easymaker 2.0.0

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",
    experiment_id="EXPERIMENT_ID", # Optional
)

# Create Experiment
experiment = easymaker.Experiment().create(
    experiment_name="experiment_name",
    experiment_description="experiment_description",
    # wait=False
)

# Delete Experiment
experiment.delete()
easymaker.Experiment("experiment_id").delete()
easymaker.experiment.delete("experiment_id")

# Create Training
training = easymaker.Training().run(
    experiment_id=experiment.experiment_id, # Optional if already set in init
    training_name="training_name",
    training_description="training_description",
    train_image_name="Ubuntu 22.04 CPU TensorFlow Training",
    train_instance_name="m2.c4m8",
    distributed_node_count=1,
    data_storage_size=300,  # minimum size : 300G
    source_dir_uri="obs://kr1-api-object-storage.nhncloudservice.com/v1/AUTH_{tenant_id}/{container_name}/{soucre_download_path}",
    entry_point="training_start.py",
    hyperparameter_list=[
        {
            "parameterName": "epochs",
            "parameterValue": "10",
        },
        {
            "parameterName": "batch-size",
            "parameterValue": "30",
        }
    ],
    timeout_hours=100, # 1~720
    model_upload_uri="obs://kr1-api-object-storage.nhncloudservice.com/v1/AUTH_{tenant_id}/{container_name}/{model_upload_path}",
    check_point_input_uri="obs://kr1-api-object-storage.nhncloudservice.com/v1/AUTH_{tenant_id}/{container_name}/{checkpoint_input_path}",
    check_point_upload_uri="obs://kr1-api-object-storage.nhncloudservice.com/v1/AUTH_{tenant_id}/{container_name}/{checkpoint_upload_path}",
    dataset_list=[
        {
            "datasetName": "train",
            "dataUri": "obs://kr1-api-object-storage.nhncloudservice.com/v1/AUTH_{tenant_id}/{container_name}/{train_data_download_path}"
        },
        {
            "datasetName": "test",
            "dataUri": "obs://kr1-api-object-storage.nhncloudservice.com/v1/AUTH_{tenant_id}/{container_name}/{test_data_download_path}"
        }
    ],
    tag_list=[  # maximum 10
        {
            "tagKey": "tag1",
            "tagValue": "test_tag_1",
        },
        {
            "tagKey": "tag2",
            "tagValue": "test_tag_2",
        }
    ],
    use_log=True,
    # wait=False
)

# Create Training By Algorithm (Image Classification)
training = easymaker.Training().run(
    experiment_id=experiment.experiment_id, # Optional if already set in init
    training_name="image_classification",
    training_description="easymaker sdk test training",
    train_image_name="Image Classification CPU",
    train_instance_name="m2.c4m8",
    distributed_node_count=1,
    algorithm_name="Image Classification",
    data_storage_size=300,  # minimum size : 300G
    hyperparameter_list=[
        {
            "parameterName": "input_size",
            "parameterValue": "28",
        },
        {
            "parameterName": "learning_rate",
            "parameterValue": "0.1",
        },
        {
            "parameterName": "per_device_train_batch_size",
            "parameterValue": "16",
        },
        {
            "parameterName": "per_device_eval_batch_size",
            "parameterValue": "16",
        },
        {
            "parameterName": "num_train_epochs",
            "parameterValue": "3",
        }
    ],
    timeout_hours=1,
    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": "validation",
            "dataUri": "obs://api-storage.cloud.toast.com/v1/AUTH_{tenant_id}/{container_name}/{validation_data_download_path}"
        }
    ],
    tag_list=[  # 최대 10개
        {
            "tagKey": "tag1",
            "tagValue": "test_tag_1",
        },
        {
            "tagKey": "tag2",
            "tagValue": "test_tag_2",
        }
    ],
    use_torchrun=True,
    nproc_per_node=1,
    use_log=True,
    # wait=False
)

# Delete Training
training.delete()
easymaker.Training("training_id").delete()
easymaker.training.delete("training_id")

# Create Hyperparameter Tuning
hyperparameter_tuning = easymaker.HyperparameterTuning().run(
    experiment_id=experiment.experiment_id, # Optional if already set in init
    hyperparameter_tuning_name="hyperparameter_tuning_name",
    hyperparameter_tuning_description="hyperparameter_tuning_description",
    image_name="Ubuntu 22.04 CPU TensorFlow Training",
    instance_name="m2.c8m16",
    distributed_node_count=1,
    parallel_trial_count=1,
    data_storage_size=300,
    source_dir_uri="obs://kr1-api-object-storage.nhncloudservice.com/v1/AUTH_{tenant_id}/{container_name}/{soucre_download_path}",
    entry_point="training_start.py",
    hyperparameter_spec_list=[
        {
            "hyperparameterName": "learning_rate",
            "hyperparameterTypeCode": easymaker.HYPERPARAMETER_TYPE_CODE.DOUBLE,
            "hyperparameterMinValue": "0.01",
            "hyperparameterMaxValue": "0.05",
        },
         {
            "hyperparameterName": "epochs",
            "hyperparameterTypeCode": easymaker.HYPERPARAMETER_TYPE_CODE.INT,
            "hyperparameterMinValue": "100",
            "hyperparameterMaxValue": "1000",
        }
    ],
    timeout_hours=10,
    model_upload_uri="obs://kr1-api-object-storage.nhncloudservice.com/v1/AUTH_{tenant_id}/{container_name}/{model_upload_path}",
    check_point_input_uri="obs://kr1-api-object-storage.nhncloudservice.com/v1/AUTH_{tenant_id}/{container_name}/{checkpoint_input_path}",
    check_point_upload_uri="obs://kr1-api-object-storage.nhncloudservice.com/v1/AUTH_{tenant_id}/{container_name}/{checkpoint_upload_path}",
    dataset_list=[
        {
            "datasetName": "train",
            "dataUri": "obs://kr1-api-object-storage.nhncloudservice.com/v1/AUTH_{tenant_id}/{container_name}/{train_data_download_path}"
        },
        {
            "datasetName": "test",
            "dataUri": "obs://kr1-api-object-storage.nhncloudservice.com/v1/AUTH_{tenant_id}/{container_name}/{test_data_download_path}"
        }
    ],
    metric_list=["val_loss", "loss", "accuracy"}],
    metric_regex="([\w|-]+)\s*:\s*([+-]?\d*(\.\d+)?([Ee][+-]?\d+)?)",
    objective_metric_name="val_loss",
    objective_type_code=easymaker.OBJECTIVE_TYPE_CODE.MINIMIZE,
    objective_goal=0.01,
    max_failed_trial_count=3,
    max_trial_count=10,
    tuning_strategy_name=easymaker.TUNING_STRATEGY.BAYESIAN_OPTIMIZATION,
    tuning_strategy_random_state=1,
    early_stopping_algorithm=easymaker.EARLY_STOPPING_ALGORITHM.MEDIAN,
    early_stopping_min_trial_count=3,
    early_stopping_start_step=4,
    tag_list=[
        {
            "tagKey": "tag1",
            "tagValue": "test_tag_1",
        }
    ],
    use_log=True,
    # wait=False,
)

# Create Hyperparameter Tuning By Algorithm (Image Classification)
hyperparameter_tuning = easymaker.HyperparameterTuning().run(
    experiment_id=experiment.experiment_id, # Optional if already set in init
    hyperparameter_tuning_name="hyperparameter_tuning_name",
    algorithm_name="Image Classification",
    image_name="Image Classification CPU",
    instance_name="m2.c2m4",
    distributed_node_count=1,
    parallel_trial_count=1,
    data_storage_size=300,
    hyperparameter_spec_list=[
        {
            "hyperparameterName": "input_size",
            "hyperparameterTypeCode": easymaker.HYPERPARAMETER_TYPE_CODE.DOUBLE,
            "hyperparameterMinValue": "4",
            "hyperparameterMaxValue": "6",
            "hyperparameterStep": "1",
        },
        {
            "hyperparameterName": "learning_rate",
            "hyperparameterTypeCode": easymaker.HYPERPARAMETER_TYPE_CODE.DOUBLE,
            "hyperparameterMinValue": "0",
            "hyperparameterMaxValue": "0.5",
            "hyperparameterStep": "0.1",
        },
        {
            "hyperparameterName": "per_device_train_batch_size",
            "hyperparameterTypeCode": easymaker.HYPERPARAMETER_TYPE_CODE.INT,
            "hyperparameterMinValue": "2",
            "hyperparameterMaxValue": "5",
            "hyperparameterStep": "1",
        },
        {
            "hyperparameterName": "per_device_eval_batch_size",
            "hyperparameterTypeCode": easymaker.HYPERPARAMETER_TYPE_CODE.INT,
            "hyperparameterMinValue": "2",
            "hyperparameterMaxValue": "5",
            "hyperparameterStep": "1",
        },
        {
            "hyperparameterName": "num_train_epochs",
            "hyperparameterTypeCode": easymaker.HYPERPARAMETER_TYPE_CODE.INT,
            "hyperparameterMinValue": "2",
            "hyperparameterMaxValue": "5",
            "hyperparameterStep": "1",
        },
        {
            "hyperparameterName": "save_steps",
            "hyperparameterTypeCode": easymaker.HYPERPARAMETER_TYPE_CODE.INT,
            "hyperparameterMinValue": "1",
            "hyperparameterMaxValue": "1",
            "hyperparameterStep": "1",
        },
        {
            "hyperparameterName": "logging_steps",
            "hyperparameterTypeCode": easymaker.HYPERPARAMETER_TYPE_CODE.INT,
            "hyperparameterMinValue": "1",
            "hyperparameterMaxValue": "1",
            "hyperparameterStep": "1",
        }
    ],
    timeout_hours=1,
    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": "validation",
            "dataUri": "obs://api-storage.cloud.toast.com/v1/AUTH_{tenant_id}/{container_name}/{validation_data_download_path}"
        }
    ],
    tag_list=[
        {
            "tagKey": "tag1",
            "tagValue": "test_tag_1",
        }
    ],
    objective_goal=1,
    max_failed_trial_count=2,
    max_trial_count=3,
    tuning_strategy_name=easymaker.TUNING_STRATEGY.GRID,
    tuning_strategy_random_state=1,
    early_stopping_algorithm=easymaker.EARLY_STOPPING_ALGORITHM.MEDIAN,
    early_stopping_min_trial_count=3,
    early_stopping_start_step=4,
    use_log=True,
    use_torchrun=True,
    nproc_per_node=1,
    # wait=False,
)

# Delete Hyperparameter Tuning
hyperparameter_tuning.delete()
easymaker.HyperparameterTuning("hyperparameter_tuning_id").delete()
easymaker.hyperparameter_tuning().delete("hyperparameter_tuning_id")

# Create Model
model = easymaker.Model().create(
    training_id=training.training_id,  # or hyperparameter_tuning_id=hyperparameter_tuning.hyperparameter_tuning_id,
    model_name="model_name",
    model_description="model_description",
)
model2 = easymaker.Model().create_by_model_upload_uri(
    model_type_code=easymaker.TENSORFLOW,
    model_upload_uri="obs://kr1-api-object-storage.nhncloudservice.com/v1/AUTH_{tenant_id}/{container_name}/{model_upload_path}",
    model_name="model_name",
    model_description="model_description",
)

# Create Hugging Face Model
model3 = easymaker.Model().create_hugging_face_model(
    model_name="model_name",
    parameter_list=[
        {
            "parameterName": "model_id",
            "parameterValue": "google-bert/bert-base-uncased",
        }
    ],
    model_description="model_description",
    tag_list=[],
)

# Delete Model
model.delete()
easymaker.Model("model_id").delete()
easymaker.model.delete("model_id")

# Create Endpoint
endpoint = easymaker.Endpoint().create(
    model_id=model.model_id,
    endpoint_name="endpoint_name",
    endpoint_description="endpoint_description",
    endpoint_instance_name="c2.c16m16",
    endpoint_model_resource_list=[
        {
            "modelId": model.model_id,
            "resourceOptionDetail": {
                "cpu": "15",
                "memory": "15Gi",
            },
            "description": "test",
        }
    ],
    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,
)

# Delete Endpoint
endpoint.delete()
easymaker.Endpoint("endpoint_id").delete()
easymaker.endpoint.delete_endpoint("endpoint_id")

# Create Endpoint Stage
endpoint_stage = endpoint.EndpointStage().create(
    stage_name="stage01",
    endpoint_id=endpoint.endpoint_id,
    stage_description="test endpoint",
    endpoint_instance_name="c2.c16m16",
    endpoint_model_resource_list=[
        {
            "modelId": model.model_id,
            "resourceOptionDetail": {
                "cpu": "15",
                "memory": "15Gi",
            },
            "description": "test",
        }
    ],
    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,
)

# Delete Endpoint Stage
endpoint_stage.delete()
easymaker.EndpointStage("endpoint_stage_id").delete()
easymaker.endpoint.delete_endpoint_stage("endpoint_stage_id")

# Inference
easymaker.EndpointStage("endpoint_stage_id").predict(model_id=model_id, json={"instances": [[6.8, 2.8, 4.8, 1.4]]})

# Delete Endpoint Model
easymaker.EndpointModel("endpoint_model_id").delete()
easymaker.endpoint.delete_endpoint_model("endpoint_model_id")

# Batch Inference
batch_inference = easymaker.BatchInference().run(
    batch_inference_name="test_batch_2",
    instance_count=1,
    timeout_hours=720,
    instance_name="m2.c2m4",
    model_name="model_create_test3",
    #
    pod_count=1,
    batch_size=120,
    inference_timeout_seconds=120,
    #
    input_data_uri="obs://kr1-api-object-storage.nhncloudservice.com/v1/AUTH_bd47a7932e3f464982cef083a7780f94/dev-test/batch_inference/input/case_1_500_record",
    input_data_type="JSONL",
    include_glob_pattern=None,
    exclude_glob_pattern=None,
    output_upload_uri=f"obs://kr1-api-object-storage.nhncloudservice.com/v1/AUTH_33634be0ec1340f3aa966a610eea77f0/model/batch_inference/output",
    #
    data_storage_size=300,
    #
    description=None,
    tag_list=None,
    use_log=False,
    wait=True,
)

# Delete Batch Inference
endpoint.delete()
easymaker.BatchInference("batch_inference_id").delete()
easymaker.batch_inference.delete("batch_inference_id")

# Upload Pipeline
pipeline = easymaker.Pipeline().upload(
    pipeline_name="pipeline_01",
    pipeline_spec_manifest_path="./sample-pipeline.yaml",
    description="test",
    tag_list=[],
)

# Delete Pipeline
pipeline.delete()
easymaker.Pipeline("pipeline_id").delete()
easymaker.pipeline.delete("pipeline_id")

# Create Pipeline Run
pipeline_run = easymaker.PipelineRun().create(
    pipeline_run_name="pipeline_run",
    description="test",
    pipeline_id=pipeline.pipeline_id,
    experiment_id=experiment.experiment_id, # Optional if already set in init
    instance_name="m2.c2m4",
    instance_count=1,
    boot_storage_size=50,
)

# Delete Pipeline Run
easymaker.PipelineRun("pipeline_run_id").delete()
easymaker.pipeline_run.delete("pipeline_run_id")

# Create Pipeline Recurring Run
pipeline_recurring_run = easymaker.PipelineRecurringRun().create(
    pipeline_recurring_run_name="pipeline_recurring_run",
    description="test",
    pipeline_id=pipeline.pipeline_id,
    experiment_id=experiment.experiment_id, # Optional if already set in init
    instance_name="m2.c2m4",
    instance_count=1,
    boot_storage_size=50,
    schedule_periodic_minutes=60,
    max_concurrency_count=1,
)

# Stop Pipeline Recurring Run
pipeline_recurring_run.stop()

# Start Pipeline Recurring Run
pipeline_recurring_run.start()

# Delete Pipeline Recurring Run
pipeline_recurring_run.delete()
easymaker.PipelineRecurringRun("pipeline_recurring_run_id").delete()
easymaker.pipeline_recurring_run.delete("pipeline_recurring_run_id")

# 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


# NHN Cloud ObjectStorage download, upload, delete
easymaker_obs = easymaker.ObjectStorage(
    easymaker_region="kr1",
    username="[e-mail]",
    password="nhn_object_storage_api_password"
)

easymaker_obs.download(
    easymaker_obs_uri="obs://api-storage.cloud.toast.com/v1/AUTH_{tenant_id}/{container_name}/{source_dir}",
    download_dir_path="./source_dir",
)

easymaker_obs.upload(
    easymaker_obs_uri="obs://api-storage.cloud.toast.com/v1/AUTH_{tenant_id}/{container_name}/{upload_path}",
    local_path="./local_dir",
)

easymaker_obs.delete(
    easymaker_obs_uri="obs://api-storage.cloud.toast.com/v1/AUTH_{tenant_id}/{container_name}/{object_path}",
    # file_extension=".json",  # Delete files with specific extensions
)

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
• hyperparameter tuning list : python -m easymaker -tuning --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