The burn-train crate encapsulates multiple utilities for training deep learning models. The goal of the crate is to provide users with a well-crafted and flexible training loop, so that projects do not have to write such components from the ground up. Most of the interactions with burn-train will be with the LearnerBuilder struct, briefly presented in the previous training section. This struct enables you to configure the training loop, offering support for registering metrics, enabling logging, checkpointing states, using multiple devices, and so on.

There are still some assumptions in the current provided APIs, which may make them inappropriate for your learning requirements. Indeed, they assume your model will learn from a training dataset and be validated against another dataset. This is the most common paradigm, allowing users to do both supervised and unsupervised learning as well as fine-tuning. However, for more complex requirements, creating a custom training loop might be what you need.


The learner builder provides numerous options when it comes to configurations.

Training MetricRegister a training metric
Validation MetricRegister a validation metric
Training Metric PlotRegister a training metric with plotting (requires the metric to be numeric)
Validation Metric PlotRegister a validation metric with plotting (requires the metric to be numeric)
Metric LoggerConfigure the metric loggers (default is saving them to files)
RendererConfigure how to render metrics (default is CLI)
Grad AccumulationConfigure the number of steps before applying gradients
File CheckpointerConfigure how the model, optimizer and scheduler states are saved
Num EpochsSet the number of epochs.
DevicesSet the devices to be used
CheckpointRestart training from a checkpoint

When the builder is configured at your liking, you can then move forward to build the learner. The build method requires three inputs: the model, the optimizer and the learning rate scheduler. Note that the latter can be a simple float if you want it to be constant during training.

The result will be a newly created Learner struct, which has only one method, the fit function which must be called with the training and validation dataloaders. This will start the training and return the trained model once finished.

Again, please refer to the training section for a relevant code snippet.


When creating a new builder, all the collected data will be saved under the directory provided as the argument to the new method. Here is an example of the data layout for a model recorded using the compressed message pack format, with the accuracy and loss metrics registered:

├── experiment.log
├── checkpoint
│   ├── model-1.mpk.gz
│   ├── optim-1.mpk.gz
│   └── scheduler-1.mpk.gz
│   ├── model-2.mpk.gz
│   ├── optim-2.mpk.gz
│   └── scheduler-2.mpk.gz
├── train
│   ├── epoch-1
│   │   ├── Accuracy.log
│   │   └── Loss.log
│   └── epoch-2
│       ├── Accuracy.log
│       └── Loss.log
└── valid
    ├── epoch-1
    │   ├── Accuracy.log
    │   └── Loss.log
    └── epoch-2
        ├── Accuracy.log
        └── Loss.log

You can choose to save or synchronize that local directory with a remote file system, if desired. The file checkpointer is capable of automatically deleting old checkpoints according to a specified configuration.