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Fixed the priority of parameters defined in register curl cmd vs model-config.yaml #2858
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Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,142 @@ | ||
import shutil | ||
from pathlib import Path | ||
from unittest.mock import patch | ||
|
||
import pytest | ||
import test_utils | ||
from model_archiver import ModelArchiverConfig | ||
|
||
CURR_FILE_PATH = Path(__file__).parent | ||
REPO_ROOT_DIR = CURR_FILE_PATH.parent.parent | ||
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||
MODEL_PY = """ | ||
import torch | ||
import torch.nn as nn | ||
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||
class Foo(nn.Module): | ||
def __init__(self): | ||
super().__init__() | ||
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def forward(self, x): | ||
return x | ||
""" | ||
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HANDLER_PY = """ | ||
import os | ||
import torch | ||
from ts.torch_handler.base_handler import BaseHandler | ||
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class FooHandler(BaseHandler): | ||
def initialize(self, ctx): | ||
super().initialize(ctx) | ||
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def preprocess(self, data): | ||
return torch.as_tensor(int(data[0].get('body').decode('utf-8')), device=self.device) | ||
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def postprocess(self, x): | ||
return [x.item()] | ||
""" | ||
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MODEL_CONFIG_YAML = f""" | ||
#frontend settings | ||
# TorchServe frontend parameters | ||
minWorkers: 1 | ||
maxWorkers: 4 | ||
maxBatchDelay: 100 | ||
batchSize: 4 | ||
""" | ||
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||
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@pytest.fixture(scope="module") | ||
def model_name(): | ||
yield "foo" | ||
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@pytest.fixture(scope="module") | ||
def work_dir(tmp_path_factory, model_name): | ||
return Path(tmp_path_factory.mktemp(model_name)) | ||
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||
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@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") | ||
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model_config_yaml_file = work_dir / "model_config.yaml" | ||
model_config_yaml_file.write_text(MODEL_CONFIG_YAML) | ||
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model_py_file = work_dir / "model.py" | ||
model_py_file.write_text(MODEL_PY) | ||
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handler_py_file = work_dir / "handler.py" | ||
handler_py_file.write_text(HANDLER_PY) | ||
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config = ModelArchiverConfig( | ||
model_name=model_name, | ||
version="1.0", | ||
serialized_file=None, | ||
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(), | ||
) | ||
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with patch("archiver.ArgParser.export_model_args_parser", return_value=config): | ||
model_archiver.generate_model_archive() | ||
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assert mar_file_path.exists() | ||
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yield mar_file_path.as_posix() | ||
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# Clean up files | ||
mar_file_path.unlink(missing_ok=True) | ||
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def register_model(mar_file_path, model_store, params, torchserve): | ||
shutil.copy(mar_file_path, model_store) | ||
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file_name = Path(mar_file_path).name | ||
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model_name = Path(file_name).stem | ||
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params = params + ( | ||
("model_name", model_name), | ||
("url", file_name), | ||
) | ||
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test_utils.reg_resp = test_utils.register_model_with_params(params) | ||
return model_name | ||
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def test_register_model_with_batch_size(mar_file_path, model_store, torchserve): | ||
params = ( | ||
("initial_workers", "2"), | ||
("synchronous", "true"), | ||
("batch_size", "2"), | ||
) | ||
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model_name = register_model(mar_file_path, model_store, params, torchserve) | ||
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describe_resp = test_utils.describe_model(model_name, "1.0") | ||
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assert describe_resp[0]["batchSize"] == 2 | ||
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test_utils.unregister_model(model_name) | ||
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def test_register_model_without_batch_size(mar_file_path, model_store, torchserve): | ||
params = ( | ||
("initial_workers", "2"), | ||
("synchronous", "true"), | ||
) | ||
model_name = register_model(mar_file_path, model_store, params, torchserve) | ||
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describe_resp = test_utils.describe_model(model_name, "1.0") | ||
|
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assert describe_resp[0]["batchSize"] == 4 | ||
|
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test_utils.unregister_model(model_name) |
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nit: Do we need this? Can't we achieve the same with
json
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According to the description, it is fastest json lib. I'm thinking if we need apply orjson in our handler for json input.