/
test_remote_training.py
2167 lines (1917 loc) · 77 KB
/
test_remote_training.py
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# -*- coding: utf-8 -*-
# Copyright 2023 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.
#
import copy
from importlib import reload
import os
import re
import sys
from unittest import mock
from unittest.mock import patch
import cloudpickle
from google import auth
from google.api_core import exceptions
from google.auth import credentials as auth_credentials
from google.cloud import aiplatform
from google.cloud.aiplatform import utils
from google.cloud.aiplatform.compat.services import (
job_service_client_v1beta1 as job_service_client,
tensorboard_service_client,
)
from google.cloud.aiplatform.compat.types import (
custom_job_v1beta1 as gca_custom_job_compat,
)
from google.cloud.aiplatform.compat.types import (
execution_v1beta1 as gca_execution,
)
from google.cloud.aiplatform.compat.types import io_v1beta1 as gca_io_compat
from google.cloud.aiplatform.compat.types import (
job_state_v1beta1 as gca_job_state_compat,
)
from google.cloud.aiplatform.compat.types import (
tensorboard as gca_tensorboard,
)
from google.cloud.aiplatform.metadata import constants as metadata_constants
from google.cloud.aiplatform.preview import resource_pool_utils
from google.cloud.aiplatform_v1 import (
Context as GapicContext,
MetadataServiceClient,
MetadataStore as GapicMetadataStore,
TensorboardServiceClient,
)
import vertexai
from vertexai.preview._workflow.executor import (
training,
)
from vertexai.preview._workflow.serialization_engine import (
any_serializer,
serializers_base,
)
from vertexai.preview._workflow.shared import configs
from vertexai.preview._workflow.shared import (
supported_frameworks,
)
from vertexai.preview.developer import remote_specs
import numpy as np
import pytest
import sklearn
from sklearn.datasets import load_iris
from sklearn.linear_model import _logistic
from sklearn.model_selection import train_test_split
import tensorflow as tf
import tensorflow.keras
# Manually set tensorflow version for b/295580335
tf.__version__ = "2.12.0"
# vertexai constants
_TEST_PROJECT = "test-project"
_TEST_PROJECT_NUMBER = 12345678
_TEST_LOCATION = "us-central1"
_TEST_PARENT = f"projects/{_TEST_PROJECT}/locations/{_TEST_LOCATION}"
_TEST_BUCKET_NAME = "gs://test-bucket"
_TEST_UNIQUE_NAME = "test-unique-name"
_TEST_REMOTE_JOB_NAME = f"remote-job-{_TEST_UNIQUE_NAME}"
_TEST_REMOTE_JOB_BASE_PATH = os.path.join(_TEST_BUCKET_NAME, _TEST_REMOTE_JOB_NAME)
_TEST_EXPERIMENT = "test-experiment"
_TEST_EXPERIMENT_RUN = "test-experiment-run"
_TEST_SERVICE_ACCOUNT = f"{_TEST_PROJECT_NUMBER}-compute@developer.gserviceaccount.com"
_TEST_CREDENTIALS = mock.Mock(spec=auth_credentials.AnonymousCredentials())
# dataset constants
dataset = load_iris()
_X_TRAIN, _X_TEST, _Y_TRAIN, _Y_TEST = train_test_split(
dataset.data, dataset.target, test_size=0.2, random_state=42
)
# custom job constants
_TEST_CUSTOM_JOB_NAME = f"{_TEST_PARENT}/customJobs/12345"
_TEST_UPGRADE_PIP_COMMAND = (
"export PIP_ROOT_USER_ACTION=ignore && " "pip install --upgrade pip && "
)
_TEST_BASE_DEPS = f"'{training.VERTEX_AI_DEPENDENCY_PATH}' 'absl-py==1.4.0' "
_TEST_CUSTOM_COMMAND = "apt-get update && " "apt-get install -y git && "
_TEST_DEPS = (
f"'scikit-learn=={sklearn.__version__}' "
f"'numpy=={np.__version__}' "
f"'cloudpickle=={cloudpickle.__version__}' "
)
_TEST_USER_DEPS = (
f"'torch_cv' "
f"'xgboost==1.6.0' "
f"'numpy' "
f"'scikit-learn=={sklearn.__version__}' "
f"'cloudpickle=={cloudpickle.__version__}' "
)
_TEST_TRAINING_COMMAND = (
"python3 -m vertexai.preview._workflow.executor.training_script "
"--pass_through_int_args= "
"--pass_through_float_args= "
"--pass_through_str_args= "
"--pass_through_bool_args= "
f"--input_path={os.path.join(_TEST_REMOTE_JOB_BASE_PATH, 'input').replace('gs://', '/gcs/', 1)} "
f"--output_path={os.path.join(_TEST_REMOTE_JOB_BASE_PATH, 'output').replace('gs://', '/gcs/', 1)} "
"--method_name=fit "
f"--arg_names=X,y "
"--enable_cuda=False "
"--enable_distributed=False "
"--accelerator_count=0"
)
_TEST_AUTOLOG_COMMAND = (
_TEST_UPGRADE_PIP_COMMAND
+ "pip install "
+ _TEST_BASE_DEPS.replace(
training.VERTEX_AI_DEPENDENCY_PATH,
training.VERTEX_AI_DEPENDENCY_PATH_AUTOLOGGING,
)
+ _TEST_DEPS
+ "&& "
+ _TEST_TRAINING_COMMAND
+ " --enable_autolog"
)
_TEST_REPLICA_COUNT = 1
_TEST_MACHINE_TYPE = "n1-standard-4"
_TEST_ACCELERATOR_TYPE = "NVIDIA_TESLA_K80"
_TEST_ACCELERATOR_COUNT = 2
_TEST_WORKER_POOL_SPEC = [
{
"machine_spec": {
"machine_type": _TEST_MACHINE_TYPE,
},
"replica_count": _TEST_REPLICA_COUNT,
"container_spec": {
"image_uri": f"python:{supported_frameworks._get_python_minor_version()}",
"command": ["sh", "-c"]
+ [
_TEST_UPGRADE_PIP_COMMAND
+ "pip install "
+ _TEST_BASE_DEPS
+ _TEST_DEPS
+ "&& "
+ _TEST_TRAINING_COMMAND
],
"args": [],
},
}
]
_TEST_CUSTOM_JOB_PROTO = gca_custom_job_compat.CustomJob(
display_name=_TEST_REMOTE_JOB_NAME,
job_spec={
"worker_pool_specs": _TEST_WORKER_POOL_SPEC,
"base_output_directory": gca_io_compat.GcsDestination(
output_uri_prefix=_TEST_REMOTE_JOB_BASE_PATH
),
},
)
# RemoteConfig constants
_TEST_TRAINING_CONFIG_DISPLAY_NAME = "test-training-config-display-name"
_TEST_TRAINING_CONFIG_STAGING_BUCKET = "gs://test-training-config-staging-bucket"
_TEST_TRAINING_CONFIG_CONTAINER_URI = "gcr.io/custom-image"
_TEST_TRAINING_CONFIG_MACHINE_TYPE = "n1-highmem-4"
_TEST_TRAINING_CONFIG_ACCELERATOR_TYPE = "NVIDIA_TESLA_K80"
_TEST_TRAINING_CONFIG_ACCELERATOR_COUNT = 4
_TEST_REQUIREMENTS = ["torch_cv", "xgboost==1.6.0", "numpy"]
_TEST_CUSTOM_COMMANDS = ["apt-get update", "apt-get install -y git"]
_TEST_BOOT_DISK_TYPE = "test_boot_disk_type"
_TEST_BOOT_DISK_SIZE_GB = 10
_TEST_TRAINING_CONFIG_WORKER_POOL_SPECS = remote_specs.WorkerPoolSpecs(
chief=remote_specs.WorkerPoolSpec(
machine_type=_TEST_TRAINING_CONFIG_MACHINE_TYPE,
replica_count=1,
boot_disk_type=_TEST_BOOT_DISK_TYPE,
boot_disk_size_gb=_TEST_BOOT_DISK_SIZE_GB,
)
)
_TEST_TRAINING_CONFIG_WORKER_POOL_SPECS_GPU = remote_specs.WorkerPoolSpecs(
chief=remote_specs.WorkerPoolSpec(
machine_type=_TEST_TRAINING_CONFIG_MACHINE_TYPE,
accelerator_count=_TEST_TRAINING_CONFIG_ACCELERATOR_COUNT,
accelerator_type=_TEST_TRAINING_CONFIG_ACCELERATOR_TYPE,
replica_count=1,
boot_disk_type=_TEST_BOOT_DISK_TYPE,
boot_disk_size_gb=_TEST_BOOT_DISK_SIZE_GB,
)
)
_TEST_CONTEXT_ID = _TEST_EXPERIMENT
_TEST_CONTEXT_NAME = f"{_TEST_PARENT}/contexts/{_TEST_CONTEXT_ID}"
_TEST_EXPERIMENT_DESCRIPTION = "test-experiment-description"
_TEST_ID = "1028944691210842416"
_TEST_TENSORBOARD_NAME = f"{_TEST_PARENT}/tensorboards/{_TEST_ID}"
_TEST_EXECUTION_ID = f"{_TEST_EXPERIMENT}-{_TEST_EXPERIMENT_RUN}"
_TEST_EXPERIMENT_RUN_CONTEXT_NAME = f"{_TEST_PARENT}/contexts/{_TEST_EXECUTION_ID}"
_TEST_METADATASTORE = (
f"projects/{_TEST_PROJECT}/locations/{_TEST_LOCATION}/metadataStores/default"
)
_EXPERIMENT_MOCK = GapicContext(
name=_TEST_CONTEXT_NAME,
display_name=_TEST_EXPERIMENT,
description=_TEST_EXPERIMENT_DESCRIPTION,
schema_title=metadata_constants.SYSTEM_EXPERIMENT,
schema_version=metadata_constants.SCHEMA_VERSIONS[
metadata_constants.SYSTEM_EXPERIMENT
],
metadata={**metadata_constants.EXPERIMENT_METADATA},
)
_EXPERIMENT_MOCK.metadata[
metadata_constants._BACKING_TENSORBOARD_RESOURCE_KEY
] = _TEST_TENSORBOARD_NAME
_EXPERIMENT_RUN_MOCK = GapicContext(
name=_TEST_EXPERIMENT_RUN_CONTEXT_NAME,
display_name=_TEST_EXPERIMENT_RUN,
schema_title=metadata_constants.SYSTEM_EXPERIMENT_RUN,
schema_version=metadata_constants.SCHEMA_VERSIONS[
metadata_constants.SYSTEM_EXPERIMENT_RUN
],
metadata={
metadata_constants._PARAM_KEY: {},
metadata_constants._METRIC_KEY: {},
metadata_constants._STATE_KEY: gca_execution.Execution.State.RUNNING.name,
},
)
_TEST_DEFAULT_TENSORBOARD_NAME = "test-tensorboard-default-name"
_TEST_DEFAULT_TENSORBOARD_GCA = gca_tensorboard.Tensorboard(
name=_TEST_DEFAULT_TENSORBOARD_NAME,
is_default=True,
)
_TEST_PERSISTENT_RESOURCE_ID = "test-cluster"
_TEST_PERSISTENT_RESOURCE_CONFIG = configs.PersistentResourceConfig(
name=_TEST_PERSISTENT_RESOURCE_ID,
resource_pools=[
remote_specs.ResourcePool(
replica_count=_TEST_REPLICA_COUNT,
),
remote_specs.ResourcePool(
machine_type="n1-standard-8",
replica_count=2,
),
],
)
_TEST_PERSISTENT_RESOURCE_CONFIG_SERVICE_ACCOUNT = configs.PersistentResourceConfig(
name=_TEST_PERSISTENT_RESOURCE_ID,
resource_pools=[
remote_specs.ResourcePool(
replica_count=_TEST_REPLICA_COUNT,
),
remote_specs.ResourcePool(
machine_type="n1-standard-8",
replica_count=2,
),
],
service_account=_TEST_SERVICE_ACCOUNT,
)
_TEST_PERSISTENT_RESOURCE_CONFIG_DISABLE = configs.PersistentResourceConfig(
name=_TEST_PERSISTENT_RESOURCE_ID,
resource_pools=[
remote_specs.ResourcePool(
replica_count=_TEST_REPLICA_COUNT,
),
remote_specs.ResourcePool(
machine_type="n1-standard-8",
replica_count=2,
),
],
disable=True,
)
@pytest.fixture
def list_default_tensorboard_mock():
with patch.object(
TensorboardServiceClient, "list_tensorboards"
) as list_default_tensorboard_mock:
list_default_tensorboard_mock.side_effect = [
[_TEST_DEFAULT_TENSORBOARD_GCA],
]
yield list_default_tensorboard_mock
def _get_custom_job_proto(
display_name=None,
staging_bucket=None,
container_uri=None,
machine_type=None,
accelerator_type=None,
accelerator_count=None,
replica_count=None,
boot_disk_type=None,
boot_disk_size_gb=None,
service_account=None,
experiment=None,
experiment_run=None,
autolog_enabled=False,
cuda_enabled=False,
distributed_enabled=False,
model=None,
user_requirements=False,
custom_commands=False,
persistent_resource_id=None,
):
job = copy.deepcopy(_TEST_CUSTOM_JOB_PROTO)
if display_name:
job.display_name = display_name
if container_uri:
job.job_spec.worker_pool_specs[0].container_spec.image_uri = container_uri
job.job_spec.worker_pool_specs[0].container_spec.command[-1] = (
_TEST_UPGRADE_PIP_COMMAND
+ "pip install "
+ _TEST_BASE_DEPS
+ "&& "
+ _TEST_TRAINING_COMMAND
)
if user_requirements:
job.job_spec.worker_pool_specs[0].container_spec.command[-1] = (
_TEST_UPGRADE_PIP_COMMAND
+ "pip install "
+ _TEST_BASE_DEPS
+ _TEST_USER_DEPS
+ "&& "
+ _TEST_TRAINING_COMMAND
)
if custom_commands:
job.job_spec.worker_pool_specs[0].container_spec.command[-1] = (
_TEST_UPGRADE_PIP_COMMAND.replace("&& ", f"&& {_TEST_CUSTOM_COMMAND}", 1)
+ "pip install "
+ _TEST_BASE_DEPS
+ _TEST_DEPS
+ "&& "
+ _TEST_TRAINING_COMMAND
)
if autolog_enabled:
job.job_spec.worker_pool_specs[0].container_spec.command[
-1
] = _TEST_AUTOLOG_COMMAND
if isinstance(model, tf.Module):
command = job.job_spec.worker_pool_specs[0].container_spec.command
for i, s in enumerate(command):
s = s.replace(
f"scikit-learn=={sklearn.__version__}", f"tensorflow=={tf.__version__}"
)
s = s.replace("--arg_names=X,y", "--arg_names=x,y")
command[i] = s
job.job_spec.worker_pool_specs[0].container_spec.command = command
if cuda_enabled:
if not container_uri:
job.job_spec.worker_pool_specs[
0
].container_spec.image_uri = supported_frameworks._get_gpu_container_uri(
model
)
job.job_spec.worker_pool_specs[0].machine_spec.machine_type = "n1-standard-16"
job.job_spec.worker_pool_specs[
0
].machine_spec.accelerator_type = "NVIDIA_TESLA_P100"
job.job_spec.worker_pool_specs[0].machine_spec.accelerator_count = 1
command = job.job_spec.worker_pool_specs[0].container_spec.command
job.job_spec.worker_pool_specs[0].container_spec.command = [
s.replace("--enable_cuda=False", "--enable_cuda=True") for s in command
]
if distributed_enabled:
command = job.job_spec.worker_pool_specs[0].container_spec.command
job.job_spec.worker_pool_specs[0].container_spec.command = [
s.replace("--enable_distributed=False", "--enable_distributed=True")
for s in command
]
if machine_type:
job.job_spec.worker_pool_specs[0].machine_spec.machine_type = machine_type
if accelerator_type:
job.job_spec.worker_pool_specs[
0
].machine_spec.accelerator_type = accelerator_type
if accelerator_count:
job.job_spec.worker_pool_specs[
0
].machine_spec.accelerator_count = accelerator_count
if not distributed_enabled:
command = job.job_spec.worker_pool_specs[0].container_spec.command
job.job_spec.worker_pool_specs[0].container_spec.command = [
s.replace(
"--accelerator_count=0",
f"--accelerator_count={accelerator_count}",
)
for s in command
]
if replica_count:
job.job_spec.worker_pool_specs[0].replica_count = replica_count
if boot_disk_type:
job.job_spec.worker_pool_specs[0].disk_spec.boot_disk_type = boot_disk_type
if boot_disk_size_gb:
job.job_spec.worker_pool_specs[
0
].disk_spec.boot_disk_size_gb = boot_disk_size_gb
if staging_bucket:
job.job_spec.base_output_directory = gca_io_compat.GcsDestination(
output_uri_prefix=os.path.join(staging_bucket, _TEST_REMOTE_JOB_NAME)
)
command = job.job_spec.worker_pool_specs[0].container_spec.command
job.job_spec.worker_pool_specs[0].container_spec.command = [
s.replace(_TEST_BUCKET_NAME[5:], staging_bucket[5:]) for s in command
]
if service_account:
job.job_spec.service_account = service_account
if experiment:
env = job.job_spec.worker_pool_specs[0].container_spec.env
env.append({"name": metadata_constants.ENV_EXPERIMENT_KEY, "value": experiment})
if experiment_run:
env = job.job_spec.worker_pool_specs[0].container_spec.env
env.append(
{"name": metadata_constants.ENV_EXPERIMENT_RUN_KEY, "value": experiment_run}
)
if persistent_resource_id:
job.job_spec.persistent_resource_id = persistent_resource_id
job.labels = ({"trained_by_vertex_ai": "true"},)
return job
@pytest.fixture
def mock_timestamped_unique_name():
with patch.object(utils, "timestamped_unique_name") as mock_timestamped_unique_name:
mock_timestamped_unique_name.return_value = _TEST_UNIQUE_NAME
yield mock_timestamped_unique_name
@pytest.fixture
def mock_autolog_enabled():
with patch.object(
utils.autologging_utils, "_is_autologging_enabled"
) as autolog_enabled:
autolog_enabled.return_value = True
yield autolog_enabled
@pytest.fixture
def mock_autolog_disabled():
with patch.object(
utils.autologging_utils, "_is_autologging_enabled"
) as autolog_disabled:
autolog_disabled.return_value = False
yield autolog_disabled
@pytest.fixture
def mock_get_project_number():
with patch.object(
utils.resource_manager_utils, "get_project_number"
) as mock_get_project_number:
mock_get_project_number.return_value = _TEST_PROJECT_NUMBER
yield mock_get_project_number
@pytest.fixture
def mock_get_experiment_run():
with patch.object(MetadataServiceClient, "get_context") as mock_get_experiment_run:
mock_get_experiment_run.side_effect = [
_EXPERIMENT_MOCK,
_EXPERIMENT_RUN_MOCK,
_EXPERIMENT_RUN_MOCK,
]
yield mock_get_experiment_run
@pytest.fixture
def mock_get_metadata_store():
with patch.object(
MetadataServiceClient, "get_metadata_store"
) as mock_get_metadata_store:
mock_get_metadata_store.return_value = GapicMetadataStore(
name=_TEST_METADATASTORE,
)
yield mock_get_metadata_store
@pytest.fixture
def get_artifact_not_found_mock():
with patch.object(MetadataServiceClient, "get_artifact") as get_artifact_mock:
get_artifact_mock.side_effect = exceptions.NotFound("")
yield get_artifact_mock
# we've tested AnySerializer in `test_serializers.py`
# so here we mock the SDK methods directly
@pytest.fixture
def mock_any_serializer_serialize_sklearn():
with patch.object(
any_serializer.AnySerializer,
"serialize",
side_effect=[
{
serializers_base.SERIALIZATION_METADATA_DEPENDENCIES_KEY: [
f"scikit-learn=={sklearn.__version__}"
],
serializers_base.SERIALIZATION_METADATA_CUSTOM_COMMANDS_KEY: [],
},
{
serializers_base.SERIALIZATION_METADATA_DEPENDENCIES_KEY: [
f"numpy=={np.__version__}",
f"cloudpickle=={cloudpickle.__version__}",
],
serializers_base.SERIALIZATION_METADATA_CUSTOM_COMMANDS_KEY: [],
},
{
serializers_base.SERIALIZATION_METADATA_DEPENDENCIES_KEY: [
f"numpy=={np.__version__}",
f"cloudpickle=={cloudpickle.__version__}",
],
serializers_base.SERIALIZATION_METADATA_CUSTOM_COMMANDS_KEY: [],
},
{
serializers_base.SERIALIZATION_METADATA_DEPENDENCIES_KEY: [
f"numpy=={np.__version__}",
f"cloudpickle=={cloudpickle.__version__}",
],
serializers_base.SERIALIZATION_METADATA_CUSTOM_COMMANDS_KEY: [],
},
],
) as mock_any_serializer_serialize:
yield mock_any_serializer_serialize
@pytest.fixture
def mock_any_serializer_save_global_metadata():
with patch.object(
any_serializer.AnySerializer,
"save_global_metadata",
) as mock_any_serializer_save_global_metadata:
yield mock_any_serializer_save_global_metadata
@pytest.fixture
def mock_any_serializer_load_global_metadata():
with patch.object(
any_serializer.AnySerializer,
"load_global_metadata",
) as mock_any_serializer_load_global_metadata:
yield mock_any_serializer_load_global_metadata
@pytest.fixture
def mock_any_serializer_sklearn(
mock_any_serializer_serialize_sklearn, mock_any_serializer_deserialize_sklearn
):
with patch.object(
any_serializer,
"AnySerializer",
) as mock_any_serializer_obj:
model = _logistic.LogisticRegression()
model.fit(_X_TRAIN, _Y_TRAIN)
mock_any_serializer_obj.return_value.deserialize = (
mock_any_serializer_deserialize_sklearn
)
mock_any_serializer_obj.return_value.serialize = (
mock_any_serializer_serialize_sklearn
)
yield mock_any_serializer_obj
@pytest.fixture
def mock_any_serializer_deserialize_sklearn():
with patch.object(
any_serializer.AnySerializer, "deserialize"
) as mock_any_serializer_deserialize_sklearn:
model = _logistic.LogisticRegression()
returned_model = model.fit(_X_TRAIN, _Y_TRAIN)
mock_any_serializer_deserialize_sklearn.side_effect = [model, returned_model]
yield mock_any_serializer_deserialize_sklearn
@pytest.fixture
def mock_any_serializer_keras(
mock_any_serializer_serialize_keras, mock_any_serializer_deserialize_keras
):
with patch.object(
any_serializer,
"AnySerializer",
) as mock_any_serializer_obj:
model = _logistic.LogisticRegression()
model.fit(_X_TRAIN, _Y_TRAIN)
mock_any_serializer_obj.return_value.deserialize = (
mock_any_serializer_deserialize_keras
)
mock_any_serializer_obj.return_value.serialize = (
mock_any_serializer_serialize_keras
)
yield mock_any_serializer_obj
@pytest.fixture
def mock_any_serializer_serialize_keras():
with patch.object(
any_serializer.AnySerializer,
"serialize",
side_effect=[
{
serializers_base.SERIALIZATION_METADATA_DEPENDENCIES_KEY: [
f"tensorflow=={tf.__version__}"
],
serializers_base.SERIALIZATION_METADATA_CUSTOM_COMMANDS_KEY: [],
},
{
serializers_base.SERIALIZATION_METADATA_DEPENDENCIES_KEY: [
f"numpy=={np.__version__}",
f"cloudpickle=={cloudpickle.__version__}",
],
serializers_base.SERIALIZATION_METADATA_CUSTOM_COMMANDS_KEY: [],
},
{
serializers_base.SERIALIZATION_METADATA_DEPENDENCIES_KEY: [
f"numpy=={np.__version__}",
f"cloudpickle=={cloudpickle.__version__}",
],
serializers_base.SERIALIZATION_METADATA_CUSTOM_COMMANDS_KEY: [],
},
{
serializers_base.SERIALIZATION_METADATA_DEPENDENCIES_KEY: [
f"numpy=={np.__version__}",
f"cloudpickle=={cloudpickle.__version__}",
],
serializers_base.SERIALIZATION_METADATA_CUSTOM_COMMANDS_KEY: [],
},
],
) as mock_any_serializer_serialize:
yield mock_any_serializer_serialize
@pytest.fixture
def mock_any_serializer_deserialize_keras():
with patch.object(
any_serializer.AnySerializer, "deserialize"
) as mock_any_serializer_deserialize_keras:
model = tf.keras.Sequential(
[tf.keras.layers.Dense(5, input_shape=(4,)), tf.keras.layers.Softmax()]
)
model.compile(optimizer="adam", loss="mean_squared_error")
returned_history = model.fit(_X_TRAIN, _Y_TRAIN)
mock_any_serializer_deserialize_keras.side_effect = [model, returned_history]
yield mock_any_serializer_deserialize_keras
@pytest.fixture
def mock_create_custom_job():
with mock.patch.object(
job_service_client.JobServiceClient, "create_custom_job"
) as mock_create_custom_job:
custom_job_proto = _get_custom_job_proto()
custom_job_proto.name = _TEST_CUSTOM_JOB_NAME
custom_job_proto.state = gca_job_state_compat.JobState.JOB_STATE_PENDING
mock_create_custom_job.return_value = custom_job_proto
yield mock_create_custom_job
@pytest.fixture
def mock_get_custom_job():
with patch.object(
job_service_client.JobServiceClient, "get_custom_job"
) as mock_get_custom_job:
custom_job_proto = _get_custom_job_proto()
custom_job_proto.name = _TEST_CUSTOM_JOB_NAME
custom_job_proto.state = gca_job_state_compat.JobState.JOB_STATE_SUCCEEDED
mock_get_custom_job.return_value = custom_job_proto
yield mock_get_custom_job
@pytest.fixture
def update_context_mock():
with patch.object(MetadataServiceClient, "update_context") as update_context_mock:
update_context_mock.side_effect = [_EXPERIMENT_RUN_MOCK] * 4
yield update_context_mock
@pytest.fixture
def aiplatform_autolog_mock():
with patch.object(aiplatform, "autolog") as aiplatform_autolog_mock:
yield aiplatform_autolog_mock
@pytest.fixture(scope="module")
def google_auth_mock():
with mock.patch.object(auth, "default") as auth_mock:
auth_mock.return_value = (
auth_credentials.AnonymousCredentials(),
"test-project",
)
yield auth_mock
@pytest.fixture
def get_tensorboard_mock():
with patch.object(
tensorboard_service_client.TensorboardServiceClient, "get_tensorboard"
) as get_tensorboard_mock:
get_tensorboard_mock.return_value = _TEST_DEFAULT_TENSORBOARD_GCA
yield get_tensorboard_mock
# unittest `assert_any_call` method doesn't work when arguments contain `np.ndarray`
# https://stackoverflow.com/questions/56644729/mock-assert-mock-calls-with-a-numpy-array-as-argument-raises-valueerror-and-np
# tentatively runtime patch `assert_any_call` to solve this issue
def assert_any_call_for_numpy(self, **kwargs):
"""Used by vertexai Serializer mock, only check kwargs."""
found = False
for call in self.call_args_list:
equal = True
actual_kwargs = call[1]
for k, v in actual_kwargs.items():
if k not in kwargs:
equal = False
break
try:
equal = v == kwargs[k]
except ValueError:
equal = False
equal = equal.all() if isinstance(equal, np.ndarray) else equal
if not equal:
break
if equal and len(actual_kwargs) == len(kwargs):
found = True
break
if not found:
raise AssertionError(f"{kwargs} not found.")
mock.Mock.assert_any_call = assert_any_call_for_numpy
# TODO(zhenyiqi) fix external unit test failure caused by this method
training._add_indirect_dependency_versions = lambda x: x
@pytest.mark.usefixtures("google_auth_mock", "mock_cloud_logging_list_entries")
class TestRemoteTraining:
def setup_method(self):
reload(aiplatform.initializer)
reload(aiplatform)
reload(vertexai.preview.initializer)
reload(vertexai)
reload(_logistic)
reload(tf.keras)
def teardown_method(self):
aiplatform.initializer.global_pool.shutdown(wait=True)
@pytest.mark.usefixtures(
"mock_timestamped_unique_name", "mock_get_custom_job", "mock_autolog_disabled"
)
def test_remote_training_sklearn(
self,
mock_any_serializer_sklearn,
mock_create_custom_job,
):
vertexai.init(
project=_TEST_PROJECT,
location=_TEST_LOCATION,
staging_bucket=_TEST_BUCKET_NAME,
)
vertexai.preview.init(remote=True)
LogisticRegression = vertexai.preview.remote(_logistic.LogisticRegression)
model = LogisticRegression()
model.fit(_X_TRAIN, _Y_TRAIN)
# check that model is serialized correctly
mock_any_serializer_sklearn.return_value.serialize.assert_any_call(
to_serialize=model,
gcs_path=os.path.join(_TEST_REMOTE_JOB_BASE_PATH, "input/input_estimator"),
)
# check that args are serialized correctly
mock_any_serializer_sklearn.return_value.serialize.assert_any_call(
to_serialize=_X_TRAIN,
gcs_path=os.path.join(_TEST_REMOTE_JOB_BASE_PATH, "input/X"),
)
mock_any_serializer_sklearn.return_value.serialize.assert_any_call(
to_serialize=_Y_TRAIN,
gcs_path=os.path.join(_TEST_REMOTE_JOB_BASE_PATH, "input/y"),
)
# ckeck that CustomJob is created correctly
expected_custom_job = _get_custom_job_proto()
mock_create_custom_job.assert_called_once_with(
parent=_TEST_PARENT,
custom_job=expected_custom_job,
timeout=None,
)
# check that trained model is deserialized correctly
mock_any_serializer_sklearn.return_value.deserialize.assert_has_calls(
[
mock.call(
os.path.join(_TEST_REMOTE_JOB_BASE_PATH, "output/output_estimator")
),
mock.call(
os.path.join(_TEST_REMOTE_JOB_BASE_PATH, "output/output_data")
),
]
)
# change to `vertexai.preview.init(remote=False)` to use local prediction
vertexai.preview.init(remote=False)
# check that local model is updated in place
# `model.score` raises NotFittedError if the model is not updated
model.score(_X_TEST, _Y_TEST)
@pytest.mark.usefixtures(
"mock_timestamped_unique_name", "mock_get_custom_job", "mock_autolog_disabled"
)
def test_remote_training_sklearn_with_user_requirements(
self,
mock_any_serializer_sklearn,
mock_create_custom_job,
):
vertexai.init(
project=_TEST_PROJECT,
location=_TEST_LOCATION,
staging_bucket=_TEST_BUCKET_NAME,
)
vertexai.preview.init(remote=True)
LogisticRegression = vertexai.preview.remote(_logistic.LogisticRegression)
model = LogisticRegression()
model.fit.vertex.remote_config.requirements = _TEST_REQUIREMENTS
model.fit(_X_TRAIN, _Y_TRAIN)
# check that model is serialized correctly
mock_any_serializer_sklearn.return_value.serialize.assert_any_call(
to_serialize=model,
gcs_path=os.path.join(_TEST_REMOTE_JOB_BASE_PATH, "input/input_estimator"),
)
# check that args are serialized correctly
mock_any_serializer_sklearn.return_value.serialize.assert_any_call(
to_serialize=_X_TRAIN,
gcs_path=os.path.join(_TEST_REMOTE_JOB_BASE_PATH, "input/X"),
)
mock_any_serializer_sklearn.return_value.serialize.assert_any_call(
to_serialize=_Y_TRAIN,
gcs_path=os.path.join(_TEST_REMOTE_JOB_BASE_PATH, "input/y"),
)
# ckeck that CustomJob is created correctly
expected_custom_job = _get_custom_job_proto(user_requirements=True)
mock_create_custom_job.assert_called_once_with(
parent=_TEST_PARENT,
custom_job=expected_custom_job,
timeout=None,
)
# check that trained model is deserialized correctly
mock_any_serializer_sklearn.return_value.deserialize.assert_has_calls(
[
mock.call(
os.path.join(_TEST_REMOTE_JOB_BASE_PATH, "output/output_estimator")
),
mock.call(
os.path.join(_TEST_REMOTE_JOB_BASE_PATH, "output/output_data")
),
]
)
# change to `vertexai.preview.init(remote=False)` to use local prediction
vertexai.preview.init(remote=False)
# check that local model is updated in place
# `model.score` raises NotFittedError if the model is not updated
model.score(_X_TEST, _Y_TEST)
@pytest.mark.usefixtures(
"mock_timestamped_unique_name", "mock_get_custom_job", "mock_autolog_disabled"
)
def test_remote_training_sklearn_with_custom_commands(
self,
mock_any_serializer_sklearn,
mock_create_custom_job,
):
vertexai.init(
project=_TEST_PROJECT,
location=_TEST_LOCATION,
staging_bucket=_TEST_BUCKET_NAME,
)
vertexai.preview.init(remote=True)
LogisticRegression = vertexai.preview.remote(_logistic.LogisticRegression)
model = LogisticRegression()
model.fit.vertex.remote_config.custom_commands = _TEST_CUSTOM_COMMANDS
model.fit(_X_TRAIN, _Y_TRAIN)
# check that model is serialized correctly
mock_any_serializer_sklearn.return_value.serialize.assert_any_call(
to_serialize=model,
gcs_path=os.path.join(_TEST_REMOTE_JOB_BASE_PATH, "input/input_estimator"),
)
# check that args are serialized correctly
mock_any_serializer_sklearn.return_value.serialize.assert_any_call(
to_serialize=_X_TRAIN,
gcs_path=os.path.join(_TEST_REMOTE_JOB_BASE_PATH, "input/X"),
)
mock_any_serializer_sklearn.return_value.serialize.assert_any_call(
to_serialize=_Y_TRAIN,
gcs_path=os.path.join(_TEST_REMOTE_JOB_BASE_PATH, "input/y"),
)
# ckeck that CustomJob is created correctly
expected_custom_job = _get_custom_job_proto(custom_commands=True)
mock_create_custom_job.assert_called_once_with(
parent=_TEST_PARENT,
custom_job=expected_custom_job,
timeout=None,
)
# check that trained model is deserialized correctly
mock_any_serializer_sklearn.return_value.deserialize.assert_has_calls(
[
mock.call(
os.path.join(_TEST_REMOTE_JOB_BASE_PATH, "output/output_estimator")
),
mock.call(
os.path.join(_TEST_REMOTE_JOB_BASE_PATH, "output/output_data")
),
]
)
# change to `vertexai.preview.init(remote=False)` to use local prediction
vertexai.preview.init(remote=False)
# check that local model is updated in place
# `model.score` raises NotFittedError if the model is not updated
model.score(_X_TEST, _Y_TEST)
@pytest.mark.usefixtures(
"mock_timestamped_unique_name", "mock_get_custom_job", "mock_autolog_disabled"
)
def test_remote_training_sklearn_with_remote_configs(
self,
mock_any_serializer_sklearn,
mock_create_custom_job,
):
vertexai.init(
project=_TEST_PROJECT,
location=_TEST_LOCATION,
staging_bucket=_TEST_BUCKET_NAME,
)
vertexai.preview.init(remote=True)
LogisticRegression = vertexai.preview.remote(_logistic.LogisticRegression)
model = LogisticRegression()
# set all training configs
model.fit.vertex.remote_config.display_name = _TEST_TRAINING_CONFIG_DISPLAY_NAME
model.fit.vertex.remote_config.staging_bucket = (
_TEST_TRAINING_CONFIG_STAGING_BUCKET
)
model.fit.vertex.remote_config.container_uri = (
_TEST_TRAINING_CONFIG_CONTAINER_URI
)
model.fit.vertex.remote_config.machine_type = _TEST_TRAINING_CONFIG_MACHINE_TYPE
model.fit.vertex.remote_config.serializer_args[model] = {"extra_params": 1}
# X_TRAIN is a numpy array that is not hashable.
model.fit.vertex.remote_config.serializer_args[_X_TRAIN] = {"extra_params": 2}