forked from phoenix-oss/llama-stack-mirror
e6bbf8d20b
# What does this PR do? Implemetation of NeMO Datastore register, unregister API. Open Issues: - provider_id gets set to `localfs` in client.datasets.register() as it is specified in routing_tables.py: DatasetsRoutingTable see: #1860 Currently I have passed `"provider_id":"nvidia"` in metadata and have parsed that in `DatasetsRoutingTable` (Not the best approach, but just a quick workaround to make it work for now.) ## Test Plan - Unit test cases: `pytest tests/unit/providers/nvidia/test_datastore.py` ```bash ========================================================== test session starts =========================================================== platform linux -- Python 3.10.0, pytest-8.3.5, pluggy-1.5.0 rootdir: /home/ubuntu/llama-stack configfile: pyproject.toml plugins: anyio-4.9.0, asyncio-0.26.0, nbval-0.11.0, metadata-3.1.1, html-4.1.1, cov-6.1.0 asyncio: mode=strict, asyncio_default_fixture_loop_scope=None, asyncio_default_test_loop_scope=function collected 2 items tests/unit/providers/nvidia/test_datastore.py .. [100%] ============================================================ warnings summary ============================================================ ====================================================== 2 passed, 1 warning in 0.84s ====================================================== ``` cc: @dglogo, @mattf, @yanxi0830
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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:
llama stack build --template nvidia --image-type conda
Basic Usage using the LlamaStack Python Client
Initialize the client
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
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
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
client.datasets.unregister(dataset_id="my-training-dataset")