address issues building mlflow model in GHA test env
This commit is contained in:
Andrew Tolopko
2022-07-29 11:17:04 -04:00
committed by GitHub
parent 03d9e8e6aa
commit a9ef01a6f9
3 changed files with 21 additions and 14 deletions
+5
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@@ -0,0 +1,5 @@
from .mlflow_model_fixture import FakeModel
def _load_pyfunc(data_path):
return FakeModel()
@@ -1,20 +1,11 @@
import shutil
from tempfile import TemporaryDirectory, mkstemp
import mlflow
def write_model(model) -> str:
with TemporaryDirectory() as mlflow_model_dir:
mlflow.pyfunc.save_model(mlflow_model_dir, python_model=model)
return shutil.make_archive(mkstemp()[1], "zip", mlflow_model_dir)
class FakeModel(mlflow.pyfunc.PythonModel):
def __init__(self, input_to_output: dict = {}):
self.input_to_output = input_to_output
def predict(self, context, model_input) -> None:
def predict(self, model_input) -> None:
# this stdout output is useful for validating the input in a test, noting that this model will be invoked in a
# subprocess, so stdout is one means of communicating information back to the test code
print(f"__MODEL_INPUT__={model_input.iloc[0][0]}")
+15 -4
View File
@@ -1,10 +1,22 @@
import os
import shutil
import unittest
from tempfile import mkstemp
from tempfile import mkstemp, TemporaryDirectory
import mlflow
from click.testing import CliRunner
from server.cli.annotate import annotate
from test.unit.cli.mlflow_model_fixture import FakeModel, write_model
from test.unit.cli.fixtures.mlflow_model_fixture import FakeModel
def write_model(model) -> str:
with TemporaryDirectory() as mlflow_model_dir:
fixtures_path = os.path.join(os.path.dirname(__file__), 'fixtures')
mlflow.pyfunc.save_model(mlflow_model_dir,
loader_module='fixtures',
code_path=[fixtures_path])
return shutil.make_archive(mkstemp()[1], "zip", mlflow_model_dir)
class TestCliAnnotate(unittest.TestCase):
@@ -29,8 +41,7 @@ class TestCliAnnotate(unittest.TestCase):
"""
_, query_dataset_file_path = mkstemp()
model = FakeModel()
model_file_path = write_model(model)
model_file_path = write_model(FakeModel())
result = CliRunner().invoke(
annotate,