# Design Doc: Save Model

## Overview

The model is the output of the training process. There are two ways from which user can obtain a model:

- Save model triggered by user code: user code asks PaddlePaddle to save a model.
- Convert model from the checkpoint: model being converted from pservers’ periodic checkpoint. In this way, the user can cancel a job at any time, and still have a relatively fresh model (we checkpoint around every 5 minutes).

### Trainer Saving Model vs. Pservers Saving Model

Both trainers and pservers have access to the model. So the model can be saved from a trainer or pservers. We need to decide where the model is saved from.

#### Dense Update vs. Sparse Update

There are two types of model update methods: dense update and sparse update (when the model parameter is configured to be sparse).

- Dense update

  Every trainer has it’s own full copy of the model. Every model update will update the entire model.

- Sparse update

  The training input is sparse, and the trainer does not have the entire model. It will only download the sub-model necessary related to the input. When updating the model, only the sub-model related to the training input is updated.

#### Pservers Saving Model

The benefit of letting pservers save model is they have the entire model all the time. However, since pservers are on different nodes, it requires a merging process to merge model shards into the same model. Thus requires the pservers to write models to a distributed filesystem, making the checkpoint shards visible to the merge program.

#### Trainer Saving Model

The benefit of letting one trainer to save the model is it does not require a distributed filesystem. And it’s reusing the same save model logic when training locally - except when doing sparse update, the trainer needs to download the entire model during the saving process.

#### Conclusion

Given trainer saving model does not require a distributed filesystem, and is an intuitive extension to trainer saving model when training locally, we decide to let the trainer save the model when doing distributed training.

### Convert Model from Checkpoint

TODO

## Timeline

We first implement trainer save the model. Converting the latest snapshot to a model will be a TODO for future.

## Trainer Save Model

### Trainer Election

One trainer will be elected as the one to save the model. When using etcd, trainer ID is a randomly generated UUID, the trainer will contact the master server requesting to save the model, and find out if itself is elected. When the master server is not used, unique trainer IDs will be given by the administrator, the trainer whose ID is “0” is elected to save the model.

### Model Save Path

Each trainer will be given the directory to save the model. The elected trainer will save the model to `given-directory/trainerID`. Since the trainer ID is unique, this would prevent concurrent save to the same file when multiple trainers are elected to save the model when split-brain problem happens.

### What Happens When Model Is Saving

It takes some time to save model, we need to define what will happen when save model is taking place.

When doing dense update, the trainer uses the local model. Pservers does not need to pause model update.

When doing sparse update. The trainer needs to download the entire model while saving. To get the most accurate model, the model update needs to be paused before the download starts and resumed after the download finishes. Otherwise, the trainer gets a model that is “polluted”: some part of the model is old, some part of the model is new.

It’s unclear that the “polluted” model will be inferior due to the stochastic nature of deep learning, and pausing the model update will add more complexity to the system. Since supporting sparse update is a TODO item. We defer the evaluation of pause the model update or not during saving model to the future.