mmanager 2.0.0

Modelmanager API With Azure ML Integration

Welcome to Modelmanager-api!

This is a api model for interacting with modelmanager.

Note:

• Example files are are in example_script directory.
• Example assets are in api_assets directory.
• It contains scripts for different actions(Add, Update, Delete).

---

Example Codes

Add Project

from mmanager.mmanager import Usecase
secret_key = 'Secret-Key'
url = 'URL'
data = {
		"name": "UsecaseName",
		"author": "AuthorName",
		"description": "UsecaseDescription",
		"source": "UsecasSource",
		"contributor": "UsecaseContributor",
		"image": 'image.jpg' , #path to image file
		"banner": 'banner.jpg' , #path to banner file
	}
Usecase(secret_key, url).post_usecase(data)

---

Update Project

from mmanager.mmanager import Usecase
secret_key = 'Secret-Key'
url = 'URL'
project_id = Project_id #use model_id number to update
data = {
		"author": "AuthorName",
		"description": "UsecaseDescription",
		"source": "UsecasSource",
		"contributor": "UsecaseContributor",
		"image": 'image.jpg' , #path to image file
		"banner": 'banner.jpg' , #path to banner file
	}
Usecase(secret_key, url).patch_usecase(data, project_id)

---

Delete Project

from mmanager.mmanager import Usecase
secret_key = 'Secret-Key'
url = 'URL'
project_id = Project_id #use project_id number to delete
Usecase(secret_key,url).delete_usecase(project_id)

---

---

Add Model, Dataset OnPrem

from mmanager.mmanager import Model
secret_key = 'Secret-Key'
url = 'URL'
model_data = {
    "project": "<project-id>", #project-id
    "transformerType": "Classification",
    "algorithmType": "Xgboost",
    "modelFramework": "other",
    "weight": "",
    "datasetinsertionType": "Manual", #To upload datasets and model form local to server
    "training_dataset": "%s/model_assets/train.csv" % path, #path to csv file
    "pred_dataset": "%s/model_assets/pred.csv" % path, #path to csv file
    "actual_dataset": "%s/model_assets/truth.csv" % path, #path to csv file
    "test_dataset": "%s/model_assets/test.csv" % path, #path to csv file
    "model_image_path": "%s/model_assets/model_image.jpg" % path, #path to model image file
    "model_summary_path": "%s/model_assets/summary.json" % path,
    "model_file_path": "%s/model_assets/model.h5" % path, #path to model file
    "scoring_file_path": "",
    "target_column": "label",
    "note": "",
    "model_area": "apiUpload",
    "model_dependencies": "",
    "model_usage": "",
    "model_adjustment": "",
    "model_developer": "",
    "model_approver": "",
    "model_maintenance": "",
    "documentation_code": "",
    "implementation_plateform": "",
    "error_traceback": "",
    "distribution_error": False,
    "current_date": "",
    "production": "observation",
    "regulations": "",
    "score_data": "",
    "sweetviz": "",
    "error_traceback_distribution": "",
    "binarize_scoring_flag": False, #True if binary data
    "model_input_data": "",
    "modelscore_compute": False,
	"amlCred": "",
}
ml_options = {
    "credPath": "config.json",
    "datasetUploadPath": "api_upload_test",
    "fetchOption": "", #Not required
    "modelName": "model-name",
    "dataPath": "", #Not required
    "registryOption": ["Model"], #To register model, add ["Model", "Dataset"] to register both
}
Model(secret_key, url).post_model(model_data, ml_options)

Add Model, Dataset From Azure ML

from mmanager.mmanager import Model
secret_key = 'Secret-Key'
url = 'URL'
model_data = {
    "project": "<project-id>", #project-id
    "transformerType": "Classification",
    "algorithmType": "Xgboost",
    "modelFramework": "other",
    "weight": "",
    "datasetinsertionType": "AzureML",
    "training_dataset": "",
    "pred_dataset": "",
    "actual_dataset": "",
    "test_dataset": "",
    "model_image_path": "",
    "model_summary_path": "",
    "model_file_path": "",
    "scoring_file_path": "",
    "target_column": "label",
    "note": "",
    "model_area": "apiUpload",
    "model_dependencies": "",
    "model_usage": "",
    "model_adjustment": "",
    "model_developer": "",
    "model_approver": "",
    "model_maintenance": "",
    "documentation_code": "",
    "implementation_plateform": "",
    "error_traceback": "",
    "distribution_error": False,
    "current_date": "",
    "production": "observation",
    "regulations": "",
    "score_data": "",
    "sweetviz": "",
    "error_traceback_distribution": "",
    "binarize_scoring_flag": False, #True if binary data
    "model_input_data": "",
    "modelscore_compute": False,
    "amlCred": "",
}
ml_options = {"credPath": "config.json",
              "datasetUploadPath": "",
              "fetchOption": ["Model"], #To fetch model, add ["Model", "Dataset"] to fetch both model and datasets
              "modelName": "model-name",
              "dataPath": "dataset-name",
              "registryOption": "",
              }
Model(secret_key, url).post_model(model_data, ml_options)

---

Update Model

from mmanager.mmanager import Model
secret_key = 'Secret-Key'
url = 'URL'
model_id = Model_id #use model_id number to update
data = {
		"transformerType": "logistic",
		"target_column": "id",
		"note": "api script Model",
		"model_area": "api script Model",
		"model_dependencies": "api script Model",
		"model_usage": "api script Model",
		"model_audjustment": "api script Model",
		"model_developer": "api script Model",
		"model_approver": "api script Model",
		"model_maintenance": "api script Model",
		"documentation_code": "api script Model",
		"implementation_plateform": "api script Model",
		"training_dataset": "train.csv", #path to csv file
		"pred_dataset": "submissionsample.csv", #path to csv file
		"actual_dataset": "truth.csv", #path to csv file
		"test_dataset": "test.csv", #path to csv file
		"model_file_path":"",
	    "scoring_file_path":"",
		"model_image_path":"" ,
    	"model_summary_path":"",
	}
Model(secret_key, url).patch_model(data, model_id)

---

Delete Model

---

from mmanager.mmanager import Model
secret_key = 'Secret-Key'
url = 'URL'
model_id = Model_id #use model_id number to delete
Model(secret_key,url).delete_model(model_id)

---