forked from phoenix-oss/llama-stack-mirror
- Added new template `dell` and its documentation - Update docs - [minor] uv fix i came across - codegen for all templates Tested with ```bash export INFERENCE_PORT=8181 export DEH_URL=http://0.0.0.0:$INFERENCE_PORT export INFERENCE_MODEL=meta-llama/Llama-3.1-8B-Instruct export CHROMADB_HOST=localhost export CHROMADB_PORT=6601 export CHROMA_URL=[http://$CHROMADB_HOST:$CHROMADB_PORT](about:blank) export CUDA_VISIBLE_DEVICES=0 export LLAMA_STACK_PORT=8321 # build the stack template llama stack build --template=dell # start the TGI inference server podman run --rm -it --network host -v $HOME/.cache/huggingface:/data -e HF_TOKEN=$HF_TOKEN -p $INFERENCE_PORT:$INFERENCE_PORT --gpus $CUDA_VISIBLE_DEVICES [ghcr.io/huggingface/text-generation-inference](http://ghcr.io/huggingface/text-generation-inference) --dtype bfloat16 --usage-stats off --sharded false --cuda-memory-fraction 0.7 --model-id $INFERENCE_MODEL --port $INFERENCE_PORT --hostname 0.0.0.0 # start chroma-db for vector-io ( aka RAG ) podman run --rm -it --network host --name chromadb -v .:/chroma/chroma -e IS_PERSISTENT=TRUE chromadb/chroma:latest --port $CHROMADB_PORT --host $(hostname) # build docker llama stack build --template=dell --image-type=container # run llama stack server ( via docker ) podman run -it \ --network host \ -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \ -v ~/.llama:/root/.llama \ # NOTE: mount the llama-stack / llama-model directories if testing local changes -v /home/hjshah/git/llama-stack:/app/llama-stack-source -v /home/hjshah/git/llama-models:/app/llama-models-source \ localhost/distribution-dell:dev \ --port $LLAMA_STACK_PORT \ --env INFERENCE_MODEL=$INFERENCE_MODEL \ --env DEH_URL=$DEH_URL \ --env CHROMA_URL=$CHROMA_URL # test the server cd <PATH_TO_LLAMA_STACK_REPO> LLAMA_STACK_BASE_URL=http://0.0.0.0:$LLAMA_STACK_PORT pytest -s -v tests/client-sdk/agents/test_agents.py ``` --------- Co-authored-by: Hardik Shah <hjshah@fb.com>
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2.2 KiB
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 |
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::rag-runtime , remote::model-context-protocol |
vector_io | inline::faiss , remote::chromadb , remote::pgvector |
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