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I used a variety of data enhancement methods to pre-train 5 epochs on 20~22 data, and then fine-tuned on the 23-year data set. The final submission score was only 0.73. Why? Is it because there are too few epochs for pre-training? Because I submitted a Pretrain: 2 epoch, train: 30 epoch, the submission score is 0.72 and I found that the data and the model are on the GPU during training, but the GPU occupancy rate is always 0, I don’t know why it appears This kind of problem?
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I used a variety of data enhancement methods to pre-train 5 epochs on 20~22 data, and then fine-tuned on the 23-year data set. The final submission score was only 0.73. Why? Is it because there are too few epochs for pre-training? Because I submitted a Pretrain: 2 epoch, train: 30 epoch, the submission score is 0.72 and I found that the data and the model are on the GPU during training, but the GPU occupancy rate is always 0, I don’t know why it appears This kind of problem?
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How do you create your input data? If you're using mel-spectrograms generally the bottleneck comes from loading the data and doing the transformation on CPU. If this is the case (which was for me) you can pre-transform the dataset and save it to the disk once and use it later on. This way I reduced the training time to 20-30 mins for single fold from 6 hours 👀.
\nOr you can do the transformation on GPU. torchaudio library might be handy for you.
", "rawMarkdown": "How do you create your input data? If you're using mel-spectrograms generally the bottleneck comes from loading the data and doing the transformation on CPU. If this is the case (which was for me) you can pre-transform the dataset and save it to the disk once and use it later on. This way I reduced the training time to 20-30 mins for single fold from 6 hours 👀. \n\nOr you can do the transformation on GPU. [torchaudio](https://github.com/pytorch/audio) library might be handy for you.", "votes": null }, { "id": "2257252", "postDate": "05/13/2023 07:17:04", "content": "Thank you for your answer. I use torchaudio to load audio data in Dataset and convert it to mel-spectrograms. In the model, I perform data enhancement on pictures.
", "rawMarkdown": "Thank you for your answer. I use torchaudio to load audio data in Dataset and convert it to mel-spectrograms. In the model, I perform data enhancement on pictures.", "votes": null } ], "comments": [ { "id": 2254737, "author_name": "snnclsr", "author_url": "", "post_date": "05/11/2023 07:34:20", "content": "How do you create your input data? If you're using mel-spectrograms generally the bottleneck comes from loading the data and doing the transformation on CPU. If this is the case (which was for me) you can pre-transform the dataset and save it to the disk once and use it later on. This way I reduced the training time to 20-30 mins for single fold from 6 hours 👀.
\nOr you can do the transformation on GPU. torchaudio library might be handy for you.
", "votes": null, "replies": [ { "id": 2257252, "author_name": "gentlezdh", "author_url": "", "post_date": "05/13/2023 07:17:04", "content": "Thank you for your answer. I use torchaudio to load audio data in Dataset and convert it to mel-spectrograms. In the model, I perform data enhancement on pictures.
", "votes": null, "replies": [] } ] } ], "raw_markdown_by_id": { "2254024": "I used a variety of data enhancement methods to pre-train 5 epochs on 20~22 data, and then fine-tuned on the 23-year data set. The final submission score was only 0.73. Why? Is it because there are too few epochs for pre-training? Because I submitted a Pretrain: 2 epoch, train: 30 epoch, the submission score is 0.72 and I found that the data and the model are on the GPU during training, but the GPU occupancy rate is always 0, I don’t know why it appears This kind of problem?\n\n\n", "2254737": "How do you create your input data? If you're using mel-spectrograms generally the bottleneck comes from loading the data and doing the transformation on CPU. If this is the case (which was for me) you can pre-transform the dataset and save it to the disk once and use it later on. This way I reduced the training time to 20-30 mins for single fold from 6 hours 👀. \n\nOr you can do the transformation on GPU. [torchaudio](https://github.com/pytorch/audio) library might be handy for you.", "2257252": "Thank you for your answer. I use torchaudio to load audio data in Dataset and convert it to mel-spectrograms. In the model, I perform data enhancement on pictures." }, "source": "meta" }