LLAMA_STACK_PORT: Port for the Llama Stack distribution server (default: 5001)

Models

The following models are available by default:

meta-llama/Llama-3.1-8B-Instruct (meta.llama3-1-8b-instruct-v1:0)
meta-llama/Llama-3.1-70B-Instruct (meta.llama3-1-70b-instruct-v1:0)
meta-llama/Llama-3.1-405B-Instruct-FP8 (meta.llama3-1-405b-instruct-v1:0)

Prerequisite: API Keys

Make sure you have access to a AWS Bedrock API Key. You can get one by visiting AWS Bedrock.

Running Llama Stack with AWS Bedrock

You can do this via Conda (build code) 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.

LLAMA_STACK_PORT=5001
docker run \
  -it \
  -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
  llamastack/distribution-bedrock \
  --port $LLAMA_STACK_PORT \
  --env AWS_ACCESS_KEY_ID=$AWS_ACCESS_KEY_ID \
  --env AWS_SECRET_ACCESS_KEY=$AWS_SECRET_ACCESS_KEY \
  --env AWS_SESSION_TOKEN=$AWS_SESSION_TOKEN

Via Conda

llama stack build --template bedrock --image-type conda
llama stack run ./run.yaml \
  --port $LLAMA_STACK_PORT \
  --env AWS_ACCESS_KEY_ID=$AWS_ACCESS_KEY_ID \
  --env AWS_SECRET_ACCESS_KEY=$AWS_SECRET_ACCESS_KEY \
  --env AWS_SESSION_TOKEN=$AWS_SESSION_TOKEN

2.1 KiB Raw Blame History