Optimizing PyTorch performance on CentOS can be achieved in a variety of ways, and here are some key optimization tips:
Data loading optimization
- Using multi-process data loading : Using workers in DataLoaders enables asynchronous data loading, thereby reducing the waiting time of the main training process. It is recommended to set num_workers based on the storage location of the workload, CPU, GPU and training data.
- Fixed Memory : Enable pin_memory to speed up data transmission from the host to the GPU. When using GPU, it is recommended to set pin_memory to True.
Model parallelism and data parallelism
- Using DistributedDataParallel : For multi-GPU training, using DistributedDataParallel instead of DataParallel can reduce the data transfer overhead between GPUs. DistributedDataParallel creates a copy of the model on each GPU and makes only a portion of the data available to that GPU.
Precision optimization
- Use 16-bit precision : Using 16-bit precision (such as torch.float16) during training can reduce memory usage and speed up training. Some GPUs support TensorCore, which can run at lower accuracy, further improving performance.
Other optimization tips
- Avoid unnecessary CPU-to-GPU transmission : minimize the use of calls such as .item(), .cpu() or .numpy(), because these calls will transfer data from the GPU to the CPU, reducing performance. You can use .detach() to delete the calculation graph attached to the variable.
- Building tensors directly on the GPU : specify the device directly when creating tensors, instead of creating them on the CPU and then transferring them to the GPU, which reduces the transfer time.
- Disable unnecessary gradient calculation : When inference, use with torch.no_grad() to disable gradient calculation to save video memory and improve speed.
Selection of parallelization library
- OpenMP : For simple parallel requirements, OpenMP is an easy-to-integrate option for loop parallel tasks.
- TBB : For more complex concurrent programming scenarios, TBB provides task-level parallelism and finer-grained thread management.
Installation and configuration
- Install PyTorch using Anaconda : It is recommended to use Anaconda to install PyTorch, because this ensures that PyTorch is correctly linked to the MKL library, thereby optimizing mathematical performance.
Through the above method, the performance of PyTorch can be significantly improved on CentOS. Depending on the specific application scenario and hardware configuration, you can choose the appropriate optimization strategy.
The above is the detailed content of How to optimize PyTorch's performance on CentOS. For more information, please follow other related articles on the PHP Chinese website!
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