PyTorch usage tips on CentOS

Efficiently running PyTorch on CentOS systems requires considerations from hardware configuration to code optimization. This article summarizes practical skills to improve PyTorch's performance in CentOS, covering hardware selection, performance bottleneck analysis, data loading optimization, and model training strategies.

1. Hardware configuration suggestions

• Processor (CPU): Choose CPUs with high main frequency, large cache, and multi-core to ensure data processing capabilities.
• Graphics Card (GPU): A GPU with large video memory is especially important when handling large Batch Size training.
• Memory (RAM): At least 64GB of memory. It is recommended to use 4 16GB memory sticks to increase memory bandwidth.
• Motherboard: Choose a motherboard with excellent performance to ensure that the CPU performance is fully utilized.
• Power supply: Choose a power supply with sufficient power to meet the power consumption needs of GPU for high load operation.
• Storage (SSD): Use SSD solid-state drives to store data, significantly improve data reading speed and avoid IO bottlenecks in mechanical hard disks.

2. Performance bottleneck analysis

• Use the torch.utils.bottleneck tool provided by PyTorch to accurately locate the code running bottlenecks.
• Use performance analysis tools such as cProfile to deeply analyze code execution efficiency and find long-term functions or code segments.

3. Image decoding optimization

• Avoid using the less efficient Pillow library for image decoding. It is recommended to use the TurboJPEG library to improve decoding speed.

4. Data loading optimization

• Set the workers parameter in DataLoader , use multiple processes to load data in parallel, and overcome the limitations of global interpreter locks (GIL).
• Use pinned memory to reduce CPU-to-GPU data transfer time.
• Use the .detach() method reasonably to avoid unnecessary CPU-to-GPU data transmission and release computing graph resources.

V. Distributed training

• DistributedDataParallel is used instead of DataParallel for distributed training, reducing communication overhead between GPUs and improving training efficiency.

6. Mixed precision training (16-bit)

• Reduce data accuracy from 32 bits to 16 bits, speed up training and reduce memory usage.

7. GPU resource management

• By setting the CUDA_VISIBLE_DEVICES environment variable, specify the GPU device to be used to avoid resource conflicts.

8. Model training strategy

• Gradient Clipping: Use nn.utils.clip_grad_norm_ to prevent gradient explosion.
• Image dimension adjustment: flexibly adjust the dimension of the picture according to task requirements.
• Single-hot encoding: Use the one-hot encoding function built in PyTorch.
• Learning rate adjustment: Use learning rate decay strategy to dynamically adjust the learning rate to improve model performance.
• Parameter freezing: Freeze some layer parameters in the early stage of training to speed up convergence.
• Layered learning rate: Set different learning rates according to the characteristics of different layers.

9. Model debugging and analysis

• Use torchsummary to view the detailed information of each layer of the model output, which is convenient for network debugging.
• Reasonably initialize network parameters to improve model training effect.
• Make full use of the pre-trained models provided by PyTorch to speed up the progress of model development.

10. Continuous learning and optimization

Please note that the above techniques are not universally applicable and need to be adjusted according to actual hardware configuration and task requirements. At the same time, PyTorch version is updated frequently, so it is recommended to refer to the latest official documents to obtain the most accurate information.

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