How to Fix ResourceExhaustedError: OOM when allocating tensor in PyTorch

ResourceExhaustedError: OOM when allocating tensor error occurs when your “system has run out of GPU or CPU memory while running a machine learning model, typically a deep learning model using frameworks like TensorFlow or PyTorch.”

This error can be frustrating, especially if you have spent hours or even days training your model. However, there are several solutions that you can try to fix the error and complete your work successfully.

Common reasons for the error

Large Data Size

When working with large datasets, the entire dataset might not fit into your GPU memory, leading to out-of-memory errors. Some strategies to handle this include:

  • Data Generators: Use data generators to load data batch-by-batch instead of the entire dataset into memory.
  • Data Sharding: Split the dataset into smaller parts and load them individually.
  • Feature Engineering: Reduce the dimensionality of the data.

Complex Model

A more complex model requires more memory to store intermediate activations, gradients, and other information. Strategies to handle this include:

  1. Reduce Model Complexity: This involves simplifying the architecture by reducing the number of layers or units per layer.
  2. Pruning: Remove less important connections in a trained model to reduce its size without sacrificing too much accuracy.

Inefficient Code

  1. Graph Optimization: In TensorFlow, use the tf.function to compile your computation graph for optimizations.
  2. In-Place Operations: Use in-place operations that don’t require additional memory allocation whenever possible.
  3. Clear Unused Variables: Make sure to delete any variables that are no longer needed.
  4. Close Sessions: In TensorFlow 1.x, close the session after the model training is complete to free up resources.

How to Fix the ResourceExhaustedError

Here are several solutions to fix the ResourceExhaustedError.

Reduce Batch Size

Reduce the batch size in the training loop. A smaller batch requires less memory but may result in a noisier gradient update. 

By reducing your batch size, you can reduce the memory required to train your model. You can do this by changing the value of the batch_size parameter in your code.

Using a Smaller Model

Reducing the model size is a viable strategy when you have already minimized the batch size but still encounter ResourceExhaustedError issues.

Smaller models have fewer parameters and thus require less memory for storing intermediate calculations, gradients, and other variables during training.

Using Pre-trained Smaller Models

Many popular architectures come in smaller variants. For example, MobileNetV2 and EfficientNet have multiple versions with varying parameters and are designed to be efficient in terms of memory and computational resources. You can use these smaller versions as a starting point and fine-tune them for your specific task.

Using Mixed Precision Training

Mixed-precision training is a robust method for reducing memory usage and speeding up the training process. By using lower-precision data types like float16 instead of float32, you can store more data in the same amount of memory, which often allows for larger batch sizes or models.

Using Gradient Checkpointing

Gradient checkpointing is especially useful for deep networks or models with many branches, which would otherwise not fit in GPU memory. The idea is not to store intermediate activations during the forward pass and recompute them during the backward pass as needed, thus saving memory.

Using TPU

TPUs (Tensor Processing Units) are an excellent alternative when facing resource limitations while training deep learning models. They are custom-built by Google for machine learning tasks and offer several advantages over GPUs.

That’s it!

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