Model manager and model governance 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 assets directory.
- It contains scripts for different actions(Add, Update, Delete).
Example Codes
Add Project / Usecase
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)
Create Config File For Azure ML Credentials
- Get Credentials from your existing Azure ML account.
- Create a config file in following format
- Give credential file path in credPath field to enable using AML integration service.
{
"subscription_id": "<subscription-id>",
"resource_group": "<resource_group>",
"workspace_name": "<workspace_name>",
"tenant-id": "<tenant-id>",
"datastore_name": "<datastore_name>"
}
Add Model No ML Integration
from mmanager.mmanager import Model
secret_key = 'Secret-Key'
url = 'URL'
path = 'assets' #path to csv file
model_data = {
"project": "<project-id>", #Project ID or Usecase ID
"transformerType": "model-type", #Options: Classification, Regression, Forcasting
"training_dataset": "%s/model_assets/train.csv" % path, #path to csv file
"test_dataset": "%s/model_assets/test.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
"model_file_path": "%s/model_assets/model.h5" % path, #path to model file
"target_column": "target-column-name", #Target Column
"note": "" #Short description of Model
}
Model(secret_key, url).post_model(model_data)
Additional model fields
{
"model_area": "",
"model_dependencies": "",
"model_usage": "",
"model_audjustment": "",
"model_developer": "",
"model_approver": "",
"model_maintenance": "",
}
Add Model, Fetch Datasets And Model From Azure ML
from mmanager.mmanager import Model
secret_key = 'Secret-Key'
url = 'URL'
model_data = {
"project": "<project-id>", #Project ID or Usecase ID
"transformerType": "model-type", #Options: Classification, Regression, Forcasting
"training_dataset": "",
"test_dataset": "",
"pred_dataset": "",
"actual_dataset": "",
"model_file_path": "",
"target_column": "target-column-name", #Target Column
"note": "" #Short description of Model
}
ml_options = {
"credPath": "config.json", #Path to Azure ML credential files.
"datasetinsertionType": "AzureML", #Option: AzureML, Manual
"fetchOption": ["Model"], #To fetch model, add ["Model", "Dataset"] to fetch both model and datasets.
"modelName": "model-name", #Fetch model file registered with model name.
"dataPath": "dataset-name", #Get datasets registered with dataset name.
}
Model(secret_key, url).post_model(model_data, ml_options)
Add Model, Upload Datasets And Model Manually And Register To Azure ML
from mmanager.mmanager import Model
secret_key = 'Secret-Key'
url = 'URL'
path = 'assets' #path to csv file
model_data = {
"project": "<project-id>", #Project ID or Usecase ID
"transformerType": "model-type", #Options: Classification, Regression, Forcasting
"training_dataset": "%s/model_assets/train.csv" % path, #path to csv file
"test_dataset": "%s/model_assets/test.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
"model_file_path": "%s/model_assets/model.h5" % path, #path to model file
"target_column": "target-column-name", #Target Column
"note": "", #Short description of Model
"model_area": "Area API test."
}
ml_options = {
"credPath": "config.json", #Path to Azure ML credential files.
"datasetinsertionType": "Manual", #Option: AzureML, Manual
"registryOption": ["Model"], #To register model, add ["Model", "Dataset"] to register both model and datasets.
"datasetUploadPath": "dataset-name", #To registere dataset on path.
}
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)
Generate Model Report
from mmanager.mmanager import Model
secret_key = 'Secret-Key'
url = 'URL'
model_id = Model_id #use model_id number
Model(secret_key,url).generate_report(model_id)
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