I Used 35 Datasets For Testing On A100, With A Total Of 3000 Steps. Every Time I Run To 35 Steps, An OOM Error Is Reported

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Introduction

In this article, we will delve into the issue of Out of Memory (OOM) errors on A100 GPUs, specifically when running a deep learning model with 35 datasets and a total of 3000 steps. We will analyze the error message, identify the potential causes, and provide solutions to mitigate the issue.

Error Message

The error message is as follows:

scepter [INFO] 2025-04-21 13:43:59,521 [File: val_loss.py Function: save_record at line 175]  Step 0 validation loss: all:  0.4590 edit_key_:  0.4590 
Traceback (most recent call last):
  File "/media/star/8T/jiangshuai/ACE_plus/run_train.py", line 70, in <module>
    we.init_env(cfg, logger=None, fn=run_task)
  File "/home/star/miniconda3/envs/JS-ace/lib/python3.10/site-packages/scepter/modules/utils/distribute.py", line 679, in init_env
    fn(config)
  File "/media/star/8T/jiangshuai/ACE_plus/run_train.py", line 30, in run_task
    solver.solve()
  File "/home/star/miniconda3/envs/JS-ace/lib/python3.10/site-packages/scepter/modules/solver/diffusion_solver.py", line 609, in solve
    self.run_train()
  File "/home/star/miniconda3/envs/JS-ace/lib/python3.10/site-packages/scepter/modules/solver/diffusion_solver.py", line 646, in run_train
    self.after_iter(self.hooks_dict[self._mode])
  File "/home/star/miniconda3/envs/JS-ace/lib/python3.10/site-packages/scepter/modules/solver/base_solver.py", line 630, in after_iter
    [t.after_iter(self) for t in hooks]
  File "/home/star/miniconda3/envs/JS-ace/lib/python3.10/site-packages/scepter/modules/solver/base_solver.py", line 630, in <listcomp>
    [t.after_iter(self) for t in hooks]
  File "/media/star/8T/jiangshuai/ACE_plus/modules/checkpoint.py", line 104, in after_iter
    solver.scaler.step(solver.optimizer)
  File "/home/star/miniconda3/envs/JS-ace/lib/python3.10/site-packages/torch/amp/grad_scaler.py", line 380, in step
    return optimizer.step(*args, **kwargs)
  File "/home/star/miniconda3/envs/JS-ace/lib/python3.10/site-packages/torch/optim/optimizer.py", line 493, in wrapper
    out = func(*args, **kwargs)
  File "/home/star/miniconda3/envs/JS-ace/lib/python3.10/site-packages/torch/optim/optimizer.py", line 91, in _use_grad
    ret = func(self, *args, **kwargs)
  File "/home/star/miniconda3/envs/JS-ace/lib/python3.10/site-packages/torch/optim/adamw.py", line 232, in step
    has_complex = self._init_group(
  File "/home/star/miniconda3/envs/JS-ace/lib/python3.10/site-packages/torch/optim/adamw.py", line 175, in _init_group
    state["exp_avg_sq"] = torch.zeros_like(
torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 54.00 MiB. GPU 0 has a total capacity of 79.25 GiB of which 12.19 MiB is free. Including non-PyTorch memory, this process has 79.21 GiB memory in use. Of the allocated memory 77.65 GiB is allocated by PyTorch, and 1.05 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)

Setting PYTORCH_CUDA_LOC_CONF=expandable segments: True is also OOM

Analysis

The error message indicates that the GPU (A100) is running out of memory, specifically when trying to allocate 54.00 MiB of memory. The GPU has a total capacity of 79.25 GiB, but only 12.19 MiB is free. This suggests that the model is consuming a significant amount of memory, likely due to the large number of datasets and steps.

Potential Causes

  1. Large Model Size: The model may be too large, consuming a significant amount of memory.
  2. Large Dataset Size: The datasets may be too large, requiring a significant amount of memory to process.
  3. Memory Fragmentation: Memory fragmentation may be occurring, causing the GPU to run out of memory.
  4. PyTorch Memory Management: PyTorch's memory management may be inefficient, leading to memory leaks or fragmentation.

Solutions

  1. Reduce Model Size: Consider reducing the model size by pruning or quantizing the weights.
  2. Reduce Dataset Size: Consider reducing the dataset size by sampling or downsampling the data.
  3. Use Gradient Accumulation: Use gradient accumulation to reduce the number of backward passes and alleviate memory pressure.
  4. Use Mixed Precision Training: Use mixed precision training to reduce the memory requirements of the model.
  5. Use CUDA Memory Management: Use CUDA memory management to optimize memory allocation and deallocation.
  6. Monitor Memory Usage: Monitor memory usage to identify potential memory leaks or fragmentation.
  7. Use PyTorch's Memory Management: Use PyTorch's memory management features, such as torch.cuda.empty_cache() and torch.cuda.reset_max_memory_allocated().

Conclusion

OOM errors on A100 GPUs can be caused by a variety of factors, including large model size, large dataset size, memory fragmentation, and PyTorch memory management inefficiencies. By analyzing the error message and identifying potential causes, we can implement solutions to mitigate the issue and improve the performance of our deep learning models.

Recommendations

  1. Monitor Memory Usage: Monitor memory usage to identify potential memory leaks or fragmentation.
  2. Use Gradient Accumulation: Use gradient accumulation to reduce the number of backward passes and alleviate memory pressure.
  3. Use Mixed Precision Training: Use mixed precision training to reduce the memory requirements of the model.
  4. Use CUDA Memory Management: Use CUDA memory management to optimize memory allocation and deallocation.
  5. Use PyTorch's Memory Management: Use PyTorch's memory management features, such as torch.cuda.empty_cache() and torch.cuda.reset_max_memory_allocated().
    OOM Error on A100 GPU: A Comprehensive Q&A =====================================================

Q: What is an OOM error on A100 GPU?

A: An OOM error on A100 GPU stands for Out of Memory error, which occurs when the GPU runs out of memory to allocate for the model's computations.

Q: What are the common causes of OOM errors on A100 GPU?

A: The common causes of OOM errors on A100 GPU include:

  1. Large Model Size: The model may be too large, consuming a significant amount of memory.
  2. Large Dataset Size: The datasets may be too large, requiring a significant amount of memory to process.
  3. Memory Fragmentation: Memory fragmentation may be occurring, causing the GPU to run out of memory.
  4. PyTorch Memory Management: PyTorch's memory management may be inefficient, leading to memory leaks or fragmentation.

Q: How can I reduce the model size to alleviate OOM errors?

A: You can reduce the model size by:

  1. Pruning: Prune the model's weights to reduce the number of parameters.
  2. Quantization: Quantize the model's weights to reduce the precision of the weights.
  3. Knowledge Distillation: Use knowledge distillation to transfer knowledge from a larger model to a smaller model.

Q: How can I reduce the dataset size to alleviate OOM errors?

A: You can reduce the dataset size by:

  1. Sampling: Sample a subset of the data to reduce the size of the dataset.
  2. Downsampling: Downsample the data to reduce the size of the dataset.
  3. Data Augmentation: Use data augmentation to increase the size of the dataset without increasing the memory requirements.

Q: What is gradient accumulation and how can it help alleviate OOM errors?

A: Gradient accumulation is a technique that allows you to accumulate the gradients of multiple backward passes and then update the model's weights. This can help alleviate OOM errors by reducing the number of backward passes and the memory requirements.

Q: What is mixed precision training and how can it help alleviate OOM errors?

A: Mixed precision training is a technique that allows you to train the model using a combination of 32-bit and 16-bit floating point numbers. This can help alleviate OOM errors by reducing the memory requirements of the model.

Q: How can I monitor memory usage to identify potential memory leaks or fragmentation?

A: You can monitor memory usage by:

  1. Using PyTorch's built-in memory monitoring tools: PyTorch provides built-in tools to monitor memory usage, such as torch.cuda.memory_stats().
  2. Using third-party tools: There are several third-party tools available that can help you monitor memory usage, such as NVIDIA's nvprof tool.

Q: What are some best practices for avoiding OOM errors on A100 GPU?

A: Some best practices for avoiding OOM errors on A100 GPU include:

  1. Regularly monitoring memory usage: Regularly monitor memory usage to identify potential memory leaks or fragmentation.
  2. Using gradient accumulation: Use gradient to reduce the number of backward passes and the memory requirements.
  3. Using mixed precision training: Use mixed precision training to reduce the memory requirements of the model.
  4. Using PyTorch's memory management features: Use PyTorch's memory management features, such as torch.cuda.empty_cache() and torch.cuda.reset_max_memory_allocated().