Understanding AI Model Checkpoints: A Simplified Guide
Training artificial intelligence (AI) takes a lot of time. It runs over multiple hours, and could even last days–if these processes stop suddenly for any reason, whether from a power outage or an unforeseen error, you’ll have to start everything over again from scratch.
To prevent this from happening when you train a Stable Diffusion model, or if you believe you’ll encounter situations in your project where you’ll want to train from a known state or try experiments at certain epochs, you must use machine learning checkpointing. You can learn a more in-depth lesson on AI model checkpoints with Stable Diffusion model learning resources, but in this article, we’ll give you a simplified guide to get you started on the basics.
What Are Checkpoints?
Before diving into checkpoints, it’s important to understand the key steps in a machine-learning pipeline. After preparing datasets for AI training, it typically moves through the following succession:
- Training
- Evaluation
- Export
- Deployment
Checkpoints go between periods of training and evaluation, then repeat those steps again before beginning export and deployment, giving you better control of the initial loops. They’re an intermediate dump or a snapshot of a model’s entire internal state, including its weights, learning rate, number of epochs executed, etc., and act as a jumping-off point so that the framework can pick up on its training from here whenever needed.
Checkpointing, then, is a fault tolerance technique; it’s a way to save the progress of a training job in a current state so that if something happens, the process can be resumed from a known state.
Benefits of Checkpoints
Here are some reasons why you should be applying checkpoints in your training projects:
Saved Progress
Long training jobs are subject to a lot of risks, with the likelihood of machine failure increasing the longer it goes on. With checkpoints, you can resume from the last saved state instead of having to work from the very beginning of the project.
Early Stopping
The longer the training, the lower the loss of the training dataset, and it may be possible that errors on the evaluation dataset might stop decreasing—or worse, even increase. If you have checkpoints, you can go back and stop the project at the state that had the best validation error, also known as “early stopping.”
Better Fine Tuning
If you need to retrain your model on fresh data, you’ll want to emphasise the new information, instead of pulling from the past datasets. With checkpoints, you can pick out certain states from which you can easily start your new training or experiment with new paths.
Uses of Checkpoints
Checkpoints are generally used for interval training because you can stop, pause, and resume training from specific states in your training job. Aside from this (and other small applications within this process), checkpoints can also be used for the following:
Prediction Accuracy Improvement
Checkpointing can be used to improve inference prediction accuracy–this happens when the learning rate is lowered to increase the model’s accuracy. That means that even while the model is still being trained, you can use it to make predictions.
Multi-System Training
When you pick up from a checkpoint, you can either continue training the model with the existing dataset or start with training across different nodes or clusters. This is helpful when you need to do a training job that requires input from multiple systems.
Transfer Learning
At some point during a long training job, you might find that your goals have changed. When this happens, you can use checkpoints to perform transfer learning.
How to Implement Checkpoints
Here’s a step-by-step guide on how to implement checkpoints on your AI model:
Create the Model
First, you need to create your model. Build the architecture, making sure to include optimisers, metrics, and loss functions.
Create and Apply the Callback
Create the callback function to save the model. This is the most common checkpointing method. These functions are applied at different epochs of training to give an overview of the internal states. You can choose to save the weights only and specify the frequency at which the checkpoints appear.
Start the training, then apply the callback and evaluate the model.
Load or Restore Weights
Once training ensues, you can then choose to load or restore weights as needed. You can use the checkpoints to stop and then continue training, resume training after an interruption, or predict inferences.
AI Model Checkpoints on Custom Models From Nightcafe
Nightcafe, a sophisticated AI image generator, allows you to apply custom models for better creations. When training your models for this purpose, we encourage checkpointing to ensure that your work derives the desired results.