本文整理汇总了Python中sagemaker.tensorflow.TensorFlow.create_model方法的典型用法代码示例。如果您正苦于以下问题：Python TensorFlow.create_model方法的具体用法？Python TensorFlow.create_model怎么用？Python TensorFlow.create_model使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类sagemaker.tensorflow.TensorFlow的用法示例。
在下文中一共展示了TensorFlow.create_model方法的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_create_model_with_custom_image
# 需要导入模块: from sagemaker.tensorflow import TensorFlow [as 别名]
# 或者: from sagemaker.tensorflow.TensorFlow import create_model [as 别名]
def test_create_model_with_custom_image(sagemaker_session):
container_log_level = '"logging.INFO"'
source_dir = 's3://mybucket/source'
custom_image = 'tensorflow:1.0'
tf = TensorFlow(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
training_steps=1000, evaluation_steps=10, train_instance_count=INSTANCE_COUNT,
train_instance_type=INSTANCE_TYPE, image_name=custom_image,
container_log_level=container_log_level, base_job_name='job',
source_dir=source_dir)
job_name = 'doing something'
tf.fit(inputs='s3://mybucket/train', job_name=job_name)
model = tf.create_model()
assert model.image == custom_image示例2: test_create_model_with_optional_params
# 需要导入模块: from sagemaker.tensorflow import TensorFlow [as 别名]
# 或者: from sagemaker.tensorflow.TensorFlow import create_model [as 别名]
def test_create_model_with_optional_params(sagemaker_session):
container_log_level = '"logging.INFO"'
source_dir = 's3://mybucket/source'
enable_cloudwatch_metrics = 'true'
tf = TensorFlow(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
training_steps=1000, evaluation_steps=10, train_instance_count=INSTANCE_COUNT,
train_instance_type=INSTANCE_TYPE, container_log_level=container_log_level, base_job_name='job',
source_dir=source_dir, enable_cloudwatch_metrics=enable_cloudwatch_metrics)
job_name = 'doing something'
tf.fit(inputs='s3://mybucket/train', job_name=job_name)
new_role = 'role'
model_server_workers = 2
model = tf.create_model(role=new_role, model_server_workers=2)
assert model.role == new_role
assert model.model_server_workers == model_server_workers示例3: test_create_model
# 需要导入模块: from sagemaker.tensorflow import TensorFlow [as 别名]
# 或者: from sagemaker.tensorflow.TensorFlow import create_model [as 别名]
def test_create_model(sagemaker_session, tf_version):
container_log_level = '"logging.INFO"'
source_dir = 's3://mybucket/source'
tf = TensorFlow(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
training_steps=1000, evaluation_steps=10, train_instance_count=INSTANCE_COUNT,
train_instance_type=INSTANCE_TYPE, framework_version=tf_version,
container_log_level=container_log_level, base_job_name='job',
source_dir=source_dir)
job_name = 'doing something'
tf.fit(inputs='s3://mybucket/train', job_name=job_name)
model = tf.create_model()
assert model.sagemaker_session == sagemaker_session
assert model.framework_version == tf_version
assert model.py_version == tf.py_version
assert model.entry_point == SCRIPT_PATH
assert model.role == ROLE
assert model.name == job_name
assert model.container_log_level == container_log_level
assert model.source_dir == source_dir示例4: test_tf
# 需要导入模块: from sagemaker.tensorflow import TensorFlow [as 别名]
# 或者: from sagemaker.tensorflow.TensorFlow import create_model [as 别名]
def test_tf(m_tar, e_tar, time, strftime, sagemaker_session, tf_version):
tf = TensorFlow(entry_point=SCRIPT_FILE, role=ROLE, sagemaker_session=sagemaker_session, training_steps=1000,
evaluation_steps=10, train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
framework_version=tf_version, requirements_file=REQUIREMENTS_FILE, source_dir=DATA_DIR)
inputs = 's3://mybucket/train'
s3_prefix = 's3://{}/{}/source/sourcedir.tar.gz'.format(BUCKET_NAME, JOB_NAME)
e_tar.return_value = UploadedCode(s3_prefix=s3_prefix, script_name=SCRIPT_FILE)
s3_prefix = 's3://{}/{}/sourcedir.tar.gz'.format(BUCKET_NAME, JOB_NAME)
m_tar.return_value = UploadedCode(s3_prefix=s3_prefix, script_name=SCRIPT_FILE)
tf.fit(inputs=inputs)
call_names = [c[0] for c in sagemaker_session.method_calls]
assert call_names == ['train', 'logs_for_job']
expected_train_args = _create_train_job(tf_version)
expected_train_args['input_config'][0]['DataSource']['S3DataSource']['S3Uri'] = inputs
actual_train_args = sagemaker_session.method_calls[0][2]
assert actual_train_args == expected_train_args
model = tf.create_model()
environment = {
'Environment': {
'SAGEMAKER_SUBMIT_DIRECTORY': 's3://{}/{}/sourcedir.tar.gz'.format(BUCKET_NAME, JOB_NAME),
'SAGEMAKER_PROGRAM': 'dummy_script.py', 'SAGEMAKER_REQUIREMENTS': 'dummy_requirements.txt',
'SAGEMAKER_ENABLE_CLOUDWATCH_METRICS': 'false', 'SAGEMAKER_REGION': 'us-west-2',
'SAGEMAKER_CONTAINER_LOG_LEVEL': '20'
},
'Image': create_image_uri('us-west-2', "tensorflow", INSTANCE_TYPE, tf_version, "py2"),
'ModelDataUrl': 's3://m/m.tar.gz'
}
assert environment == model.prepare_container_def(INSTANCE_TYPE)
assert 'cpu' in model.prepare_container_def(INSTANCE_TYPE)['Image']
predictor = tf.deploy(1, INSTANCE_TYPE)
assert isinstance(predictor, TensorFlowPredictor)