feat: Add nemo customizer (#1448)

# What does this PR do?

This PR adds support for NVIDIA's NeMo Customizer API to the Llama Stack
post-training module. The integration enables users to fine-tune models
using NVIDIA's cloud-based customization service through a consistent
Llama Stack interface.


[//]: # (If resolving an issue, uncomment and update the line below)
[//]: # (Closes #[issue-number])

## Test Plan
[Describe the tests you ran to verify your changes with result
summaries. *Provide clear instructions so the plan can be easily
re-executed.*]
Yet to be done

Things pending under this PR:

- [x] Integration of fine-tuned model(new checkpoint) for inference with
nvidia llm distribution
- [x] distribution integration of API
- [x] Add test cases for customizer(In Progress)
- [x] Documentation

```

LLAMA_STACK_BASE_URL=http://localhost:5002 pytest -v tests/client-sdk/post_training/test_supervised_fine_tuning.py 

============================================================================================================================================================================ test session starts =============================================================================================================================================================================
platform linux -- Python 3.10.0, pytest-8.3.4, pluggy-1.5.0 -- /home/ubuntu/llama-stack/.venv/bin/python
cachedir: .pytest_cache
metadata: {'Python': '3.10.0', 'Platform': 'Linux-6.8.0-1021-gcp-x86_64-with-glibc2.35', 'Packages': {'pytest': '8.3.4', 'pluggy': '1.5.0'}, 'Plugins': {'nbval': '0.11.0', 'metadata': '3.1.1', 'anyio': '4.8.0', 'html': '4.1.1', 'asyncio': '0.25.3'}}
rootdir: /home/ubuntu/llama-stack
configfile: pyproject.toml
plugins: nbval-0.11.0, metadata-3.1.1, anyio-4.8.0, html-4.1.1, asyncio-0.25.3
asyncio: mode=strict, asyncio_default_fixture_loop_scope=None
collected 2 items                                                                                                                                                                                                                                                                                                                                                            

tests/client-sdk/post_training/test_supervised_fine_tuning.py::test_post_training_provider_registration[txt=8B] PASSED                                                                                                                                                                                                                                                 [ 50%]
tests/client-sdk/post_training/test_supervised_fine_tuning.py::test_list_training_jobs[txt=8B] PASSED                                                                                                                                                                                                                                                                  [100%]

======================================================================================================================================================================== 2 passed, 1 warning in 0.10s ========================================================================================================================================================================
```
cc: @mattf @dglogo @sumitb

---------

Co-authored-by: Ubuntu <ubuntu@llama-stack-customizer-dev-inst-2tx95fyisatvlic4we8hidx5tfj.us-central1-a.c.brevdevprod.internal>

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)
+```