def test_create_prediction_job(self, version):
job_1.status = "completed"
responses.add("POST", '/v1/models/1/versions/1/jobs',
body=json.dumps(job_1.to_dict()),
status=200,
content_type='application/json')
bq_src = BigQuerySource(table="project.dataset.source_table",
features=["feature_1", "feature2"],
options={"key": "val"})
bq_sink = BigQuerySink(table="project.dataset.result_table",
result_column="prediction",
save_mode=SaveMode.OVERWRITE,
staging_bucket="gs://test",
options={"key": "val"})
job_config = PredictionJobConfig(source=bq_src,
sink=bq_sink,
service_account_name="my-service-account",
result_type=ResultType.INTEGER)
j = version.create_prediction_job(job_config=job_config)
assert j.status == JobStatus.COMPLETED
assert j.id == job_1.id
assert j.error == job_1.error
assert j.name == job_1.name
actual_req = json.loads(responses.calls[0].request.body)
assert actual_req["config"]["job_config"]["bigquery_source"] == bq_src.to_dict()
assert actual_req["config"]["job_config"]["bigquery_sink"] == bq_sink.to_dict()
assert actual_req["config"]["job_config"]["model"]["result"]["type"] == ResultType.INTEGER.value
assert actual_req["config"]["job_config"]["model"]["uri"] == f"{version.artifact_uri}/model"
assert actual_req["config"]["job_config"]["model"]["type"] == ModelType.PYFUNC_V2.value.upper()
assert actual_req["config"]["service_account_name"] == "my-service-account"
def test_batch_pyfunc_v2_batch(integration_test_url, project_name,
service_account, use_google_oauth,
batch_bigquery_source, batch_bigquery_sink,
batch_gcs_staging_bucket):
merlin.set_url(integration_test_url, use_google_oauth=use_google_oauth)
merlin.set_project(project_name)
merlin.set_model("batch-iris", ModelType.PYFUNC_V2)
service_account_name = "*****@*****.**"
_create_secret(merlin.active_project(), service_account_name,
service_account)
clf = svm.SVC(gamma='scale')
iris = load_iris()
X, y = iris.data, iris.target
clf.fit(X, y)
joblib.dump(clf, MODEL_PATH)
# Create new version of the model
mdl = merlin.active_model()
v = mdl.new_model_version()
v.start()
# Upload the serialized model to MLP
v.log_pyfunc_model(model_instance=IrisClassifier(),
conda_env=ENV_PATH,
code_dir=["test"],
artifacts={MODEL_PATH_ARTIFACT_KEY: MODEL_PATH})
v.finish()
bq_source = BigQuerySource(batch_bigquery_source,
features=[
"sepal_length", "sepal_width",
"petal_length", "petal_width"
])
bq_sink = BigQuerySink(batch_bigquery_sink,
staging_bucket=batch_gcs_staging_bucket,
result_column="prediction",
save_mode=SaveMode.OVERWRITE)
job_config = PredictionJobConfig(source=bq_source,
sink=bq_sink,
service_account_name=service_account_name,
env_vars={"ALPHA": "0.2"})
job = v.create_prediction_job(job_config=job_config)
assert job.status == JobStatus.COMPLETED
job = v.create_prediction_job(job_config=job_config, sync=False)
while job.status == JobStatus.PENDING:
sleep(20)
job = job.refresh()
job = job.stop()
assert job.status == JobStatus.TERMINATED
def test_stop_prediction_job(self, version):
job_1.status = "pending"
responses.add("POST",
'/v1/models/1/versions/1/jobs',
body=json.dumps(job_1.to_dict()),
status=200,
content_type='application/json')
responses.add("PUT",
'/v1/models/1/versions/1/jobs/1/stop',
status=204,
content_type='application/json')
job_1.status = "terminated"
responses.add("GET",
'/v1/models/1/versions/1/jobs/1',
body=json.dumps(job_1.to_dict()),
status=200,
content_type='application/json')
bq_src = BigQuerySource(table="project.dataset.source_table",
features=["feature_1", "feature2"],
options={"key": "val"})
bq_sink = BigQuerySink(table="project.dataset.result_table",
result_column="prediction",
save_mode=SaveMode.OVERWRITE,
staging_bucket="gs://test",
options={"key": "val"})
job_config = PredictionJobConfig(
source=bq_src,
sink=bq_sink,
service_account_name="my-service-account",
result_type=ResultType.INTEGER)
j = version.create_prediction_job(job_config=job_config, sync=False)
j = j.stop()
assert j.status == JobStatus.TERMINATED
assert j.id == job_1.id
assert j.error == job_1.error
assert j.name == job_1.name
def test_create_prediction_job_with_retry_failed(self, version):
job_1.status = "pending"
responses.add("POST",
'/v1/models/1/versions/1/jobs',
body=json.dumps(job_1.to_dict()),
status=200,
content_type='application/json')
for i in range(5):
responses.add("GET",
'/v1/models/1/versions/1/jobs/1',
body=json.dumps(job_1.to_dict()),
status=500,
content_type='application/json')
bq_src = BigQuerySource(table="project.dataset.source_table",
features=["feature_1", "feature2"],
options={"key": "val"})
bq_sink = BigQuerySink(table="project.dataset.result_table",
result_column="prediction",
save_mode=SaveMode.OVERWRITE,
staging_bucket="gs://test",
options={"key": "val"})
job_config = PredictionJobConfig(
source=bq_src,
sink=bq_sink,
service_account_name="my-service-account",
result_type=ResultType.INTEGER)
with pytest.raises(ValueError):
j = version.create_prediction_job(job_config=job_config)
assert j.id == job_1.id
assert j.error == job_1.error
assert j.name == job_1.name
assert len(responses.calls) == 6
def test_create_prediction_job_with_retry_pending_then_failed(
self, version):
job_1.status = "pending"
responses.add("POST",
'/v1/models/1/versions/1/jobs',
body=json.dumps(job_1.to_dict()),
status=200,
content_type='application/json')
# Patch the method as currently it is not supported in the library
# https://github.com/getsentry/responses/issues/135
def _find_match(self, request):
for match in self._urls:
if request.method == match['method'] and \
self._has_url_match(match, request.url):
return match
def _find_match_patched(self, request):
for index, match in enumerate(self._urls):
if request.method == match['method'] and \
self._has_url_match(match, request.url):
if request.method == "GET" and request.url == "/v1/models/1/versions/1/jobs/1":
return self._urls.pop(index)
else:
return match
responses._find_match = types.MethodType(_find_match_patched,
responses)
for i in range(3):
responses.add("GET",
'/v1/models/1/versions/1/jobs/1',
body=json.dumps(job_1.to_dict()),
status=500,
content_type='application/json')
responses.add("GET",
'/v1/models/1/versions/1/jobs/1',
body=json.dumps(job_1.to_dict()),
status=200,
content_type='application/json')
job_1.status = "failed"
for i in range(5):
responses.add("GET",
'/v1/models/1/versions/1/jobs/1',
body=json.dumps(job_1.to_dict()),
status=500,
content_type='application/json')
bq_src = BigQuerySource(table="project.dataset.source_table",
features=["feature_1", "feature2"],
options={"key": "val"})
bq_sink = BigQuerySink(table="project.dataset.result_table",
result_column="prediction",
save_mode=SaveMode.OVERWRITE,
staging_bucket="gs://test",
options={"key": "val"})
job_config = PredictionJobConfig(
source=bq_src,
sink=bq_sink,
service_account_name="my-service-account",
result_type=ResultType.INTEGER)
with pytest.raises(ValueError):
j = version.create_prediction_job(job_config=job_config)
assert j.id == job_1.id
assert j.error == job_1.error
assert j.name == job_1.name
# unpatch
responses._find_match = types.MethodType(_find_match, responses)
assert len(responses.calls) == 10
def test_create_prediction_job_with_retry_success(self, version):
job_1.status = "pending"
responses.add("POST",
'/v1/models/1/versions/1/jobs',
body=json.dumps(job_1.to_dict()),
status=200,
content_type='application/json')
# Patch the method as currently it is not supported in the library
# https://github.com/getsentry/responses/issues/135
def _find_match(self, request):
for match in self._urls:
if request.method == match['method'] and \
self._has_url_match(match, request.url):
return match
def _find_match_patched(self, request):
for index, match in enumerate(self._urls):
if request.method == match['method'] and \
self._has_url_match(match, request.url):
if request.method == "GET" and request.url == "/v1/models/1/versions/1/jobs/1":
return self._urls.pop(index)
else:
return match
responses._find_match = types.MethodType(_find_match_patched,
responses)
for i in range(4):
responses.add("GET",
'/v1/models/1/versions/1/jobs/1',
body=json.dumps(job_1.to_dict()),
status=500,
content_type='application/json')
job_1.status = "completed"
responses.add("GET",
'/v1/models/1/versions/1/jobs/1',
body=json.dumps(job_1.to_dict()),
status=200,
content_type='application/json')
bq_src = BigQuerySource(table="project.dataset.source_table",
features=["feature_1", "feature2"],
options={"key": "val"})
bq_sink = BigQuerySink(table="project.dataset.result_table",
result_column="prediction",
save_mode=SaveMode.OVERWRITE,
staging_bucket="gs://test",
options={"key": "val"})
job_config = PredictionJobConfig(
source=bq_src,
sink=bq_sink,
service_account_name="my-service-account",
result_type=ResultType.INTEGER)
j = version.create_prediction_job(job_config=job_config)
assert j.status == JobStatus.COMPLETED
assert j.id == job_1.id
assert j.error == job_1.error
assert j.name == job_1.name
actual_req = json.loads(responses.calls[0].request.body)
assert actual_req["config"]["job_config"][
"bigquery_source"] == bq_src.to_dict()
assert actual_req["config"]["job_config"][
"bigquery_sink"] == bq_sink.to_dict()
assert actual_req["config"]["job_config"]["model"]["result"][
"type"] == ResultType.INTEGER.value
assert actual_req["config"]["job_config"]["model"][
"uri"] == f"{version.artifact_uri}/model"
assert actual_req["config"]["job_config"]["model"][
"type"] == ModelType.PYFUNC_V2.value.upper()
assert actual_req["config"][
"service_account_name"] == "my-service-account"
assert len(responses.calls) == 6
# unpatch
responses._find_match = types.MethodType(_find_match, responses)