feat: Add open benchmark template codegen (#1579)

## What does this PR do?

As title, add codegen for open-benchmark template

## test 

checked the new generated run.yaml file and it's identical before and
after the change

Also add small improvement to together template so that missing
TOGETHER_API_KEY won't crash the server which is the consistent user
experience as other remote providers

distributions/dependencies.json

@@ -453,6 +453,40 @@
     "transformers",
     "uvicorn"
   ],
+  "open-benchmark": [
+    "aiosqlite",
+    "autoevals",
+    "blobfile",
+    "chardet",
+    "chromadb-client",
+    "datasets",
+    "fastapi",
+    "fire",
+    "httpx",
+    "litellm",
+    "matplotlib",
+    "mcp",
+    "nltk",
+    "numpy",
+    "openai",
+    "opentelemetry-exporter-otlp-proto-http",
+    "opentelemetry-sdk",
+    "pandas",
+    "pillow",
+    "psycopg2-binary",
+    "pymongo",
+    "pypdf",
+    "redis",
+    "requests",
+    "scikit-learn",
+    "scipy",
+    "sentencepiece",
+    "sqlite-vec",
+    "together",
+    "tqdm",
+    "transformers",
+    "uvicorn"
+  ],
   "remote-vllm": [
     "aiosqlite",
     "autoevals",

llama_stack/providers/remote/inference/together/config.py

@@ -26,5 +26,5 @@ class TogetherImplConfig(BaseModel):
     def sample_run_config(cls, **kwargs) -> Dict[str, Any]:
         return {
             "url": "https://api.together.xyz/v1",
-            "api_key": "${env.TOGETHER_API_KEY}",
+            "api_key": "${env.TOGETHER_API_KEY:}",
         }

llama_stack/templates/open-benchmark/__init__.py

@@ -0,0 +1,7 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from .open_benchmark import get_distribution_template  # noqa: F401

llama_stack/templates/open-benchmark/open_benchmark.py

@@ -0,0 +1,293 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from typing import List, Tuple
+
+from llama_stack.apis.models.models import ModelType
+from llama_stack.distribution.datatypes import (
+    BenchmarkInput,
+    DatasetInput,
+    ModelInput,
+    Provider,
+    ShieldInput,
+    ToolGroupInput,
+)
+from llama_stack.providers.inline.vector_io.sqlite_vec.config import SQLiteVectorIOConfig
+from llama_stack.providers.remote.inference.anthropic.config import AnthropicConfig
+from llama_stack.providers.remote.inference.gemini.config import GeminiConfig
+from llama_stack.providers.remote.inference.groq.config import GroqConfig
+from llama_stack.providers.remote.inference.openai.config import OpenAIConfig
+from llama_stack.providers.remote.inference.together.config import TogetherImplConfig
+from llama_stack.providers.remote.vector_io.chroma.config import ChromaVectorIOConfig
+from llama_stack.providers.remote.vector_io.pgvector.config import PGVectorVectorIOConfig
+from llama_stack.providers.utils.inference.model_registry import (
+    ProviderModelEntry,
+)
+from llama_stack.templates.template import DistributionTemplate, RunConfigSettings, get_model_registry
+
+
+def get_inference_providers() -> Tuple[List[Provider], List[ModelInput]]:
+    # in this template, we allow each API key to be optional
+    providers = [
+        (
+            "openai",
+            [
+                ProviderModelEntry(
+                    provider_model_id="openai/gpt-4o",
+                    model_type=ModelType.llm,
+                )
+            ],
+            OpenAIConfig.sample_run_config(api_key="${env.OPENAI_API_KEY:}"),
+        ),
+        (
+            "anthropic",
+            [
+                ProviderModelEntry(
+                    provider_model_id="anthropic/claude-3-5-sonnet-latest",
+                    model_type=ModelType.llm,
+                )
+            ],
+            AnthropicConfig.sample_run_config(api_key="${env.ANTHROPIC_API_KEY:}"),
+        ),
+        (
+            "gemini",
+            [
+                ProviderModelEntry(
+                    provider_model_id="gemini/gemini-1.5-flash",
+                    model_type=ModelType.llm,
+                )
+            ],
+            GeminiConfig.sample_run_config(api_key="${env.GEMINI_API_KEY:}"),
+        ),
+        (
+            "groq",
+            [],
+            GroqConfig.sample_run_config(api_key="${env.GROQ_API_KEY:}"),
+        ),
+        (
+            "together",
+            [],
+            TogetherImplConfig.sample_run_config(api_key="${env.TOGETHER_API_KEY:}"),
+        ),
+    ]
+    inference_providers = []
+    available_models = {}
+    for provider_id, model_entries, config in providers:
+        inference_providers.append(
+            Provider(
+                provider_id=provider_id,
+                provider_type=f"remote::{provider_id}",
+                config=config,
+            )
+        )
+        available_models[provider_id] = model_entries
+    return inference_providers, available_models
+
+
+def get_distribution_template() -> DistributionTemplate:
+    inference_providers, available_models = get_inference_providers()
+    providers = {
+        "inference": [p.provider_type for p in inference_providers],
+        "vector_io": ["inline::sqlite-vec", "remote::chromadb", "remote::pgvector"],
+        "safety": ["inline::llama-guard"],
+        "agents": ["inline::meta-reference"],
+        "telemetry": ["inline::meta-reference"],
+        "eval": ["inline::meta-reference"],
+        "datasetio": ["remote::huggingface", "inline::localfs"],
+        "scoring": ["inline::basic", "inline::llm-as-judge", "inline::braintrust"],
+        "tool_runtime": [
+            "remote::brave-search",
+            "remote::tavily-search",
+            "inline::code-interpreter",
+            "inline::rag-runtime",
+            "remote::model-context-protocol",
+        ],
+    }
+    name = "open-benchmark"
+
+    vector_io_providers = [
+        Provider(
+            provider_id="sqlite-vec",
+            provider_type="inline::sqlite-vec",
+            config=SQLiteVectorIOConfig.sample_run_config(f"~/.llama/distributions/{name}"),
+        ),
+        Provider(
+            provider_id="${env.ENABLE_CHROMADB+chromadb}",
+            provider_type="remote::chromadb",
+            config=ChromaVectorIOConfig.sample_run_config(url="${env.CHROMADB_URL:}"),
+        ),
+        Provider(
+            provider_id="${env.ENABLE_PGVECTOR+pgvector}",
+            provider_type="remote::pgvector",
+            config=PGVectorVectorIOConfig.sample_run_config(
+                db="${env.PGVECTOR_DB:}",
+                user="${env.PGVECTOR_USER:}",
+                password="${env.PGVECTOR_PASSWORD:}",
+            ),
+        ),
+    ]
+
+    default_tool_groups = [
+        ToolGroupInput(
+            toolgroup_id="builtin::websearch",
+            provider_id="tavily-search",
+        ),
+        ToolGroupInput(
+            toolgroup_id="builtin::rag",
+            provider_id="rag-runtime",
+        ),
+        ToolGroupInput(
+            toolgroup_id="builtin::code_interpreter",
+            provider_id="code-interpreter",
+        ),
+    ]
+
+    default_models = get_model_registry(available_models) + [
+        ModelInput(
+            model_id="meta-llama/Llama-3.3-70B-Instruct",
+            provider_id="groq",
+            provider_model_id="groq/llama-3.3-70b-versatile",
+            model_type=ModelType.llm,
+        ),
+        ModelInput(
+            model_id="meta-llama/Llama-3.1-405B-Instruct",
+            provider_id="together",
+            provider_model_id="meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo",
+            model_type=ModelType.llm,
+        ),
+    ]
+
+    default_datasets = [
+        DatasetInput(
+            dataset_id="simpleqa",
+            provider_id="huggingface",
+            url={"uri": "https://huggingface.co/datasets/llamastack/simpleqa"},
+            metadata={
+                "path": "llamastack/simpleqa",
+                "split": "train",
+            },
+            dataset_schema={
+                "input_query": {"type": "string"},
+                "expected_answer": {"type": "string"},
+                "chat_completion_input": {"type": "string"},
+            },
+        ),
+        DatasetInput(
+            dataset_id="mmlu_cot",
+            provider_id="huggingface",
+            url={"uri": "https://huggingface.co/datasets/llamastack/mmlu_cot"},
+            metadata={
+                "path": "llamastack/mmlu_cot",
+                "name": "all",
+                "split": "test",
+            },
+            dataset_schema={
+                "input_query": {"type": "string"},
+                "expected_answer": {"type": "string"},
+                "chat_completion_input": {"type": "string"},
+            },
+        ),
+        DatasetInput(
+            dataset_id="gpqa_cot",
+            provider_id="huggingface",
+            url={"uri": "https://huggingface.co/datasets/llamastack/gpqa_0shot_cot"},
+            metadata={
+                "path": "llamastack/gpqa_0shot_cot",
+                "name": "gpqa_main",
+                "split": "train",
+            },
+            dataset_schema={
+                "input_query": {"type": "string"},
+                "expected_answer": {"type": "string"},
+                "chat_completion_input": {"type": "string"},
+            },
+        ),
+        DatasetInput(
+            dataset_id="math_500",
+            provider_id="huggingface",
+            url={"uri": "https://huggingface.co/datasets/llamastack/math_500"},
+            metadata={
+                "path": "llamastack/math_500",
+                "split": "test",
+            },
+            dataset_schema={
+                "input_query": {"type": "string"},
+                "expected_answer": {"type": "string"},
+                "chat_completion_input": {"type": "string"},
+            },
+        ),
+    ]
+
+    default_benchmarks = [
+        BenchmarkInput(
+            benchmark_id="meta-reference-simpleqa",
+            dataset_id="simpleqa",
+            scoring_functions=["llm-as-judge::405b-simpleqa"],
+        ),
+        BenchmarkInput(
+            benchmark_id="meta-reference-mmlu-cot",
+            dataset_id="mmlu_cot",
+            scoring_functions=["basic::regex_parser_multiple_choice_answer"],
+        ),
+        BenchmarkInput(
+            benchmark_id="meta-reference-gpqa-cot",
+            dataset_id="gpqa_cot",
+            scoring_functions=["basic::regex_parser_multiple_choice_answer"],
+        ),
+        BenchmarkInput(
+            benchmark_id="meta-reference-math-500",
+            dataset_id="math_500",
+            scoring_functions=["basic::regex_parser_math_response"],
+        ),
+    ]
+    return DistributionTemplate(
+        name=name,
+        distro_type="self_hosted",
+        description="Distribution for running open benchmarks",
+        container_image=None,
+        template_path=None,
+        providers=providers,
+        available_models_by_provider=available_models,
+        run_configs={
+            "run.yaml": RunConfigSettings(
+                provider_overrides={
+                    "inference": inference_providers,
+                    "vector_io": vector_io_providers,
+                },
+                default_models=default_models,
+                default_tool_groups=default_tool_groups,
+                default_shields=[ShieldInput(shield_id="meta-llama/Llama-Guard-3-8B")],
+                default_datasets=default_datasets,
+                default_benchmarks=default_benchmarks,
+            ),
+        },
+        run_config_env_vars={
+            "LLAMA_STACK_PORT": (
+                "5001",
+                "Port for the Llama Stack distribution server",
+            ),
+            "TOGETHER_API_KEY": (
+                "",
+                "Together API Key",
+            ),
+            "OPENAI_API_KEY": (
+                "",
+                "OpenAI API Key",
+            ),
+            "GEMINI_API_KEY": (
+                "",
+                "Gemini API Key",
+            ),
+            "ANTHROPIC_API_KEY": (
+                "",
+                "Anthropic API Key",
+            ),
+            "GROQ_API_KEY": (
+                "",
+                "Groq API Key",
+            ),
+        },
+    )

llama_stack/templates/open-benchmark/run.yaml

@@ -38,7 +38,7 @@ providers:
   - provider_id: sqlite-vec
     provider_type: inline::sqlite-vec
     config:
-      db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/dev}/sqlite_vec.db
+      db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/open-benchmark}/sqlite_vec.db
   - provider_id: ${env.ENABLE_CHROMADB+chromadb}
     provider_type: remote::chromadb
     config:
@@ -62,14 +62,14 @@ providers:
       persistence_store:
         type: sqlite
         namespace: null
-        db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/dev}/agents_store.db
+        db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/open-benchmark}/agents_store.db
   telemetry:
   - provider_id: meta-reference
     provider_type: inline::meta-reference
     config:
       service_name: ${env.OTEL_SERVICE_NAME:llama-stack}
       sinks: ${env.TELEMETRY_SINKS:console,sqlite}
-      sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/dev/trace_store.db}
+      sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/open-benchmark/trace_store.db}
   eval:
   - provider_id: meta-reference
     provider_type: inline::meta-reference
@@ -114,18 +114,13 @@ providers:
     config: {}
 metadata_store:
   type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/dev}/registry.db
+  db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/open-benchmark}/registry.db
 models:
 - metadata: {}
   model_id: openai/gpt-4o
   provider_id: openai
   provider_model_id: openai/gpt-4o
   model_type: llm
-- metadata: {}
-  model_id: meta-llama/Llama-3.1-405B-Instruct
-  provider_id: together
-  provider_model_id: meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo
-  model_type: llm
 - metadata: {}
   model_id: anthropic/claude-3-5-sonnet-latest
   provider_id: anthropic
@@ -141,84 +136,95 @@ models:
   provider_id: groq
   provider_model_id: groq/llama-3.3-70b-versatile
   model_type: llm
+- metadata: {}
+  model_id: meta-llama/Llama-3.1-405B-Instruct
+  provider_id: together
+  provider_model_id: meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo
+  model_type: llm
 shields:
 - shield_id: meta-llama/Llama-Guard-3-8B
 vector_dbs: []
 datasets:
-  - dataset_id: simpleqa
+- dataset_schema:
-    provider_id: huggingface
-    url:
-      uri: https://huggingface.co/datasets/llamastack/simpleqa
-    metadata:
-      path: llamastack/simpleqa
-      name:
-      split: train
-    dataset_schema:
     input_query:
       type: string
     expected_answer:
       type: string
     chat_completion_input:
       type: string
-  - dataset_id: mmlu_cot
+  url:
+    uri: https://huggingface.co/datasets/llamastack/simpleqa
+  metadata:
+    path: llamastack/simpleqa
+    split: train
+  dataset_id: simpleqa
   provider_id: huggingface
+- dataset_schema:
+    input_query:
+      type: string
+    expected_answer:
+      type: string
+    chat_completion_input:
+      type: string
   url:
     uri: https://huggingface.co/datasets/llamastack/mmlu_cot
   metadata:
     path: llamastack/mmlu_cot
     name: all
     split: test
-    dataset_schema:
+  dataset_id: mmlu_cot
+  provider_id: huggingface
+- dataset_schema:
     input_query:
       type: string
     expected_answer:
       type: string
     chat_completion_input:
       type: string
-  - dataset_id: gpqa_cot
-    provider_id: huggingface
   url:
     uri: https://huggingface.co/datasets/llamastack/gpqa_0shot_cot
   metadata:
     path: llamastack/gpqa_0shot_cot
     name: gpqa_main
     split: train
-    dataset_schema:
+  dataset_id: gpqa_cot
+  provider_id: huggingface
+- dataset_schema:
     input_query:
       type: string
     expected_answer:
       type: string
     chat_completion_input:
       type: string
-  - dataset_id: math_500
-    provider_id: huggingface
   url:
     uri: https://huggingface.co/datasets/llamastack/math_500
   metadata:
     path: llamastack/math_500
-      name:
     split: test
-    dataset_schema:
+  dataset_id: math_500
-      input_query:
+  provider_id: huggingface
-        type: string
-      expected_answer:
-        type: string
-      chat_completion_input:
-        type: string
 scoring_fns: []
 benchmarks:
-  - benchmark_id: meta-reference-simpleqa
+- dataset_id: simpleqa
-    dataset_id: simpleqa
+  scoring_functions:
-    scoring_functions: ["llm-as-judge::405b-simpleqa"]
+  - llm-as-judge::405b-simpleqa
-  - benchmark_id: meta-reference-mmlu-cot
+  metadata: {}
-    dataset_id: mmlu_cot
+  benchmark_id: meta-reference-simpleqa
-    scoring_functions: ["basic::regex_parser_multiple_choice_answer"]
+- dataset_id: mmlu_cot
-  - benchmark_id: meta-reference-gpqa-cot
+  scoring_functions:
-    dataset_id: gpqa_cot
+  - basic::regex_parser_multiple_choice_answer
-    scoring_functions: ["basic::regex_parser_multiple_choice_answer"]
+  metadata: {}
-  - benchmark_id: meta-reference-math-500
+  benchmark_id: meta-reference-mmlu-cot
-    dataset_id: math_500
+- dataset_id: gpqa_cot
-    scoring_functions: ["basic::regex_parser_math_response"]
+  scoring_functions:
+  - basic::regex_parser_multiple_choice_answer
+  metadata: {}
+  benchmark_id: meta-reference-gpqa-cot
+- dataset_id: math_500
+  scoring_functions:
+  - basic::regex_parser_math_response
+  metadata: {}
+  benchmark_id: meta-reference-math-500
 tool_groups:
 - toolgroup_id: builtin::websearch
   provider_id: tavily-search

llama_stack/templates/template.py

@@ -14,7 +14,9 @@ from pydantic import BaseModel, Field
 from llama_stack.apis.models.models import ModelType
 from llama_stack.distribution.datatypes import (
     Api,
+    BenchmarkInput,
     BuildConfig,
+    DatasetInput,
     DistributionSpec,
     ModelInput,
     Provider,
@@ -56,6 +58,8 @@ class RunConfigSettings(BaseModel):
     default_models: Optional[List[ModelInput]] = None
     default_shields: Optional[List[ShieldInput]] = None
     default_tool_groups: Optional[List[ToolGroupInput]] = None
+    default_datasets: Optional[List[DatasetInput]] = None
+    default_benchmarks: Optional[List[BenchmarkInput]] = None
 
     def run_config(
         self,
@@ -113,6 +117,8 @@ class RunConfigSettings(BaseModel):
             models=self.default_models or [],
             shields=self.default_shields or [],
             tool_groups=self.default_tool_groups or [],
+            datasets=self.default_datasets or [],
+            benchmarks=self.default_benchmarks or [],
         )
 
 

llama_stack/templates/together/run-with-safety.yaml

@@ -16,7 +16,7 @@ providers:
     provider_type: remote::together
     config:
       url: https://api.together.xyz/v1
-      api_key: ${env.TOGETHER_API_KEY}
+      api_key: ${env.TOGETHER_API_KEY:}
   - provider_id: sentence-transformers
     provider_type: inline::sentence-transformers
     config: {}

llama_stack/templates/together/run.yaml

@@ -16,7 +16,7 @@ providers:
     provider_type: remote::together
     config:
       url: https://api.together.xyz/v1
-      api_key: ${env.TOGETHER_API_KEY}
+      api_key: ${env.TOGETHER_API_KEY:}
   - provider_id: sentence-transformers
     provider_type: inline::sentence-transformers
     config: {}