Modelmanager API With Insight Generation and Pycausal, MLFlow Integration, Drivers Analysis

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Project description

ModelManager API

A Python API for managing machine learning models, usecases, datasets, and assets with ModelManager.

Overview
Features
Installation
Quick Start
Usecase Management
Model Management
Database Integration
Advanced & Reference
Tips & Resources

Overview

ModelManager API lets you:

Register, update, and delete ML usecases and models
Manage datasets and assets
Integrate with MLFlow, AzureML, and DVC
Track versions and metrics

Features

Easy Python interface for model lifecycle management
Support for classification, regression, and forecasting
Integration with popular ML tools (MLFlow, AzureML)
Version control and what-if analysis

Installation

pip install mmanager  # Or your preferred installation method

Quick Start

from mmanager.mmanager import Usecase, Model

secret_key = 'YOUR_SECRET_KEY'
url = 'YOUR_API_URL'

# Add a Usecase
usecase_data = {"name": "My Usecase", "description": "Short description"}
Usecase(secret_key, url).post_usecase(usecase_data)

# Add a Model
model_data = {
    "project": "<usecase-id>",
    "transformerType": "Classification",
    "target_column": "target_column_name"
}
Model(secret_key, url).post_model(model_data)

Usecase Management

from mmanager.mmanager import Usecase

# Create a usecase
usecase_data = {"name": "Fraud Detection", "description": "Detect fraud in transactions"}
Usecase(secret_key, url).post_usecase(usecase_data)

# List all usecases
Usecase(secret_key, url).get_usecases()

# Get usecase details
Usecase(secret_key, url).get_detail(usecase_id)

# Update a usecase
update_data = {"description": "Updated description"}
Usecase(secret_key, url).patch_usecase(update_data, usecase_id)

# Delete a usecase
Usecase(secret_key, url).delete_usecase(usecase_id)

Model Management

from mmanager.mmanager import Model

# Add a model
model_data = {
    "project": usecase_id,
    "transformerType": "Classification", # or Regression, Forecasting
    "training_dataset": "/path/train.csv",
    "test_dataset": "/path/test.csv",
    "target_column": "Class"
}
Model(secret_key, url).post_model(model_data)

# List models under a usecase
Model(secret_key, url).get_models(usecase_id)

# Get model details
Model(secret_key, url).get_details(model_id)

# Delete a model
Model(secret_key, url).delete_model(model_id)

Database Integration

from mmanager.mmanager import TableInfo, FieldInfo, Usecase

# Add a related database table
table_data = {
    "table_type": "actual",
    "table_name": "daily_act2",
    "db_link": 11
}
TableInfo(secret_key, url).post_table_info(data=table_data)

# Add fields to the table
field_data = {
    "table_id": 9,
    "display_name": "actual2",
    "field_type": "",
    "field_name": ""
}
FieldInfo(secret_key, url).post_field_info(data=field_data)

# Load database cache for a usecase
Usecase(secret_key, url).load_cache(usecase_id=7)

Advanced & Reference

Forecasting usecases
MLFlow and AzureML integration
Version control (Git/DVC)
Metrics and causal inference
What-if analysis

See the [full documentation] or original README for advanced features.

Tips & Resources

Example scripts: example_script/
Example assets: assets/
Logs: mmanager_log.log
For more advanced examples, see the [original README] or full documentation.

For advanced integrations, troubleshooting, or to contribute, see the [full documentation] or contact the maintainer.

| source | Forina, M. et al, PARVUS -An Extendible Package for Data Exploration, Classification and Correlation.,Institute of Pharmaceutical and Food Analysis and Technologies, Via Brigata Salerno,16147 Genoa, Italy. | | contributor | S. Aeberhard, D. Coomans and O. de Vel, Comparison of Classifiers in High Dimensional Settings, Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of Mathematics and Statistics, James Cook, University of North Queensland. | | is_private | Set True to keep the usecase private. Default False. | | trustability | Set True to list in trustability. Default False. | | explainability | Set True to list in explainability. Default False. | | hide_model | Set True to hide model related to this usecase. Default False. | | notification_emails | List of emails that will be notified. Eg: johndoe@qausal.com, adams_mary@qausal.com |

Forecasting Usecase

from mmanager.mmanager import Usecase

secret_key = 'YOUR_SECRET_KEY'
url = 'YOUR_API_URL'

# Define forecasting usecase information
usecase_info = {
    "name": "",
    "usecase_type": "Forecasting",
    "author": "Jane Doe",
    "description": "Forecast future sales based on historical data",
    "source": "Internal Data",
    "contributor": "Analytics Team",
    "image": "",  # Optional: path to image
    "performance_data_selection": "",
    "applications": ""
}

# Forecasting-specific fields
forecasting_fields = {
    "performance_data_selection": "{'from':'2023-01-01', 'to':'2023-12-31'}",
    "notification_emails": ["jane.doe@example.com"],
    "forecasting_template": "two_conditions"
}

# Feature tabs for the forecasting UI
forecasting_feature_tabs = {
    "result_tab": True,
    "series_tab": True,
    "condition_tab": True,
    "performance_tab": True,
    "ab_testing_tab": True,
    "release_tab": True
}

# Create the forecasting usecase
Usecase(secret_key, url).post_usecase(
    usecase_info,
    forecasting_fields,
    forecasting_feature_tabs
)

Update Usecase

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)

Get All Usecases Uploaded By Authenticated User

from mmanager.mmanager import Usecase
secret_key = 'Secret-Key'
url = 'URL'
usecases = Usecase(secret_key,url).get_usecases()
print(usecases)

Get Usecase Detail

from mmanager.mmanager import Usecase
secret_key = 'Secret-Key'
url = 'URL' 
usecase_id = "Usecase-Id"

# GET USECASE DETAIL
usecase_detail = Usecase(secret_key,url).get_detail(usecase_id)
print(usecase_detail)

# GET ALL USECASE UPLOATED BY AUTHENTICATED USER
_usecases = Usecase(secret_key,url).get_usecases()
print(_usecases)

# GET ALL MODEL ID REGISTERED UNDER USECASE
model_list = Usecase(secret_key,url).get_models(usecase_id)
print(model_list)

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 Related Database

from mmanager.mmanager import ExternalDatabase
secret_key = 'Secret-Key'
url = 'URL'
# db_type: Postgres, MySQL
related_db_data ={
    "db_type":"Postgres",
    "db_name":"db_name",
    "db_user":"username",
    "db_password":"db_pass",
    "db_host":"localhost",
    "db_port":"5432"
}
ExternalDatabase(secret_key,url).post_related_db(data=related_db_data)

Link External Database

from mmanager.mmanager import ExternalDatabase
secret_key = 'Secret-Key'
url = 'URL'
# link_type: Client, System
external_db_data = {
    "link_type":"System",
    "usecase":"usecase_id",
    "external_db":"related_db_id",
    "train_table":"",
    "test_table":"",
    "pred_table":"",
    "actuals_table":""
}
ExternalDatabase(secret_key,url).link_externaldb(data=external_db_data)

Add Tables

from mmanager.mmanager import TableInfo
secret_key = 'Secret-Key'
url = 'URL'
table_data = {
    "table_type":"" #eg:"actual",
    "table_name": "" #eg:"daily_act2",
    "db_link": #eg:11
    }

TableInfo(secret_key,url).post_table_info(data=table_data)

Add Fields

from mmanager.mmanager import FieldInfo
secret_key = 'Secret-Key'
url = 'URL'
field_data = {
    "table_id": "" #eg:9,
    "display_name": "" #eg:actual2,
    "field_type": "",
    "field_name":""
    }
FieldInfo(secret_key,url).post_field_info(data=field_data)

Load Database Cache

from mmanager.mmanager import Usecase
secret_key = 'Secret-Key'
url = 'URL'
Usecase(secret_key,url).load_cache(usecase_id=7)

Add Model

from mmanager.mmanager import Model
secret_key = 'Secret-Key'
url = 'URL'
path = 'assets' #path to csv file

model_data = {
    "project": "1", #Project ID or Usecase ID
    "transformerType": "Classification", #Options: Classification, Regression, Forcasting
    "datasetinsertionType": "Manual" #Options: AzureML, External DB, Manual
    "training_dataset": "/path/train.csv"
    "test_dataset": "/path/test.csv"
    "pred_dataset": "/path/pred.csv"
    "actual_dataset": "/path/truth.csv"
    "model_file_path": "/path/model.h5"
    "target_column": "Class"
}
Model(secret_key, url).post_model(model_data)

Other optional data

Key word	Example
note
model_area
model_dependencies
model_usage
model_audjustment
model_developer
model_approver
model_maintenance
documentation_code
production	Production (Options: production, observation, retired)
model_input_data	"/path/input.csv" (Path to input data.)
computing_type	Classical (Options: Classical, Qantum, Hybrid)
binarize_scoring_flag	Set True to label binarize. Default False.
algorithmType	Xgboost (Options: Xgboost, GBM)
modelFramework	driverless_ai (Options: driverless_ai, tensorflow, keras, scikit, statmodlib, other)

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, 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 
    "target_column": "target-column-name", #Target Column
    }

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": "1", #Project ID or Usecase ID
    "transformerType": "Classification", #Options: Classification, Regression, Forcasting
    "datasetinsertionType": "Manual" #Options: AzureML, External DB, Manual
    "training_dataset": "/path/train.csv"
    "test_dataset": "/path/test.csv"
    "pred_dataset": "/path/pred.csv"
    "actual_dataset": "/path/truth.csv"
    "model_file_path": "/path/model.h5"
    "target_column": "Class"
}

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 = Model(secret_key, url).post_model(model_data, ml_options)
model.json()

ADD MODEL: MLFLOW

Add MLFlow Creds

from mmanager.mmanager import MLFlow
secret_key = 'Secret-Key'
url = 'URL' 

mlflow_cred_data = {
    "name": "", #eg:"Test Credentials"
    "aws_secret_access_key": "", #eg:"ueCepWaPlDIb/nATh7wYibgBMKXG3qn9PSZhk"
    "aws_access_key_id": "", #eg:"DO0MT6XN0CACQQ"
    "mlflow_s3_endpoint_url": "", #eg:"https://sfo3.digitaloceanspaces.com"
    "artifact_path": "", #eg:"pathtomodelfiles"
    "tracking_uri": "", #eg:"https://example.mlflow.com"
    "usecase": usecase_id #Usecase ID
}
mlflow_cred = MLFlow(secret_key, url).post_mlflow_creds(mlflow_cred_data)
mlflow_cred_id = mlflow_cred.json().get('id')
mlflow_cred.json()

Get MLFlow Creds

from mmanager.mmanager import MLFlow
secret_key = 'Secret-Key'
url = 'URL' 

creds_id = #eg:1
mlflow_cred = MLFlow(secret_key, url).get_mlflow_creds(mlflow_creds_id=creds_id)
mlflow_cred_dict = mlflow_cred.json()

usecase_id = mlflow_cred_dict.get('usecase')
mlflow_cred_id = mlflow_cred_dict.get('id')
mlflow_tracking_uri = mlflow_cred_dict.get('tracking_uri')
mlflow_aws_secret_access_key = mlflow_cred_dict.get('aws_secret_access_key')
mlflow_aws_access_key_id = mlflow_cred_dict.get('aws_access_key_id')
mlflow_s3_endpoint_url = mlflow_cred_dict.get('mlflow_s3_endpoint_url')

Download Datasets And Model Files From MLFlow Server

from mmanager.mmanager import MLFlow
secret_key = 'Secret-Key'
url = 'URL' 

mlflow_exp_name = experiment_name #Not Optional
run_id = None #Optional
artifact_path = None #Optional

# Note: This will download the files in temp location eg:/opt/tmp/dataset/train.csv
mlflow_datafiles_details = MLFlow(secret_key, url).download_dataset_model(mlflow_cred_id, mlflow_exp_name)
mlflow_datafiles_details_dict = mlflow_datafiles_details.json()

modelfile_dict = mlflow_datafiles_details_dict.get("model",{})
datasets_dict = mlflow_datafiles_details_dict.get("datasets",{})

# Note: File names might vary, as user will upload those in the MLFlow
trainfile_path = datasets_dict.get("train.csv", None)
testfile_path = datasets_dict.get("test.csv", None)
predfile_path = datasets_dict.get("pred.csv", None)
actualfile_path = datasets_dict.get("actual.csv", None)
modelfile_path = modelfile_dict.get("model_file", None)

mlflow_data_is_local = mlflow_datafiles_details_dict.get("is_local") #If the location of datasets are not in s3 bucket or any other external storages is_local = True.

Add Model

from mmanager.mmanager import Model
secret_key = 'Secret-Key'
url = 'URL' 
model_data = {
    "project": usecase_id, #Project ID or Usecase ID
    "transformerType": "Classification", #Options: Classification, Regression, Forcasting
    "datasetinsertionType": "MLFlow", #Options: Manual, AzureML, MLFlow
    "training_dataset": trainfile_path, #path to csv file
    "test_dataset": testfile_path, #path to csv file
    "pred_dataset": predfile_path, #path to csv file
    "actual_dataset": actualfile_path, #path to csv file
    "model_file_path": modelfile_path, #path to model file|
    "is_mlflow_local": mlflow_data_is_local,
    "target_column": "", #Target Column, eg:Class
    "note": "", #Short description of Model, eg: MLFlow mmanager test model.
}
model = Model(secret_key, url).post_model(model_data)
model.json()

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",
		"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(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)

Get Model Details

from mmanager.mmanager import Model
secret_key = 'Secret-Key'
url = 'URL'
model_id = "Model_id" 
Model(secret_key,url).get_details(model_id)

Get Metrics

Get latest metrics recorded under Model
Metric Type
- Developement Metric
- Scoring Metric

from mmanager.mmanager import Model
secret_key = 'Secret-Key'
url = 'URL'
metric = Model(secret_key,url).get_latest_metrics(model_id="Model-Id", metric_type="Metric-Type")

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)

Get Model Report

from mmanager.mmanager import Model
secret_key = 'Secret-Key'
url = 'URL'
model_id = "Model-Id" #use model_id number
all_report = Model(secret_key,url).get_all_reports(model_id=model_id)

Get Causal Analysis Graphs

Get Causal Discovery Graphs

from mmanager.mmanager import Model
secret_key = 'Secret-Key'
url = 'URL'
model_id = "Model-Id" #use model_id number
graph_type = "HeatMap" #Options : 3D_CausalDiscovery_Comparision, 2D_CausalDiscovery_Comparision, HeatMap
causal_dicovery = Model(secret_key, url).get_causal_discovery_graphs(model_id, graph_type=graph_type)
causal_dicovery

Get Causal Inference Graphs

Get Causal Inference Top 5 Effects

from mmanager.mmanager import Model
secret_key = 'Secret-Key'
url = 'URL'
model_id = "Model-Id" #use model_id number
graph_type = "coeff_graph" #Options : top_effect_p_values, top_effect_rsquared, coeff_graph
causal_inference_top5_effects = Model(secret_key, url).get_causal_inference_graphs(model_id, graph_type=graph_type)
causal_inference_top5_effects

Get Causal Inference Effects Comparision

from mmanager.mmanager import Model
secret_key = 'Secret-Key'
url = 'URL'
model_id = "Model-Id" #use model_id number
graph_type = "coeff_graph" #Options : top_effect_p_values, top_effect_rsquared, coeff_graph
treatment=""
outcome=""
causal_inference_effects_comparision = Model(secret_key, url).get_causal_inference_graphs(model_id, graph_type=graph_type, treatment=treatment, outcome=outcome)
causal_inference_effects_comparision

Get Causal Inference Correlations

from mmanager.mmanager import Model
secret_key = 'Secret-Key'
url = 'URL'
model_id = "Model-Id" #use model_id number
graph_type = "correlation_graph" #Options : correlation_graph, causal_correlation_summary
treatment=""
outcome=""
causal_inference_correlation = Model(secret_key, url).get_causal_inference_correlation(model_id, graph_type=graph_type, treatment=treatment, outcome=outcome)
causal_inference_correlation

Get Drivers Analysis

from mmanager.mmanager import Model
secret_key = 'Secret-Key'
url = 'URL'
file_path = "file/path"
treatment = "treatment-variable"
outcome = "outcome-variable"
drivers_analysis = Model(secret_key, url).get_drivers_analysis({"file_path": file_path, "treatment": treatment, "outcome": outcome})
drivers_analysis

Get Model Features

Display What If Analysis Tool

from mmanager.mmanager import Model
secret_key = 'Secret-Key'
url = 'URL'
model_id = "Model-Id" #use model_id number
what_if_analysis = Model(secret_key, url).get_wit(model_id)
what_if_analysis

Display Model Detail Tool

from mmanager.mmanager import Model
secret_key = 'Secret-Key'
url = 'URL'
model_id = "Model-Id" #use model_id number
model_detail_graph = Model(secret_key, url).get_netron(model_id)
model_detail_graph

Display Data Distribution Tool

from mmanager.mmanager import Model
secret_key = 'Secret-Key'
url = 'URL'
model_id = "Model-Id" #use model_id number
model_detail_graph = Model(secret_key, url).get_data_distribution(model_id)
model_detail_graph

Build What If Analysis Tool

Add What If Resources For Image Classification Usecase

from mmanager.mmanager import WhatIf
secret_key = 'Secret-Key'
url = 'URL'
data = {
    "usecase":"",
    "model":"",
    "label":""
    "imgclass_datatype":"Local" #Resource Type: Options: Dicom(Pass Dicom data for image processing), Local (Upload data from the locally), Bucket (Get data from the storage bucket)
    "input_model":"", #If imgclass_datatype is Local (Model file should be in h5 extension. (eg: model.h5)).
    "input_zip":"", #If imgclass_datatype is Local (eg: histo.zip).
    "modelfile_url":"", #If imgclass_datatype is Bucket (Model file should be in h5 extension. (eg: https://bucket.example.com/model.h5)).
    "zip_url":"", #If imgclass_datatype is Bucket (eg: https://bucket.example.com/histo.zip).
}
dicom_fields = {
    "dicom_datatype":"", #Dicom Resource Link Type Options: Local(Upload dicom data from the locally), Bucket (Get data from the storage bucket)
    "dicom_zipfile":"", #Add your dicom zip file. (If dicom_datatype is Local.)
    "dicom_labelfile":"", #Add your dicom label file. eg: data.csv (If dicom_datatype is Local.)
    "dicom_url":"", #Add URL to your dicom file. (eg: https://bucket.example.com/histo_dicom.zip) (If dicom_datatype is Bucket.)
    "dicom_labelfile_url":"", #Add URL to your dicom label file. (eg: https://bucket.example.com/data.csv) (If dicom_datatype is Bucket.)
    "dicom_id_col":"", # Label File ID Column eg: id, entry etc.
    "dicom_target_col":"", #Label File Target Column eg:Finding, Label, Score etc.
}
# If imgclass_datatype is Dicom.
data.update({dicom_fields})
wit_imgcls_resource_files = Model(secret_key, url).post_img_cls_wit_files(data)
wit_imgcls_resource_files

Build What If Analysis

from mmanager.mmanager import WhatIf
secret_key = 'Secret-Key'
url = 'URL'
model_id = "Model-Id" #use model_id number
wit = Model(secret_key, url).build_wit(model_id)
wit

Version Control

# ADD GIT CONFIG
from mmanager.mmanager import VersionControl
data = {
    "tag": "dvc_example",
    "git_url": "github.com",
    "git_repo": "https://github.com/jhondoe/example.git",
    "git_branch": "main",
    "username": "jhondoe",
    "email":"jhondoe@gmail.com",
    "access_token":"",
    "is_active": True,
  }
git_config = VersionControl(secret_key, url).git_config(data)
git_config.json()

# DVC SETUP
from mmanager.mmanager import VersionControl
git_config_id = ""
dvc_set = VersionControl(secret_key, url).dvc_set(git_config_id)
dvc_set.json()

# ADD MODEL: NO ML INTEGRATION
from mmanager.mmanager import Model
usecase_id =""
model_data = {
    "project": usecase_id, #Project ID or Usecase ID
    "transformerType": "Classification", #Options: Classification, Regression, Forcasting
    "training_dataset": "train.csv", #path to csv file
    "test_dataset": "test.csv", #path to csv file
    "pred_dataset": "pred_score_data.csv", #path to csv file
    "actual_dataset": "truth_data.csv", #path to csv file
    "model_file_path": "model.h5", #path to model file
    "target_column": "class", #Target Column
    "note": "Wine", #Short description of Model
    "model_area": "",
    "model_dependencies": "",
    "model_usage": "",
    "model_audjustment": "",
    "model_developer": "",
    "model_approver": "",
    "data_version_tags": "Classification_Model_V1", #Auto Generated If Not Provided
    "data_version_comment": "Classification Model Uploaded Jan 2025" #Add Custom Commit Message
    
}
model = Model(secret_key, url).post_model(model_data)
model.json()

# ADD MODEL: NO ML INTEGRATION
from mmanager.mmanager import Model
model_id = ""
model_data = {
    "transformerType": "Classification", #Options: Classification, Regression, Forcasting
    "training_dataset": "train_updated.csv", #path to csv file
    "target_column": "class", #Target Column
    "note": "Updated", #Short description of Model
    "model_area": "",
    "model_dependencies": "",
    "model_usage": "",
    "model_audjustment": "Updated",
    "model_developer": "",
    "model_approver": "",
    "data_version_tags": "Classification_Model_V2", #Auto Generated If Not Provided of UsecaseId_ModelId_DateTimeStamp eg: "42_273_2023_06_15_095950"
    "data_version_comment": "Classification Model Updated Feb 2025" #Add Custom Commit Message
}
model = Model(secret_key, url).patch_model(model_data, model_id)
model.json()

# GET DATA VERSION
from mmanager.mmanager import VersionControl
model_id = ""
versions = VersionControl(secret_key, url).get_version_tags(model_id, usecase_id)
versions.json()

# GET DATA VERSION DETAIL
from mmanager.mmanager import VersionControl
tag_name = ""
version = VersionControl(secret_key, url).get_version_details(tag_name)
version.json()

# SWITCH DATA VERSION
from mmanager.mmanager import VersionControl
model_id = ""
usecase_id = ""
tag_name = ""
versions = VersionControl(secret_key, url).switch_data_version(model_id, usecase_id, tag_name)
versions.json()

Project details

These details have not been verified by PyPI

License
- OSI Approved :: MIT License
Operating System
- OS Independent
Programming Language
- Python :: 3

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2.4.2

Apr 10, 2026

2.4.1

Feb 26, 2026

2.4.0

Dec 30, 2025

This version

2.3.9

Jun 5, 2025

2.3.8

May 28, 2025

2.3.7

May 13, 2025

2.3.6

May 13, 2025

2.3.5

May 13, 2025

2.3.4

Feb 4, 2025

2.3.3

Jan 21, 2025

2.3.2

Jul 5, 2024

2.3.1

May 28, 2024

2.2.0

Sep 20, 2023

2.1.2

Nov 3, 2022

2.1.1

Nov 2, 2022

2.1.0

Oct 25, 2022

2.0.5

Oct 24, 2022

2.0.3

Jun 9, 2022

2.0.2

Jan 18, 2022

2.0.1

Jan 18, 2022

2.0.0

Jan 10, 2022

1.0.6

Jan 20, 2021

1.0.5

Dec 8, 2020

1.0.4

Mar 8, 2020

1.0.3

Mar 8, 2020

1.0.2

Feb 16, 2020

1.0.1

Feb 14, 2020

1.0.0

Feb 14, 2020

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mmanager 2.3.9

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Project description

ModelManager API

Table of Contents

Overview

Features

Installation

Quick Start

Usecase Management

Model Management

Database Integration

Advanced & Reference

Tips & Resources

Forecasting Usecase

Update Usecase

Get All Usecases Uploaded By Authenticated User

Get Usecase Detail

Delete Project

Add Related Database

Link External Database

Add Tables

Add Fields

Load Database Cache

Add Model

Other optional data

Create Config File For Azure ML Credentials

Add Model, Fetch Datasets And Model From Azure ML

Add Model, Upload Datasets And Model Manually And Register To Azure ML

ADD MODEL: MLFLOW

Add MLFlow Creds

Get MLFlow Creds

Download Datasets And Model Files From MLFlow Server

Add Model

Update Model

Delete Model

Get Model Details

Get Metrics

Generate Model Report

Get Model Report

Get Causal Analysis Graphs

Get Causal Discovery Graphs

Get Causal Inference Graphs

Get Causal Inference Top 5 Effects

Get Causal Inference Effects Comparision

Get Causal Inference Correlations

Get Drivers Analysis

Get Model Features

Display What If Analysis Tool

Display Model Detail Tool

Display Data Distribution Tool

Build What If Analysis Tool

Add What If Resources For Image Classification Usecase

Build What If Analysis

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