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chore: upgrade backend dependencies (#2641)

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chore: upgrade backend dependencies (#2641)
This commit is contained in:
atarashansky
2023-11-29 14:16:39 -08:00
committed by GitHub
parent 4bb9a2b834
commit 6505f6cbf5
36 changed files with 161 additions and 315 deletions
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test/unit/cli/fixtures/__init__.py
-5
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@@ -1,5 +0,0 @@
from .mlflow_model_fixture import FakeModel
def _load_pyfunc(data_path):
return FakeModel()
test/unit/cli/test_annotate.py
-130
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@@ -1,130 +0,0 @@
import os
import shutil
import unittest
from tempfile import mkstemp, TemporaryDirectory, NamedTemporaryFile
import mlflow
from click.testing import CliRunner
from server.cli.annotate import annotate
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):
def test__annotate__loads_and_runs(self):
"""
Invokes the `annotate` subcommand of cellxgene CLI, using a CliRunner() programmatic invocation.
This tests the happy path case:
1) Command line options are parsed;
2) An MLflow model zip archive can be read in (from local disk), unpacked, and invoked;
3) The correct options are passed to the MLflow model.
4) The annotate subcommand exits successfully.
This does not verify model output or predictions (it's a fake MLflow model, after all); it's up to the real model
to output its predictions as it wants, but this is specific to the model and so not tested here.
The CliRunner() invokes the subcommand in a subprocess, and the annotate subcommand itself invokes the MLflow
model in yet another subprocess. So while this test can help determine if everything is working, it is not a
simple matter to debug in the case of a failure. However, the stdout/stderr of the MLflow process is captured
by the CliRunner() subprocess, so errors can be inspected in result.stdout when debugging this test. Hope this
helps!
"""
_, query_dataset_file_path = mkstemp()
model_file_path = write_model(FakeModel())
result = CliRunner().invoke(
annotate,
[
query_dataset_file_path,
"--model-url",
model_file_path,
"--output-h5ad-file",
f"{query_dataset_file_path}.output",
# avoid having mflow create conda env or virtualenv when in test env;
# this avoids making pip remote requests and is also faster
"--mlflow-env-manager",
"local",
],
)
# to help debugging, show the output from the CliRunner and MLflow stdout
if result.exit_code:
print(result.stdout)
self.assertEqual(0, result.exit_code, "runs successfully")
# The FakeModel will print it inputs to stdout, as "__MODEL_INPUT__={...}", allowing us to assert that it received valid inputs.
self.assertIn(
"__MODEL_INPUT__={"
f'"query_dataset_h5ad_path": "{query_dataset_file_path}", '
f'"output_h5ad_path": "{query_dataset_file_path}.output", '
'"annotation_prefix": "cxg_cell_type", "classifier": "default", '
'"organism": "Homo sapiens", "use_gpu": true}',
result.stdout,
"inputs passed correctly",
)
self.assertIn(
f"Wrote annotations to {query_dataset_file_path}.output",
result.stdout,
"success message is correct",
)
def test__annotate__requires_overwrite_option_when_output_file_exists(self):
with NamedTemporaryFile() as input_h5ad, NamedTemporaryFile() as existing_file:
required_options = [input_h5ad.name, "--output-h5ad-file", existing_file.name, "--model-url", "some_url"]
result = CliRunner().invoke(
annotate,
required_options + [],
)
self.assertNotEqual(0, result.exit_code, "aborts with non-success code")
self.assertIn(
"try using the flag --overwrite",
result.stdout,
"error message displayed",
)
def test__annotate__overwrite_option_allows_overwrite_of_existing_output_file(self):
model_file_path = write_model(FakeModel())
with NamedTemporaryFile() as existing_file:
required_options = [
existing_file.name,
"--output-h5ad-file",
existing_file.name,
"--overwrite",
"--model-url",
model_file_path,
]
result = CliRunner().invoke(
annotate,
required_options + [],
)
print(result.stdout)
self.assertNotEqual(1, result.exit_code, "aborts with non-success code")
self.assertIn(
f"Wrote annotations to {existing_file.name}",
result.stdout,
"success message is correct on output file overwrite",
)
# TODO:
# Test annotate cli args more comprehensively
# Test server.cli.annotate._validate_options
# Test model caching feature works
# Test model loading from s3 works (maybe w/just a real model)
if __name__ == "__main__":
unittest.main()
test/unit/cli/test_prepare.py
+1 -1
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@@ -6,7 +6,7 @@ from server.cli.prepare import make_index_unique
class CLIPrepareTests(unittest.TestCase):
""" Test cases for CLI prepare logic """
"""Test cases for CLI prepare logic"""
def test_make_index_unique(self):
index = pd.Index(["SNORD113", "SNORD113", "SNORD113-1"])
test/unit/cli/test_upgrade.py
+1 -1
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@@ -4,7 +4,7 @@ from server.cli.upgrade import validate_version_str, split_version, version_gt
class CLIUpgradeTests(unittest.TestCase):
""" Test cases for CLI logic """
"""Test cases for CLI logic"""
def test_validate_version_str(self):
self.assertTrue(validate_version_str("0.1.2"))