Modelmanager API With Azure ML Integration
Project description
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)
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