Janusz Lisiecki

Hi @zhengjs, While CV-CUDA and nvImageCodec work great for inference, they may not be well suited to the multiprocess data loading approach PyTorch applies for training. Have you heard and...

Hi @rachelglenn, Thank you for reaching out. Have you tried the [`source_info`](https://docs.nvidia.com/deeplearning/dali/user-guide/docs/data_types.html#nvidia.dali.tensors.TensorCPU.source_info) method for the sample in the output batch? ``` o = pipe.run() print(o[0][0].source_info()) ```

I'm afraid the method mentioned is a property of the DALI tensor, not the Torch one, which is returned by the iterator. Another solution you can test in this case...

@rachelglenn have you checked [this example](https://docs.nvidia.com/deeplearning/dali/user-guide/docs/examples/general/data_loading/parallel_external_source.html)?

Hi @lyyi599, Thank you for reaching out and using DALI. > Under the same settings, the DALI pipeline achieves only 67.6% accuracy, while using PyTorch transforms achieves the paper's reported...

> Therefore, the issue is likely not related to the .txt file used for indexing. > > In the trainer.py code above, the only difference is how iNaturalist18 is loaded—specifically,...

Hi @aafaqin, Thank you for reaching out. You can read more about the meaning of the operator backend [here](https://docs.nvidia.com/deeplearning/dali/user-guide/docs/pipeline.html#id2). The `mixed` backed is used for operators that consume the input...

Hi @aafaqin, > I am curious if we can integrate GPU Direct Storage (GDS) with DALI to streamline data transfers directly from storage to GPU memory, bypassing the CPU to...

Hi @aafaqin, > Still my CPU core is just 1 CPU core being used(100% utilisation) i have a 64 core CPU how to spread it. It means you use only...

Can you try capturing the profile of the processing using [nsight](https://developer.nvidia.com/nsight-systems) and see how it looks like/share?