Lr Schedulers Adaptive Optimizers Pytorch Training Performance Guide

A long long time ago, almost all neural networks were trained using a fixed learning rate and the stochastic gradient descent (SGD) optimizer. Then the whole deep learning revolution thing happened, leading to a whirlwind of new techniques and ideas. In the area of model optimization, the two most influential of these new ideas have been learning rate schedulers and adaptive optimizers. In this chapter, we will discuss the history of learning rate schedulers and optimizers, leading up to the two techniques best-known among practitioners today: OneCycleLR and the Adam optimizer. We will discuss the relative merits of these two techniques. TLDR: you can stick to Adam (or one of its derivatives) during the development stage of the project, but you should try additionally incorporating OneCycleLR into your model as well eventually.

All optimizers have a learning rate hyperparameter, which is one of the most important hyperparameters affecting model performance. Created On: Jun 13, 2025 | Last Updated On: Aug 24, 2025 torch.optim is a package implementing various optimization algorithms. Most commonly used methods are already supported, and the interface is general enough, so that more sophisticated ones can also be easily integrated in the future. To use torch.optim you have to construct an optimizer object that will hold the current state and will update the parameters based on the computed gradients. To construct an Optimizer you have to give it an iterable containing the parameters (all should be Parameter s) or named parameters (tuples of (str, Parameter)) to optimize.

Then, you can specify optimizer-specific options such as the learning rate, weight decay, etc. In the realm of deep learning, PyTorch stands as a beacon, illuminating the path for researchers and practitioners to traverse the complex landscapes of artificial intelligence. Its dynamic computational graph and user-friendly interface have solidified its position as a preferred framework for developing neural networks. As we delve into the nuances of model training, one essential aspect that demands meticulous attention is the learning rate. To navigate the fluctuating terrains of optimization effectively, PyTorch introduces a potent ally—the learning rate scheduler. This article aims to demystify the PyTorch learning rate scheduler, providing insights into its syntax, parameters, and indispensable role in enhancing the efficiency and efficacy of model training.

PyTorch, an open-source machine learning library, has gained immense popularity for its dynamic computation graph and ease of use. Developed by Facebook's AI Research lab (FAIR), PyTorch has become a go-to framework for building and training deep learning models. Its flexibility and dynamic nature make it particularly well-suited for research and experimentation, allowing practitioners to iterate swiftly and explore innovative approaches in the ever-evolving field of artificial intelligence. At the heart of effective model training lies the learning rate—a hyperparameter crucial for controlling the step size during optimization. PyTorch provides a sophisticated mechanism, known as the learning rate scheduler, to dynamically adjust this hyperparameter as the training progresses. The syntax for incorporating a learning rate scheduler into your PyTorch training pipeline is both intuitive and flexible.

At its core, the scheduler is integrated into the optimizer, working hand in hand to regulate the learning rate based on predefined policies. The typical syntax for implementing a learning rate scheduler involves instantiating an optimizer and a scheduler, then stepping through epochs or batches, updating the learning rate accordingly. The versatility of the scheduler is reflected in its ability to accommodate various parameters, allowing practitioners to tailor its behavior to meet specific training requirements. The importance of learning rate schedulers becomes evident when considering the dynamic nature of model training. As models traverse complex loss landscapes, a fixed learning rate may hinder convergence or cause overshooting. Learning rate schedulers address this challenge by adapting the learning rate based on the model's performance during training.

This adaptability is crucial for avoiding divergence, accelerating convergence, and facilitating the discovery of optimal model parameters. The provided test accuracy of approximately 95.6% suggests that the trained neural network model performs well on the test set. A blog about data science and machine learning In deep learning, optimizing the learning rate is an important for training neural networks effectively. Learning rate schedulers in PyTorch adjust the learning rate during training to improve convergence and performance. This tutorial will guide you through implementing and using various learning rate schedulers in PyTorch.

The tutorial covers: The learning rate is a critical hyperparameter in the training of machine learning models, particularly in neural networks and other iterative optimization algorithms. It determines the step size at each iteration while moving towards a minimum of the loss function. Before you start, ensure you have the torch library installed: This command will download and install the necessary dependencies in your Python environment. In deep learning, optimizing the learning rate is crucial for training efficient and effective models.

PyTorch, a popular deep learning framework, provides a powerful set of tools for adjusting the learning rate during the training process through learning rate schedulers. These schedulers allow us to control how the learning rate changes over time, which can significantly impact the convergence speed and the performance of the model. In this blog post, we will explore the fundamental concepts of PyTorch learning rate schedulers, their usage methods, common practices, and best practices. The learning rate is a hyperparameter that controls the step size at each iteration while updating the model's parameters during training. A large learning rate can cause the model to converge quickly but may also lead to overshooting the optimal solution. On the other hand, a small learning rate can result in slow convergence and may get stuck in local minima.

A learning rate scheduler adjusts the learning rate during the training process based on a predefined strategy. PyTorch provides several built - in learning rate schedulers, such as StepLR, MultiStepLR, ExponentialLR, CosineAnnealingLR, etc. These schedulers can be used to adapt the learning rate according to the number of epochs, the validation loss, or other criteria. StepLR decays the learning rate of each parameter group by a given factor every step_size epochs. MultiStepLR decays the learning rate of each parameter group by a given factor at specified epochs. PyTorch implementation of the "Learning an Adaptive Learning Rate Schedule" paper found here: https://arxiv.org/abs/1909.09712.

Work in progress! A controller is optimized by PPO to generate adaptive learning rate schedules. Both the actor and the critic are MLPs with 2 hidden layers of size 32. Three distinct child network architectures are used: 1) an MLP with 3 hidden layers, 2) LeNet-5 and 3) ResNet-18. Learning rate schedules are evaluated on three different datasets: 1) MNIST, 2) Fashion-MNIST and 3) CIFAR10. Original paper experiments with combinations of Fashion-MNIST, CIFAR10, LeNet-5 and ResNet-18 only.

In each of the three settings, child networks are optimized using Adam with an initial learning rate in (1e-2, 1e-3, 1e-4) and are trained for 1000 steps on the full training set (40-50k samples)... 20-25 epochs. Learning rate schedules are evaluated based on validation loss over the course of training. Test loss and test accuracies are in the pipeline. Experiments are made in both a discrete and continuous setting. In the discrete setting, the controller controls the learning rate by proposing one of the following actions every 10 steps: 1) increase the learning rate, 2) decrease the learning rate, 3) do nothing.

In the continuous setting, the controller instead proposes a real-valued scaling factor, which allows the controller to modify learning rates with finer granularity. Maximum change per LR update has been set to 5% for simplicity (action space is not stated in the paper). In both the discrete and the continuous setting, Gaussian noise is optionally applied to learning rate updates. Observations for the controller contain information about current training loss, validation loss, variance of predictions, variance of prediction changes, mean and variance of the weights of the output layer as well as the previous... To make credit assignment easier, the validation loss at each step is used as reward signal rather than the final validation loss. Both observations and rewards are normalized by a running mean.

DeBERTa-v3 large layer-wise learning rate scheduler. Reference: https://github.com/gilfernandes/commonlit Model based on Huggingface Transformers. Starting index of the head parameters (end of backbone). The optimizer for which to schedule the learning rate. © 2025 ApX Machine LearningEngineered with @keyframes heartBeat { 0%, 100% { transform: scale(1); } 25% { transform: scale(1.3); } 50% { transform: scale(1.1); } 75% { transform: scale(1.2); } }

Neural networks have many hyperparameters that affect the model’s performance. One of the essential hyperparameters is the learning rate (LR), which determines how much the model weights change between training steps. In the simplest case, the LR value is a fixed value between 0 and 1. However, choosing the correct LR value can be challenging. On the one hand, a large learning rate can help the algorithm to converge quickly. But it can also cause the algorithm to bounce around the minimum without reaching it or even jumping over it if it is too large.

On the other hand, a small learning rate can converge better to the minimum. However, the optimizer may take too long to converge or get stuck in a plateau if it is too small. One solution to help the algorithm converge quickly to an optimum is to use a learning rate scheduler. A learning rate scheduler adjusts the learning rate according to a pre-defined schedule during the training process. One solution to help the algorithm converge quickly to an optimum is to use a learning rate scheduler. Usually, the learning rate is set to a higher value at the beginning of the training to allow faster convergence.

As the training progresses, the learning rate is reduced to enable convergence to the optimum and thus leading to better performance. Reducing the learning rate over the training process is also known as annealing or decay. Go to the end to download the full example code. Created On: May 21, 2024 | Last Updated: May 21, 2024 | Last Verified: Nov 05, 2024 The optimizer is a key algorithm for training any deep learning model. In this example, we will show how to pair the optimizer, which has been compiled using torch.compile, with the LR schedulers to accelerate training convergence.

This tutorial requires PyTorch 2.3.0 or later. For this example, we’ll use a simple sequence of linear layers.

People Also Search

• LR Schedulers, Adaptive Optimizers — PyTorch Training Performance Guide
• torch.optim — PyTorch 2.9 documentation
• Understanding PyTorch Learning Rate Scheduling - GeeksforGeeks
• Implementing Learning Rate Schedulers in PyTorch - DataTechNotes
• Optimizer PyTorch Schedule: A Comprehensive Guide
• GitHub - nicklashansen/adaptive-learning-rate-schedule: PyTorch ...
• LR Scheduler - pytorch-optimizer
• Advanced Learning Rate Schedules in PyTorch - apxml.com
• A Visual Guide to Learning Rate Schedulers in PyTorch
• (beta) Running the compiled optimizer with an LR Scheduler - PyTorch