Clearing GPU Memory After PyTorch Training Without Kernel Restart Last Updated : 23 Jul, 2025 Comments Improve Suggest changes Like Article Like Report Managing GPU memory effectively is crucial when training deep learning models using PyTorch, especially when working with limited resources or large models. This article will guide you through various techniques to clear GPU memory after PyTorch model training without restarting the kernel. We will explore different methods, including using PyTorch's built-in functions and best practices to optimize memory usage.Table of ContentUnderstanding GPU Memory Management in PyTorchTechniques to Clear GPU Memory1. Use torch.cuda.empty_cache()2. Delete Unused Variables - References3. Invoke Python's Garbage CollectorBest Practices for Efficient GPU Memory ManagementUnderstanding GPU Memory Management in PyTorchPyTorch employs a caching memory allocator to manage GPU memory efficiently. This caching mechanism speeds up memory allocations but can lead to situations where memory appears to be used even after the tensors are no longer needed. Understanding how to manage this memory is key to preventing memory overflow issues and ensuring efficient utilization of GPU resource.Why Clear GPU Memory?Clearing GPU memory is essential for several reasons:Resource Optimization: Freeing up memory allows other processes or models to use the GPU resources, which is vital in multi-tasking environments.Avoiding Memory Overflow: Large models or datasets can quickly consume available memory, leading to out-of-memory (OOM) errors.Improving Workflow Efficiency: By clearing memory, you can continue working in the same session without restarting the kernel, preserving your workflow and results.Techniques to Clear GPU Memory1. Use torch.cuda.empty_cache()The torch.cuda.empty_cache() function releases all unused cached memory held by the caching allocator. This does not free the memory occupied by tensors but helps in releasing some memory that might be cached.import torch# Clear GPU cachetorch.cuda.empty_cache()Techniques to Clear GPU Memory2. Delete Unused Variables - ReferencesEnsure that all variables and objects that reference GPU memory are deleted. This includes models, optimizers, and any intermediate tensors that are no longer needed. Use the del statement to remove these references.# Delete model and optimizerdel modeldel optimizer# Clear cachetorch.cuda.empty_cache()To demonstrate the process of deleting references to objects that are using GPU memory in Python (specifically with PyTorch), let's create a small example. This example will involve creating a model, optimizer, and some tensors on the GPU, and then we'll see how deleting references and clearing GPU memory works. Python import torch import torch.nn as nn import torch.optim as optim # Helper function to check GPU memory usage def print_gpu_memory(): print(f"Allocated memory: {torch.cuda.memory_allocated() / 1024**2:.2f} MB") print(f"Cached memory: {torch.cuda.memory_reserved() / 1024**2:.2f} MB") # Step 1: Create a model and optimizer, and allocate some tensors on the GPU device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') model = nn.Linear(1000, 1000).to(device) # Model on GPU optimizer = optim.Adam(model.parameters()) # Optimizer for the model tensor = torch.randn(1000, 1000, device=device) # Tensor on GPU print("Before deleting references:") print_gpu_memory() # Step 2: Delete the references to these objects del model del optimizer del tensor print("\nAfter deleting references:") print_gpu_memory() # Step 3: Free up GPU memory manually using torch.cuda.empty_cache() torch.cuda.empty_cache() print("\nAfter emptying cache:") print_gpu_memory() Output:Before deleting references:Allocated memory: 389.63 MBCached memory: 404.00 MBAfter deleting references:Allocated memory: 382.00 MBCached memory: 404.00 MBAfter emptying cache:Allocated memory: 382.00 MBCached memory: 382.00 MB3. Invoke Python's Garbage CollectorThe Python garbage collector can be invoked to reclaim memory that is no longer reachable. This can be done using the gc.collect() function.import gc# Invoke garbage collectorgc.collect()# Clear GPU cachetorch.cuda.empty_cache()To demonstrate how to invoke the garbage collector and clear the GPU cache in Python, you can create a small example that uses both the gc and torch libraries. This example will simulate a scenario where memory is allocated, and then garbage collection and GPU cache clearing are performed. Python import gc import torch # Simulate some memory usage by creating a large tensor large_tensor = torch.randn(10000, 10000, device='cuda') print(f"Memory allocated before clearing cache: {torch.cuda.memory_allocated()} bytes") # Manually invoke garbage collection gc.collect() # Clear GPU cache torch.cuda.empty_cache() print(f"Memory allocated after clearing cache: {torch.cuda.memory_allocated()} bytes") Output:Techniques to Clear GPU MemoryBest Practices for Efficient GPU Memory ManagementPlan Model Architecture: Design models with memory constraints in mind, optimizing layer sizes and types.Batch Size Considerations: Adjust batch sizes based on available memory to prevent OOM errors.Regular Memory Checks: Incorporate regular memory checks in your workflow to catch potential issues early.Profile Memory Usage: Use profiling tools to understand memory allocation patterns and identify areas for optimization.ConclusionClearing GPU memory after PyTorch model training is a critical step in maintaining efficient workflows and optimizing resource usage. By employing the techniques outlined in this article, you can manage GPU memory effectively, avoid memory overflow issues, and continue working seamlessly without restarting your kernel. Whether you're working with large models or limited resources, these strategies will help you make the most of your GPU capabilities. Comment More infoAdvertise with us Next Article Introduction to Deep Learning L lakshaymbnwg Follow Improve Article Tags : Deep Learning AI-ML-DS Python-PyTorch AI-ML-DS With Python Similar Reads Deep Learning Tutorial Deep Learning is a subset of Artificial Intelligence (AI) that helps machines to learn from large datasets using multi-layered neural networks. It automatically finds patterns and makes predictions and eliminates the need for manual feature extraction. Deep Learning tutorial covers the basics to adv 5 min read Deep Learning BasicsIntroduction to Deep LearningDeep Learning is transforming the way machines understand, learn and interact with complex data. 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