Model Registry

[ChakshuGautam]

• [ ] Model Version Management (commit hash, semantic version) - should happen while training
• [ ] Provide model files (onnx, pt, bin) through a CDN
• [ ] Rollback to an older version
• [ ] Deployment by a version number
• [ ] Track costs during training

Clicking train button on Admi Panel

ML Pod:

• [ ] #142

Admin panel :

• [ ] when train button is clicked ,it'll hit model registry API to get the:

  • Base Model Branch on HF - the base model which will be used to train the dataset with
  • task_type: classfication/NER etc
  • model_format: onnx/pytorch - safetensors
  • model_name (purpose for which model is getting trained ) like agri_classification in AKAI/KMAI {can be same as service_name}
  • epochs (number of epochs the model is getting trained for)
  • args : training arguements used to fine tune the model
  • quantization: None mostly unless specified)

• [ ] Admin Panel will hit dataset registry to get dataset id for the given model-botid

• [ ] Admin Panel will hit /train API with the following parameters:

  
  {
  "model": Base Model Branch on HF (from model registry)
  "epochs":  (from model registry)
    "task_type":  (from model registry)
  "dataset":  (from dataset registry)
      "versioning": {
       "owner": botid   
      "environment": bot environment 
      “model_name ': (from model registry) 
  },
  “args”: (from model registry) 
  }

## Dataset service:  

-  To create dataset for models with the following for each model-botid t least : 

     -  Base Model Branch on HF - the base model which will be used to train the dataset with 
     - task_type:  classfication/NER etc 
     - model_format: onnx/pytorch - safetensors 
     - model_name (purpose for which model is getting trained ) like agri_classification in AKAI/KMAI {can be same as service_name}
     - epochs (number of epochs the model is getting trained for) 
     - args : training arguements used to fine tune the model
     - quantization: None mostly unless specified) 

- to create dataset for datasets with : 
 datasetid for each model for each bot 

[ChakshuGautam]

@suresh12 to review the Doc

[Gautam-Rajeev]

Clicking train button on Admi Panel

ML Pod:

• [ ] Modify the train API to support versioning

Admin panel :

• [ ] when train button is clicked ,it'll hit model registry API to get the:

  • Base Model Branch on HF - the base model which will be used to train the dataset with
  • task_type: classfication/NER etc
  • model_format: onnx/pytorch - safetensors
  • model_name (purpose for which model is getting trained ) like agri_classification in AKAI/KMAI {can be same as service_name}
  • epochs (number of epochs the model is getting trained for)
  • args : training arguements used to fine tune the model
  • quantization: None mostly unless specified)

• [ ] Admin Panel will hit dataset registry to get dataset id for the given model-botid

• [ ] Admin Panel will hit /train API with the following parameters:

  
  {
  "model": Base Model Branch on HF (from model registry)
  "epochs":  (from model registry)
    "task_type":  (from model registry)
  "dataset":  (from dataset registry)
      "versioning": {
       "owner": botid   
      "environment": bot environment 
      “model_name ': (from model registry) 
  },
  “args”: (from model registry) 
  }

## Dataset service:  

-  To create dataset for models with the following for each model-botid t least : 

     -  Base Model Branch on HF - the base model which will be used to train the dataset with 
     - task_type:  classfication/NER etc 
     - model_format: onnx/pytorch - safetensors 
     - model_name (purpose for which model is getting trained ) like agri_classification in AKAI/KMAI {can be same as service_name}
     - epochs (number of epochs the model is getting trained for) 
     - args : training arguements used to fine tune the model
     - quantization: None mostly unless specified) 

- to create dataset for datasets with : 
 datasetid for each model for each bot 

[KDwevedi]

Scoping Model Registry from ML Flow us lift and use directly.

Desirable Features

• Model Metadata Storage
• Version Management + Finetuning
• Deployment
• Utilising CDNs for making BIN and other model files available
• Metrics for model use recorded
• [ ] PoC