forked from phoenix-oss/llama-stack-mirror
c9e5578151
See https://github.com/meta-llama/llama-stack/issues/827 for the broader design. This PR finishes off all the stragglers and migrates everything to the new naming.
75 lines
2.1 KiB
Markdown
75 lines
2.1 KiB
Markdown
# 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::memory-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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