forked from phoenix-oss/llama-stack-mirror
a84e7669f0
- 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>
76 lines
2.2 KiB
Markdown
76 lines
2.2 KiB
Markdown
<!-- This file was auto-generated by distro_codegen.py, please edit source -->
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# Bedrock Distribution
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```{toctree}
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:maxdepth: 2
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:hidden:
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self
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```
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The `llamastack/distribution-bedrock` distribution consists of the following provider configurations:
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| API | Provider(s) |
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|-----|-------------|
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| agents | `inline::meta-reference` |
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| datasetio | `remote::huggingface`, `inline::localfs` |
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| eval | `inline::meta-reference` |
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| inference | `remote::bedrock` |
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| safety | `remote::bedrock` |
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| scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` |
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| telemetry | `inline::meta-reference` |
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| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::code-interpreter`, `inline::rag-runtime`, `remote::model-context-protocol` |
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| vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
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### Environment Variables
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The following environment variables can be configured:
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- `LLAMA_STACK_PORT`: Port for the Llama Stack distribution server (default: `5001`)
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### Models
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The following models are available by default:
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- `meta-llama/Llama-3.1-8B-Instruct (meta.llama3-1-8b-instruct-v1:0)`
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- `meta-llama/Llama-3.1-70B-Instruct (meta.llama3-1-70b-instruct-v1:0)`
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- `meta-llama/Llama-3.1-405B-Instruct-FP8 (meta.llama3-1-405b-instruct-v1:0)`
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### Prerequisite: API Keys
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Make sure you have access to a AWS Bedrock API Key. You can get one by visiting [AWS Bedrock](https://aws.amazon.com/bedrock/).
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## Running Llama Stack with AWS Bedrock
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You can do this via Conda (build code) or Docker which has a pre-built image.
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### Via Docker
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This method allows you to get started quickly without having to build the distribution code.
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```bash
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LLAMA_STACK_PORT=5001
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docker run \
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-it \
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-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
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llamastack/distribution-bedrock \
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--port $LLAMA_STACK_PORT \
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--env AWS_ACCESS_KEY_ID=$AWS_ACCESS_KEY_ID \
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--env AWS_SECRET_ACCESS_KEY=$AWS_SECRET_ACCESS_KEY \
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--env AWS_SESSION_TOKEN=$AWS_SESSION_TOKEN
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```
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### Via Conda
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```bash
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llama stack build --template bedrock --image-type conda
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llama stack run ./run.yaml \
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--port $LLAMA_STACK_PORT \
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--env AWS_ACCESS_KEY_ID=$AWS_ACCESS_KEY_ID \
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--env AWS_SECRET_ACCESS_KEY=$AWS_SECRET_ACCESS_KEY \
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--env AWS_SESSION_TOKEN=$AWS_SESSION_TOKEN
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```
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