Question: How to Maximize throughput and fps about dali HOT 2 CLOSED

Hi,

1. The current idea of DALI is to allow easy offload of data loading and augmentation to the GPU. It was designed for scenarios where CPU is the bottleneck. In your case CPU shouldn't be and what you can do is to construct pipeline by assigning some operations to CPU and the rest to GPU that CPU is also utilized.
2. This relates to a number of CPU thread that is used to perform CPU operators. When you create pipeline you may assign it to given GPU by providing device_id, by providing num_threads you tell how big CPU thread pool should be. There is one thing that we need to document better, nvJpeg is executed partially by CPU, partially by GPU. For CPU it also creates a thread pool which size can be defined by passing num_threads argument. If you set num_threads to low value it could hurt performance. Please check how your different values work for you.
3. It is true, additional memory is required so data processing could be performed by DALI on GPU. We are working on reducing memory pressure as @ptrendx stated in #21. In your case it makes you use small batch sizes and this could affect overall performance.
4. If you are asking for speed results for configurations where CPU processing power is not a bottleneck (like 1xGTX1080P), it should be almost the same comparing to test without DALI (even may be a bit slower due to DALI overhead). In such case, the main benefit of DALI is flexibility and ease of pipeline construction. That is why we don't provide general performance reports. Nevertheless, it is a good point and we may prepare a more thorough performance report.
5. nvJpeg is designed to provide Jpeg loading and decoding (mostly), it is not planned to be image processing library. For that DALI can be used and it is not necessarily limited only to Deep Learning applications. If you really need to build an own and custom processing pipeline how about mixing nvJpeg and NPP for processing?"

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@JanuszL Thank you！

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