Progressive Compressed Records: Taking a Byte Out of Deep Learning Data
Deep learning training accesses vast amounts of data at high velocity, posing challenges for datasets retrieved over commodity networks and storage devices. We introduce a way to dynamically reduce the overhead of fetching and transporting training data with a method we term Progressive Compressed Records (PCRs). PCRs deviate from previous formats by leveraging progressive compression to split each training example into multiple examples of increasingly higher fidelity, without adding to the total data size. Training examples of similar fidelity are grouped together, which reduces both the system overhead and data bandwidth needed to train a model. We show that models can be trained on aggressively compressed representations of the training data and still retain high accuracy, and that PCRs can enable a 2x speedup on average over baseline formats using JPEG compression. Our results hold across deep learning architectures for a wide range of datasets: ImageNet, HAM10000, Stanford Cars, and CelebA-HQ.
The paper proposes using progressive encoding of images and re-arrange of data blocks in images to improve reading speed and therefore training speed. To fully analyze the maximum possible speed of training, it would be great to the measure upper bound of images/sec, when avoiding reading from disk and just using images from memory. Decoding a typical progressive JPEG image usually takes about 2-3 times as much time as decoding a non-progressive JPEG, for full resolution, analyzing the time to read vs time to decode the images would be great. It is not clear how changing the number of total groups would affect the image size and the reading speed. Based on the current experiments it is not clear what is the impact of the batch size when creating PCRs and when reading the image blocks, or the impact of the batch size on the training speed. Figure 3 is really hard to read and compare times to convergence, authors should provide a table with times to X% accuracy. Although time to convergence is the key metric, it would be great to know the difference in images/sec of different settings. Using ImageNet 100 classes (not clear how the 100 classes were chosen) instead of the usual 1000 classes, can distort the results, since it is not clear if higher resolution would be needed to distinguish more classes or not. Have the authors considered other image compression formats like WebP? How tie is the proposed record encoding with the image compression?
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Progressive Compressed Records: Taking a Byte Out of Deep Learning Data
Deep learning training accesses vast amounts of data at high velocity, posing challenges for datasets retrieved over commodity networks and storage devices. We introduce a way to dynamically reduce the overhead of fetching and transporting training data with a method we term Progressive Compressed Records (PCRs). PCRs deviate from previous formats by leveraging progressive compression to split each training example into multiple examples of increasingly higher fidelity, without adding to the total data size. Training examples of similar fidelity are grouped together, which reduces both the system overhead and data bandwidth needed to train a model. We show that models can be trained on aggressively compressed representations of the training data and still retain high accuracy, and that PCRs can enable a 2x speedup on average over baseline formats using JPEG compression. Our results hold across deep learning architectures for a wide range of datasets: ImageNet, HAM10000, Stanford Cars, and CelebA-HQ.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



The paper proposes using progressive encoding of images and re-arrange of data blocks in images to improve reading speed and therefore training speed. To fully analyze the maximum possible speed of training, it would be great to the measure upper bound of images/sec, when avoiding reading from disk and just using images from memory. Decoding a typical progressive JPEG image usually takes about 2-3 times as much time as decoding a non-progressive JPEG, for full resolution, analyzing the time to read vs time to decode the images would be great. It is not clear how changing the number of total groups would affect the image size and the reading speed. Based on the current experiments it is not clear what is the impact of the batch size when creating PCRs and when reading the image blocks, or the impact of the batch size on the training speed. Figure 3 is really hard to read and compare times to convergence, authors should provide a table with times to X% accuracy. Although time to convergence is the key metric, it would be great to know the difference in images/sec of different settings. Using ImageNet 100 classes (not clear how the 100 classes were chosen) instead of the usual 1000 classes, can distort the results, since it is not clear if higher resolution would be needed to distinguish more classes or not. Have the authors considered other image compression formats like WebP? How tie is the proposed record encoding with the image compression?
assistant
The paper proposes using progressive encoding of images and re-arrange of data blocks in images to improve reading speed and therefore training speed. To fully analyze the maximum possible speed of training, it would be great to the measure upper bound of images/sec, when avoiding reading from disk and just using images from memory. Decoding a typical progressive JPEG image usually takes about 2-3 times as much time as decoding a non-progressive JPEG, for full resolution, analyzing the time to read vs time to decode the images would be great. It is not clear how changing the number of total groups would affect the image size and the reading speed. Based on the current experiments it is not clear what is the impact of the batch size when creating PCRs and when reading the image blocks, or the impact of the batch size on the training speed. Figure 3 is really hard to read and compare times to convergence, authors should provide a table with times to X% accuracy. Although time to convergence is the key metric, it would be great to know the difference in images/sec of different settings. Using ImageNet 100 classes (not clear how the 100 classes were chosen) instead of the usual 1000 classes, can distort the results, since it is not clear if higher resolution would be needed to distinguish more classes or not. Have the authors considered other image compression formats like WebP? How tie is the proposed record encoding with the image compression?