forked from phoenix-oss/llama-stack-mirror
Auto-generate distro yamls + docs (#468)
# What does this PR do? Automatically generates - build.yaml - run.yaml - run-with-safety.yaml - parts of markdown docs for the distributions. ## Test Plan At this point, this only updates the YAMLs and the docs. Some testing (especially with ollama and vllm) has been performed but needs to be much more tested.
This commit is contained in:
Ashwin Bharambe
GitHub
parent
0784284ab5
commit
2a31163178
88 changed files with 3008 additions and 852 deletions
7
llama_stack/templates/tgi/__init__.py
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7
llama_stack/templates/tgi/__init__.py
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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from .tgi import get_distribution_template # noqa: F401
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version: '2'
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name: tgi
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distribution_spec:
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description: Use TGI for running LLM inference
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description: Use (an external) TGI server for running LLM inference
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docker_image: llamastack/distribution-tgi:test-0.0.52rc3
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providers:
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inference: remote::tgi
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inference:
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- remote::tgi
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memory:
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- inline::faiss
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- remote::chromadb
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- remote::pgvector
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safety: inline::llama-guard
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agents: inline::meta-reference
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telemetry: inline::meta-reference
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safety:
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- inline::llama-guard
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agents:
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- inline::meta-reference
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telemetry:
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- inline::meta-reference
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image_type: conda
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119
llama_stack/templates/tgi/doc_template.md
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119
llama_stack/templates/tgi/doc_template.md
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# TGI Distribution
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The `llamastack/distribution-{{ name }}` distribution consists of the following provider configurations.
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{{ providers_table }}
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You can use this distribution if you have GPUs and want to run an independent TGI server container for running inference.
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{% if run_config_env_vars %}
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### Environment Variables
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The following environment variables can be configured:
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{% for var, (default_value, description) in run_config_env_vars.items() %}
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- `{{ var }}`: {{ description }} (default: `{{ default_value }}`)
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{% endfor %}
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{% endif %}
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## Setting up TGI server
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Please check the [TGI Getting Started Guide](https://github.com/huggingface/text-generation-inference?tab=readme-ov-file#get-started) to get a TGI endpoint. Here is a sample script to start a TGI server locally via Docker:
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```bash
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export INFERENCE_PORT=8080
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export INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
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export CUDA_VISIBLE_DEVICES=0
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docker run --rm -it \
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-v $HOME/.cache/huggingface:/data \
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-p $INFERENCE_PORT:$INFERENCE_PORT \
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--gpus $CUDA_VISIBLE_DEVICES \
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ghcr.io/huggingface/text-generation-inference:2.3.1 \
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--dtype bfloat16 \
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--usage-stats off \
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--sharded false \
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--cuda-memory-fraction 0.7 \
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--model-id $INFERENCE_MODEL \
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--port $INFERENCE_PORT
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```
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If you are using Llama Stack Safety / Shield APIs, then you will need to also run another instance of a TGI with a corresponding safety model like `meta-llama/Llama-Guard-3-1B` using a script like:
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```bash
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export SAFETY_PORT=8081
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export SAFETY_MODEL=meta-llama/Llama-Guard-3-1B
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export CUDA_VISIBLE_DEVICES=1
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docker run --rm -it \
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-v $HOME/.cache/huggingface:/data \
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-p $SAFETY_PORT:$SAFETY_PORT \
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--gpus $CUDA_VISIBLE_DEVICES \
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ghcr.io/huggingface/text-generation-inference:2.3.1 \
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--dtype bfloat16 \
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--usage-stats off \
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--sharded false \
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--model-id $SAFETY_MODEL \
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--port $SAFETY_PORT
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```
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## Running Llama Stack
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Now you are ready to run Llama Stack with TGI as the inference provider. 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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-v ./run.yaml:/root/my-run.yaml \
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llamastack/distribution-{{ name }} \
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/root/my-run.yaml \
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--port $LLAMA_STACK_PORT \
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--env INFERENCE_MODEL=$INFERENCE_MODEL \
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--env TGI_URL=http://host.docker.internal:$INFERENCE_PORT
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```
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If you are using Llama Stack Safety / Shield APIs, use:
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```bash
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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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-v ./run-with-safety.yaml:/root/my-run.yaml \
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llamastack/distribution-{{ name }} \
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/root/my-run.yaml \
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--port $LLAMA_STACK_PORT \
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--env INFERENCE_MODEL=$INFERENCE_MODEL \
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--env TGI_URL=http://host.docker.internal:$INFERENCE_PORT \
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--env SAFETY_MODEL=$SAFETY_MODEL \
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--env TGI_SAFETY_URL=http://host.docker.internal:$SAFETY_PORT
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```
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### Via Conda
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Make sure you have done `pip install llama-stack` and have the Llama Stack CLI available.
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```bash
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llama stack build --template {{ name }} --image-type conda
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llama stack run ./run.yaml
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--port 5001
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--env INFERENCE_MODEL=$INFERENCE_MODEL
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--env TGI_URL=http://127.0.0.1:$INFERENCE_PORT
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```
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If you are using Llama Stack Safety / Shield APIs, use:
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```bash
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llama stack run ./run-with-safety.yaml
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--port 5001
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--env INFERENCE_MODEL=$INFERENCE_MODEL
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--env TGI_URL=http://127.0.0.1:$INFERENCE_PORT
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--env SAFETY_MODEL=$SAFETY_MODEL
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--env TGI_SAFETY_URL=http://127.0.0.1:$SAFETY_PORT
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```
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66
llama_stack/templates/tgi/run-with-safety.yaml
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66
llama_stack/templates/tgi/run-with-safety.yaml
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version: '2'
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image_name: tgi
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docker_image: llamastack/distribution-tgi:test-0.0.52rc3
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conda_env: null
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apis:
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- agents
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- inference
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- memory
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- safety
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- telemetry
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providers:
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inference:
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- provider_id: tgi-inference
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provider_type: remote::tgi
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config:
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url: ${env.TGI_URL}
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- provider_id: tgi-safety
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provider_type: remote::tgi
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config:
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url: ${env.TGI_SAFETY_URL}
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memory:
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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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namespace: null
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db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/tgi}/faiss_store.db
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safety:
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- provider_id: llama-guard
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provider_type: inline::llama-guard
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config: {}
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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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namespace: null
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db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/tgi}/agents_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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metadata_store:
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namespace: null
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type: sqlite
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db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/tgi}/registry.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: tgi-inference
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provider_model_id: null
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- metadata: {}
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model_id: ${env.SAFETY_MODEL}
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provider_id: tgi-safety
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provider_model_id: null
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shields:
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- params: null
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shield_id: ${env.SAFETY_MODEL}
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provider_id: null
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provider_shield_id: null
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memory_banks: []
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datasets: []
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scoring_fns: []
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eval_tasks: []
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54
llama_stack/templates/tgi/run.yaml
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54
llama_stack/templates/tgi/run.yaml
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version: '2'
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image_name: tgi
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docker_image: llamastack/distribution-tgi:test-0.0.52rc3
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conda_env: null
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apis:
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- agents
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- inference
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- memory
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- safety
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- telemetry
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providers:
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inference:
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- provider_id: tgi-inference
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provider_type: remote::tgi
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config:
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url: ${env.TGI_URL}
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memory:
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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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namespace: null
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db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/tgi}/faiss_store.db
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safety:
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- provider_id: llama-guard
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provider_type: inline::llama-guard
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config: {}
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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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namespace: null
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db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/tgi}/agents_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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metadata_store:
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namespace: null
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type: sqlite
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db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/tgi}/registry.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: tgi-inference
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provider_model_id: null
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shields: []
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memory_banks: []
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datasets: []
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scoring_fns: []
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eval_tasks: []
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97
llama_stack/templates/tgi/tgi.py
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97
llama_stack/templates/tgi/tgi.py
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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from pathlib import Path
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from llama_stack.distribution.datatypes import ModelInput, Provider, ShieldInput
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from llama_stack.providers.remote.inference.tgi import TGIImplConfig
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from llama_stack.templates.template import DistributionTemplate, RunConfigSettings
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def get_distribution_template() -> DistributionTemplate:
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providers = {
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"inference": ["remote::tgi"],
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"memory": ["inline::faiss", "remote::chromadb", "remote::pgvector"],
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"safety": ["inline::llama-guard"],
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"agents": ["inline::meta-reference"],
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"telemetry": ["inline::meta-reference"],
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}
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inference_provider = Provider(
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provider_id="tgi-inference",
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provider_type="remote::tgi",
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config=TGIImplConfig.sample_run_config(
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url="${env.TGI_URL}",
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),
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)
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inference_model = ModelInput(
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model_id="${env.INFERENCE_MODEL}",
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provider_id="tgi-inference",
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)
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safety_model = ModelInput(
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model_id="${env.SAFETY_MODEL}",
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provider_id="tgi-safety",
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)
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return DistributionTemplate(
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name="tgi",
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distro_type="self_hosted",
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description="Use (an external) TGI server for running LLM inference",
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docker_image="llamastack/distribution-tgi:test-0.0.52rc3",
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template_path=Path(__file__).parent / "doc_template.md",
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providers=providers,
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default_models=[inference_model, safety_model],
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run_configs={
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"run.yaml": RunConfigSettings(
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provider_overrides={
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"inference": [inference_provider],
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},
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default_models=[inference_model],
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),
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"run-with-safety.yaml": RunConfigSettings(
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provider_overrides={
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"inference": [
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inference_provider,
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Provider(
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provider_id="tgi-safety",
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provider_type="remote::tgi",
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config=TGIImplConfig.sample_run_config(
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url="${env.TGI_SAFETY_URL}",
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),
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),
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],
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},
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default_models=[
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inference_model,
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safety_model,
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],
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default_shields=[ShieldInput(shield_id="${env.SAFETY_MODEL}")],
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),
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},
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run_config_env_vars={
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"LLAMASTACK_PORT": (
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"5001",
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"Port for the Llama Stack distribution server",
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),
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"INFERENCE_MODEL": (
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"meta-llama/Llama-3.2-3B-Instruct",
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"Inference model loaded into the TGI server",
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),
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"TGI_URL": (
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"http://127.0.0.1:8080}/v1",
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"URL of the TGI server with the main inference model",
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),
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"TGI_SAFETY_URL": (
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"http://127.0.0.1:8081/v1",
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"URL of the TGI server with the safety model",
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),
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"SAFETY_MODEL": (
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"meta-llama/Llama-Guard-3-1B",
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"Name of the safety (Llama-Guard) model to use",
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),
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},
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)
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