mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-12-04 10:10:36 +00:00
chore(package): migrate to src/ layout (#3920)
Migrates package structure to src/ layout following Python packaging best practices. All code moved from `llama_stack/` to `src/llama_stack/`. Public API unchanged - imports remain `import llama_stack.*`. Updated build configs, pre-commit hooks, scripts, and GitHub workflows accordingly. All hooks pass, package builds cleanly. **Developer note**: Reinstall after pulling: `pip install -e .`
This commit is contained in:
Ashwin Bharambe
GitHub
parent
98a5047f9d
commit
471b1b248b
791 changed files with 2983 additions and 456 deletions
|
|
@ -1,7 +0,0 @@
|
|||
# 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 .nvidia import get_distribution_template # noqa: F401
|
||||
|
|
@ -1,141 +0,0 @@
|
|||
---
|
||||
orphan: true
|
||||
---
|
||||
# NVIDIA Distribution
|
||||
|
||||
The `llamastack/distribution-{{ name }}` distribution consists of the following provider configurations.
|
||||
|
||||
{{ providers_table }}
|
||||
|
||||
{% if run_config_env_vars %}
|
||||
### Environment Variables
|
||||
|
||||
The following environment variables can be configured:
|
||||
|
||||
{% for var, (default_value, description) in run_config_env_vars.items() %}
|
||||
- `{{ var }}`: {{ description }} (default: `{{ default_value }}`)
|
||||
{% endfor %}
|
||||
{% endif %}
|
||||
|
||||
{% if default_models %}
|
||||
### Models
|
||||
|
||||
The following models are available by default:
|
||||
|
||||
{% for model in default_models %}
|
||||
- `{{ model.model_id }} {{ model.doc_string }}`
|
||||
{% endfor %}
|
||||
{% endif %}
|
||||
|
||||
|
||||
## Prerequisites
|
||||
### NVIDIA API Keys
|
||||
|
||||
Make sure you have access to a NVIDIA API Key. You can get one by visiting [https://build.nvidia.com/](https://build.nvidia.com/). Use this key for the `NVIDIA_API_KEY` environment variable.
|
||||
|
||||
### Deploy NeMo Microservices Platform
|
||||
The NVIDIA NeMo microservices platform supports end-to-end microservice deployment of a complete AI flywheel on your Kubernetes cluster through the NeMo Microservices Helm Chart. Please reference the [NVIDIA NeMo Microservices documentation](https://docs.nvidia.com/nemo/microservices/latest/about/index.html) for platform prerequisites and instructions to install and deploy the platform.
|
||||
|
||||
## Supported Services
|
||||
Each Llama Stack API corresponds to a specific NeMo microservice. The core microservices (Customizer, Evaluator, Guardrails) are exposed by the same endpoint. The platform components (Data Store) are each exposed by separate endpoints.
|
||||
|
||||
### Inference: NVIDIA NIM
|
||||
NVIDIA NIM is used for running inference with registered models. There are two ways to access NVIDIA NIMs:
|
||||
1. Hosted (default): Preview APIs hosted at https://integrate.api.nvidia.com (Requires an API key)
|
||||
2. Self-hosted: NVIDIA NIMs that run on your own infrastructure.
|
||||
|
||||
The deployed platform includes the NIM Proxy microservice, which is the service that provides to access your NIMs (for example, to run inference on a model). Set the `NVIDIA_BASE_URL` environment variable to use your NVIDIA NIM Proxy deployment.
|
||||
|
||||
### Datasetio API: NeMo Data Store
|
||||
The NeMo Data Store microservice serves as the default file storage solution for the NeMo microservices platform. It exposts APIs compatible with the Hugging Face Hub client (`HfApi`), so you can use the client to interact with Data Store. The `NVIDIA_DATASETS_URL` environment variable should point to your NeMo Data Store endpoint.
|
||||
|
||||
See the [NVIDIA Datasetio docs](https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/datasetio/nvidia/README.md) for supported features and example usage.
|
||||
|
||||
### Eval API: NeMo Evaluator
|
||||
The NeMo Evaluator microservice supports evaluation of LLMs. Launching an Evaluation job with NeMo Evaluator requires an Evaluation Config (an object that contains metadata needed by the job). A Llama Stack Benchmark maps to an Evaluation Config, so registering a Benchmark creates an Evaluation Config in NeMo Evaluator. The `NVIDIA_EVALUATOR_URL` environment variable should point to your NeMo Microservices endpoint.
|
||||
|
||||
See the [NVIDIA Eval docs](https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/eval/nvidia/README.md) for supported features and example usage.
|
||||
|
||||
### Post-Training API: NeMo Customizer
|
||||
The NeMo Customizer microservice supports fine-tuning models. You can reference [this list of supported models](https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/post_training/nvidia/models.py) that can be fine-tuned using Llama Stack. The `NVIDIA_CUSTOMIZER_URL` environment variable should point to your NeMo Microservices endpoint.
|
||||
|
||||
See the [NVIDIA Post-Training docs](https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/post_training/nvidia/README.md) for supported features and example usage.
|
||||
|
||||
### Safety API: NeMo Guardrails
|
||||
The NeMo Guardrails microservice sits between your application and the LLM, and adds checks and content moderation to a model. The `GUARDRAILS_SERVICE_URL` environment variable should point to your NeMo Microservices endpoint.
|
||||
|
||||
See the [NVIDIA Safety docs](https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/safety/nvidia/README.md) for supported features and example usage.
|
||||
|
||||
## Deploying models
|
||||
In order to use a registered model with the Llama Stack APIs, ensure the corresponding NIM is deployed to your environment. For example, you can use the NIM Proxy microservice to deploy `meta/llama-3.2-1b-instruct`.
|
||||
|
||||
Note: For improved inference speeds, we need to use NIM with `fast_outlines` guided decoding system (specified in the request body). This is the default if you deployed the platform with the NeMo Microservices Helm Chart.
|
||||
```sh
|
||||
# URL to NeMo NIM Proxy service
|
||||
export NEMO_URL="http://nemo.test"
|
||||
|
||||
curl --location "$NEMO_URL/v1/deployment/model-deployments" \
|
||||
-H 'accept: application/json' \
|
||||
-H 'Content-Type: application/json' \
|
||||
-d '{
|
||||
"name": "llama-3.2-1b-instruct",
|
||||
"namespace": "meta",
|
||||
"config": {
|
||||
"model": "meta/llama-3.2-1b-instruct",
|
||||
"nim_deployment": {
|
||||
"image_name": "nvcr.io/nim/meta/llama-3.2-1b-instruct",
|
||||
"image_tag": "1.8.3",
|
||||
"pvc_size": "25Gi",
|
||||
"gpu": 1,
|
||||
"additional_envs": {
|
||||
"NIM_GUIDED_DECODING_BACKEND": "fast_outlines"
|
||||
}
|
||||
}
|
||||
}
|
||||
}'
|
||||
```
|
||||
This NIM deployment should take approximately 10 minutes to go live. [See the docs](https://docs.nvidia.com/nemo/microservices/latest/get-started/tutorials/deploy-nims.html) for more information on how to deploy a NIM and verify it's available for inference.
|
||||
|
||||
You can also remove a deployed NIM to free up GPU resources, if needed.
|
||||
```sh
|
||||
export NEMO_URL="http://nemo.test"
|
||||
|
||||
curl -X DELETE "$NEMO_URL/v1/deployment/model-deployments/meta/llama-3.1-8b-instruct"
|
||||
```
|
||||
|
||||
## Running Llama Stack with NVIDIA
|
||||
|
||||
You can do this via venv (build code), or Docker which has a pre-built image.
|
||||
|
||||
### Via Docker
|
||||
|
||||
This method allows you to get started quickly without having to build the distribution code.
|
||||
|
||||
```bash
|
||||
LLAMA_STACK_PORT=8321
|
||||
docker run \
|
||||
-it \
|
||||
--pull always \
|
||||
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
|
||||
-v ./run.yaml:/root/my-run.yaml \
|
||||
-e NVIDIA_API_KEY=$NVIDIA_API_KEY \
|
||||
llamastack/distribution-{{ name }} \
|
||||
--config /root/my-run.yaml \
|
||||
--port $LLAMA_STACK_PORT
|
||||
```
|
||||
|
||||
### Via venv
|
||||
|
||||
If you've set up your local development environment, you can also install the distribution dependencies using your local virtual environment.
|
||||
|
||||
```bash
|
||||
INFERENCE_MODEL=meta-llama/Llama-3.1-8B-Instruct
|
||||
llama stack list-deps nvidia | xargs -L1 uv pip install
|
||||
NVIDIA_API_KEY=$NVIDIA_API_KEY \
|
||||
INFERENCE_MODEL=$INFERENCE_MODEL \
|
||||
llama stack run ./run.yaml \
|
||||
--port 8321
|
||||
```
|
||||
|
||||
## Example Notebooks
|
||||
For examples of how to use the NVIDIA Distribution to run inference, fine-tune, evaluate, and run safety checks on your LLMs, you can reference the example notebooks in [docs/notebooks/nvidia](https://github.com/meta-llama/llama-stack/tree/main/docs/notebooks/nvidia).
|
||||
|
|
@ -1,154 +0,0 @@
|
|||
# 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 llama_stack.core.datatypes import BuildProvider, ModelInput, Provider, ShieldInput, ToolGroupInput
|
||||
from llama_stack.distributions.template import DistributionTemplate, RunConfigSettings
|
||||
from llama_stack.providers.inline.files.localfs.config import LocalfsFilesImplConfig
|
||||
from llama_stack.providers.remote.datasetio.nvidia import NvidiaDatasetIOConfig
|
||||
from llama_stack.providers.remote.eval.nvidia import NVIDIAEvalConfig
|
||||
from llama_stack.providers.remote.inference.nvidia import NVIDIAConfig
|
||||
from llama_stack.providers.remote.safety.nvidia import NVIDIASafetyConfig
|
||||
|
||||
|
||||
def get_distribution_template(name: str = "nvidia") -> DistributionTemplate:
|
||||
providers = {
|
||||
"inference": [BuildProvider(provider_type="remote::nvidia")],
|
||||
"vector_io": [BuildProvider(provider_type="inline::faiss")],
|
||||
"safety": [BuildProvider(provider_type="remote::nvidia")],
|
||||
"agents": [BuildProvider(provider_type="inline::meta-reference")],
|
||||
"eval": [BuildProvider(provider_type="remote::nvidia")],
|
||||
"post_training": [BuildProvider(provider_type="remote::nvidia")],
|
||||
"datasetio": [
|
||||
BuildProvider(provider_type="inline::localfs"),
|
||||
BuildProvider(provider_type="remote::nvidia"),
|
||||
],
|
||||
"scoring": [BuildProvider(provider_type="inline::basic")],
|
||||
"tool_runtime": [BuildProvider(provider_type="inline::rag-runtime")],
|
||||
"files": [BuildProvider(provider_type="inline::localfs")],
|
||||
}
|
||||
|
||||
inference_provider = Provider(
|
||||
provider_id="nvidia",
|
||||
provider_type="remote::nvidia",
|
||||
config=NVIDIAConfig.sample_run_config(),
|
||||
)
|
||||
safety_provider = Provider(
|
||||
provider_id="nvidia",
|
||||
provider_type="remote::nvidia",
|
||||
config=NVIDIASafetyConfig.sample_run_config(),
|
||||
)
|
||||
datasetio_provider = Provider(
|
||||
provider_id="nvidia",
|
||||
provider_type="remote::nvidia",
|
||||
config=NvidiaDatasetIOConfig.sample_run_config(),
|
||||
)
|
||||
eval_provider = Provider(
|
||||
provider_id="nvidia",
|
||||
provider_type="remote::nvidia",
|
||||
config=NVIDIAEvalConfig.sample_run_config(),
|
||||
)
|
||||
files_provider = Provider(
|
||||
provider_id="meta-reference-files",
|
||||
provider_type="inline::localfs",
|
||||
config=LocalfsFilesImplConfig.sample_run_config(f"~/.llama/distributions/{name}"),
|
||||
)
|
||||
inference_model = ModelInput(
|
||||
model_id="${env.INFERENCE_MODEL}",
|
||||
provider_id="nvidia",
|
||||
)
|
||||
safety_model = ModelInput(
|
||||
model_id="${env.SAFETY_MODEL}",
|
||||
provider_id="nvidia",
|
||||
)
|
||||
|
||||
default_tool_groups = [
|
||||
ToolGroupInput(
|
||||
toolgroup_id="builtin::rag",
|
||||
provider_id="rag-runtime",
|
||||
),
|
||||
]
|
||||
|
||||
return DistributionTemplate(
|
||||
name=name,
|
||||
distro_type="self_hosted",
|
||||
description="Use NVIDIA NIM for running LLM inference, evaluation and safety",
|
||||
container_image=None,
|
||||
template_path=Path(__file__).parent / "doc_template.md",
|
||||
providers=providers,
|
||||
run_configs={
|
||||
"run.yaml": RunConfigSettings(
|
||||
provider_overrides={
|
||||
"inference": [inference_provider],
|
||||
"datasetio": [datasetio_provider],
|
||||
"eval": [eval_provider],
|
||||
"files": [files_provider],
|
||||
},
|
||||
default_tool_groups=default_tool_groups,
|
||||
),
|
||||
"run-with-safety.yaml": RunConfigSettings(
|
||||
provider_overrides={
|
||||
"inference": [
|
||||
inference_provider,
|
||||
safety_provider,
|
||||
],
|
||||
"eval": [eval_provider],
|
||||
"files": [files_provider],
|
||||
},
|
||||
default_models=[inference_model, safety_model],
|
||||
default_shields=[ShieldInput(shield_id="${env.SAFETY_MODEL}", provider_id="nvidia")],
|
||||
default_tool_groups=default_tool_groups,
|
||||
),
|
||||
},
|
||||
run_config_env_vars={
|
||||
"NVIDIA_API_KEY": (
|
||||
"",
|
||||
"NVIDIA API Key",
|
||||
),
|
||||
"NVIDIA_APPEND_API_VERSION": (
|
||||
"True",
|
||||
"Whether to append the API version to the base_url",
|
||||
),
|
||||
## Nemo Customizer related variables
|
||||
"NVIDIA_DATASET_NAMESPACE": (
|
||||
"default",
|
||||
"NVIDIA Dataset Namespace",
|
||||
),
|
||||
"NVIDIA_PROJECT_ID": (
|
||||
"test-project",
|
||||
"NVIDIA Project ID",
|
||||
),
|
||||
"NVIDIA_CUSTOMIZER_URL": (
|
||||
"https://customizer.api.nvidia.com",
|
||||
"NVIDIA Customizer URL",
|
||||
),
|
||||
"NVIDIA_OUTPUT_MODEL_DIR": (
|
||||
"test-example-model@v1",
|
||||
"NVIDIA Output Model Directory",
|
||||
),
|
||||
"GUARDRAILS_SERVICE_URL": (
|
||||
"http://0.0.0.0:7331",
|
||||
"URL for the NeMo Guardrails Service",
|
||||
),
|
||||
"NVIDIA_GUARDRAILS_CONFIG_ID": (
|
||||
"self-check",
|
||||
"NVIDIA Guardrail Configuration ID",
|
||||
),
|
||||
"NVIDIA_EVALUATOR_URL": (
|
||||
"http://0.0.0.0:7331",
|
||||
"URL for the NeMo Evaluator Service",
|
||||
),
|
||||
"INFERENCE_MODEL": (
|
||||
"Llama3.1-8B-Instruct",
|
||||
"Inference model",
|
||||
),
|
||||
"SAFETY_MODEL": (
|
||||
"meta/llama-3.1-8b-instruct",
|
||||
"Name of the model to use for safety",
|
||||
),
|
||||
},
|
||||
)
|
||||
Loading…
Add table
Add a link
Reference in a new issue