mirror of
https://github.com/meta-llama/llama-stack.git
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7519b73fcc
The starter distribution added post-training which added torch dependencies which pulls in all the nvidia CUDA libraries. This made our starter container very big. We have worked hard to keep the starter container small so it serves its purpose as a starter. This PR tries to get it back to its size by forking off duplicate "-gpu" providers for post-training. These forked providers are then used for a new `starter-gpu` distribution which can pull in all dependencies.
282 lines
10 KiB
Python
282 lines
10 KiB
Python
# 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 typing import Any
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from llama_stack.core.datatypes import (
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BuildProvider,
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Provider,
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ProviderSpec,
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ShieldInput,
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ToolGroupInput,
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)
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from llama_stack.core.utils.dynamic import instantiate_class_type
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from llama_stack.distributions.template import DistributionTemplate, RunConfigSettings
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from llama_stack.providers.datatypes import RemoteProviderSpec
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from llama_stack.providers.inline.files.localfs.config import LocalfsFilesImplConfig
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from llama_stack.providers.inline.inference.sentence_transformers import (
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SentenceTransformersInferenceConfig,
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)
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from llama_stack.providers.inline.vector_io.faiss.config import FaissVectorIOConfig
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from llama_stack.providers.inline.vector_io.milvus.config import MilvusVectorIOConfig
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from llama_stack.providers.inline.vector_io.sqlite_vec.config import (
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SQLiteVectorIOConfig,
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)
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from llama_stack.providers.registry.inference import available_providers
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from llama_stack.providers.remote.vector_io.chroma.config import ChromaVectorIOConfig
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from llama_stack.providers.remote.vector_io.pgvector.config import (
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PGVectorVectorIOConfig,
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)
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from llama_stack.providers.utils.sqlstore.sqlstore import PostgresSqlStoreConfig
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def _get_config_for_provider(provider_spec: ProviderSpec) -> dict[str, Any]:
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"""Get configuration for a provider using its adapter's config class."""
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config_class = instantiate_class_type(provider_spec.config_class)
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if hasattr(config_class, "sample_run_config"):
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config: dict[str, Any] = config_class.sample_run_config()
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return config
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return {}
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ENABLED_INFERENCE_PROVIDERS = [
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"ollama",
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"vllm",
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"tgi",
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"fireworks",
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"together",
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"gemini",
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"vertexai",
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"groq",
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"sambanova",
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"anthropic",
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"openai",
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"cerebras",
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"nvidia",
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"bedrock",
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]
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INFERENCE_PROVIDER_IDS = {
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"ollama": "${env.OLLAMA_URL:+ollama}",
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"vllm": "${env.VLLM_URL:+vllm}",
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"tgi": "${env.TGI_URL:+tgi}",
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"cerebras": "${env.CEREBRAS_API_KEY:+cerebras}",
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"nvidia": "${env.NVIDIA_API_KEY:+nvidia}",
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"vertexai": "${env.VERTEX_AI_PROJECT:+vertexai}",
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}
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def get_remote_inference_providers() -> list[Provider]:
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# Filter out inline providers and some others - the starter distro only exposes remote providers
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remote_providers = [
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provider
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for provider in available_providers()
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if isinstance(provider, RemoteProviderSpec) and provider.adapter.adapter_type in ENABLED_INFERENCE_PROVIDERS
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]
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inference_providers = []
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for provider_spec in remote_providers:
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provider_type = provider_spec.adapter.adapter_type
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if provider_type in INFERENCE_PROVIDER_IDS:
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provider_id = INFERENCE_PROVIDER_IDS[provider_type]
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else:
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provider_id = provider_type.replace("-", "_").replace("::", "_")
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config = _get_config_for_provider(provider_spec)
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inference_providers.append(
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Provider(
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provider_id=provider_id,
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provider_type=f"remote::{provider_type}",
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config=config,
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)
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)
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return inference_providers
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def get_distribution_template() -> DistributionTemplate:
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remote_inference_providers = get_remote_inference_providers()
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name = "starter"
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providers = {
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"inference": [BuildProvider(provider_type=p.provider_type, module=p.module) for p in remote_inference_providers]
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+ [BuildProvider(provider_type="inline::sentence-transformers")],
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"vector_io": [
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BuildProvider(provider_type="inline::faiss"),
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BuildProvider(provider_type="inline::sqlite-vec"),
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BuildProvider(provider_type="inline::milvus"),
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BuildProvider(provider_type="remote::chromadb"),
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BuildProvider(provider_type="remote::pgvector"),
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],
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"files": [BuildProvider(provider_type="inline::localfs")],
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"safety": [
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BuildProvider(provider_type="inline::llama-guard"),
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BuildProvider(provider_type="inline::code-scanner"),
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],
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"agents": [BuildProvider(provider_type="inline::meta-reference")],
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"telemetry": [BuildProvider(provider_type="inline::meta-reference")],
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"post_training": [BuildProvider(provider_type="inline::huggingface-cpu")],
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"eval": [BuildProvider(provider_type="inline::meta-reference")],
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"datasetio": [
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BuildProvider(provider_type="remote::huggingface"),
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BuildProvider(provider_type="inline::localfs"),
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],
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"scoring": [
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BuildProvider(provider_type="inline::basic"),
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BuildProvider(provider_type="inline::llm-as-judge"),
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BuildProvider(provider_type="inline::braintrust"),
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],
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"tool_runtime": [
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BuildProvider(provider_type="remote::brave-search"),
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BuildProvider(provider_type="remote::tavily-search"),
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BuildProvider(provider_type="inline::rag-runtime"),
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BuildProvider(provider_type="remote::model-context-protocol"),
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],
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"batches": [
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BuildProvider(provider_type="inline::reference"),
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],
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}
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files_provider = Provider(
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provider_id="meta-reference-files",
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provider_type="inline::localfs",
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config=LocalfsFilesImplConfig.sample_run_config(f"~/.llama/distributions/{name}"),
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)
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embedding_provider = Provider(
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provider_id="sentence-transformers",
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provider_type="inline::sentence-transformers",
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config=SentenceTransformersInferenceConfig.sample_run_config(),
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)
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default_tool_groups = [
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ToolGroupInput(
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toolgroup_id="builtin::websearch",
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provider_id="tavily-search",
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),
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ToolGroupInput(
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toolgroup_id="builtin::rag",
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provider_id="rag-runtime",
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),
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]
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default_shields = [
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# if the
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ShieldInput(
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shield_id="llama-guard",
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provider_id="${env.SAFETY_MODEL:+llama-guard}",
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provider_shield_id="${env.SAFETY_MODEL:=}",
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),
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ShieldInput(
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shield_id="code-scanner",
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provider_id="${env.CODE_SCANNER_MODEL:+code-scanner}",
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provider_shield_id="${env.CODE_SCANNER_MODEL:=}",
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),
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]
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return DistributionTemplate(
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name=name,
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distro_type="self_hosted",
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description="Quick start template for running Llama Stack with several popular providers. This distribution is intended for CPU-only environments.",
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container_image=None,
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template_path=None,
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providers=providers,
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additional_pip_packages=PostgresSqlStoreConfig.pip_packages(),
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run_configs={
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"run.yaml": RunConfigSettings(
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provider_overrides={
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"inference": remote_inference_providers + [embedding_provider],
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"vector_io": [
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Provider(
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provider_id="faiss",
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provider_type="inline::faiss",
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config=FaissVectorIOConfig.sample_run_config(f"~/.llama/distributions/{name}"),
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),
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Provider(
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provider_id="sqlite-vec",
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provider_type="inline::sqlite-vec",
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config=SQLiteVectorIOConfig.sample_run_config(f"~/.llama/distributions/{name}"),
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),
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Provider(
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provider_id="${env.MILVUS_URL:+milvus}",
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provider_type="inline::milvus",
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config=MilvusVectorIOConfig.sample_run_config(f"~/.llama/distributions/{name}"),
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),
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Provider(
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provider_id="${env.CHROMADB_URL:+chromadb}",
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provider_type="remote::chromadb",
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config=ChromaVectorIOConfig.sample_run_config(
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f"~/.llama/distributions/{name}/",
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url="${env.CHROMADB_URL:=}",
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),
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),
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Provider(
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provider_id="${env.PGVECTOR_DB:+pgvector}",
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provider_type="remote::pgvector",
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config=PGVectorVectorIOConfig.sample_run_config(
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f"~/.llama/distributions/{name}",
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db="${env.PGVECTOR_DB:=}",
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user="${env.PGVECTOR_USER:=}",
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password="${env.PGVECTOR_PASSWORD:=}",
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),
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),
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],
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"files": [files_provider],
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},
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default_models=[],
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default_tool_groups=default_tool_groups,
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default_shields=default_shields,
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),
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},
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run_config_env_vars={
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"LLAMA_STACK_PORT": (
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"8321",
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"Port for the Llama Stack distribution server",
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),
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"FIREWORKS_API_KEY": (
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"",
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"Fireworks API Key",
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),
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"OPENAI_API_KEY": (
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"",
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"OpenAI API Key",
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),
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"GROQ_API_KEY": (
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"",
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"Groq API Key",
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),
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"ANTHROPIC_API_KEY": (
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"",
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"Anthropic API Key",
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),
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"GEMINI_API_KEY": (
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"",
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"Gemini API Key",
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),
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"VERTEX_AI_PROJECT": (
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"",
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"Google Cloud Project ID for Vertex AI",
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),
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"VERTEX_AI_LOCATION": (
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"us-central1",
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"Google Cloud Location for Vertex AI",
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),
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"SAMBANOVA_API_KEY": (
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"",
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"SambaNova API Key",
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),
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"VLLM_URL": (
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"http://localhost:8000/v1",
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"vLLM URL",
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),
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"VLLM_INFERENCE_MODEL": (
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"",
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"Optional vLLM Inference Model to register on startup",
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),
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"OLLAMA_URL": (
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"http://localhost:11434",
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"Ollama URL",
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),
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},
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)
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