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https://github.com/meta-llama/llama-stack.git
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5bb3817c49
# What does this PR do? The `nvidia` distro was previously collapsed into the `starter` distro. However, the `nvidia` distro was setup specifically to use NVIDIA NeMo microservices as providers for all APIs and not just inference, which means it was doing quite a bit more than what the `starter` distro covers today. We should work with our friends at NVIDIA to determine the best place to maintain this distro long-term, but for now this restores the `nvidia` distro and its docs back to where they were so that things continue to work for their users. ## Test Plan I ensure the `nvidia` distro could build, and run at least to the point of complaining that I didn't provide the necessary API keys. ``` uv run llama stack build --template nvidia --image-type venv uv run llama stack run llama_stack/templates/nvidia/run.yaml ``` I also made sure the docs website built and looks reasonable, with the `nvidia` distro docs at the same URL it was previously (because it has incoming links from official NVIDIA NeMo docs, among other places). ``` uv run --group docs sphinx-autobuild docs/source docs/build/html --write-all ``` Signed-off-by: Ben Browning <bbrownin@redhat.com>
119 lines
3.5 KiB
YAML
119 lines
3.5 KiB
YAML
version: 2
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image_name: nvidia
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apis:
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- agents
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- datasetio
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- eval
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- inference
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- post_training
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- safety
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- scoring
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- telemetry
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- tool_runtime
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- vector_io
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providers:
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inference:
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- provider_id: nvidia
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provider_type: remote::nvidia
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config:
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url: ${env.NVIDIA_BASE_URL:=https://integrate.api.nvidia.com}
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api_key: ${env.NVIDIA_API_KEY:=}
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append_api_version: ${env.NVIDIA_APPEND_API_VERSION:=True}
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- provider_id: nvidia
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provider_type: remote::nvidia
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config:
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guardrails_service_url: ${env.GUARDRAILS_SERVICE_URL:=http://localhost:7331}
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config_id: ${env.NVIDIA_GUARDRAILS_CONFIG_ID:=self-check}
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vector_io:
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- provider_id: faiss
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provider_type: inline::faiss
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config:
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kvstore:
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type: sqlite
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db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/faiss_store.db
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safety:
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- provider_id: nvidia
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provider_type: remote::nvidia
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config:
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guardrails_service_url: ${env.GUARDRAILS_SERVICE_URL:=http://localhost:7331}
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config_id: ${env.NVIDIA_GUARDRAILS_CONFIG_ID:=self-check}
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agents:
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- provider_id: meta-reference
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provider_type: inline::meta-reference
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config:
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persistence_store:
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type: sqlite
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db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/agents_store.db
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responses_store:
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type: sqlite
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db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/responses_store.db
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telemetry:
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- provider_id: meta-reference
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provider_type: inline::meta-reference
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config:
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service_name: "${env.OTEL_SERVICE_NAME:=\u200B}"
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sinks: ${env.TELEMETRY_SINKS:=console,sqlite}
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sqlite_db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/trace_store.db
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otel_exporter_otlp_endpoint: ${env.OTEL_EXPORTER_OTLP_ENDPOINT:=}
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eval:
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- provider_id: nvidia
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provider_type: remote::nvidia
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config:
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evaluator_url: ${env.NVIDIA_EVALUATOR_URL:=http://localhost:7331}
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post_training:
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- provider_id: nvidia
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provider_type: remote::nvidia
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config:
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api_key: ${env.NVIDIA_API_KEY:=}
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dataset_namespace: ${env.NVIDIA_DATASET_NAMESPACE:=default}
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project_id: ${env.NVIDIA_PROJECT_ID:=test-project}
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customizer_url: ${env.NVIDIA_CUSTOMIZER_URL:=http://nemo.test}
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datasetio:
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- provider_id: localfs
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provider_type: inline::localfs
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config:
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kvstore:
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type: sqlite
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db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/localfs_datasetio.db
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- provider_id: nvidia
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provider_type: remote::nvidia
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config:
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api_key: ${env.NVIDIA_API_KEY:=}
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dataset_namespace: ${env.NVIDIA_DATASET_NAMESPACE:=default}
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project_id: ${env.NVIDIA_PROJECT_ID:=test-project}
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datasets_url: ${env.NVIDIA_DATASETS_URL:=http://nemo.test}
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scoring:
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- provider_id: basic
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provider_type: inline::basic
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config: {}
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tool_runtime:
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- provider_id: rag-runtime
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provider_type: inline::rag-runtime
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config: {}
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metadata_store:
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type: sqlite
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db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/registry.db
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inference_store:
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type: sqlite
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db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/inference_store.db
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models:
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- metadata: {}
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model_id: ${env.INFERENCE_MODEL}
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provider_id: nvidia
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model_type: llm
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- metadata: {}
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model_id: ${env.SAFETY_MODEL}
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provider_id: nvidia
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model_type: llm
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shields:
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- shield_id: ${env.SAFETY_MODEL}
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provider_id: nvidia
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vector_dbs: []
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datasets: []
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scoring_fns: []
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benchmarks: []
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tool_groups:
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- toolgroup_id: builtin::rag
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provider_id: rag-runtime
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server:
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port: 8321
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