Merge branch 'main' into chore/add-upstream-to-sl-config

2025-12-22 22:42:25 +00:00 · 2025-07-18 09:52:32 -07:00 · 2025-07-18 09:52:32 -07:00 · 36c8196d17
commit 36c8196d17
parent de1e8128f1 2bb9039173
22 changed files with 583 additions and 521 deletions
--- a/.coveragerc
+++ b/.coveragerc
@ -4,3 +4,9 @@ omit =
    */llama_stack/providers/*
    */llama_stack/templates/*
    .venv/*
+    */llama_stack/cli/scripts/*
+    */llama_stack/ui/*
+    */llama_stack/distribution/ui/*
+    */llama_stack/strong_typing/*
+    */llama_stack/env.py
+    */__init__.py
--- a/.github/workflows/coverage-badge.yml
+++ b/.github/workflows/coverage-badge.yml
@ -0,0 +1,57 @@
+name: Coverage Badge
+
+on:
+  push:
+    branches: [ main ]
+    paths:
+      - 'llama_stack/**'
+      - 'tests/unit/**'
+      - 'uv.lock'
+      - 'pyproject.toml'
+      - 'requirements.txt'
+      - '.github/workflows/unit-tests.yml'
+      - '.github/workflows/coverage-badge.yml' # This workflow
+  workflow_dispatch:
+
+jobs:
+  unit-tests:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+
+      - name: Install dependencies
+        uses: ./.github/actions/setup-runner
+
+      - name: Run unit tests
+        run: |
+          ./scripts/unit-tests.sh
+
+      - name: Coverage Badge
+        uses: tj-actions/coverage-badge-py@1788babcb24544eb5bbb6e0d374df5d1e54e670f # v2.0.4
+
+      - name: Verify Changed files
+        uses: tj-actions/verify-changed-files@a1c6acee9df209257a246f2cc6ae8cb6581c1edf # v20.0.4
+        id: verify-changed-files
+        with:
+          files: coverage.svg
+
+      - name: Commit files
+        if: steps.verify-changed-files.outputs.files_changed == 'true'
+        run: |
+          git config --local user.email "github-actions[bot]@users.noreply.github.com"
+          git config --local user.name "github-actions[bot]"
+          git add coverage.svg
+          git commit -m "Updated coverage.svg"
+
+      - name: Create Pull Request
+        if: steps.verify-changed-files.outputs.files_changed == 'true'
+        uses: peter-evans/create-pull-request@271a8d0340265f705b14b6d32b9829c1cb33d45e # v7.0.8
+        with:
+          token: ${{ secrets.GITHUB_TOKEN }}
+          title: "ci: [Automatic] Coverage Badge Update"
+          body: |
+            This PR updates the coverage badge based on the latest coverage report.
+
+            Automatically generated by the [workflow coverage-badge.yaml](.github/workflows/coverage-badge.yaml)
+          delete-branch: true
--- a/.github/workflows/integration-tests.yml
+++ b/.github/workflows/integration-tests.yml
@ -7,7 +7,7 @@ on:
    branches: [ main ]
    paths:
      - 'llama_stack/**'
-      - 'tests/integration/**'
+      - 'tests/**'
      - 'uv.lock'
      - 'pyproject.toml'
      - 'requirements.txt'
--- a/.github/workflows/unit-tests.yml
+++ b/.github/workflows/unit-tests.yml
@ -36,7 +36,7 @@ jobs:

      - name: Run unit tests
        run: |
-          PYTHON_VERSION=${{ matrix.python }} ./scripts/unit-tests.sh --cov=llama_stack --junitxml=pytest-report-${{ matrix.python }}.xml --cov-report=html:htmlcov-${{ matrix.python }}
+          PYTHON_VERSION=${{ matrix.python }} ./scripts/unit-tests.sh --junitxml=pytest-report-${{ matrix.python }}.xml

      - name: Upload test results
        if: always()
--- a/README.md
+++ b/README.md
@ -6,6 +6,7 @@
 [![Discord](https://img.shields.io/discord/1257833999603335178?color=6A7EC2&logo=discord&logoColor=ffffff)](https://discord.gg/llama-stack)
 [![Unit Tests](https://github.com/meta-llama/llama-stack/actions/workflows/unit-tests.yml/badge.svg?branch=main)](https://github.com/meta-llama/llama-stack/actions/workflows/unit-tests.yml?query=branch%3Amain)
 [![Integration Tests](https://github.com/meta-llama/llama-stack/actions/workflows/integration-tests.yml/badge.svg?branch=main)](https://github.com/meta-llama/llama-stack/actions/workflows/integration-tests.yml?query=branch%3Amain)
+![coverage badge](./coverage.svg)

 [**Quick Start**](https://llama-stack.readthedocs.io/en/latest/getting_started/index.html) | [**Documentation**](https://llama-stack.readthedocs.io/en/latest/index.html) | [**Colab Notebook**](./docs/getting_started.ipynb) | [**Discord**](https://discord.gg/llama-stack)

--- a/coverage.svg
+++ b/coverage.svg
@ -0,0 +1,21 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<svg xmlns="http://www.w3.org/2000/svg" width="99" height="20">
+    <linearGradient id="b" x2="0" y2="100%">
+        <stop offset="0" stop-color="#bbb" stop-opacity=".1"/>
+        <stop offset="1" stop-opacity=".1"/>
+    </linearGradient>
+    <mask id="a">
+        <rect width="99" height="20" rx="3" fill="#fff"/>
+    </mask>
+    <g mask="url(#a)">
+        <path fill="#555" d="M0 0h63v20H0z"/>
+        <path fill="#fe7d37" d="M63 0h36v20H63z"/>
+        <path fill="url(#b)" d="M0 0h99v20H0z"/>
+    </g>
+    <g fill="#fff" text-anchor="middle" font-family="DejaVu Sans,Verdana,Geneva,sans-serif" font-size="11">
+        <text x="31.5" y="15" fill="#010101" fill-opacity=".3">coverage</text>
+        <text x="31.5" y="14">coverage</text>
+        <text x="80" y="15" fill="#010101" fill-opacity=".3">44%</text>
+        <text x="80" y="14">44%</text>
+    </g>
+</svg>
--- a/docs/source/distributions/self_hosted_distro/starter.md
+++ b/docs/source/distributions/self_hosted_distro/starter.md
@ -167,7 +167,7 @@ When using the `:` pattern (like `${env.OLLAMA_INFERENCE_MODEL:__disabled__}`),

 ## Running the Distribution

-You can run the starter distribution via Docker or Conda.
+You can run the starter distribution via Docker, Conda, or venv.

 ### Via Docker

@ -186,17 +186,12 @@ docker run \
  --port $LLAMA_STACK_PORT
 ```

-### Via Conda
+### Via Conda or venv

-Make sure you have done `uv pip install llama-stack` and have the Llama Stack CLI available.
+Ensure you have configured the starter distribution using the environment variables explained above.

 ```bash
-llama stack build --template starter --image-type conda
-llama stack run distributions/starter/run.yaml \
-  --port 8321 \
-  --env OPENAI_API_KEY=your_openai_key \
-  --env FIREWORKS_API_KEY=your_fireworks_key \
-  --env TOGETHER_API_KEY=your_together_key
+uv run --with llama-stack llama stack build --template starter --image-type <conda|venv> --run
 ```

 ## Example Usage
--- a/docs/source/getting_started/quickstart.md
+++ b/docs/source/getting_started/quickstart.md
@ -19,7 +19,7 @@ ollama run llama3.2:3b --keepalive 60m
 #### Step 2: Run the Llama Stack server
 We will use `uv` to run the Llama Stack server.
 ```bash
-INFERENCE_MODEL=llama3.2:3b uv run --with llama-stack llama stack build --template starter --image-type venv --run
+ENABLE_OLLAMA=ollama OLLAMA_INFERENCE_MODEL=llama3.2:3b uv run --with llama-stack llama stack build --template starter --image-type venv --run
 ```
 #### Step 3: Run the demo
 Now open up a new terminal and copy the following script into a file named `demo_script.py`.
@ -111,6 +111,12 @@ Ultimately, great work is about making a meaningful contribution and leaving a l
 ```
 Congratulations! You've successfully built your first RAG application using Llama Stack! 🎉🥳

+```{admonition} HuggingFace access
+:class: tip
+
+If you are getting a **401 Client Error** from HuggingFace for the **all-MiniLM-L6-v2** model, try setting **HF_TOKEN** to a valid HuggingFace token in your environment
+```
+
 ### Next Steps

 Now you're ready to dive deeper into Llama Stack!
--- a/llama_stack/models/llama/llama3/chat_format.py
+++ b/llama_stack/models/llama/llama3/chat_format.py
@ -8,6 +8,7 @@ import io
 import json
 import uuid
 from dataclasses import dataclass
+from typing import Any

 from PIL import Image as PIL_Image

@ -184,16 +185,26 @@ class ChatFormat:
            content = content[: -len("<|eom_id|>")]
            stop_reason = StopReason.end_of_message

-        tool_name = None
-        tool_arguments = {}
+        tool_name: str | BuiltinTool | None = None
+        tool_arguments: dict[str, Any] = {}

        custom_tool_info = ToolUtils.maybe_extract_custom_tool_call(content)
        if custom_tool_info is not None:
-            tool_name, tool_arguments = custom_tool_info
+            # Type guard: ensure custom_tool_info is a tuple of correct types
+            if isinstance(custom_tool_info, tuple) and len(custom_tool_info) == 2:
+                extracted_tool_name, extracted_tool_arguments = custom_tool_info
+                # Handle both dict and str return types from the function
+                if isinstance(extracted_tool_arguments, dict):
+                    tool_name, tool_arguments = extracted_tool_name, extracted_tool_arguments
+                else:
+                    # If it's a string, treat it as a query parameter
+                    tool_name, tool_arguments = extracted_tool_name, {"query": extracted_tool_arguments}
+            else:
+                tool_name, tool_arguments = None, {}
            # Sometimes when agent has custom tools alongside builin tools
            # Agent responds for builtin tool calls in the format of the custom tools
            # This code tries to handle that case
-            if tool_name in BuiltinTool.__members__:
+            if tool_name is not None and tool_name in BuiltinTool.__members__:
                tool_name = BuiltinTool[tool_name]
                if isinstance(tool_arguments, dict):
                    tool_arguments = {
--- a/llama_stack/providers/inline/inference/meta_reference/parallel_utils.py
+++ b/llama_stack/providers/inline/inference/meta_reference/parallel_utils.py
@ -98,7 +98,7 @@ class ProcessingMessageWrapper(BaseModel):


 def mp_rank_0() -> bool:
-    return get_model_parallel_rank() == 0
+    return bool(get_model_parallel_rank() == 0)


 def encode_msg(msg: ProcessingMessage) -> bytes:
@ -125,7 +125,7 @@ def retrieve_requests(reply_socket_url: str):
        reply_socket.send_multipart([client_id, encode_msg(obj)])

    while True:
-        tasks = [None]
+        tasks: list[ProcessingMessage | None] = [None]
        if mp_rank_0():
            client_id, maybe_task_json = maybe_get_work(reply_socket)
            if maybe_task_json is not None:
@ -152,7 +152,7 @@ def retrieve_requests(reply_socket_url: str):
                    break

                for obj in out:
-                    updates = [None]
+                    updates: list[ProcessingMessage | None] = [None]
                    if mp_rank_0():
                        _, update_json = maybe_get_work(reply_socket)
                        update = maybe_parse_message(update_json)
--- a/pyproject.toml
+++ b/pyproject.toml
@ -91,6 +91,7 @@ unit = [
    "pymilvus>=2.5.12",
    "litellm",
    "together",
+    "coverage",
 ]
 # These are the core dependencies required for running integration tests. They are shared across all
 # providers. If a provider requires additional dependencies, please add them to your environment
@ -242,7 +243,6 @@ exclude = [
    "^llama_stack/distribution/store/registry\\.py$",
    "^llama_stack/distribution/utils/exec\\.py$",
    "^llama_stack/distribution/utils/prompt_for_config\\.py$",
-    "^llama_stack/models/llama/llama3/chat_format\\.py$",
    "^llama_stack/models/llama/llama3/interface\\.py$",
    "^llama_stack/models/llama/llama3/tokenizer\\.py$",
    "^llama_stack/models/llama/llama3/tool_utils\\.py$",
@ -255,7 +255,6 @@ exclude = [
    "^llama_stack/models/llama/llama3/generation\\.py$",
    "^llama_stack/models/llama/llama3/multimodal/model\\.py$",
    "^llama_stack/models/llama/llama4/",
-    "^llama_stack/providers/inline/inference/meta_reference/parallel_utils\\.py$",
    "^llama_stack/providers/inline/inference/meta_reference/quantization/fp8_impls\\.py$",
    "^llama_stack/providers/inline/inference/sentence_transformers/sentence_transformers\\.py$",
    "^llama_stack/providers/inline/inference/vllm/",
--- a/scripts/unit-tests.sh
+++ b/scripts/unit-tests.sh
@ -16,4 +16,9 @@ if [ $FOUND_PYTHON -ne 0 ]; then
     uv python install "$PYTHON_VERSION"
 fi

-uv run --python "$PYTHON_VERSION" --with-editable . --group unit pytest -s -v tests/unit/ $@
+# Run unit tests with coverage
+uv run --python "$PYTHON_VERSION" --with-editable . --group unit \
+    coverage run --source=llama_stack -m pytest -s -v tests/unit/ "$@"
+
+# Generate HTML coverage report
+uv run --python "$PYTHON_VERSION" coverage html -d htmlcov-$PYTHON_VERSION
--- a/tests/integration/post_training/test_post_training.py
+++ b/tests/integration/post_training/test_post_training.py
@ -123,14 +123,14 @@ class TestPostTraining:
        logger.info(f"Job artifacts: {artifacts}")

    # TODO: Fix these tests to properly represent the Jobs API in training
-    # @pytest.mark.asyncio
+    #
    # async def test_get_training_jobs(self, post_training_stack):
    #     post_training_impl = post_training_stack
    #     jobs_list = await post_training_impl.get_training_jobs()
    #     assert isinstance(jobs_list, list)
    #     assert jobs_list[0].job_uuid == "1234"

-    # @pytest.mark.asyncio
+    #
    # async def test_get_training_job_status(self, post_training_stack):
    #     post_training_impl = post_training_stack
    #     job_status = await post_training_impl.get_training_job_status("1234")
@ -139,7 +139,7 @@ class TestPostTraining:
    #     assert job_status.status == JobStatus.completed
    #     assert isinstance(job_status.checkpoints[0], Checkpoint)

-    # @pytest.mark.asyncio
+    #
    # async def test_get_training_job_artifacts(self, post_training_stack):
    #     post_training_impl = post_training_stack
    #     job_artifacts = await post_training_impl.get_training_job_artifacts("1234")
--- a/tests/unit/README.md
+++ b/tests/unit/README.md
@ -1,9 +1,17 @@
 # Llama Stack Unit Tests

+## Unit Tests
+
+Unit tests verify individual components and functions in isolation. They are fast, reliable, and don't require external services.
+
+### Prerequisites
+
+1. **Python Environment**: Ensure you have Python 3.12+ installed
+2. **uv Package Manager**: Install `uv` if not already installed
+
 You can run the unit tests by running:

 ```bash
-source .venv/bin/activate
 ./scripts/unit-tests.sh [PYTEST_ARGS]
 ```

@ -19,3 +27,21 @@ If you'd like to run for a non-default version of Python (currently 3.12), pass
 source .venv/bin/activate
 PYTHON_VERSION=3.13 ./scripts/unit-tests.sh
 ```
+
+### Test Configuration
+
+- **Test Discovery**: Tests are automatically discovered in the `tests/unit/` directory
+- **Async Support**: Tests use `--asyncio-mode=auto` for automatic async test handling
+- **Coverage**: Tests generate coverage reports in `htmlcov/` directory
+- **Python Version**: Defaults to Python 3.12, but can be overridden with `PYTHON_VERSION` environment variable
+
+### Coverage Reports
+
+After running tests, you can view coverage reports:
+
+```bash
+# Open HTML coverage report in browser
+open htmlcov/index.html  # macOS
+xdg-open htmlcov/index.html  # Linux
+start htmlcov/index.html  # Windows
+```
--- a/tests/unit/models/test_prompt_adapter.py
+++ b/tests/unit/models/test_prompt_adapter.py
@ -4,7 +4,6 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.

-import pytest

 from llama_stack.apis.inference import (
    ChatCompletionRequest,
@ -32,7 +31,6 @@ MODEL = "Llama3.1-8B-Instruct"
 MODEL3_2 = "Llama3.2-3B-Instruct"


-@pytest.mark.asyncio
 async def test_system_default():
    content = "Hello !"
    request = ChatCompletionRequest(
@ -47,7 +45,6 @@ async def test_system_default():
    assert "Cutting Knowledge Date: December 2023" in interleaved_content_as_str(messages[0].content)


-@pytest.mark.asyncio
 async def test_system_builtin_only():
    content = "Hello !"
    request = ChatCompletionRequest(
@ -67,7 +64,6 @@ async def test_system_builtin_only():
    assert "Tools: brave_search" in interleaved_content_as_str(messages[0].content)


-@pytest.mark.asyncio
 async def test_system_custom_only():
    content = "Hello !"
    request = ChatCompletionRequest(
@ -98,7 +94,6 @@ async def test_system_custom_only():
    assert messages[-1].content == content


-@pytest.mark.asyncio
 async def test_system_custom_and_builtin():
    content = "Hello !"
    request = ChatCompletionRequest(
@ -132,7 +127,6 @@ async def test_system_custom_and_builtin():
    assert messages[-1].content == content


-@pytest.mark.asyncio
 async def test_completion_message_encoding():
    request = ChatCompletionRequest(
        model=MODEL3_2,
@ -174,7 +168,6 @@ async def test_completion_message_encoding():
    assert '{"type": "function", "name": "custom1", "parameters": {"param1": "value1"}}' in prompt


-@pytest.mark.asyncio
 async def test_user_provided_system_message():
    content = "Hello !"
    system_prompt = "You are a pirate"
@ -195,7 +188,6 @@ async def test_user_provided_system_message():
    assert messages[-1].content == content


-@pytest.mark.asyncio
 async def test_replace_system_message_behavior_builtin_tools():
    content = "Hello !"
    system_prompt = "You are a pirate"
@ -221,7 +213,6 @@ async def test_replace_system_message_behavior_builtin_tools():
    assert messages[-1].content == content


-@pytest.mark.asyncio
 async def test_replace_system_message_behavior_custom_tools():
    content = "Hello !"
    system_prompt = "You are a pirate"
@ -259,7 +250,6 @@ async def test_replace_system_message_behavior_custom_tools():
    assert messages[-1].content == content


-@pytest.mark.asyncio
 async def test_replace_system_message_behavior_custom_tools_with_template():
    content = "Hello !"
    system_prompt = "You are a pirate {{ function_description }}"
--- a/tests/unit/models/test_system_prompts.py
+++ b/tests/unit/models/test_system_prompts.py
@ -12,7 +12,6 @@
 # the top-level of this source tree.

 import textwrap
-import unittest
 from datetime import datetime

 from llama_stack.models.llama.llama3.prompt_templates import (
@ -24,24 +23,24 @@ from llama_stack.models.llama.llama3.prompt_templates import (
 )


-class PromptTemplateTests(unittest.TestCase):
-    def check_generator_output(self, generator):
+def check_generator_output(generator):
    for example in generator.data_examples():
        pt = generator.gen(example)
        text = pt.render()
-            # print(text)  # debugging
        if not example:
            continue
        for tool in example:
            assert tool.tool_name in text

-    def test_system_default(self):
+
+def test_system_default():
    generator = SystemDefaultGenerator()
    today = datetime.now().strftime("%d %B %Y")
    expected_text = f"Cutting Knowledge Date: December 2023\nToday Date: {today}"
    assert expected_text.strip("\n") == generator.gen(generator.data_examples()[0]).render()

-    def test_system_builtin_only(self):
+
+def test_system_builtin_only():
    generator = BuiltinToolGenerator()
    expected_text = textwrap.dedent(
        """
@ -51,21 +50,23 @@ class PromptTemplateTests(unittest.TestCase):
    )
    assert expected_text.strip("\n") == generator.gen(generator.data_examples()[0]).render()

-    def test_system_custom_only(self):
-        self.maxDiff = None
+
+def test_system_custom_only():
    generator = JsonCustomToolGenerator()
-        self.check_generator_output(generator)
+    check_generator_output(generator)

-    def test_system_custom_function_tag(self):
-        self.maxDiff = None
+
+def test_system_custom_function_tag():
    generator = FunctionTagCustomToolGenerator()
-        self.check_generator_output(generator)
+    check_generator_output(generator)

-    def test_llama_3_2_system_zero_shot(self):
+
+def test_llama_3_2_system_zero_shot():
    generator = PythonListCustomToolGenerator()
-        self.check_generator_output(generator)
+    check_generator_output(generator)

-    def test_llama_3_2_provided_system_prompt(self):
+
+def test_llama_3_2_provided_system_prompt():
    generator = PythonListCustomToolGenerator()
    user_system_prompt = textwrap.dedent(
        """
--- a/tests/unit/providers/nvidia/test_datastore.py
+++ b/tests/unit/providers/nvidia/test_datastore.py
@ -5,38 +5,34 @@
 # the root directory of this source tree.

 import os
-import unittest
 from unittest.mock import patch

 import pytest

 from llama_stack.apis.datasets import Dataset, DatasetPurpose, URIDataSource
+from llama_stack.apis.resource import ResourceType
 from llama_stack.providers.remote.datasetio.nvidia.config import NvidiaDatasetIOConfig
 from llama_stack.providers.remote.datasetio.nvidia.datasetio import NvidiaDatasetIOAdapter


-class TestNvidiaDatastore(unittest.TestCase):
-    def setUp(self):
+@pytest.fixture
+def nvidia_adapter():
+    """Fixture to set up NvidiaDatasetIOAdapter with mocked requests."""
    os.environ["NVIDIA_DATASETS_URL"] = "http://nemo.test/datasets"

    config = NvidiaDatasetIOConfig(
        datasets_url=os.environ["NVIDIA_DATASETS_URL"], dataset_namespace="default", project_id="default"
    )
-        self.adapter = NvidiaDatasetIOAdapter(config)
-        self.make_request_patcher = patch(
+    adapter = NvidiaDatasetIOAdapter(config)
+
+    with patch(
        "llama_stack.providers.remote.datasetio.nvidia.datasetio.NvidiaDatasetIOAdapter._make_request"
-        )
-        self.mock_make_request = self.make_request_patcher.start()
+    ) as mock_make_request:
+        yield adapter, mock_make_request

-    def tearDown(self):
-        self.make_request_patcher.stop()

-    @pytest.fixture(autouse=True)
-    def inject_fixtures(self, run_async):
-        self.run_async = run_async
-
-    def _assert_request(self, mock_call, expected_method, expected_path, expected_json=None):
-        """Helper method to verify request details in mock calls."""
+def _assert_request(mock_call, expected_method, expected_path, expected_json=None):
+    """Helper function to verify request details in mock calls."""
    call_args = mock_call.call_args

    assert call_args[0][0] == expected_method
@ -46,8 +42,10 @@ class TestNvidiaDatastore(unittest.TestCase):
        for key, value in expected_json.items():
            assert call_args[1]["json"][key] == value

-    def test_register_dataset(self):
-        self.mock_make_request.return_value = {
+
+def test_register_dataset(nvidia_adapter, run_async):
+    adapter, mock_make_request = nvidia_adapter
+    mock_make_request.return_value = {
        "id": "dataset-123456",
        "name": "test-dataset",
        "namespace": "default",
@ -55,7 +53,7 @@ class TestNvidiaDatastore(unittest.TestCase):

    dataset_def = Dataset(
        identifier="test-dataset",
-            type="dataset",
+        type=ResourceType.dataset,
        provider_resource_id="",
        provider_id="",
        purpose=DatasetPurpose.post_training_messages,
@ -63,11 +61,11 @@ class TestNvidiaDatastore(unittest.TestCase):
        metadata={"provider_id": "nvidia", "format": "jsonl", "description": "Test dataset description"},
    )

-        self.run_async(self.adapter.register_dataset(dataset_def))
+    run_async(adapter.register_dataset(dataset_def))

-        self.mock_make_request.assert_called_once()
-        self._assert_request(
-            self.mock_make_request,
+    mock_make_request.assert_called_once()
+    _assert_request(
+        mock_make_request,
        "POST",
        "/v1/datasets",
        expected_json={
@ -80,20 +78,26 @@ class TestNvidiaDatastore(unittest.TestCase):
        },
    )

-    def test_unregister_dataset(self):
-        self.mock_make_request.return_value = {
+
+def test_unregister_dataset(nvidia_adapter, run_async):
+    adapter, mock_make_request = nvidia_adapter
+    mock_make_request.return_value = {
        "message": "Resource deleted successfully.",
        "id": "dataset-81RSQp7FKX3rdBtKvF9Skn",
        "deleted_at": None,
    }
    dataset_id = "test-dataset"

-        self.run_async(self.adapter.unregister_dataset(dataset_id))
+    run_async(adapter.unregister_dataset(dataset_id))

-        self.mock_make_request.assert_called_once()
-        self._assert_request(self.mock_make_request, "DELETE", "/v1/datasets/default/test-dataset")
+    mock_make_request.assert_called_once()
+    _assert_request(mock_make_request, "DELETE", "/v1/datasets/default/test-dataset")
+
+
+def test_register_dataset_with_custom_namespace_project(run_async):
+    """Test with custom namespace and project configuration."""
+    os.environ["NVIDIA_DATASETS_URL"] = "http://nemo.test/datasets"

-    def test_register_dataset_with_custom_namespace_project(self):
    custom_config = NvidiaDatasetIOConfig(
        datasets_url=os.environ["NVIDIA_DATASETS_URL"],
        dataset_namespace="custom-namespace",
@ -101,7 +105,10 @@ class TestNvidiaDatastore(unittest.TestCase):
    )
    custom_adapter = NvidiaDatasetIOAdapter(custom_config)

-        self.mock_make_request.return_value = {
+    with patch(
+        "llama_stack.providers.remote.datasetio.nvidia.datasetio.NvidiaDatasetIOAdapter._make_request"
+    ) as mock_make_request:
+        mock_make_request.return_value = {
            "id": "dataset-123456",
            "name": "test-dataset",
            "namespace": "custom-namespace",
@ -109,7 +116,7 @@ class TestNvidiaDatastore(unittest.TestCase):

        dataset_def = Dataset(
            identifier="test-dataset",
-            type="dataset",
+            type=ResourceType.dataset,
            provider_resource_id="",
            provider_id="",
            purpose=DatasetPurpose.post_training_messages,
@ -117,11 +124,11 @@ class TestNvidiaDatastore(unittest.TestCase):
            metadata={"format": "jsonl"},
        )

-        self.run_async(custom_adapter.register_dataset(dataset_def))
+        run_async(custom_adapter.register_dataset(dataset_def))

-        self.mock_make_request.assert_called_once()
-        self._assert_request(
-            self.mock_make_request,
+        mock_make_request.assert_called_once()
+        _assert_request(
+            mock_make_request,
            "POST",
            "/v1/datasets",
            expected_json={
@ -132,7 +139,3 @@ class TestNvidiaDatastore(unittest.TestCase):
                "format": "jsonl",
            },
        )
-
-
-if __name__ == "__main__":
-    unittest.main()
--- a/tests/unit/providers/nvidia/test_parameters.py
+++ b/tests/unit/providers/nvidia/test_parameters.py
@ -5,7 +5,6 @@
 # the root directory of this source tree.

 import os
-import unittest
 import warnings
 from unittest.mock import patch

@ -27,14 +26,13 @@ from llama_stack.providers.remote.post_training.nvidia.post_training import (
 )


-class TestNvidiaParameters(unittest.TestCase):
-    def setUp(self):
-        os.environ["NVIDIA_BASE_URL"] = "http://nemo.test"
+class TestNvidiaParameters:
+    @pytest.fixture(autouse=True)
+    def setup_and_teardown(self):
+        """Setup and teardown for each test method."""
        os.environ["NVIDIA_CUSTOMIZER_URL"] = "http://nemo.test"

-        config = NvidiaPostTrainingConfig(
-            base_url=os.environ["NVIDIA_BASE_URL"], customizer_url=os.environ["NVIDIA_CUSTOMIZER_URL"], api_key=None
-        )
+        config = NvidiaPostTrainingConfig(customizer_url=os.environ["NVIDIA_CUSTOMIZER_URL"], api_key=None)
        self.adapter = NvidiaPostTrainingAdapter(config)

        self.make_request_patcher = patch(
@ -48,7 +46,8 @@ class TestNvidiaParameters(unittest.TestCase):
            "updated_at": "2025-03-04T13:07:47.543605",
        }

-    def tearDown(self):
+        yield
+
        self.make_request_patcher.stop()

    def _assert_request_params(self, expected_json):
@ -166,8 +165,8 @@ class TestNvidiaParameters(unittest.TestCase):

            self.run_async(
                self.adapter.supervised_fine_tune(
-                    job_uuid=required_job_uuid,  # Required parameter
-                    model=required_model,  # Required parameter
+                    job_uuid=required_job_uuid,
+                    model=required_model,
                    checkpoint_dir="",
                    algorithm_config=algorithm_config,
                    training_config=convert_pydantic_to_json_value(training_config),
@ -198,7 +197,6 @@ class TestNvidiaParameters(unittest.TestCase):
        data_config = DataConfig(
            dataset_id="test-dataset",
            batch_size=8,
-            # Unsupported parameters
            shuffle=True,
            data_format=DatasetFormat.instruct,
            validation_dataset_id="val-dataset",
@ -207,20 +205,16 @@ class TestNvidiaParameters(unittest.TestCase):
        optimizer_config = OptimizerConfig(
            lr=0.0001,
            weight_decay=0.01,
-            # Unsupported parameters
            optimizer_type=OptimizerType.adam,
            num_warmup_steps=100,
        )

-        efficiency_config = EfficiencyConfig(
-            enable_activation_checkpointing=True  # Unsupported parameter
-        )
+        efficiency_config = EfficiencyConfig(enable_activation_checkpointing=True)

        training_config = TrainingConfig(
            n_epochs=1,
            data_config=data_config,
            optimizer_config=optimizer_config,
-            # Unsupported parameters
            efficiency_config=efficiency_config,
            max_steps_per_epoch=1000,
            gradient_accumulation_steps=4,
@ -228,7 +222,6 @@ class TestNvidiaParameters(unittest.TestCase):
            dtype="bf16",
        )

-        # Capture warnings
        with warnings.catch_warnings(record=True) as w:
            warnings.simplefilter("always")

@ -236,7 +229,7 @@ class TestNvidiaParameters(unittest.TestCase):
                self.adapter.supervised_fine_tune(
                    job_uuid="test-job",
                    model="meta-llama/Llama-3.1-8B-Instruct",
-                    checkpoint_dir="test-dir",  # Unsupported parameter
+                    checkpoint_dir="test-dir",
                    algorithm_config=LoraFinetuningConfig(
                        type="LoRA",
                        apply_lora_to_mlp=True,
@ -246,8 +239,8 @@ class TestNvidiaParameters(unittest.TestCase):
                        lora_attn_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
                    ),
                    training_config=convert_pydantic_to_json_value(training_config),
-                    logger_config={"test": "value"},  # Unsupported parameter
-                    hyperparam_search_config={"test": "value"},  # Unsupported parameter
+                    logger_config={"test": "value"},
+                    hyperparam_search_config={"test": "value"},
                )
            )

@ -265,7 +258,6 @@ class TestNvidiaParameters(unittest.TestCase):
                "gradient_accumulation_steps",
                "max_validation_steps",
                "dtype",
-                # required unsupported parameters
                "rank",
                "apply_lora_to_output",
                "lora_attn_modules",
@ -273,7 +265,3 @@ class TestNvidiaParameters(unittest.TestCase):
            ]
            for field in fields:
                assert any(field in text for text in warning_texts)
-
-
-if __name__ == "__main__":
-    unittest.main()
--- a/tests/unit/providers/nvidia/test_supervised_fine_tuning.py
+++ b/tests/unit/providers/nvidia/test_supervised_fine_tuning.py
@ -5,13 +5,11 @@
 # the root directory of this source tree.

 import os
-import unittest
 import warnings
-from unittest.mock import AsyncMock, patch
+from unittest.mock import patch

 import pytest

-from llama_stack.apis.models import Model, ModelType
 from llama_stack.apis.post_training.post_training import (
    DataConfig,
    DatasetFormat,
@ -22,7 +20,6 @@ from llama_stack.apis.post_training.post_training import (
    TrainingConfig,
 )
 from llama_stack.distribution.library_client import convert_pydantic_to_json_value
-from llama_stack.providers.remote.inference.nvidia.nvidia import NVIDIAConfig, NVIDIAInferenceAdapter
 from llama_stack.providers.remote.post_training.nvidia.post_training import (
    ListNvidiaPostTrainingJobs,
    NvidiaPostTrainingAdapter,
@ -32,43 +29,19 @@ from llama_stack.providers.remote.post_training.nvidia.post_training import (
 )


-class TestNvidiaPostTraining(unittest.TestCase):
-    def setUp(self):
-        os.environ["NVIDIA_BASE_URL"] = "http://nemo.test"  # needed for llm inference
+@pytest.fixture
+def nvidia_post_training_adapter():
+    """Fixture to create and configure the NVIDIA post training adapter."""
    os.environ["NVIDIA_CUSTOMIZER_URL"] = "http://nemo.test"  # needed for nemo customizer

-        config = NvidiaPostTrainingConfig(
-            base_url=os.environ["NVIDIA_BASE_URL"], customizer_url=os.environ["NVIDIA_CUSTOMIZER_URL"], api_key=None
-        )
-        self.adapter = NvidiaPostTrainingAdapter(config)
-        self.make_request_patcher = patch(
-            "llama_stack.providers.remote.post_training.nvidia.post_training.NvidiaPostTrainingAdapter._make_request"
-        )
-        self.mock_make_request = self.make_request_patcher.start()
+    config = NvidiaPostTrainingConfig(customizer_url=os.environ["NVIDIA_CUSTOMIZER_URL"], api_key=None)
+    adapter = NvidiaPostTrainingAdapter(config)

-        # Mock the inference client
-        inference_config = NVIDIAConfig(base_url=os.environ["NVIDIA_BASE_URL"], api_key=None)
-        self.inference_adapter = NVIDIAInferenceAdapter(inference_config)
+    with patch.object(adapter, "_make_request") as mock_make_request:
+        yield adapter, mock_make_request

-        self.mock_client = unittest.mock.MagicMock()
-        self.mock_client.chat.completions.create = unittest.mock.AsyncMock()
-        self.inference_mock_make_request = self.mock_client.chat.completions.create
-        self.inference_make_request_patcher = patch(
-            "llama_stack.providers.remote.inference.nvidia.nvidia.NVIDIAInferenceAdapter._client",
-            new_callable=unittest.mock.PropertyMock,
-            return_value=self.mock_client,
-        )
-        self.inference_make_request_patcher.start()

-    def tearDown(self):
-        self.make_request_patcher.stop()
-        self.inference_make_request_patcher.stop()
-
-    @pytest.fixture(autouse=True)
-    def inject_fixtures(self, run_async):
-        self.run_async = run_async
-
-    def _assert_request(self, mock_call, expected_method, expected_path, expected_params=None, expected_json=None):
+def _assert_request(mock_call, expected_method, expected_path, expected_params=None, expected_json=None):
    """Helper method to verify request details in mock calls."""
    call_args = mock_call.call_args

@ -86,9 +59,11 @@ class TestNvidiaPostTraining(unittest.TestCase):
        for key, value in expected_json.items():
            assert call_args[1]["json"][key] == value

-    def test_supervised_fine_tune(self):
+
+async def test_supervised_fine_tune(nvidia_post_training_adapter):
    """Test the supervised fine-tuning API call."""
-        self.mock_make_request.return_value = {
+    adapter, mock_make_request = nvidia_post_training_adapter
+    mock_make_request.return_value = {
        "id": "cust-JGTaMbJMdqjJU8WbQdN9Q2",
        "created_at": "2024-12-09T04:06:28.542884",
        "updated_at": "2024-12-09T04:06:28.542884",
@ -163,8 +138,7 @@ class TestNvidiaPostTraining(unittest.TestCase):

    with warnings.catch_warnings(record=True):
        warnings.simplefilter("always")
-            training_job = self.run_async(
-                self.adapter.supervised_fine_tune(
+        training_job = await adapter.supervised_fine_tune(
            job_uuid="1234",
            model="meta/llama-3.2-1b-instruct@v1.0.0+L40",
            checkpoint_dir="",
@ -173,15 +147,14 @@ class TestNvidiaPostTraining(unittest.TestCase):
            logger_config={},
            hyperparam_search_config={},
        )
-            )

    # check the output is a PostTrainingJob
    assert isinstance(training_job, NvidiaPostTrainingJob)
    assert training_job.job_uuid == "cust-JGTaMbJMdqjJU8WbQdN9Q2"

-        self.mock_make_request.assert_called_once()
-        self._assert_request(
-            self.mock_make_request,
+    mock_make_request.assert_called_once()
+    _assert_request(
+        mock_make_request,
        "POST",
        "/v1/customization/jobs",
        expected_json={
@ -199,7 +172,11 @@ class TestNvidiaPostTraining(unittest.TestCase):
        },
    )

-    def test_supervised_fine_tune_with_qat(self):
+
+async def test_supervised_fine_tune_with_qat(nvidia_post_training_adapter):
+    """Test that QAT configuration raises NotImplementedError."""
+    adapter, mock_make_request = nvidia_post_training_adapter
+
    algorithm_config = QATFinetuningConfig(type="QAT", quantizer_name="quantizer_name", group_size=1)
    data_config = DataConfig(
        dataset_id="sample-basic-test", batch_size=16, shuffle=False, data_format=DatasetFormat.instruct
@ -215,10 +192,10 @@ class TestNvidiaPostTraining(unittest.TestCase):
        data_config=data_config,
        optimizer_config=optimizer_config,
    )
+
    # This will raise NotImplementedError since QAT is not supported
-        with self.assertRaises(NotImplementedError):
-            self.run_async(
-                self.adapter.supervised_fine_tune(
+    with pytest.raises(NotImplementedError):
+        await adapter.supervised_fine_tune(
            job_uuid="1234",
            model="meta/llama-3.2-1b-instruct@v1.0.0+L40",
            checkpoint_dir="",
@ -227,9 +204,12 @@ class TestNvidiaPostTraining(unittest.TestCase):
            logger_config={},
            hyperparam_search_config={},
        )
-            )

-    def test_get_training_job_status(self):
+
+async def test_get_training_job_status(nvidia_post_training_adapter):
+    """Test getting training job status with different statuses."""
+    adapter, mock_make_request = nvidia_post_training_adapter
+
    customizer_status_to_job_status = [
        ("running", "in_progress"),
        ("completed", "completed"),
@ -240,8 +220,7 @@ class TestNvidiaPostTraining(unittest.TestCase):
    ]

    for customizer_status, expected_status in customizer_status_to_job_status:
-            with self.subTest(customizer_status=customizer_status, expected_status=expected_status):
-                self.mock_make_request.return_value = {
+        mock_make_request.return_value = {
            "created_at": "2024-12-09T04:06:28.580220",
            "updated_at": "2024-12-09T04:21:19.852832",
            "status": customizer_status,
@ -255,27 +234,35 @@ class TestNvidiaPostTraining(unittest.TestCase):

        job_id = "cust-JGTaMbJMdqjJU8WbQdN9Q2"

-                status = self.run_async(self.adapter.get_training_job_status(job_uuid=job_id))
+        status = await adapter.get_training_job_status(job_uuid=job_id)

        assert isinstance(status, NvidiaPostTrainingJobStatusResponse)
        assert status.status.value == expected_status
-                assert status.steps_completed == 1210
-                assert status.epochs_completed == 2
-                assert status.percentage_done == 100.0
-                assert status.best_epoch == 2
-                assert status.train_loss == 1.718016266822815
-                assert status.val_loss == 1.8661999702453613
+        # Note: The response object inherits extra fields via ConfigDict(extra="allow")
+        # So these attributes should be accessible using getattr with defaults
+        assert getattr(status, "steps_completed", None) == 1210
+        assert getattr(status, "epochs_completed", None) == 2
+        assert getattr(status, "percentage_done", None) == 100.0
+        assert getattr(status, "best_epoch", None) == 2
+        assert getattr(status, "train_loss", None) == 1.718016266822815
+        assert getattr(status, "val_loss", None) == 1.8661999702453613

-                self._assert_request(
-                    self.mock_make_request,
+        _assert_request(
+            mock_make_request,
            "GET",
            f"/v1/customization/jobs/{job_id}/status",
            expected_params={"job_id": job_id},
        )

-    def test_get_training_jobs(self):
+        mock_make_request.reset_mock()
+
+
+async def test_get_training_jobs(nvidia_post_training_adapter):
+    """Test getting list of training jobs."""
+    adapter, mock_make_request = nvidia_post_training_adapter
+
    job_id = "cust-JGTaMbJMdqjJU8WbQdN9Q2"
-        self.mock_make_request.return_value = {
+    mock_make_request.return_value = {
        "data": [
            {
                "id": job_id,
@ -301,7 +288,7 @@ class TestNvidiaPostTraining(unittest.TestCase):
        ]
    }

-        jobs = self.run_async(self.adapter.get_training_jobs())
+    jobs = await adapter.get_training_jobs()

    assert isinstance(jobs, ListNvidiaPostTrainingJobs)
    assert len(jobs.data) == 1
@ -309,59 +296,30 @@ class TestNvidiaPostTraining(unittest.TestCase):
    assert job.job_uuid == job_id
    assert job.status.value == "completed"

-        self.mock_make_request.assert_called_once()
-        self._assert_request(
-            self.mock_make_request,
+    mock_make_request.assert_called_once()
+    _assert_request(
+        mock_make_request,
        "GET",
        "/v1/customization/jobs",
        expected_params={"page": 1, "page_size": 10, "sort": "created_at"},
    )

-    def test_cancel_training_job(self):
-        self.mock_make_request.return_value = {}  # Empty response for successful cancellation
+
+async def test_cancel_training_job(nvidia_post_training_adapter):
+    """Test canceling a training job."""
+    adapter, mock_make_request = nvidia_post_training_adapter
+
+    mock_make_request.return_value = {}  # Empty response for successful cancellation
    job_id = "cust-JGTaMbJMdqjJU8WbQdN9Q2"

-        result = self.run_async(self.adapter.cancel_training_job(job_uuid=job_id))
+    result = await adapter.cancel_training_job(job_uuid=job_id)

    assert result is None

-        self.mock_make_request.assert_called_once()
-        self._assert_request(
-            self.mock_make_request,
+    mock_make_request.assert_called_once()
+    _assert_request(
+        mock_make_request,
        "POST",
        f"/v1/customization/jobs/{job_id}/cancel",
        expected_params={"job_id": job_id},
    )
-
-    def test_inference_register_model(self):
-        model_id = "default/job-1234"
-        model_type = ModelType.llm
-        model = Model(
-            identifier=model_id,
-            provider_id="nvidia",
-            provider_model_id=model_id,
-            provider_resource_id=model_id,
-            model_type=model_type,
-        )
-
-        # simulate a NIM where default/job-1234 is an available model
-        with patch.object(self.inference_adapter, "check_model_availability", new_callable=AsyncMock) as mock_check:
-            mock_check.return_value = True
-            result = self.run_async(self.inference_adapter.register_model(model))
-        assert result == model
-        assert len(self.inference_adapter.alias_to_provider_id_map) > 1
-        assert self.inference_adapter.get_provider_model_id(model.provider_model_id) == model_id
-
-        with patch.object(self.inference_adapter, "chat_completion") as mock_chat_completion:
-            self.run_async(
-                self.inference_adapter.chat_completion(
-                    model_id=model_id,
-                    messages=[{"role": "user", "content": "Hello, model"}],
-                )
-            )
-
-            mock_chat_completion.assert_called()
-
-
-if __name__ == "__main__":
-    unittest.main()
--- a/tests/unit/providers/vector_io/remote/test_milvus.py
+++ b/tests/unit/providers/vector_io/remote/test_milvus.py
@ -8,7 +8,6 @@ from unittest.mock import MagicMock, patch

 import numpy as np
 import pytest
-import pytest_asyncio

 from llama_stack.apis.vector_io import QueryChunksResponse

@ -33,7 +32,7 @@ with patch.dict("sys.modules", {"pymilvus": pymilvus_mock}):
 MILVUS_PROVIDER = "milvus"


-@pytest_asyncio.fixture
+@pytest.fixture
 async def mock_milvus_client() -> MagicMock:
    """Create a mock Milvus client with common method behaviors."""
    client = MagicMock()
@ -84,7 +83,7 @@ async def mock_milvus_client() -> MagicMock:
    return client


-@pytest_asyncio.fixture
+@pytest.fixture
 async def milvus_index(mock_milvus_client):
    """Create a MilvusIndex with mocked client."""
    index = MilvusIndex(client=mock_milvus_client, collection_name="test_collection")
@ -92,7 +91,6 @@ async def milvus_index(mock_milvus_client):
    # No real cleanup needed since we're using mocks


-@pytest.mark.asyncio
 async def test_add_chunks(milvus_index, sample_chunks, sample_embeddings, mock_milvus_client):
    # Setup: collection doesn't exist initially, then exists after creation
    mock_milvus_client.has_collection.side_effect = [False, True]
@ -108,7 +106,6 @@ async def test_add_chunks(milvus_index, sample_chunks, sample_embeddings, mock_m
    assert len(insert_call[1]["data"]) == len(sample_chunks)


-@pytest.mark.asyncio
 async def test_query_chunks_vector(
    milvus_index, sample_chunks, sample_embeddings, embedding_dimension, mock_milvus_client
 ):
@ -125,7 +122,6 @@ async def test_query_chunks_vector(
    mock_milvus_client.search.assert_called_once()


-@pytest.mark.asyncio
 async def test_query_chunks_keyword_search(milvus_index, sample_chunks, sample_embeddings, mock_milvus_client):
    mock_milvus_client.has_collection.return_value = True
    await milvus_index.add_chunks(sample_chunks, sample_embeddings)
@ -138,7 +134,6 @@ async def test_query_chunks_keyword_search(milvus_index, sample_chunks, sample_e
    assert len(response.chunks) == 2


-@pytest.mark.asyncio
 async def test_bm25_fallback_to_simple_search(milvus_index, sample_chunks, sample_embeddings, mock_milvus_client):
    """Test that when BM25 search fails, the system falls back to simple text search."""
    mock_milvus_client.has_collection.return_value = True
@ -181,7 +176,6 @@ async def test_bm25_fallback_to_simple_search(milvus_index, sample_chunks, sampl
    assert all(score == 1.0 for score in response.scores), "Simple text search should use binary scoring"


-@pytest.mark.asyncio
 async def test_delete_collection(milvus_index, mock_milvus_client):
    # Test collection deletion
    mock_milvus_client.has_collection.return_value = True
--- a/tests/unit/rag/test_rag_query.py
+++ b/tests/unit/rag/test_rag_query.py
@ -64,7 +64,6 @@ class TestRagQuery:
        with pytest.raises(ValueError):
            RAGQueryConfig(mode="invalid_mode")

-    @pytest.mark.asyncio
    async def test_query_accepts_valid_modes(self):
        RAGQueryConfig()  # Test default (vector)
        RAGQueryConfig(mode="vector")  # Test vector
--- a/uv.lock
+++ b/uv.lock
@ -1390,6 +1390,7 @@ unit = [
    { name = "aiosqlite" },
    { name = "blobfile" },
    { name = "chardet" },
+    { name = "coverage" },
    { name = "faiss-cpu" },
    { name = "litellm" },
    { name = "mcp" },
@ -1499,6 +1500,7 @@ unit = [
    { name = "aiosqlite" },
    { name = "blobfile" },
    { name = "chardet" },
+    { name = "coverage" },
    { name = "faiss-cpu" },
    { name = "litellm" },
    { name = "mcp" },