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- Examples or explanations of pytorch dataloaders? - Stack Overflow
I'll give you an example of how to use dataloaders and will explain the steps: Dataloaders are iterables over the dataset So when you iterate over it, it will return B randomly from the dataset collected samples (including the data-sample and the target label), where B is the batch-size To create such a dataloader you will first need a class which inherits from the Dataset Pytorch class
- PyTorch: How to use DataLoaders for custom Datasets
In addition to user3693922's answer and the accepted answer, which respectively link the "quick" PyTorch documentation example to create custom dataloaders for custom datasets, and create a custom dataloader in the "simplest" case, there is a much more detailed dedicated official PyTorch tutorial on how to create a custom dataloader with the
- How do I use pinned memory with multiple workers in a PyTorch DataLoader?
-> 1146 raise RuntimeError('DataLoader worker (pid(s) {}) exited unexpectedly' format(pids_str)) from e 1147 if isinstance(e, queue Empty): 1148 return (False, None) My CUDA installation behaves fine insofar as I can train and run inference with models using either the GPU (with and without pinned memory) or my CUDA GPU when num_workers=0
- Pytorch. How does pin_memory work in Dataloader?
I want to understand how the pin_memory parameter in Dataloader works According to the documentation: pin_memory (bool, optional) – If True, the data loader will copy tensors into CUDA pinned mem
- How does the number of workers parameter in PyTorch dataloader . . .
Nope Remember DataLoader doesn't just randomly return from what's available in RAM right now, it uses batch_sampler to decide which batch to return next Each batch is assigned to a worker, and main process will wait until the desired batch is retrieved by assigned worker
- Why does my PyTorch DataLoader only use one CPU core despite setting . . .
Even though I set num_workers=16 in my DataLoader, it only uses one CPU core to load data onto my GPU This significantly slows down the process Here’s what I’ve tried: Reducing the batch size Reducing the number of tokens (token_max) Tokenizing the entire dataset beforehand to ensure the tokenizer isn’t causing the bottleneck issue
- nlp - How to pass a pytorch DataLoader to huggingface Trainer? Is that . . .
The usual steps to use the Trainer from huggingface requires that: Load the data Tokenize the data Pass tokenized data to Trainer MWE: data = generate_random_data(10000) # Generate 10,000 sample
- pytorch - Why is my DataLoader process using up to 2. 6GB of virtual . . .
Why is my DataLoader process using up to 2 6GB of virtual memory, and is there any way to reduce it? Each DataLoader process takes up 2 6GB of virtual memory, and 4 processes take up 10 4GB
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