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test_training_jobs.py
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test_training_jobs.py
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# -*- coding: utf-8 -*-
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from distutils import core
import functools
import importlib
import pathlib
import pytest
import subprocess
import shutil
import sys
import tarfile
import tempfile
from unittest import mock
from unittest.mock import patch
from google.auth import credentials as auth_credentials
from google.cloud import aiplatform
from google.cloud.aiplatform import datasets
from google.cloud.aiplatform import initializer
from google.cloud.aiplatform import schema
from google.cloud.aiplatform import training_jobs
from google.cloud.aiplatform_v1.services.model_service import (
client as model_service_client,
)
from google.cloud.aiplatform_v1.services.pipeline_service import (
client as pipeline_service_client,
)
from google.cloud.aiplatform_v1.types import (
dataset as gca_dataset,
encryption_spec as gca_encryption_spec,
env_var as gca_env_var,
io as gca_io,
model as gca_model,
pipeline_state as gca_pipeline_state,
training_pipeline as gca_training_pipeline,
)
from google.cloud import storage
from google.protobuf import json_format
from google.protobuf import struct_pb2
_TEST_BUCKET_NAME = "test-bucket"
_TEST_GCS_PATH_WITHOUT_BUCKET = "path/to/folder"
_TEST_GCS_PATH = f"{_TEST_BUCKET_NAME}/{_TEST_GCS_PATH_WITHOUT_BUCKET}"
_TEST_GCS_PATH_WITH_TRAILING_SLASH = f"{_TEST_GCS_PATH}/"
_TEST_LOCAL_SCRIPT_FILE_NAME = "____test____script.py"
_TEST_LOCAL_SCRIPT_FILE_PATH = f"path/to/{_TEST_LOCAL_SCRIPT_FILE_NAME}"
_TEST_PYTHON_SOURCE = """
print('hello world')
"""
_TEST_REQUIREMENTS = ["pandas", "numpy", "tensorflow"]
_TEST_DATASET_DISPLAY_NAME = "test-dataset-display-name"
_TEST_DATASET_NAME = "test-dataset-name"
_TEST_DISPLAY_NAME = "test-display-name"
_TEST_METADATA_SCHEMA_URI_TABULAR = schema.dataset.metadata.tabular
_TEST_TRAINING_CONTAINER_IMAGE = "gcr.io/test-training/container:image"
_TEST_TRAINING_CONTAINER_CMD = ["python3", "task.py"]
_TEST_SERVING_CONTAINER_IMAGE = "gcr.io/test-serving/container:image"
_TEST_SERVING_CONTAINER_PREDICTION_ROUTE = "predict"
_TEST_SERVING_CONTAINER_HEALTH_ROUTE = "metadata"
_TEST_METADATA_SCHEMA_URI_NONTABULAR = schema.dataset.metadata.image
_TEST_ANNOTATION_SCHEMA_URI = schema.dataset.annotation.image.classification
_TEST_BASE_OUTPUT_DIR = "gs://test-base-output-dir"
_TEST_BIGQUERY_DESTINATION = "bq://test-project"
_TEST_RUN_ARGS = ["-v", 0.1, "--test=arg"]
_TEST_REPLICA_COUNT = 1
_TEST_MACHINE_TYPE = "n1-standard-4"
_TEST_ACCELERATOR_TYPE = "NVIDIA_TESLA_K80"
_TEST_INVALID_ACCELERATOR_TYPE = "NVIDIA_DOES_NOT_EXIST"
_TEST_ACCELERATOR_COUNT = 1
_TEST_MODEL_DISPLAY_NAME = "model-display-name"
_TEST_DEFAULT_TRAINING_FRACTION_SPLIT = 0.8
_TEST_DEFAULT_VALIDATION_FRACTION_SPLIT = 0.1
_TEST_DEFAULT_TEST_FRACTION_SPLIT = 0.1
_TEST_TRAINING_FRACTION_SPLIT = 0.6
_TEST_VALIDATION_FRACTION_SPLIT = 0.2
_TEST_TEST_FRACTION_SPLIT = 0.2
_TEST_PREDEFINED_SPLIT_COLUMN_NAME = "split"
_TEST_PROJECT = "test-project"
_TEST_LOCATION = "us-central1"
_TEST_ID = "12345"
_TEST_NAME = (
f"projects/{_TEST_PROJECT}/locations/{_TEST_LOCATION}/trainingPipelines/{_TEST_ID}"
)
_TEST_ALT_PROJECT = "test-project-alt"
_TEST_ALT_LOCATION = "europe-west4"
_TEST_MODEL_INSTANCE_SCHEMA_URI = "instance_schema_uri.yaml"
_TEST_MODEL_PARAMETERS_SCHEMA_URI = "parameters_schema_uri.yaml"
_TEST_MODEL_PREDICTION_SCHEMA_URI = "prediction_schema_uri.yaml"
_TEST_MODEL_SERVING_CONTAINER_COMMAND = ["test_command"]
_TEST_MODEL_SERVING_CONTAINER_ARGS = ["test_args"]
_TEST_MODEL_SERVING_CONTAINER_ENVIRONMENT_VARIABLES = {
"learning_rate": 0.01,
"loss_fn": "mse",
}
_TEST_MODEL_SERVING_CONTAINER_PORTS = [8888, 10000]
_TEST_MODEL_DESCRIPTION = "test description"
_TEST_OUTPUT_PYTHON_PACKAGE_PATH = "gs://test/ouput/python/trainer.tar.gz"
_TEST_PYTHON_MODULE_NAME = "aiplatform.task"
_TEST_MODEL_NAME = "projects/my-project/locations/us-central1/models/12345"
_TEST_PIPELINE_RESOURCE_NAME = (
"projects/my-project/locations/us-central1/trainingPipeline/12345"
)
_TEST_CREDENTIALS = mock.Mock(spec=auth_credentials.AnonymousCredentials())
# CMEK encryption
_TEST_DEFAULT_ENCRYPTION_KEY_NAME = "key_default"
_TEST_DEFAULT_ENCRYPTION_SPEC = gca_encryption_spec.EncryptionSpec(
kms_key_name=_TEST_DEFAULT_ENCRYPTION_KEY_NAME
)
_TEST_PIPELINE_ENCRYPTION_KEY_NAME = "key_pipeline"
_TEST_PIPELINE_ENCRYPTION_SPEC = gca_encryption_spec.EncryptionSpec(
kms_key_name=_TEST_PIPELINE_ENCRYPTION_KEY_NAME
)
_TEST_MODEL_ENCRYPTION_KEY_NAME = "key_model"
_TEST_MODEL_ENCRYPTION_SPEC = gca_encryption_spec.EncryptionSpec(
kms_key_name=_TEST_MODEL_ENCRYPTION_KEY_NAME
)
def local_copy_method(path):
shutil.copy(path, ".")
return pathlib.Path(path).name
@pytest.fixture
def get_training_job_custom_mock():
with patch.object(
pipeline_service_client.PipelineServiceClient, "get_training_pipeline"
) as get_training_job_custom_mock:
get_training_job_custom_mock.return_value = gca_training_pipeline.TrainingPipeline(
name=_TEST_PIPELINE_RESOURCE_NAME,
state=gca_pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED,
model_to_upload=gca_model.Model(name=_TEST_MODEL_NAME),
training_task_definition=schema.training_job.definition.custom_task,
)
yield get_training_job_custom_mock
@pytest.fixture
def get_training_job_custom_mock_no_model_to_upload():
with patch.object(
pipeline_service_client.PipelineServiceClient, "get_training_pipeline"
) as get_training_job_custom_mock:
get_training_job_custom_mock.return_value = gca_training_pipeline.TrainingPipeline(
name=_TEST_PIPELINE_RESOURCE_NAME,
state=gca_pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED,
model_to_upload=None,
training_task_definition=schema.training_job.definition.custom_task,
)
yield get_training_job_custom_mock
@pytest.fixture
def get_training_job_tabular_mock():
with patch.object(
pipeline_service_client.PipelineServiceClient, "get_training_pipeline"
) as get_training_job_tabular_mock:
get_training_job_tabular_mock.return_value = gca_training_pipeline.TrainingPipeline(
name=_TEST_PIPELINE_RESOURCE_NAME,
state=gca_pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED,
model_to_upload=gca_model.Model(name=_TEST_MODEL_NAME),
training_task_definition=schema.training_job.definition.automl_tabular,
)
yield get_training_job_tabular_mock
@pytest.fixture
def mock_client_bucket():
with patch.object(storage.Client, "bucket") as mock_client_bucket:
def blob_side_effect(name, mock_blob, bucket):
mock_blob.name = name
mock_blob.bucket = bucket
return mock_blob
MockBucket = mock.Mock(autospec=storage.Bucket)
MockBucket.name = _TEST_BUCKET_NAME
MockBlob = mock.Mock(autospec=storage.Blob)
MockBucket.blob.side_effect = functools.partial(
blob_side_effect, mock_blob=MockBlob, bucket=MockBucket
)
mock_client_bucket.return_value = MockBucket
yield mock_client_bucket, MockBlob
class TestTrainingScriptPythonPackagerHelpers:
def setup_method(self):
importlib.reload(initializer)
importlib.reload(aiplatform)
def test_timestamp_copy_to_gcs_calls_gcs_client_with_bucket(
self, mock_client_bucket
):
mock_client_bucket, mock_blob = mock_client_bucket
gcs_path = training_jobs._timestamped_copy_to_gcs(
local_file_path=_TEST_LOCAL_SCRIPT_FILE_PATH,
gcs_dir=_TEST_BUCKET_NAME,
project=_TEST_PROJECT,
)
local_script_file_name = pathlib.Path(_TEST_LOCAL_SCRIPT_FILE_PATH).name
mock_client_bucket.assert_called_once_with(_TEST_BUCKET_NAME)
mock_client_bucket.return_value.blob.assert_called_once()
blob_arg = mock_client_bucket.return_value.blob.call_args[0][0]
assert blob_arg.startswith("aiplatform-")
assert blob_arg.endswith(_TEST_LOCAL_SCRIPT_FILE_NAME)
mock_blob.upload_from_filename.assert_called_once_with(
_TEST_LOCAL_SCRIPT_FILE_PATH
)
assert gcs_path.endswith(local_script_file_name)
assert gcs_path.startswith(f"gs://{_TEST_BUCKET_NAME}/aiplatform-")
def test_timestamp_copy_to_gcs_calls_gcs_client_with_gcs_path(
self, mock_client_bucket
):
mock_client_bucket, mock_blob = mock_client_bucket
gcs_path = training_jobs._timestamped_copy_to_gcs(
local_file_path=_TEST_LOCAL_SCRIPT_FILE_PATH,
gcs_dir=_TEST_GCS_PATH_WITH_TRAILING_SLASH,
project=_TEST_PROJECT,
)
local_script_file_name = pathlib.Path(_TEST_LOCAL_SCRIPT_FILE_PATH).name
mock_client_bucket.assert_called_once_with(_TEST_BUCKET_NAME)
mock_client_bucket.return_value.blob.assert_called_once()
blob_arg = mock_client_bucket.return_value.blob.call_args[0][0]
assert blob_arg.startswith(f"{_TEST_GCS_PATH_WITHOUT_BUCKET}/aiplatform-")
assert blob_arg.endswith(f"{_TEST_LOCAL_SCRIPT_FILE_NAME}")
mock_blob.upload_from_filename.assert_called_once_with(
_TEST_LOCAL_SCRIPT_FILE_PATH
)
assert gcs_path.startswith(f"gs://{_TEST_GCS_PATH}/aiplatform-")
assert gcs_path.endswith(local_script_file_name)
def test_timestamp_copy_to_gcs_calls_gcs_client_with_trailing_slash(
self, mock_client_bucket
):
mock_client_bucket, mock_blob = mock_client_bucket
gcs_path = training_jobs._timestamped_copy_to_gcs(
local_file_path=_TEST_LOCAL_SCRIPT_FILE_PATH,
gcs_dir=_TEST_GCS_PATH,
project=_TEST_PROJECT,
)
local_script_file_name = pathlib.Path(_TEST_LOCAL_SCRIPT_FILE_PATH).name
mock_client_bucket.assert_called_once_with(_TEST_BUCKET_NAME)
mock_client_bucket.return_value.blob.assert_called_once()
blob_arg = mock_client_bucket.return_value.blob.call_args[0][0]
assert blob_arg.startswith(f"{_TEST_GCS_PATH_WITHOUT_BUCKET}/aiplatform-")
assert blob_arg.endswith(_TEST_LOCAL_SCRIPT_FILE_NAME)
mock_blob.upload_from_filename.assert_called_once_with(
_TEST_LOCAL_SCRIPT_FILE_PATH
)
assert gcs_path.startswith(f"gs://{_TEST_GCS_PATH}/aiplatform-")
assert gcs_path.endswith(local_script_file_name)
def test_timestamp_copy_to_gcs_calls_gcs_client(self, mock_client_bucket):
mock_client_bucket, mock_blob = mock_client_bucket
gcs_path = training_jobs._timestamped_copy_to_gcs(
local_file_path=_TEST_LOCAL_SCRIPT_FILE_PATH,
gcs_dir=_TEST_BUCKET_NAME,
project=_TEST_PROJECT,
)
mock_client_bucket.assert_called_once_with(_TEST_BUCKET_NAME)
mock_client_bucket.return_value.blob.assert_called_once()
mock_blob.upload_from_filename.assert_called_once_with(
_TEST_LOCAL_SCRIPT_FILE_PATH
)
assert gcs_path.endswith(pathlib.Path(_TEST_LOCAL_SCRIPT_FILE_PATH).name)
assert gcs_path.startswith(f"gs://{_TEST_BUCKET_NAME}")
def test_get_python_executable_raises_if_None(self):
with patch.object(sys, "executable", new=None):
with pytest.raises(EnvironmentError):
training_jobs._get_python_executable()
def test_get_python_executable_returns_python_executable(self):
assert "python" in training_jobs._get_python_executable().lower()
class TestTrainingScriptPythonPackager:
def setup_method(self):
importlib.reload(initializer)
importlib.reload(aiplatform)
with open(_TEST_LOCAL_SCRIPT_FILE_NAME, "w") as fp:
fp.write(_TEST_PYTHON_SOURCE)
def teardown_method(self):
pathlib.Path(_TEST_LOCAL_SCRIPT_FILE_NAME).unlink()
python_package_file = f"{training_jobs._TrainingScriptPythonPackager._ROOT_MODULE}-{training_jobs._TrainingScriptPythonPackager._SETUP_PY_VERSION}.tar.gz"
if pathlib.Path(python_package_file).is_file():
pathlib.Path(python_package_file).unlink()
subprocess.check_output(
[
"pip3",
"uninstall",
"-y",
training_jobs._TrainingScriptPythonPackager._ROOT_MODULE,
]
)
def test_packager_creates_and_copies_python_package(self):
tsp = training_jobs._TrainingScriptPythonPackager(_TEST_LOCAL_SCRIPT_FILE_NAME)
tsp.package_and_copy(copy_method=local_copy_method)
assert pathlib.Path(
f"{tsp._ROOT_MODULE}-{tsp._SETUP_PY_VERSION}.tar.gz"
).is_file()
def test_created_package_module_is_installable_and_can_be_run(self):
tsp = training_jobs._TrainingScriptPythonPackager(_TEST_LOCAL_SCRIPT_FILE_NAME)
source_dist_path = tsp.package_and_copy(copy_method=local_copy_method)
subprocess.check_output(["pip3", "install", source_dist_path])
module_output = subprocess.check_output(
[training_jobs._get_python_executable(), "-m", tsp.module_name]
)
assert "hello world" in module_output.decode()
def test_requirements_are_in_package(self):
tsp = training_jobs._TrainingScriptPythonPackager(
_TEST_LOCAL_SCRIPT_FILE_NAME, requirements=_TEST_REQUIREMENTS
)
source_dist_path = tsp.package_and_copy(copy_method=local_copy_method)
with tarfile.open(source_dist_path) as tf:
with tempfile.TemporaryDirectory() as tmpdirname:
setup_py_path = f"{training_jobs._TrainingScriptPythonPackager._ROOT_MODULE}-{training_jobs._TrainingScriptPythonPackager._SETUP_PY_VERSION}/setup.py"
tf.extract(setup_py_path, path=tmpdirname)
setup_py = core.run_setup(
pathlib.Path(tmpdirname, setup_py_path), stop_after="init"
)
assert _TEST_REQUIREMENTS == setup_py.install_requires
def test_packaging_fails_whith_RuntimeError(self):
with patch("subprocess.Popen") as mock_popen:
mock_subprocess = mock.Mock()
mock_subprocess.communicate.return_value = (b"", b"")
mock_subprocess.returncode = 1
mock_popen.return_value = mock_subprocess
tsp = training_jobs._TrainingScriptPythonPackager(
_TEST_LOCAL_SCRIPT_FILE_NAME
)
with pytest.raises(RuntimeError):
tsp.package_and_copy(copy_method=local_copy_method)
def test_package_and_copy_to_gcs_copies_to_gcs(self, mock_client_bucket):
mock_client_bucket, mock_blob = mock_client_bucket
tsp = training_jobs._TrainingScriptPythonPackager(_TEST_LOCAL_SCRIPT_FILE_NAME)
gcs_path = tsp.package_and_copy_to_gcs(
gcs_staging_dir=_TEST_BUCKET_NAME, project=_TEST_PROJECT
)
mock_client_bucket.assert_called_once_with(_TEST_BUCKET_NAME)
mock_client_bucket.return_value.blob.assert_called_once()
mock_blob.upload_from_filename.call_args[0][0].endswith(
"/trainer/dist/aiplatform_custom_trainer_script-0.1.tar.gz"
)
assert gcs_path.endswith("-aiplatform_custom_trainer_script-0.1.tar.gz")
assert gcs_path.startswith(f"gs://{_TEST_BUCKET_NAME}")
@pytest.fixture
def mock_pipeline_service_create():
with mock.patch.object(
pipeline_service_client.PipelineServiceClient, "create_training_pipeline"
) as mock_create_training_pipeline:
mock_create_training_pipeline.return_value = gca_training_pipeline.TrainingPipeline(
name=_TEST_PIPELINE_RESOURCE_NAME,
state=gca_pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED,
model_to_upload=gca_model.Model(name=_TEST_MODEL_NAME),
)
yield mock_create_training_pipeline
@pytest.fixture
def mock_pipeline_service_cancel():
with mock.patch.object(
pipeline_service_client.PipelineServiceClient, "cancel_training_pipeline"
) as mock_cancel_training_pipeline:
yield mock_cancel_training_pipeline
@pytest.fixture
def mock_pipeline_service_create_with_no_model_to_upload():
with mock.patch.object(
pipeline_service_client.PipelineServiceClient, "create_training_pipeline"
) as mock_create_training_pipeline:
mock_create_training_pipeline.return_value = gca_training_pipeline.TrainingPipeline(
name=_TEST_PIPELINE_RESOURCE_NAME,
state=gca_pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED,
)
yield mock_create_training_pipeline
@pytest.fixture
def mock_pipeline_service_create_and_get_with_fail():
with mock.patch.object(
pipeline_service_client.PipelineServiceClient, "create_training_pipeline"
) as mock_create_training_pipeline:
mock_create_training_pipeline.return_value = gca_training_pipeline.TrainingPipeline(
name=_TEST_PIPELINE_RESOURCE_NAME,
state=gca_pipeline_state.PipelineState.PIPELINE_STATE_RUNNING,
)
with mock.patch.object(
pipeline_service_client.PipelineServiceClient, "get_training_pipeline"
) as mock_get_training_pipeline:
mock_get_training_pipeline.return_value = gca_training_pipeline.TrainingPipeline(
name=_TEST_PIPELINE_RESOURCE_NAME,
state=gca_pipeline_state.PipelineState.PIPELINE_STATE_FAILED,
)
yield mock_create_training_pipeline, mock_get_training_pipeline
@pytest.fixture
def mock_model_service_get():
with mock.patch.object(
model_service_client.ModelServiceClient, "get_model"
) as mock_get_model:
mock_get_model.return_value = gca_model.Model()
yield mock_get_model
@pytest.fixture
def mock_python_package_to_gcs():
with mock.patch.object(
training_jobs._TrainingScriptPythonPackager, "package_and_copy_to_gcs"
) as mock_package_to_copy_gcs:
mock_package_to_copy_gcs.return_value = _TEST_OUTPUT_PYTHON_PACKAGE_PATH
yield mock_package_to_copy_gcs
@pytest.fixture
def mock_tabular_dataset():
ds = mock.MagicMock(datasets.Dataset)
ds.name = _TEST_DATASET_NAME
ds._latest_future = None
ds._gca_resource = gca_dataset.Dataset(
display_name=_TEST_DATASET_DISPLAY_NAME,
metadata_schema_uri=_TEST_METADATA_SCHEMA_URI_TABULAR,
labels={},
name=_TEST_DATASET_NAME,
metadata={},
)
return ds
@pytest.fixture
def mock_nontabular_dataset():
ds = mock.MagicMock(datasets.Dataset)
ds.name = _TEST_DATASET_NAME
ds._latest_future = None
ds._gca_resource = gca_dataset.Dataset(
display_name=_TEST_DATASET_DISPLAY_NAME,
metadata_schema_uri=_TEST_METADATA_SCHEMA_URI_NONTABULAR,
labels={},
name=_TEST_DATASET_NAME,
metadata={},
)
return ds
class TestCustomTrainingJob:
def setup_method(self):
importlib.reload(initializer)
importlib.reload(aiplatform)
with open(_TEST_LOCAL_SCRIPT_FILE_NAME, "w") as fp:
fp.write(_TEST_PYTHON_SOURCE)
def teardown_method(self):
pathlib.Path(_TEST_LOCAL_SCRIPT_FILE_NAME).unlink()
initializer.global_pool.shutdown(wait=True)
@pytest.mark.parametrize("sync", [True, False])
def test_run_call_pipeline_service_create_with_tabular_dataset(
self,
mock_pipeline_service_create,
mock_python_package_to_gcs,
mock_tabular_dataset,
mock_model_service_get,
sync,
):
aiplatform.init(
project=_TEST_PROJECT,
staging_bucket=_TEST_BUCKET_NAME,
credentials=_TEST_CREDENTIALS,
encryption_spec_key_name=_TEST_DEFAULT_ENCRYPTION_KEY_NAME,
)
job = training_jobs.CustomTrainingJob(
display_name=_TEST_DISPLAY_NAME,
script_path=_TEST_LOCAL_SCRIPT_FILE_NAME,
container_uri=_TEST_TRAINING_CONTAINER_IMAGE,
model_serving_container_image_uri=_TEST_SERVING_CONTAINER_IMAGE,
model_serving_container_predict_route=_TEST_SERVING_CONTAINER_PREDICTION_ROUTE,
model_serving_container_health_route=_TEST_SERVING_CONTAINER_HEALTH_ROUTE,
model_instance_schema_uri=_TEST_MODEL_INSTANCE_SCHEMA_URI,
model_parameters_schema_uri=_TEST_MODEL_PARAMETERS_SCHEMA_URI,
model_prediction_schema_uri=_TEST_MODEL_PREDICTION_SCHEMA_URI,
model_serving_container_command=_TEST_MODEL_SERVING_CONTAINER_COMMAND,
model_serving_container_args=_TEST_MODEL_SERVING_CONTAINER_ARGS,
model_serving_container_environment_variables=_TEST_MODEL_SERVING_CONTAINER_ENVIRONMENT_VARIABLES,
model_serving_container_ports=_TEST_MODEL_SERVING_CONTAINER_PORTS,
model_description=_TEST_MODEL_DESCRIPTION,
)
model_from_job = job.run(
dataset=mock_tabular_dataset,
base_output_dir=_TEST_BASE_OUTPUT_DIR,
args=_TEST_RUN_ARGS,
replica_count=1,
machine_type=_TEST_MACHINE_TYPE,
accelerator_type=_TEST_ACCELERATOR_TYPE,
accelerator_count=_TEST_ACCELERATOR_COUNT,
model_display_name=_TEST_MODEL_DISPLAY_NAME,
training_fraction_split=_TEST_TRAINING_FRACTION_SPLIT,
validation_fraction_split=_TEST_VALIDATION_FRACTION_SPLIT,
test_fraction_split=_TEST_TEST_FRACTION_SPLIT,
predefined_split_column_name=_TEST_PREDEFINED_SPLIT_COLUMN_NAME,
sync=sync,
)
if not sync:
model_from_job.wait()
mock_python_package_to_gcs.assert_called_once_with(
gcs_staging_dir=_TEST_BUCKET_NAME,
project=_TEST_PROJECT,
credentials=initializer.global_config.credentials,
)
true_args = _TEST_RUN_ARGS
true_worker_pool_spec = {
"replicaCount": _TEST_REPLICA_COUNT,
"machineSpec": {
"machineType": _TEST_MACHINE_TYPE,
"acceleratorType": _TEST_ACCELERATOR_TYPE,
"acceleratorCount": _TEST_ACCELERATOR_COUNT,
},
"pythonPackageSpec": {
"executorImageUri": _TEST_TRAINING_CONTAINER_IMAGE,
"pythonModule": training_jobs._TrainingScriptPythonPackager.module_name,
"packageUris": [_TEST_OUTPUT_PYTHON_PACKAGE_PATH],
"args": true_args,
},
}
true_fraction_split = gca_training_pipeline.FractionSplit(
training_fraction=_TEST_TRAINING_FRACTION_SPLIT,
validation_fraction=_TEST_VALIDATION_FRACTION_SPLIT,
test_fraction=_TEST_TEST_FRACTION_SPLIT,
)
env = [
gca_env_var.EnvVar(name=str(key), value=str(value))
for key, value in _TEST_MODEL_SERVING_CONTAINER_ENVIRONMENT_VARIABLES.items()
]
ports = [
gca_model.Port(container_port=port)
for port in _TEST_MODEL_SERVING_CONTAINER_PORTS
]
true_container_spec = gca_model.ModelContainerSpec(
image_uri=_TEST_SERVING_CONTAINER_IMAGE,
predict_route=_TEST_SERVING_CONTAINER_PREDICTION_ROUTE,
health_route=_TEST_SERVING_CONTAINER_HEALTH_ROUTE,
command=_TEST_MODEL_SERVING_CONTAINER_COMMAND,
args=_TEST_MODEL_SERVING_CONTAINER_ARGS,
env=env,
ports=ports,
)
true_managed_model = gca_model.Model(
display_name=_TEST_MODEL_DISPLAY_NAME,
description=_TEST_MODEL_DESCRIPTION,
container_spec=true_container_spec,
predict_schemata=gca_model.PredictSchemata(
instance_schema_uri=_TEST_MODEL_INSTANCE_SCHEMA_URI,
parameters_schema_uri=_TEST_MODEL_PARAMETERS_SCHEMA_URI,
prediction_schema_uri=_TEST_MODEL_PREDICTION_SCHEMA_URI,
),
encryption_spec=_TEST_DEFAULT_ENCRYPTION_SPEC,
)
true_input_data_config = gca_training_pipeline.InputDataConfig(
fraction_split=true_fraction_split,
predefined_split=gca_training_pipeline.PredefinedSplit(
key=_TEST_PREDEFINED_SPLIT_COLUMN_NAME
),
dataset_id=mock_tabular_dataset.name,
gcs_destination=gca_io.GcsDestination(
output_uri_prefix=_TEST_BASE_OUTPUT_DIR
),
)
true_training_pipeline = gca_training_pipeline.TrainingPipeline(
display_name=_TEST_DISPLAY_NAME,
training_task_definition=schema.training_job.definition.custom_task,
training_task_inputs=json_format.ParseDict(
{
"workerPoolSpecs": [true_worker_pool_spec],
"baseOutputDirectory": {"output_uri_prefix": _TEST_BASE_OUTPUT_DIR},
},
struct_pb2.Value(),
),
model_to_upload=true_managed_model,
input_data_config=true_input_data_config,
encryption_spec=_TEST_DEFAULT_ENCRYPTION_SPEC,
)
mock_pipeline_service_create.assert_called_once_with(
parent=initializer.global_config.common_location_path(),
training_pipeline=true_training_pipeline,
)
assert job._gca_resource is mock_pipeline_service_create.return_value
mock_model_service_get.assert_called_once_with(name=_TEST_MODEL_NAME)
assert model_from_job._gca_resource is mock_model_service_get.return_value
assert job.get_model()._gca_resource is mock_model_service_get.return_value
assert not job.has_failed
assert job.state == gca_pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED
@pytest.mark.parametrize("sync", [True, False])
def test_run_call_pipeline_service_create_with_bigquery_destination(
self,
mock_pipeline_service_create,
mock_python_package_to_gcs,
mock_tabular_dataset,
mock_model_service_get,
sync,
):
aiplatform.init(
project=_TEST_PROJECT, staging_bucket=_TEST_BUCKET_NAME,
)
job = training_jobs.CustomTrainingJob(
display_name=_TEST_DISPLAY_NAME,
script_path=_TEST_LOCAL_SCRIPT_FILE_NAME,
container_uri=_TEST_TRAINING_CONTAINER_IMAGE,
model_serving_container_image_uri=_TEST_SERVING_CONTAINER_IMAGE,
model_serving_container_predict_route=_TEST_SERVING_CONTAINER_PREDICTION_ROUTE,
model_serving_container_health_route=_TEST_SERVING_CONTAINER_HEALTH_ROUTE,
model_instance_schema_uri=_TEST_MODEL_INSTANCE_SCHEMA_URI,
model_parameters_schema_uri=_TEST_MODEL_PARAMETERS_SCHEMA_URI,
model_prediction_schema_uri=_TEST_MODEL_PREDICTION_SCHEMA_URI,
model_serving_container_command=_TEST_MODEL_SERVING_CONTAINER_COMMAND,
model_serving_container_args=_TEST_MODEL_SERVING_CONTAINER_ARGS,
model_serving_container_environment_variables=_TEST_MODEL_SERVING_CONTAINER_ENVIRONMENT_VARIABLES,
model_serving_container_ports=_TEST_MODEL_SERVING_CONTAINER_PORTS,
model_description=_TEST_MODEL_DESCRIPTION,
training_encryption_spec_key_name=_TEST_PIPELINE_ENCRYPTION_KEY_NAME,
model_encryption_spec_key_name=_TEST_MODEL_ENCRYPTION_KEY_NAME,
)
model_from_job = job.run(
dataset=mock_tabular_dataset,
base_output_dir=_TEST_BASE_OUTPUT_DIR,
bigquery_destination=_TEST_BIGQUERY_DESTINATION,
args=_TEST_RUN_ARGS,
replica_count=1,
machine_type=_TEST_MACHINE_TYPE,
accelerator_type=_TEST_ACCELERATOR_TYPE,
accelerator_count=_TEST_ACCELERATOR_COUNT,
model_display_name=_TEST_MODEL_DISPLAY_NAME,
training_fraction_split=_TEST_TRAINING_FRACTION_SPLIT,
validation_fraction_split=_TEST_VALIDATION_FRACTION_SPLIT,
test_fraction_split=_TEST_TEST_FRACTION_SPLIT,
predefined_split_column_name=_TEST_PREDEFINED_SPLIT_COLUMN_NAME,
sync=sync,
)
if not sync:
model_from_job.wait()
true_args = _TEST_RUN_ARGS
true_worker_pool_spec = {
"replicaCount": _TEST_REPLICA_COUNT,
"machineSpec": {
"machineType": _TEST_MACHINE_TYPE,
"acceleratorType": _TEST_ACCELERATOR_TYPE,
"acceleratorCount": _TEST_ACCELERATOR_COUNT,
},
"pythonPackageSpec": {
"executorImageUri": _TEST_TRAINING_CONTAINER_IMAGE,
"pythonModule": training_jobs._TrainingScriptPythonPackager.module_name,
"packageUris": [_TEST_OUTPUT_PYTHON_PACKAGE_PATH],
"args": true_args,
},
}
true_fraction_split = gca_training_pipeline.FractionSplit(
training_fraction=_TEST_TRAINING_FRACTION_SPLIT,
validation_fraction=_TEST_VALIDATION_FRACTION_SPLIT,
test_fraction=_TEST_TEST_FRACTION_SPLIT,
)
env = [
gca_env_var.EnvVar(name=str(key), value=str(value))
for key, value in _TEST_MODEL_SERVING_CONTAINER_ENVIRONMENT_VARIABLES.items()
]
ports = [
gca_model.Port(container_port=port)
for port in _TEST_MODEL_SERVING_CONTAINER_PORTS
]
true_container_spec = gca_model.ModelContainerSpec(
image_uri=_TEST_SERVING_CONTAINER_IMAGE,
predict_route=_TEST_SERVING_CONTAINER_PREDICTION_ROUTE,
health_route=_TEST_SERVING_CONTAINER_HEALTH_ROUTE,
command=_TEST_MODEL_SERVING_CONTAINER_COMMAND,
args=_TEST_MODEL_SERVING_CONTAINER_ARGS,
env=env,
ports=ports,
)
true_managed_model = gca_model.Model(
display_name=_TEST_MODEL_DISPLAY_NAME,
description=_TEST_MODEL_DESCRIPTION,
container_spec=true_container_spec,
predict_schemata=gca_model.PredictSchemata(
instance_schema_uri=_TEST_MODEL_INSTANCE_SCHEMA_URI,
parameters_schema_uri=_TEST_MODEL_PARAMETERS_SCHEMA_URI,
prediction_schema_uri=_TEST_MODEL_PREDICTION_SCHEMA_URI,
),
encryption_spec=_TEST_MODEL_ENCRYPTION_SPEC,
)
true_input_data_config = gca_training_pipeline.InputDataConfig(
fraction_split=true_fraction_split,
predefined_split=gca_training_pipeline.PredefinedSplit(
key=_TEST_PREDEFINED_SPLIT_COLUMN_NAME
),
dataset_id=mock_tabular_dataset.name,
bigquery_destination=gca_io.BigQueryDestination(
output_uri=_TEST_BIGQUERY_DESTINATION
),
)
true_training_pipeline = gca_training_pipeline.TrainingPipeline(
display_name=_TEST_DISPLAY_NAME,
training_task_definition=schema.training_job.definition.custom_task,
training_task_inputs=json_format.ParseDict(
{
"workerPoolSpecs": [true_worker_pool_spec],
"baseOutputDirectory": {"output_uri_prefix": _TEST_BASE_OUTPUT_DIR},
},
struct_pb2.Value(),
),
model_to_upload=true_managed_model,
input_data_config=true_input_data_config,
encryption_spec=_TEST_PIPELINE_ENCRYPTION_SPEC,
)
mock_pipeline_service_create.assert_called_once_with(
parent=initializer.global_config.common_location_path(),
training_pipeline=true_training_pipeline,
)
assert job._gca_resource is mock_pipeline_service_create.return_value
mock_model_service_get.assert_called_once_with(name=_TEST_MODEL_NAME)
assert model_from_job._gca_resource is mock_model_service_get.return_value
assert job.get_model()._gca_resource is mock_model_service_get.return_value
assert not job.has_failed
assert job.state == gca_pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED
@pytest.mark.parametrize("sync", [True, False])
def test_run_called_twice_raises(
self,
mock_pipeline_service_create,
mock_python_package_to_gcs,
mock_tabular_dataset,
mock_model_service_get,
sync,
):
aiplatform.init(project=_TEST_PROJECT, staging_bucket=_TEST_BUCKET_NAME)
job = training_jobs.CustomTrainingJob(
display_name=_TEST_DISPLAY_NAME,
script_path=_TEST_LOCAL_SCRIPT_FILE_NAME,
container_uri=_TEST_TRAINING_CONTAINER_IMAGE,
model_serving_container_image_uri=_TEST_SERVING_CONTAINER_IMAGE,
model_serving_container_predict_route=_TEST_SERVING_CONTAINER_PREDICTION_ROUTE,
model_serving_container_health_route=_TEST_SERVING_CONTAINER_HEALTH_ROUTE,
)
job.run(
dataset=mock_tabular_dataset,
base_output_dir=_TEST_BASE_OUTPUT_DIR,
args=_TEST_RUN_ARGS,
replica_count=1,
machine_type=_TEST_MACHINE_TYPE,
accelerator_type=_TEST_ACCELERATOR_TYPE,
accelerator_count=_TEST_ACCELERATOR_COUNT,
model_display_name=_TEST_MODEL_DISPLAY_NAME,
training_fraction_split=_TEST_TRAINING_FRACTION_SPLIT,
validation_fraction_split=_TEST_VALIDATION_FRACTION_SPLIT,
test_fraction_split=_TEST_TEST_FRACTION_SPLIT,
sync=sync,
)
with pytest.raises(RuntimeError):
job.run(
dataset=mock_tabular_dataset,
base_output_dir=_TEST_BASE_OUTPUT_DIR,
args=_TEST_RUN_ARGS,
replica_count=1,
machine_type=_TEST_MACHINE_TYPE,
accelerator_type=_TEST_ACCELERATOR_TYPE,
accelerator_count=_TEST_ACCELERATOR_COUNT,
model_display_name=_TEST_MODEL_DISPLAY_NAME,
training_fraction_split=_TEST_TRAINING_FRACTION_SPLIT,
validation_fraction_split=_TEST_VALIDATION_FRACTION_SPLIT,
test_fraction_split=_TEST_TEST_FRACTION_SPLIT,
sync=sync,
)
if not sync:
job.wait()
@pytest.mark.parametrize("sync", [True, False])
def test_run_with_invalid_accelerator_type_raises(
self,
mock_pipeline_service_create,
mock_python_package_to_gcs,
mock_tabular_dataset,
mock_model_service_get,
sync,
):
aiplatform.init(project=_TEST_PROJECT, staging_bucket=_TEST_BUCKET_NAME)
job = training_jobs.CustomTrainingJob(
display_name=_TEST_DISPLAY_NAME,
script_path=_TEST_LOCAL_SCRIPT_FILE_NAME,
container_uri=_TEST_TRAINING_CONTAINER_IMAGE,
model_serving_container_image_uri=_TEST_SERVING_CONTAINER_IMAGE,
model_serving_container_predict_route=_TEST_SERVING_CONTAINER_PREDICTION_ROUTE,
model_serving_container_health_route=_TEST_SERVING_CONTAINER_HEALTH_ROUTE,
)
with pytest.raises(ValueError):
job.run(
dataset=mock_tabular_dataset,
base_output_dir=_TEST_BASE_OUTPUT_DIR,
args=_TEST_RUN_ARGS,
replica_count=1,
machine_type=_TEST_MACHINE_TYPE,
accelerator_type=_TEST_INVALID_ACCELERATOR_TYPE,
accelerator_count=_TEST_ACCELERATOR_COUNT,
model_display_name=_TEST_MODEL_DISPLAY_NAME,
training_fraction_split=_TEST_TRAINING_FRACTION_SPLIT,
validation_fraction_split=_TEST_VALIDATION_FRACTION_SPLIT,
test_fraction_split=_TEST_TEST_FRACTION_SPLIT,
sync=sync,
)
@pytest.mark.parametrize("sync", [True, False])
def test_run_with_incomplete_model_info_raises_with_model_to_upload(
self,
mock_pipeline_service_create,
mock_python_package_to_gcs,
mock_tabular_dataset,
mock_model_service_get,
sync,
):
aiplatform.init(project=_TEST_PROJECT, staging_bucket=_TEST_BUCKET_NAME)
job = training_jobs.CustomTrainingJob(
display_name=_TEST_DISPLAY_NAME,
script_path=_TEST_LOCAL_SCRIPT_FILE_NAME,
container_uri=_TEST_TRAINING_CONTAINER_IMAGE,
)
with pytest.raises(RuntimeError):
job.run(
dataset=mock_tabular_dataset,
base_output_dir=_TEST_BASE_OUTPUT_DIR,
args=_TEST_RUN_ARGS,
replica_count=1,
machine_type=_TEST_MACHINE_TYPE,
accelerator_type=_TEST_ACCELERATOR_TYPE,
accelerator_count=_TEST_ACCELERATOR_COUNT,
model_display_name=_TEST_MODEL_DISPLAY_NAME,
training_fraction_split=_TEST_TRAINING_FRACTION_SPLIT,
validation_fraction_split=_TEST_VALIDATION_FRACTION_SPLIT,
test_fraction_split=_TEST_TEST_FRACTION_SPLIT,
sync=sync,
)
@pytest.mark.parametrize("sync", [True, False])
def test_run_call_pipeline_service_create_with_no_dataset(
self,
mock_pipeline_service_create,
mock_python_package_to_gcs,
mock_model_service_get,
sync,
):
aiplatform.init(
project=_TEST_PROJECT,
staging_bucket=_TEST_BUCKET_NAME,
credentials=_TEST_CREDENTIALS,
)
job = training_jobs.CustomTrainingJob(
display_name=_TEST_DISPLAY_NAME,
script_path=_TEST_LOCAL_SCRIPT_FILE_NAME,
container_uri=_TEST_TRAINING_CONTAINER_IMAGE,
model_serving_container_image_uri=_TEST_SERVING_CONTAINER_IMAGE,
model_serving_container_predict_route=_TEST_SERVING_CONTAINER_PREDICTION_ROUTE,
model_serving_container_health_route=_TEST_SERVING_CONTAINER_HEALTH_ROUTE,
)
model_from_job = job.run(
base_output_dir=_TEST_BASE_OUTPUT_DIR,
args=_TEST_RUN_ARGS,
replica_count=1,
machine_type=_TEST_MACHINE_TYPE,
accelerator_type=_TEST_ACCELERATOR_TYPE,
accelerator_count=_TEST_ACCELERATOR_COUNT,
model_display_name=_TEST_MODEL_DISPLAY_NAME,
training_fraction_split=_TEST_TRAINING_FRACTION_SPLIT,
validation_fraction_split=_TEST_VALIDATION_FRACTION_SPLIT,
test_fraction_split=_TEST_TEST_FRACTION_SPLIT,
sync=sync,
)
if not sync:
model_from_job.wait()