Workflow:LaurentMazare Tch rs MNIST Training
| Knowledge Sources | |
|---|---|
| Domains | Deep_Learning, Image_Classification, Rust_ML |
| Last Updated | 2026-02-08 13:00 GMT |
Overview
End-to-end process for training an image classification model on the MNIST handwritten digit dataset using tch-rs Rust bindings for PyTorch.
Description
This workflow demonstrates the canonical deep learning training loop in Rust using tch-rs. It covers loading the MNIST dataset, constructing a neural network (from simple linear models to convolutional networks), setting up an optimizer, running the training loop with forward/backward passes, and evaluating accuracy on the test set. Three model architectures are demonstrated: a bare-tensor linear classifier, a two-layer feedforward network using the nn module, and a convolutional neural network with dropout.
Usage
Execute this workflow when you want to train a classifier from scratch in Rust on a standard dataset. This is the recommended starting point for learning the tch-rs training API, covering VarStore creation, layer construction, optimizer configuration, and the training loop pattern. Suitable for any scenario where you have labeled image data and want to build and train a model entirely in Rust.
Execution Steps
Step 1: Load the dataset
Load the MNIST dataset from binary files on disk into a structured Dataset object containing training and test image/label tensors. The tch-rs vision module provides a built-in loader that reads the IDX format files and returns normalized floating-point tensors.
Key considerations:
- Dataset files must be in IDX binary format in the specified directory
- Images are automatically flattened to 784-dimensional vectors (28x28)
- Labels are stored as integer tensors
Step 2: Initialize VarStore and select device
Create a VarStore bound to a target compute device (CPU or CUDA). The VarStore serves as the central registry for all trainable parameters in the model. All layers constructed under a VarStore path will have their weights automatically tracked for optimization and serialization.
Key considerations:
- Use Device::cuda_if_available() to automatically use GPU when present
- The VarStore owns all parameters and manages their lifecycle
- Parameter paths provide hierarchical namespacing for weights
Step 3: Define the model architecture
Construct the neural network by composing layers under the VarStore's root path. tch-rs supports both functional Sequential composition (for simple feed-forward networks) and custom struct-based architectures (for models requiring complex forward logic like dropout or residual connections).
What happens:
- Linear layers, convolutions, and normalization layers are created under named paths
- The Module or ModuleT trait defines the forward pass contract
- Sequential containers chain layers automatically; custom structs implement forward_t for train/eval behavior
Step 4: Configure the optimizer
Build an optimizer (Adam, SGD, etc.) from the VarStore. The optimizer automatically discovers all trainable parameters registered in the VarStore and manages their gradient updates with the specified learning rate and hyperparameters.
Key considerations:
- OptimizerConfig trait provides a builder pattern for all optimizer types
- The optimizer holds a reference to the VarStore's parameters
- Learning rate can be adjusted during training via set_lr
Step 5: Run the training loop
Iterate over epochs, computing the forward pass, calculating the loss, and performing a backward step. For small datasets, the entire training set can be processed at once. For larger datasets or models requiring dropout/batch-norm, use the data iterator with mini-batches and shuffle.
What happens:
- Forward pass: apply the model to input tensors to get logits
- Loss computation: cross-entropy between logits and labels
- Backward step: the optimizer computes gradients and updates parameters in a single call
- Mini-batch iteration: the Dataset::train_iter method provides shuffled batches
Step 6: Evaluate on the test set
After each epoch (or at the end of training), run the model in inference mode on the test set and compute accuracy. For ModuleT models, set train=false to disable dropout and use running statistics for batch normalization.
Key considerations:
- Use no_grad context or batch_accuracy_for_logits for memory-efficient evaluation
- For models with dropout, pass train=false to forward_t
- Accuracy is computed as the fraction of correct argmax predictions