llama-stack/llama_stack/templates/template.py
Hardik Shah a84e7669f0
feat: Add a new template for dell (#978)
- Added new template `dell` and its documentation 
- Update docs 
- [minor] uv fix i came across 
- codegen for all templates 

Tested with 

```bash
export INFERENCE_PORT=8181
export DEH_URL=http://0.0.0.0:$INFERENCE_PORT
export INFERENCE_MODEL=meta-llama/Llama-3.1-8B-Instruct
export CHROMADB_HOST=localhost
export CHROMADB_PORT=6601
export CHROMA_URL=[http://$CHROMADB_HOST:$CHROMADB_PORT](about:blank)
export CUDA_VISIBLE_DEVICES=0
export LLAMA_STACK_PORT=8321

# build the stack template 
llama stack build --template=dell 

# start the TGI inference server 
podman run --rm -it --network host -v $HOME/.cache/huggingface:/data -e HF_TOKEN=$HF_TOKEN -p $INFERENCE_PORT:$INFERENCE_PORT --gpus $CUDA_VISIBLE_DEVICES [ghcr.io/huggingface/text-generation-inference](http://ghcr.io/huggingface/text-generation-inference) --dtype bfloat16 --usage-stats off --sharded false --cuda-memory-fraction 0.7 --model-id $INFERENCE_MODEL --port $INFERENCE_PORT --hostname 0.0.0.0

# start chroma-db for vector-io ( aka RAG )
podman run --rm -it --network host --name chromadb -v .:/chroma/chroma -e IS_PERSISTENT=TRUE chromadb/chroma:latest --port $CHROMADB_PORT --host $(hostname)

# build docker 
llama stack build --template=dell --image-type=container

# run llama stack server ( via docker )
podman run -it \
--network host \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-v ~/.llama:/root/.llama \
# NOTE: mount the llama-stack / llama-model directories if testing local changes 
-v /home/hjshah/git/llama-stack:/app/llama-stack-source -v /home/hjshah/git/llama-models:/app/llama-models-source \ localhost/distribution-dell:dev \
--port $LLAMA_STACK_PORT  \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env DEH_URL=$DEH_URL \
--env CHROMA_URL=$CHROMA_URL

# test the server 
cd <PATH_TO_LLAMA_STACK_REPO>
LLAMA_STACK_BASE_URL=http://0.0.0.0:$LLAMA_STACK_PORT pytest -s -v tests/client-sdk/agents/test_agents.py

```

---------

Co-authored-by: Hardik Shah <hjshah@fb.com>
2025-02-06 14:14:39 -08:00

184 lines
6.6 KiB
Python

# 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 pathlib import Path
from typing import Dict, List, Literal, Optional, Tuple
import jinja2
import yaml
from llama_stack.apis.models.models import ModelType
from llama_stack.distribution.datatypes import (
Api,
BuildConfig,
DistributionSpec,
ModelInput,
Provider,
ShieldInput,
StackRunConfig,
ToolGroupInput,
)
from llama_stack.distribution.distribution import get_provider_registry
from llama_stack.distribution.utils.dynamic import instantiate_class_type
from llama_stack.providers.utils.kvstore.config import SqliteKVStoreConfig
from pydantic import BaseModel, Field
class RunConfigSettings(BaseModel):
provider_overrides: Dict[str, List[Provider]] = Field(default_factory=dict)
default_models: Optional[List[ModelInput]] = None
default_shields: Optional[List[ShieldInput]] = None
default_tool_groups: Optional[List[ToolGroupInput]] = None
def run_config(
self,
name: str,
providers: Dict[str, List[str]],
container_image: Optional[str] = None,
) -> StackRunConfig:
provider_registry = get_provider_registry()
provider_configs = {}
for api_str, provider_types in providers.items():
if api_providers := self.provider_overrides.get(api_str):
provider_configs[api_str] = api_providers
continue
provider_configs[api_str] = []
for provider_type in provider_types:
provider_id = provider_type.split("::")[-1]
api = Api(api_str)
if provider_type not in provider_registry[api]:
raise ValueError(f"Unknown provider type: {provider_type} for API: {api_str}")
config_class = provider_registry[api][provider_type].config_class
assert config_class is not None, (
f"No config class for provider type: {provider_type} for API: {api_str}"
)
config_class = instantiate_class_type(config_class)
if hasattr(config_class, "sample_run_config"):
config = config_class.sample_run_config(__distro_dir__=f"distributions/{name}")
else:
config = {}
provider_configs[api_str].append(
Provider(
provider_id=provider_id,
provider_type=provider_type,
config=config,
)
)
# Get unique set of APIs from providers
apis = sorted(providers.keys())
return StackRunConfig(
image_name=name,
container_image=container_image,
apis=apis,
providers=provider_configs,
metadata_store=SqliteKVStoreConfig.sample_run_config(
__distro_dir__=f"distributions/{name}",
db_name="registry.db",
),
models=self.default_models or [],
shields=self.default_shields or [],
tool_groups=self.default_tool_groups or [],
)
class DistributionTemplate(BaseModel):
"""
Represents a Llama Stack distribution instance that can generate configuration
and documentation files.
"""
name: str
description: str
distro_type: Literal["self_hosted", "remote_hosted", "ondevice"]
providers: Dict[str, List[str]]
run_configs: Dict[str, RunConfigSettings]
template_path: Optional[Path] = None
# Optional configuration
run_config_env_vars: Optional[Dict[str, Tuple[str, str]]] = None
container_image: Optional[str] = None
default_models: Optional[List[ModelInput]] = None
def build_config(self) -> BuildConfig:
return BuildConfig(
name=self.name,
distribution_spec=DistributionSpec(
description=self.description,
container_image=self.container_image,
providers=self.providers,
),
image_type="conda", # default to conda, can be overridden
)
def generate_markdown_docs(self) -> str:
providers_table = "| API | Provider(s) |\n"
providers_table += "|-----|-------------|\n"
for api, providers in sorted(self.providers.items()):
providers_str = ", ".join(f"`{p}`" for p in providers)
providers_table += f"| {api} | {providers_str} |\n"
template = "<!-- This file was auto-generated by distro_codegen.py, please edit source -->\n"
template += self.template_path.read_text()
# Render template with rich-generated table
env = jinja2.Environment(
trim_blocks=True,
lstrip_blocks=True,
# NOTE: autoescape is required to prevent XSS attacks
autoescape=True,
)
template = env.from_string(template)
return template.render(
name=self.name,
description=self.description,
providers=self.providers,
providers_table=providers_table,
run_config_env_vars=self.run_config_env_vars,
default_models=self.default_models,
)
def save_distribution(self, yaml_output_dir: Path, doc_output_dir: Path) -> None:
def enum_representer(dumper, data):
return dumper.represent_scalar("tag:yaml.org,2002:str", data.value)
# Register YAML representer for ModelType
yaml.add_representer(ModelType, enum_representer)
yaml.SafeDumper.add_representer(ModelType, enum_representer)
for output_dir in [yaml_output_dir, doc_output_dir]:
output_dir.mkdir(parents=True, exist_ok=True)
build_config = self.build_config()
with open(yaml_output_dir / "build.yaml", "w") as f:
yaml.safe_dump(
build_config.model_dump(exclude_none=True),
f,
sort_keys=False,
)
for yaml_pth, settings in self.run_configs.items():
run_config = settings.run_config(self.name, self.providers, self.container_image)
with open(yaml_output_dir / yaml_pth, "w") as f:
yaml.safe_dump(
run_config.model_dump(exclude_none=True),
f,
sort_keys=False,
)
if self.template_path:
docs = self.generate_markdown_docs()
with open(doc_output_dir / f"{self.name}.md", "w") as f:
f.write(docs if docs.endswith("\n") else docs + "\n")