feat: NVIDIA beginner e2e notebook

This commit is contained in:
Jash Gulabrai 2025-04-15 23:26:38 -04:00
parent 7cdd2a0410
commit 6927cdf5ce
31 changed files with 888 additions and 1621 deletions

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# NVIDIA DatasetIO Provider for LlamaStack
This provider enables dataset management using NVIDIA's NeMo Customizer service.
## Features
- Register datasets for fine-tuning LLMs
- Unregister datasets
## Getting Started
### Prerequisites
- LlamaStack with NVIDIA configuration
- Access to Hosted NVIDIA NeMo Microservice
- API key for authentication with the NVIDIA service
### Setup
Build the NVIDIA environment:
```bash
llama stack build --template nvidia --image-type conda
```
### Basic Usage using the LlamaStack Python Client
#### Initialize the client
```python
import os
os.environ["NVIDIA_API_KEY"] = "your-api-key"
os.environ["NVIDIA_CUSTOMIZER_URL"] = "http://nemo.test"
os.environ["NVIDIA_USER_ID"] = "llama-stack-user"
os.environ["NVIDIA_DATASET_NAMESPACE"] = "default"
os.environ["NVIDIA_PROJECT_ID"] = "test-project"
from llama_stack.distribution.library_client import LlamaStackAsLibraryClient
client = LlamaStackAsLibraryClient("nvidia")
client.initialize()
```
#### Register a dataset
```python
client.datasets.register(
purpose="post-training/messages",
dataset_id="my-training-dataset",
source={"type": "uri", "uri": "hf://datasets/default/sample-dataset"},
metadata={
"format": "json",
"description": "Dataset for LLM fine-tuning",
"provider": "nvidia",
},
)
```
#### Get a list of all registered datasets
```python
datasets = client.datasets.list()
for dataset in datasets:
print(f"Dataset ID: {dataset.identifier}")
print(f"Description: {dataset.metadata.get('description', '')}")
print(f"Source: {dataset.source.uri}")
print("---")
```
#### Unregister a dataset
```python
client.datasets.unregister(dataset_id="my-training-dataset")
```

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# 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 .config import NvidiaDatasetIOConfig
async def get_adapter_impl(
config: NvidiaDatasetIOConfig,
_deps,
):
from .datasetio import NvidiaDatasetIOAdapter
if not isinstance(config, NvidiaDatasetIOConfig):
raise RuntimeError(f"Unexpected config type: {type(config)}")
impl = NvidiaDatasetIOAdapter(config)
return impl
__all__ = ["get_adapter_impl", "NvidiaDatasetIOAdapter"]

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# 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.
import os
import warnings
from typing import Any, Dict, Optional
from pydantic import BaseModel, Field
class NvidiaDatasetIOConfig(BaseModel):
"""Configuration for NVIDIA DatasetIO implementation."""
api_key: Optional[str] = Field(
default_factory=lambda: os.getenv("NVIDIA_API_KEY"),
description="The NVIDIA API key.",
)
dataset_namespace: Optional[str] = Field(
default_factory=lambda: os.getenv("NVIDIA_DATASET_NAMESPACE", "default"),
description="The NVIDIA dataset namespace.",
)
project_id: Optional[str] = Field(
default_factory=lambda: os.getenv("NVIDIA_PROJECT_ID", "test-project"),
description="The NVIDIA project ID.",
)
datasets_url: str = Field(
default_factory=lambda: os.getenv("NVIDIA_DATASETS_URL", "http://nemo.test"),
description="Base URL for the NeMo Dataset API",
)
# warning for default values
def __post_init__(self):
default_values = []
if os.getenv("NVIDIA_PROJECT_ID") is None:
default_values.append("project_id='test-project'")
if os.getenv("NVIDIA_DATASET_NAMESPACE") is None:
default_values.append("dataset_namespace='default'")
if os.getenv("NVIDIA_DATASETS_URL") is None:
default_values.append("datasets_url='http://nemo.test'")
if default_values:
warnings.warn(
f"Using default values: {', '.join(default_values)}. \
Please set the environment variables to avoid this default behavior.",
stacklevel=2,
)
@classmethod
def sample_run_config(cls, **kwargs) -> Dict[str, Any]:
return {
"api_key": "${env.NVIDIA_API_KEY:}",
"dataset_namespace": "${env.NVIDIA_DATASET_NAMESPACE:default}",
"project_id": "${env.NVIDIA_PROJECT_ID:test-project}",
"datasets_url": "${env.NVIDIA_DATASETS_URL:http://nemo.test}",
}

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# 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 Any, Dict, List, Optional
import aiohttp
from llama_stack.apis.common.content_types import URL
from llama_stack.apis.common.responses import PaginatedResponse
from llama_stack.apis.common.type_system import ParamType
from llama_stack.apis.datasets import Dataset
from .config import NvidiaDatasetIOConfig
class NvidiaDatasetIOAdapter:
"""Nvidia NeMo DatasetIO API."""
def __init__(self, config: NvidiaDatasetIOConfig):
self.config = config
self.headers = {}
async def _make_request(
self,
method: str,
path: str,
headers: Optional[Dict[str, Any]] = None,
params: Optional[Dict[str, Any]] = None,
json: Optional[Dict[str, Any]] = None,
**kwargs,
) -> Dict[str, Any]:
"""Helper method to make HTTP requests to the Customizer API."""
url = f"{self.config.datasets_url}{path}"
request_headers = self.headers.copy()
# Set default Content-Type for JSON requests
if json is not None:
request_headers["Content-Type"] = "application/json"
if headers:
request_headers.update(headers)
async with aiohttp.ClientSession(headers=request_headers) as session:
# TODO: Remove `verify_ssl=False`. Added for testing purposes to call NMP int environment from `docs/notebooks/nvidia/`
async with session.request(method, url, params=params, json=json, verify_ssl=False, **kwargs) as response:
if response.status != 200:
error_data = await response.json()
raise Exception(f"API request failed: {error_data}")
return await response.json()
async def register_dataset(
self,
dataset_def: Dataset,
) -> Dataset:
"""Register a new dataset.
Args:
dataset_def [Dataset]: The dataset definition.
dataset_id [str]: The ID of the dataset.
source [DataSource]: The source of the dataset.
metadata [Dict[str, Any]]: The metadata of the dataset.
format [str]: The format of the dataset.
description [str]: The description of the dataset.
Returns:
Dataset
"""
## add warnings for unsupported params
request_body = {
"name": dataset_def.identifier,
"namespace": self.config.dataset_namespace,
"files_url": dataset_def.source.uri,
"project": self.config.project_id,
}
if dataset_def.metadata:
request_body["format"] = dataset_def.metadata.get("format")
request_body["description"] = dataset_def.metadata.get("description")
await self._make_request(
"POST",
"/v1/datasets",
json=request_body,
)
return dataset_def
async def update_dataset(
self,
dataset_id: str,
dataset_schema: Dict[str, ParamType],
url: URL,
provider_dataset_id: Optional[str] = None,
provider_id: Optional[str] = None,
metadata: Optional[Dict[str, Any]] = None,
) -> None:
raise NotImplementedError("Not implemented")
async def unregister_dataset(
self,
dataset_id: str,
) -> None:
await self._make_request(
"DELETE",
f"/v1/datasets/{self.config.dataset_namespace}/{dataset_id}",
headers={"Accept": "application/json", "Content-Type": "application/json"},
)
async def iterrows(
self,
dataset_id: str,
start_index: Optional[int] = None,
limit: Optional[int] = None,
) -> PaginatedResponse:
raise NotImplementedError("Not implemented")
async def append_rows(self, dataset_id: str, rows: List[Dict[str, Any]]) -> None:
raise NotImplementedError("Not implemented")