add documentation

docs/source/distributions/remote_hosted_distro/nvidia.md

@@ -9,6 +9,7 @@ The `llamastack/distribution-nvidia` distribution consists of the following prov
 | datasetio | `remote::huggingface`, `inline::localfs` |
 | eval | `inline::meta-reference` |
 | inference | `remote::nvidia` |
+| post_training | `remote::nvidia` |
 | safety | `inline::llama-guard` |
 | scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` |
 | telemetry | `inline::meta-reference` |
@@ -22,6 +23,13 @@ The following environment variables can be configured:
 
 - `LLAMASTACK_PORT`: Port for the Llama Stack distribution server (default: `5001`)
 - `NVIDIA_API_KEY`: NVIDIA API Key (default: ``)
+- `NVIDIA_CUSTOMIZER_URL`: NVIDIA Customizer URL (default: `http://nemo.test`)
+- `NVIDIA_USER_ID`: NVIDIA user ID (default: `llama-stack-user`)
+- `NVIDIA_DATASET_NAMESPACE`: NVIDIA dataset namespace (default: `default`)
+- `NVIDIA_ACCESS_POLICIES`: NVIDIA access policies (default: `{}`)
+- `NVIDIA_PROJECT_ID`: NVIDIA project ID (default: `test-project`)
+- `NVIDIA_OUTPUT_MODEL_DIR`: Directory to save the output model (default: `test-example-model@v1`)
+
 
 ### Models
 

llama_stack/providers/remote/post_training/nvidia/README.md

@@ -0,0 +1,138 @@
+# NVIDIA Post-Training Provider for LlamaStack
+
+This provider enables fine-tuning of LLMs using NVIDIA's NeMo Customizer service.
+
+## Features
+
+- Supervised fine-tuning of Llama models
+- LoRA fine-tuning support
+- Job management and status tracking
+
+## Getting Started
+
+### Prerequisites
+
+- LlamaStack with NVIDIA configuration
+- Access to Hosted NVIDIA NeMo Customizer service
+- Dataset registered in the Hosted NVIDIA NeMo Customizer service
+- Base model downloaded and available in the Hosted NVIDIA NeMo Customizer service
+
+### Setup
+
+Build the NVIDIA environment:
+
+```bash
+llama stack build --template nvidia --image-type conda
+```
+
+### Basic Usage using the LlamaStack Python Client
+
+### Create Customization Job
+
+#### 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"
+os.environ["NVIDIA_OUTPUT_MODEL_DIR"] = "test-example-model@v1"
+
+from llama_stack.distribution.library_client import LlamaStackAsLibraryClient
+
+client = LlamaStackAsLibraryClient("nvidia")
+client.initialize()
+```
+
+#### Configure fine-tuning parameters
+
+```python
+from llama_stack_client.types.post_training_supervised_fine_tune_params import (
+    TrainingConfig,
+    TrainingConfigDataConfig,
+    TrainingConfigOptimizerConfig,
+)
+from llama_stack_client.types.algorithm_config_param import LoraFinetuningConfig
+```
+
+#### Set up LoRA configuration
+
+```python
+algorithm_config = LoraFinetuningConfig(type="LoRA", adapter_dim=16)
+```
+
+#### Configure training data
+
+```python
+data_config = TrainingConfigDataConfig(
+    dataset_id="your-dataset-id",  # Use client.datasets.list() to see available datasets
+    batch_size=16,
+)
+```
+
+#### Configure optimizer
+
+```python
+optimizer_config = TrainingConfigOptimizerConfig(
+    lr=0.0001,
+)
+```
+
+#### Set up training configuration
+
+```python
+training_config = TrainingConfig(
+    n_epochs=2,
+    data_config=data_config,
+    optimizer_config=optimizer_config,
+)
+```
+
+#### Start fine-tuning job
+
+```python
+training_job = client.post_training.supervised_fine_tune(
+    job_uuid="unique-job-id",
+    model="meta-llama/Llama-3.1-8B-Instruct",
+    checkpoint_dir="",
+    algorithm_config=algorithm_config,
+    training_config=training_config,
+    logger_config={},
+    hyperparam_search_config={},
+)
+```
+
+### List all jobs
+
+```python
+jobs = client.post_training.job.list()
+```
+
+###  Check job status
+
+```python
+job_status = client.post_training.job.status(job_uuid="your-job-id")
+```
+
+### Cancel a job
+
+```python
+client.post_training.job.cancel(job_uuid="your-job-id")
+```
+
+### Inference with the fine-tuned model
+
+```python
+response = client.inference.completion(
+    content="Complete the sentence using one word: Roses are red, violets are ",
+    stream=False,
+    model_id="test-example-model@v1",
+    sampling_params={
+        "max_tokens": 50,
+    },
+)
+print(response.content)
+```

llama_stack/providers/remote/post_training/nvidia/config.py

@@ -40,7 +40,7 @@ class NvidiaPostTrainingConfig(BaseModel):
 
     # ToDO: validate this, add default value
     customizer_url: str = Field(
-        default_factory=lambda: os.getenv("NVIDIA_CUSTOMIZER_URL"),
+        default_factory=lambda: os.getenv("NVIDIA_CUSTOMIZER_URL", "http://nemo.test"),
         description="Base URL for the NeMo Customizer API",
     )