easymaker 1.0.4

AI EasyMaker SDK for Python.

NHN AI EasyMaker SDK

# Initialize EasyMaker SDK
import easymaker

easymaker.init(
    appkey='EASYMAKER_APPKEY',
    region='kr1',
    secret_key='EASYMAKER_SECRET_KEY',
)

# NHN Cloud ObjectStorage upload/download
easymaker.download(
    easymaker_obs_uri='obs://api-storage.cloud.toast.com/v1/AUTH_59ed815ebc6b4391802d4848329cebe7/SDK/sample/source_dir',
    download_dir_path='./source_dir2',
    username='[e-mail]',
    password='easymaker!@#123'
)
easymaker.upload(
    easymaker_obs_uri='obs://api-storage.cloud.toast.com/v1/AUTH_{tenant_id}/{container_name}/{upload_path}',
    src_dir_path='./local_dir',
    username='[e-mail]',
    password='nhn_object_storage_api_password'
)

# Create Experiment
experiment_id = easymaker.Experiment().create(
    experiment_name='experiment_name',
    experiment_description='experiment_description',
    # wait=False
)

# Create Training
training_id = easymaker.Training().run(
    experiment_id=experiment_id,
    training_name='training_name',
    training_description='training_description',
    train_image_name='Ubuntu 18.04 CPU TensorFlow Training',
    train_instance_name='m2.c4m8',
    train_instance_count=1,
    data_storage_size=300,  # minimum size : 300G
    source_dir_uri='obs://api-storage.cloud.toast.com/v1/AUTH_{tenant_id}/{container_name}/{soucre_download_path}',
    entry_point='training_start.py',
    hyperparameter_list=[
        {
            "hyperparameterKey": "epochs",
            "hyperparameterValue": "10",
        },
        {
            "hyperparameterKey": "batch-size",
            "hyperparameterValue": "30",
        }
    ],
    timeout_hours=100, # 1~720
    model_upload_uri='obs://api-storage.cloud.toast.com/v1/AUTH_{tenant_id}/{container_name}/{model_upload_path}',
    check_point_upload_uri='obs://api-storage.cloud.toast.com/v1/AUTH_{tenant_id}/{container_name}/{checkpoint_upload_path}',
    dataset_list=[
        {
            "datasetName": "train",
            "dataUri": "obs://api-storage.cloud.toast.com/v1/AUTH_{tenant_id}/{container_name}/{train_data_download_path}"
        },
        {
            "datasetName": "test",
            "dataUri": "obs://api-storage.cloud.toast.com/v1/AUTH_{tenant_id}/{container_name}/{test_data_download_path}"
        }
    ],
    tag_list=[  # maximum 10
        {
            "tagKey": "tag_num",
            "tagValue": "test_tag_1",
        },
        {
            "tagKey": "tag2",
            "tagValue": "test_tag_2",
        }
    ],
    use_log=True,
    # wait=False
)

# Create Model
model_id = easymaker.Model().create(
    training_id=training_id,
    model_name='model_name',
    model_description='model_description',
)
model_id2 = easymaker.Model().create_by_model_uri(
    framework_code=easymaker.TENSORFLOW,
    model_uri='obs://api-storage.cloud.toast.com/v1/AUTH_{tenant_id}/{container_name}/{model_upload_path}',
    model_name='model_name',
    model_description='model_description',
)

# Create Endpoint
endpoint = easymaker.Endpoint()
endpoint_id = endpoint.create(
    model_id=model_id,
    endpoint_name='endpoint_name',
    endpoint_description='endpoint_description',
    endpoint_instance_name='c2.c16m16',
    apigw_resource_uri='/api-path',
    endpoint_instance_count=1,
    use_log=True,
    # wait=False,
    # autoscaler_enable=True,  # default False
    # autoscaler_min_node_count=1,
    # autoscaler_max_node_count=10,
    # autoscaler_scale_down_enable=True,
    # autoscaler_scale_down_util_threshold=50,
    # autoscaler_scale_down_unneeded_time=10,
    # autoscaler_scale_down_delay_after_add=10,
)
# Create Endpoint Stage
stage_id = endpoint.create_stage(
    model_id=model_id,
    stage_name='stage01',
    stage_description='test endpoint',
    endpoint_instance_name='c2.c16m16',
    apigw_resource_uri='/test-api',
    endpoint_instance_count=1,
    # wait=False,
    # autoscaler_enable=True,  # default False
    # autoscaler_min_node_count=1,
    # autoscaler_max_node_count=10,
    # autoscaler_scale_down_enable=True,
    # autoscaler_scale_down_util_threshold=50,
    # autoscaler_scale_down_unneeded_time=10,
    # autoscaler_scale_down_delay_after_add=10,
)

# Get an endpoint that already exists
endpoint = easymaker.Endpoint(endpoint_id)

# get endpoint list
endpoint_stage_info_list = endpoint.get_endpoint_stage_info_list()

# Inference
endpoint.predict(json={'instances': [[6.8, 2.8, 4.8, 1.4]]})
endpoint.predict(endpoint_stage_info=endpoint_stage_info_list[1],  # If endpoint_stage_info is not set, use the default endpoint
                 json={'instances': [[6.8, 2.8, 4.8, 1.4]]})

# Log (Log & Crash)
easymaker_logger = easymaker.logger(logncrash_appkey='log&crash_product_app_key')
easymaker_logger.send('test log meassage')  # Output to stdout & send log to log&crash product
easymaker_logger.send(log_message='log meassage',
                      log_level='INFO',  # default: INFO
                      project_version='1.0.0',  # default: 1.0.0
                      parameters={'serviceType': 'EasyMakerSample'})  # Add custom parameters

CLI Command

• instance type list : python -m easymaker -instance --region kr1 --appkey EM_APPKEY --secret_key EM_SECRET_KEY
• image list : python -m easymaker -image --region kr1 --appkey EM_APPKEY --secret_key EM_SECRET_KEY
• experiment list : python -m easymaker -experiment --region kr1 --appkey EM_APPKEY --secret_key EM_SECRET_KEY
• training list : 'python -m easymaker -training --region kr1 --appkey EM_APPKEY --secret_key EM_SECRET_KEY'
• model list : 'python -m easymaker -model --region kr1 --appkey EM_APPKEY --secret_key EM_SECRET_KEY'
• endpoint list : 'python -m easymaker -endpoint --region kr1 --appkey EM_APPKEY --secret_key EM_SECRET_KEY'