Add test template

CONTRIBUTING.md

@@ -40,7 +40,21 @@ Your contributions will fall into two categories:
 
 Once you finish implementing a feature or bug-fix, please send a Pull Request to https://github.com/pytorch/serve.
 
-For more non-technical guidance about how to contribute to PyTorch, see the Contributing Guide.
+New features should always be covered by at least one integration test.
+For guidance please have a look at our [current suite of pytest tests](https://github.com/pytorch/serve/tree/master/test/pytest) and orient yourself on a test that covers a similar use case as your new feature.
+A simplified version of an example test can be found in the [mnist template test](https://github.com/pytorch/serve/blob/master/test/pytest/test_mnist_template.py) which shows how to create a mar file on the fly and register it with TorchServe from within a test.
+You can run most tests by simply executing:
+```bash
+pytest test/pytest/test_mnist_template.py
+```
+To have a look at the TorchServe and/or test output add `-s` like this:
+```bash
+pytest -s test/pytest/test_mnist_template.py
+```
+To run only a subset or a single test from a file use `-k` like this:
+```bash
+pytest -k  test/pytest/test_mnist_template.py
+```
 
 ### Install TorchServe for development
 
@@ -50,7 +64,7 @@ Ensure that you have `python3` installed, and the user has access to the site-pa
 
 Run the following script from the top of the source directory.
 
-NOTE: This script force reinstalls `torchserve`, `torch-model-archiver` and `torch-workflow-archiver` if existing installations are found
+NOTE: This script force re-installs `torchserve`, `torch-model-archiver` and `torch-workflow-archiver` if existing installations are found
 
 #### For Debian Based Systems/ MacOS
 

test/pytest/test_mnist_template.py

@@ -0,0 +1,120 @@
+import shutil
+from pathlib import Path
+from unittest.mock import patch
+
+import pytest
+import requests
+import test_utils
+from model_archiver import ModelArchiverConfig
+
+CURR_FILE_PATH = Path(__file__).parent
+REPO_ROOT_DIR = CURR_FILE_PATH.parents[1]
+config_file = REPO_ROOT_DIR / "test/resources/config_token.properties"
+data_file_zero = REPO_ROOT_DIR / "test/pytest/test_data/0.png"
+handler_py_file = REPO_ROOT_DIR / "examples/image_classifier/mnist/mnist_handler.py"
+model_py_file = REPO_ROOT_DIR / "examples/image_classifier/mnist/mnist.py"
+model_pt_file = REPO_ROOT_DIR / "examples/image_classifier/mnist/mnist_cnn.pt"
+
+
+HANDLER_PY = """
+import torch
+from ts.torch_handler.base_handler import BaseHandler
+
+class customHandler(BaseHandler):
+
+    def initialize(self, context):
+        super().initialize(context)
+"""
+
+MODEL_CONFIG_YAML = """
+    #frontend settings
+    # TorchServe frontend parameters
+    minWorkers: 1
+    batchSize: 1
+    maxWorkers: 1
+    """
+
+
+@pytest.fixture(scope="module")
+def model_name():
+    yield "some_model"
+
+
+@pytest.fixture(scope="module")
+def work_dir(tmp_path_factory, model_name):
+    return Path(tmp_path_factory.mktemp(model_name))
+
+
+@pytest.fixture(scope="module", name="mar_file_path")
+def create_mar_file(work_dir, model_archiver, model_name):
+    mar_file_path = work_dir.joinpath(model_name + ".mar")
+
+    model_config_yaml_file = work_dir / "model_config.yaml"
+    model_config_yaml_file.write_text(MODEL_CONFIG_YAML)
+
+    config = ModelArchiverConfig(
+        model_name=model_name,
+        version="1.0",
+        serialized_file=model_pt_file.as_posix(),
+        model_file=model_py_file.as_posix(),
+        handler=handler_py_file.as_posix(),
+        extra_files=None,
+        export_path=work_dir,
+        requirements_file=None,
+        runtime="python",
+        force=False,
+        archive_format="default",
+        config_file=model_config_yaml_file.as_posix(),
+    )
+
+    with patch("archiver.ArgParser.export_model_args_parser", return_value=config):
+        model_archiver.generate_model_archive()
+
+        assert mar_file_path.exists()
+
+        yield mar_file_path.as_posix()
+
+        # Clean up files
+
+    mar_file_path.unlink(missing_ok=True)
+
+    # Clean up files
+
+
+@pytest.fixture(scope="module", name="model_name")
+def register_model(mar_file_path, model_store, torchserve):
+    """
+    Register the model in torchserve
+    """
+    shutil.copy(mar_file_path, model_store)
+
+    file_name = Path(mar_file_path).name
+
+    model_name = Path(file_name).stem
+
+    params = (
+        ("model_name", model_name),
+        ("url", file_name),
+        ("initial_workers", "1"),
+        ("synchronous", "true"),
+        ("batch_size", "1"),
+    )
+
+    test_utils.reg_resp = test_utils.register_model_with_params(params)
+
+    yield model_name
+
+    test_utils.unregister_model(model_name)
+
+
+def test_mnist_template(model_name):
+    response = requests.get(f"http://localhost:8081/models/{model_name}")
+    assert response.status_code == 200, "Describe Failed"
+
+    with open(data_file_zero, "rb") as f:
+        response = requests.post(
+            f"http://localhost:8080/predictions/{model_name}",
+            data=f,
+        )
+
+    assert response.content.decode("utf-8") == "0", "Wrong prediction"

ts_scripts/spellcheck_conf/wordlist.txt

@@ -1238,3 +1238,4 @@ lora
 vllm
 sql
 TimeUnit
+Aopen