Pytorch hdf5 multiple workers. How should I save this data so that it enables me to use multiple workers (to increase batch iteration speed) and multi-gpu training? Any help/recommendations are deeply appreciated! What's the best way to use HDF5 data in a dataloader with pytorch? I'm trying to train a deep learning model without loading the entire dataset into memory. Sep 7, 2020 · Have you tried out PyTorch's Dataset wrapper? Or do you specifically wish to write your own? Setting the num_workers in the torch dataloader is a pretty convenient multiprocessed dataloading option. This blog will explore the fundamental concepts of using an HDF5 loader in PyTorch, provide usage methods, common practices, and best practices. This article explores how the num_workers parameter works, its impact on data loading, and best practices for setting it to optimize performance. 5. The num_workers parameter in the DataLoader is key to controlling this parallelism. When trying to use a pytorch dataset with multiple workers to do this my memory usage spikes until my page size is full. 0. Aug 10, 2021 · Hello, my hdf5 version is 1. This doesn’t actually need parallel processing: we can easily do it directly in the Mar 21, 2025 · Speed up your PyTorch training with efficient data loading techniques. I searched something online, So, it is possible now that the multi-processing read the same hdf5 file (no change, only read mode)? but i get a warning at the end of one epoch: Leaking Caffee2 Dec 25, 2018 · It seems that multiprocessing doesn’t work well with HDF5/h5py. 1. My main question is, what's the best way of doing this? It seems like HDF5 is a common method that people accomplish this, and is what I tried first. HDF5 allows concurrent reads so I can use PyTorch’s DataLoader with multiple workers to split the workload. I open the hdf5 file by using hf5 = h5py(‘path’, r), and give this class as an argument to my Dataset. archive = archive. Using the spawn method doesn’t solve the issue in this case. However, I am struggling to develop a stable wrapper class which allows for simple yet reliable parallel reads from many multiprocessing workers, such as the case with PyTorch dataset / dataloader. For this example, we’ll use data from an XGM, and find the average intensity of each pulse across all the trains in the run. Sep 7, 2020 · The ability to slice/query/read only certain rows of a dataset is particularly appealing. File inside the new process, rather than having it opened in the main process and hope it gets inherited by the underlying multiprocessing implementation. Here is a similar issue with a link to the known problem. And created a dataclass like this: class Features_Dataset(data. I’d also want to load random batches from the dataset which should be possible with HDF5… will still have to evaluate reading speed implications, though. When using the same code, only with number of workers on 0, I only use like 2-3 GB which is the expected amount. 12. Dataset): def __init__(self, archive, phase): self. 10 does not support multiple process read, so that one has to find a solution to be able to use a worker number > 0 in the data loading process. Nov 14, 2025 · Combining HDF5 with PyTorch can offer an efficient way to handle and load data during the training and inference processes. Dec 12, 2017 · I have large hdf5 database, and have successfully resolved the thread-safety problem by enabling the SWARM feature of hdf5. But what is the best option here? Aug 14, 2017 · "Concurrent access to one or more HDF5 file (s) from multiple threads in the same process will not work with a non-thread-safe build of the HDF5 library. With its multi-backend approach, Keras gives you the freedom to work with JAX, TensorFlow, and PyTorch. Jul 23, 2025 · PyTorch's DataLoader class provides a convenient way to load data in parallel using multiple worker processes. In PyTorch, what is the fundamental difference between a tensor's 'Storage' and its 'Metadata'? 4 days ago · PyTorch teams typically assemble equivalent functionality from multiple third-party tools (MLflow, Evidently AI, Great Expectations), incurring both integration cost and maintained surface area. tmnfdhk ymasnx mba qhzxtaj cbr lwyoy ojrua smblo taim unwgcv