# NHN AI EasyMaker SDK
```
# Initialize EasyMaker SDK
import easymaker
easymaker.init(
appkey='EASYMAKER_APPKEY',
region='kr1',
secret_key='EASYMAKER_SECRET_KEY',
)
# Create Experiment
experiment_id = easymaker.Experiment().create(
experiment_name='experiment_name',
experiment_description='experiment_description',
# wait=False
)
# Delete Experiment
easymaker.Experiment().delete(experiment_id)
# Create Training
training_id = easymaker.Training().run(
experiment_id=experiment_id,
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=[
{
"hyperparameterKey": "epochs",
"hyperparameterValue": "10",
},
{
"hyperparameterKey": "batch-size",
"hyperparameterValue": "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_id = easymaker.Training().run(
experiment_id=experiment_id,
training_name='image_classification',
training_description='easymaker sdk test training',
train_image_name='Ubuntu 22.04 CPU PyTorch Training',
train_instance_name='m2.c4m8',
distributed_node_count=1,
algorithm_name='Image Classification',
data_storage_size=300, # minimum size : 300G
hyperparameter_list=[
{
"hyperparameterKey": "input_size",
"hyperparameterValue": "28",
},
{
"hyperparameterKey": "learning_rate",
"hyperparameterValue": "0.1",
},
{
"hyperparameterKey": "per_device_train_batch_size",
"hyperparameterValue": "16",
},
{
"hyperparameterKey": "per_device_eval_batch_size",
"hyperparameterValue": "16",
},
{
"hyperparameterKey": "num_train_epochs",
"hyperparameterValue": "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_log=True,
# wait=False
)
# Delete Training
easymaker.Training().delete(training_id)
# Create Hyperparameter Tuning
hyperparameter_tuning_id = easymaker.HyperparameterTuning().run(
experiment_id=experiment_id,
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_id = easymaker.HyperparameterTuning().run(
experiment_id=experiment_id,
hyperparameter_tuning_name='hyperparameter_tuning_name',
algorithm_name='Image Classification',
image_name='Ubuntu 22.04 CPU PyTorch Training',
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,
# wait=False,
)
# Delete Hyperparameter Tuning
easymaker.HyperparameterTuning().delete(hyperparameter_tuning_id)
# Create Model
model_id = easymaker.Model().create(
training_id=training_id, # or hyperparameter_tuning_id=hyperparameter_tuning_id,
model_name='model_name',
model_description='model_description',
)
model_id2 = easymaker.Model().create_by_model_uri(
framework_code=easymaker.TENSORFLOW,
model_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',
)
# Delete Model
easymaker.Model().delete(model_id)
# 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,
)
# Delete Endpoint
endpoint.Endpoint().delete_endpoint(endpoint_id)
# 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,
)
# Delete Endpoint Stage
endpoint.Endpoint().delete_endpoint_stage(stage_id)
# Get an endpoint that already exists
endpoint = easymaker.Endpoint(endpoint_id)
# get endpoint stage info
endpoint_stage_info = endpoint.get_endpoint_stage_by_id(endpoint_stage_id=stage_id)
# Inference
endpoint.predict(endpoint_stage_info=endpoint_stage_info, model_id=model_id, 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
# NHN Cloud ObjectStorage download, upload, delete
easymaker_obs = easymaker.ObjectStorage(
easymaker_region='kr1',
username='username@nhn.com',
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`
Raw data
{
"_id": null,
"home_page": "https://www.nhncloud.com",
"name": "easymaker",
"maintainer": null,
"docs_url": null,
"requires_python": null,
"maintainer_email": null,
"keywords": "NHN Cloud AI EasyMaker",
"author": "NHN Cloud AI EasyMaker Services",
"author_email": null,
"download_url": null,
"platform": null,
"description": "# NHN AI EasyMaker SDK\n\n```\n# Initialize EasyMaker SDK\nimport easymaker\n\neasymaker.init(\n appkey='EASYMAKER_APPKEY',\n region='kr1',\n secret_key='EASYMAKER_SECRET_KEY',\n)\n\n# Create Experiment\nexperiment_id = easymaker.Experiment().create(\n experiment_name='experiment_name',\n experiment_description='experiment_description',\n # wait=False\n)\n\n# Delete Experiment\neasymaker.Experiment().delete(experiment_id)\n\n# Create Training\ntraining_id = easymaker.Training().run(\n experiment_id=experiment_id,\n training_name='training_name',\n training_description='training_description',\n train_image_name='Ubuntu 22.04 CPU TensorFlow Training',\n train_instance_name='m2.c4m8',\n distributed_node_count=1,\n data_storage_size=300, # minimum size : 300G\n source_dir_uri='obs://kr1-api-object-storage.nhncloudservice.com/v1/AUTH_{tenant_id}/{container_name}/{soucre_download_path}',\n entry_point='training_start.py',\n hyperparameter_list=[\n {\n \"hyperparameterKey\": \"epochs\",\n \"hyperparameterValue\": \"10\",\n },\n {\n \"hyperparameterKey\": \"batch-size\",\n \"hyperparameterValue\": \"30\",\n }\n ],\n timeout_hours=100, # 1~720\n model_upload_uri='obs://kr1-api-object-storage.nhncloudservice.com/v1/AUTH_{tenant_id}/{container_name}/{model_upload_path}',\n check_point_input_uri='obs://kr1-api-object-storage.nhncloudservice.com/v1/AUTH_{tenant_id}/{container_name}/{checkpoint_input_path}',\n check_point_upload_uri='obs://kr1-api-object-storage.nhncloudservice.com/v1/AUTH_{tenant_id}/{container_name}/{checkpoint_upload_path}',\n dataset_list=[\n {\n \"datasetName\": \"train\",\n \"dataUri\": \"obs://kr1-api-object-storage.nhncloudservice.com/v1/AUTH_{tenant_id}/{container_name}/{train_data_download_path}\"\n },\n {\n \"datasetName\": \"test\",\n \"dataUri\": \"obs://kr1-api-object-storage.nhncloudservice.com/v1/AUTH_{tenant_id}/{container_name}/{test_data_download_path}\"\n }\n ],\n tag_list=[ # maximum 10\n {\n \"tagKey\": \"tag1\",\n \"tagValue\": \"test_tag_1\",\n },\n {\n \"tagKey\": \"tag2\",\n \"tagValue\": \"test_tag_2\",\n }\n ],\n use_log=True,\n # wait=False\n)\n\n# Create Training By Algorithm (Image Classification)\ntraining_id = easymaker.Training().run(\n experiment_id=experiment_id,\n training_name='image_classification',\n training_description='easymaker sdk test training',\n train_image_name='Ubuntu 22.04 CPU PyTorch Training',\n train_instance_name='m2.c4m8',\n distributed_node_count=1,\n algorithm_name='Image Classification',\n data_storage_size=300, # minimum size : 300G\n hyperparameter_list=[\n {\n \"hyperparameterKey\": \"input_size\",\n \"hyperparameterValue\": \"28\",\n },\n {\n \"hyperparameterKey\": \"learning_rate\",\n \"hyperparameterValue\": \"0.1\",\n },\n {\n \"hyperparameterKey\": \"per_device_train_batch_size\",\n \"hyperparameterValue\": \"16\",\n },\n {\n \"hyperparameterKey\": \"per_device_eval_batch_size\",\n \"hyperparameterValue\": \"16\",\n },\n {\n \"hyperparameterKey\": \"num_train_epochs\",\n \"hyperparameterValue\": \"3\",\n }\n ],\n timeout_hours=1,\n model_upload_uri='obs://api-storage.cloud.toast.com/v1/AUTH_{tenant_id}/{container_name}/{model_upload_path}',\n check_point_upload_uri='obs://api-storage.cloud.toast.com/v1/AUTH_{tenant_id}/{container_name}/{checkpoint_upload_path}',\n dataset_list=[\n {\n \"datasetName\": \"train\",\n \"dataUri\": \"obs://api-storage.cloud.toast.com/v1/AUTH_{tenant_id}/{container_name}/{train_data_download_path}\"\n },\n {\n \"datasetName\": \"validation\",\n \"dataUri\": \"obs://api-storage.cloud.toast.com/v1/AUTH_{tenant_id}/{container_name}/{validation_data_download_path}\"\n }\n ],\n tag_list=[ # \ucd5c\ub300 10\uac1c\n {\n \"tagKey\": \"tag1\",\n \"tagValue\": \"test_tag_1\",\n },\n {\n \"tagKey\": \"tag2\",\n \"tagValue\": \"test_tag_2\",\n }\n ],\n use_log=True,\n # wait=False\n)\n\n# Delete Training\neasymaker.Training().delete(training_id)\n\n# Create Hyperparameter Tuning\nhyperparameter_tuning_id = easymaker.HyperparameterTuning().run(\n experiment_id=experiment_id,\n hyperparameter_tuning_name='hyperparameter_tuning_name',\n hyperparameter_tuning_description='hyperparameter_tuning_description',\n image_name='Ubuntu 22.04 CPU TensorFlow Training',\n instance_name='m2.c8m16',\n distributed_node_count=1,\n parallel_trial_count=1,\n data_storage_size=300,\n source_dir_uri='obs://kr1-api-object-storage.nhncloudservice.com/v1/AUTH_{tenant_id}/{container_name}/{soucre_download_path}',\n entry_point='training_start.py',\n hyperparameter_spec_list=[\n {\n \"hyperparameterName\": \"learning_rate\",\n \"hyperparameterTypeCode\": easymaker.HYPERPARAMETER_TYPE_CODE.DOUBLE,\n \"hyperparameterMinValue\": \"0.01\",\n \"hyperparameterMaxValue\": \"0.05\",\n },\n {\n \"hyperparameterName\": \"epochs\",\n \"hyperparameterTypeCode\": easymaker.HYPERPARAMETER_TYPE_CODE.INT,\n \"hyperparameterMinValue\": \"100\",\n \"hyperparameterMaxValue\": \"1000\",\n }\n ],\n timeout_hours=10,\n model_upload_uri='obs://kr1-api-object-storage.nhncloudservice.com/v1/AUTH_{tenant_id}/{container_name}/{model_upload_path}',\n check_point_input_uri='obs://kr1-api-object-storage.nhncloudservice.com/v1/AUTH_{tenant_id}/{container_name}/{checkpoint_input_path}',\n check_point_upload_uri='obs://kr1-api-object-storage.nhncloudservice.com/v1/AUTH_{tenant_id}/{container_name}/{checkpoint_upload_path}',\n dataset_list=[\n {\n \"datasetName\": \"train\",\n \"dataUri\": \"obs://kr1-api-object-storage.nhncloudservice.com/v1/AUTH_{tenant_id}/{container_name}/{train_data_download_path}\"\n },\n {\n \"datasetName\": \"test\",\n \"dataUri\": \"obs://kr1-api-object-storage.nhncloudservice.com/v1/AUTH_{tenant_id}/{container_name}/{test_data_download_path}\"\n }\n ],\n metric_list=[\"val_loss\", \"loss\", \"accuracy\"}],\n metric_regex='([\\w|-]+)\\s*:\\s*([+-]?\\d*(\\.\\d+)?([Ee][+-]?\\d+)?)',\n objective_metric_name=\"val_loss\",\n objective_type_code=easymaker.OBJECTIVE_TYPE_CODE.MINIMIZE,\n objective_goal=0.01,\n max_failed_trial_count=3,\n max_trial_count=10,\n tuning_strategy_name=easymaker.TUNING_STRATEGY.BAYESIAN_OPTIMIZATION,\n tuning_strategy_random_state=1,\n early_stopping_algorithm=easymaker.EARLY_STOPPING_ALGORITHM.MEDIAN,\n early_stopping_min_trial_count=3,\n early_stopping_start_step=4,\n tag_list=[\n {\n \"tagKey\": \"tag1\",\n \"tagValue\": \"test_tag_1\",\n }\n ],\n use_log=True,\n # wait=False,\n)\n\n# Create Hyperparameter Tuning By Algorithm (Image Classification)\nhyperparameter_tuning_id = easymaker.HyperparameterTuning().run(\n experiment_id=experiment_id,\n hyperparameter_tuning_name='hyperparameter_tuning_name',\n algorithm_name='Image Classification',\n image_name='Ubuntu 22.04 CPU PyTorch Training',\n instance_name='m2.c2m4',\n distributed_node_count=1,\n parallel_trial_count=1,\n data_storage_size=300,\n hyperparameter_spec_list=[\n {\n \"hyperparameterName\": \"input_size\",\n \"hyperparameterTypeCode\": easymaker.HYPERPARAMETER_TYPE_CODE.DOUBLE,\n \"hyperparameterMinValue\": \"4\",\n \"hyperparameterMaxValue\": \"6\",\n \"hyperparameterStep\": \"1\",\n },\n {\n \"hyperparameterName\": \"learning_rate\",\n \"hyperparameterTypeCode\": easymaker.HYPERPARAMETER_TYPE_CODE.DOUBLE,\n \"hyperparameterMinValue\": \"0\",\n \"hyperparameterMaxValue\": \"0.5\",\n \"hyperparameterStep\": \"0.1\",\n },\n {\n \"hyperparameterName\": \"per_device_train_batch_size\",\n \"hyperparameterTypeCode\": easymaker.HYPERPARAMETER_TYPE_CODE.INT,\n \"hyperparameterMinValue\": \"2\",\n \"hyperparameterMaxValue\": \"5\",\n \"hyperparameterStep\": \"1\",\n },\n {\n \"hyperparameterName\": \"per_device_eval_batch_size\",\n \"hyperparameterTypeCode\": easymaker.HYPERPARAMETER_TYPE_CODE.INT,\n \"hyperparameterMinValue\": \"2\",\n \"hyperparameterMaxValue\": \"5\",\n \"hyperparameterStep\": \"1\",\n },\n {\n \"hyperparameterName\": \"num_train_epochs\",\n \"hyperparameterTypeCode\": easymaker.HYPERPARAMETER_TYPE_CODE.INT,\n \"hyperparameterMinValue\": \"2\",\n \"hyperparameterMaxValue\": \"5\",\n \"hyperparameterStep\": \"1\",\n },\n {\n \"hyperparameterName\": \"save_steps\",\n \"hyperparameterTypeCode\": easymaker.HYPERPARAMETER_TYPE_CODE.INT,\n \"hyperparameterMinValue\": \"1\",\n \"hyperparameterMaxValue\": \"1\",\n \"hyperparameterStep\": \"1\",\n },\n {\n \"hyperparameterName\": \"logging_steps\",\n \"hyperparameterTypeCode\": easymaker.HYPERPARAMETER_TYPE_CODE.INT,\n \"hyperparameterMinValue\": \"1\",\n \"hyperparameterMaxValue\": \"1\",\n \"hyperparameterStep\": \"1\",\n }\n ],\n timeout_hours=1,\n model_upload_uri='obs://api-storage.cloud.toast.com/v1/AUTH_{tenant_id}/{container_name}/{model_upload_path}',\n check_point_upload_uri='obs://api-storage.cloud.toast.com/v1/AUTH_{tenant_id}/{container_name}/{checkpoint_upload_path}',\n dataset_list=[\n {\n \"datasetName\": \"train\",\n \"dataUri\": \"obs://api-storage.cloud.toast.com/v1/AUTH_{tenant_id}/{container_name}/{train_data_download_path}\"\n },\n {\n \"datasetName\": \"validation\",\n \"dataUri\": \"obs://api-storage.cloud.toast.com/v1/AUTH_{tenant_id}/{container_name}/{validation_data_download_path}\"\n }\n ],\n tag_list=[\n {\n \"tagKey\": \"tag1\",\n \"tagValue\": \"test_tag_1\",\n }\n ],\n objective_goal=1,\n max_failed_trial_count=2,\n max_trial_count=3,\n tuning_strategy_name=easymaker.TUNING_STRATEGY.GRID,\n tuning_strategy_random_state=1,\n early_stopping_algorithm=easymaker.EARLY_STOPPING_ALGORITHM.MEDIAN,\n early_stopping_min_trial_count=3,\n early_stopping_start_step=4,\n use_log=True,\n # wait=False,\n)\n\n# Delete Hyperparameter Tuning\neasymaker.HyperparameterTuning().delete(hyperparameter_tuning_id)\n\n# Create Model\nmodel_id = easymaker.Model().create(\n training_id=training_id, # or hyperparameter_tuning_id=hyperparameter_tuning_id,\n model_name='model_name',\n model_description='model_description',\n)\nmodel_id2 = easymaker.Model().create_by_model_uri(\n framework_code=easymaker.TENSORFLOW,\n model_uri='obs://kr1-api-object-storage.nhncloudservice.com/v1/AUTH_{tenant_id}/{container_name}/{model_upload_path}',\n model_name='model_name',\n model_description='model_description',\n)\n\n# Delete Model\neasymaker.Model().delete(model_id)\n\n# Create Endpoint\nendpoint = easymaker.Endpoint()\nendpoint_id = endpoint.create(\n model_id=model_id,\n endpoint_name='endpoint_name',\n endpoint_description='endpoint_description',\n endpoint_instance_name='c2.c16m16',\n apigw_resource_uri='/api-path',\n endpoint_instance_count=1,\n use_log=True,\n # wait=False,\n # autoscaler_enable=True, # default False\n # autoscaler_min_node_count=1,\n # autoscaler_max_node_count=10,\n # autoscaler_scale_down_enable=True,\n # autoscaler_scale_down_util_threshold=50,\n # autoscaler_scale_down_unneeded_time=10,\n # autoscaler_scale_down_delay_after_add=10,\n)\n\n# Delete Endpoint\nendpoint.Endpoint().delete_endpoint(endpoint_id)\n\n# Create Endpoint Stage\nstage_id = endpoint.create_stage(\n model_id=model_id,\n stage_name='stage01',\n stage_description='test endpoint',\n endpoint_instance_name='c2.c16m16',\n apigw_resource_uri='/test-api',\n endpoint_instance_count=1,\n # wait=False,\n # autoscaler_enable=True, # default False\n # autoscaler_min_node_count=1,\n # autoscaler_max_node_count=10,\n # autoscaler_scale_down_enable=True,\n # autoscaler_scale_down_util_threshold=50,\n # autoscaler_scale_down_unneeded_time=10,\n # autoscaler_scale_down_delay_after_add=10,\n)\n\n# Delete Endpoint Stage\nendpoint.Endpoint().delete_endpoint_stage(stage_id)\n\n# Get an endpoint that already exists\nendpoint = easymaker.Endpoint(endpoint_id)\n\n# get endpoint stage info\nendpoint_stage_info = endpoint.get_endpoint_stage_by_id(endpoint_stage_id=stage_id)\n\n# Inference\nendpoint.predict(endpoint_stage_info=endpoint_stage_info, model_id=model_id, json={'instances': [[6.8, 2.8, 4.8, 1.4]]})\n\n# Log (Log & Crash)\neasymaker_logger = easymaker.logger(logncrash_appkey='log&crash_product_app_key')\neasymaker_logger.send('test log meassage') # Output to stdout & send log to log&crash product\neasymaker_logger.send(log_message='log meassage',\n log_level='INFO', # default: INFO\n project_version='1.0.0', # default: 1.0.0\n parameters={'serviceType': 'EasyMakerSample'}) # Add custom parameters\n\n\n# NHN Cloud ObjectStorage download, upload, delete\neasymaker_obs = easymaker.ObjectStorage(\n easymaker_region='kr1',\n username='username@nhn.com',\n password='nhn_object_storage_api_password'\n)\n\neasymaker_obs.download(\n easymaker_obs_uri='obs://api-storage.cloud.toast.com/v1/AUTH_{tenant_id}/{container_name}/{source_dir}',\n download_dir_path='./source_dir',\n)\n\neasymaker_obs.upload(\n easymaker_obs_uri='obs://api-storage.cloud.toast.com/v1/AUTH_{tenant_id}/{container_name}/{upload_path}',\n local_path='./local_dir',\n)\n\neasymaker_obs.delete(\n easymaker_obs_uri='obs://api-storage.cloud.toast.com/v1/AUTH_{tenant_id}/{container_name}/{object_path}',\n # file_extension='.json', # Delete files with specific extensions\n)\n\n\n```\n\n## CLI Command\n- instance type list : `python -m easymaker -instance --region kr1 --appkey EM_APPKEY --secret_key EM_SECRET_KEY`\n- image list : `python -m easymaker -image --region kr1 --appkey EM_APPKEY --secret_key EM_SECRET_KEY`\n- experiment list : `python -m easymaker -experiment --region kr1 --appkey EM_APPKEY --secret_key EM_SECRET_KEY`\n- training list : `python -m easymaker -training --region kr1 --appkey EM_APPKEY --secret_key EM_SECRET_KEY`\n- hyperparameter tuning list : `python -m easymaker -tuning --region kr1 --appkey EM_APPKEY --secret_key EM_SECRET_KEY`\n- model list : `python -m easymaker -model --region kr1 --appkey EM_APPKEY --secret_key EM_SECRET_KEY`\n- endpoint list : `python -m easymaker -endpoint --region kr1 --appkey EM_APPKEY --secret_key EM_SECRET_KEY`\n",
"bugtrack_url": null,
"license": "Apache License 2.0",
"summary": "AI EasyMaker SDK for Python.",
"version": "1.1.5",
"project_urls": {
"Homepage": "https://www.nhncloud.com"
},
"split_keywords": [
"nhn",
"cloud",
"ai",
"easymaker"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "26b4e2890db07df15d0ae42d98fd6d7fbdd71a07f5b91aafbef1bd63c571def5",
"md5": "b052c82d8dd3f870993c75c8575fb9a0",
"sha256": "df553b4fa5a6c117f725a9633562abdc055776ac1bf4a5a7d0344de1d4ec2412"
},
"downloads": -1,
"filename": "easymaker-1.1.5-py3-none-any.whl",
"has_sig": false,
"md5_digest": "b052c82d8dd3f870993c75c8575fb9a0",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": null,
"size": 28508,
"upload_time": "2024-04-23T00:17:17",
"upload_time_iso_8601": "2024-04-23T00:17:17.702404Z",
"url": "https://files.pythonhosted.org/packages/26/b4/e2890db07df15d0ae42d98fd6d7fbdd71a07f5b91aafbef1bd63c571def5/easymaker-1.1.5-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-04-23 00:17:17",
"github": false,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"lcname": "easymaker"
}
JSON