{
  "id": 569547,
  "title": "Why Does My Model Achieve Zero Loss but Take Too Long to Train? (TPU vs. CPU Performance Analysis)",
  "url": "/competitions/byu-locating-bacterial-flagellar-motors-2025/discussion/569547",
  "author_name": "",
  "post_date": "2025-03-22T14:21:32.566039700Z",
  "votes": 5,
  "comment_count": 1,
  "views": 0,
  "content": "<p>The issue of zero loss but long training time<br>\nThe comparison between TPU and CPU training<br>\nThe broader goal of optimizing training speed</p>\n<p>My model detects everything, and the loss is 0, but the training time is too long. I don’t know how it figured it out. Can anyone give me a suggestion?<img src=\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F8486392%2Fdb144815b67c11ef3c6855095dc5a539%2FScreenshot%202025-03-22%20194449.png?generation=1742652915034510&amp;alt=media\" alt=\"\"> .                                                                                                   I don't know how this model trains so fast. How is it possible? pls anyone explain this ??  <img src=\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F8486392%2F751ee235b3f967c96bffdccbe79027aa%2FScreenshot%202025-03-22%20195100.png?generation=1742653283685342&amp;alt=media\" alt=\"\"></p>",
  "messages": [
    {
      "id": "3156778",
      "postDate": "03/22/2025 14:21:32",
      "content": "<p>The issue of zero loss but long training time<br>\nThe comparison between TPU and CPU training<br>\nThe broader goal of optimizing training speed</p>\n<p>My model detects everything, and the loss is 0, but the training time is too long. I don’t know how it figured it out. Can anyone give me a suggestion?<img src=\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F8486392%2Fdb144815b67c11ef3c6855095dc5a539%2FScreenshot%202025-03-22%20194449.png?generation=1742652915034510&amp;alt=media\" alt=\"\"> .                                                                                                   I don't know how this model trains so fast. How is it possible? pls anyone explain this ??  <img src=\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F8486392%2F751ee235b3f967c96bffdccbe79027aa%2FScreenshot%202025-03-22%20195100.png?generation=1742653283685342&amp;alt=media\" alt=\"\"></p>",
      "rawMarkdown": "The issue of zero loss but long training time\nThe comparison between TPU and CPU training\nThe broader goal of optimizing training speed\n\nMy model detects everything, and the loss is 0, but the training time is too long. I don’t know how it figured it out. Can anyone give me a suggestion?![](https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F8486392%2Fdb144815b67c11ef3c6855095dc5a539%2FScreenshot%202025-03-22%20194449.png?generation=1742652915034510&alt=media) .                                                                                                   I don't know how this model trains so fast. How is it possible? pls anyone explain this ??  ![](https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F8486392%2F751ee235b3f967c96bffdccbe79027aa%2FScreenshot%202025-03-22%20195100.png?generation=1742653283685342&alt=media)",
      "votes": null
    },
    {
      "id": "3158773",
      "postDate": "03/24/2025 21:34:10",
      "content": "<p>It is hard to debug your situation without seeing the training code or model definition.  If you are using TPU instead of GPU then your device is going to go to CPU because cuda available will be false on TPU resulting in no speed up.  I recommend switching to a GPU if you can for an easier implementation or look at using TPU and pytorch together (torch_xla.core.xla_model for example).</p>\n<p>As for 0 loss it depends on your dataset definition and loss function but if I had to guess it is because the target is very sparse in this dataset so the model is predicting background for everything resulting in a near 0 loss but not actually correctly identifying the target.</p>",
      "rawMarkdown": "It is hard to debug your situation without seeing the training code or model definition.  If you are using TPU instead of GPU then your device is going to go to CPU because cuda available will be false on TPU resulting in no speed up.  I recommend switching to a GPU if you can for an easier implementation or look at using TPU and pytorch together (torch_xla.core.xla_model for example).\n\nAs for 0 loss it depends on your dataset definition and loss function but if I had to guess it is because the target is very sparse in this dataset so the model is predicting background for everything resulting in a near 0 loss but not actually correctly identifying the target.",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 3158773,
      "author_name": "connorjd",
      "author_url": "",
      "post_date": "03/24/2025 21:34:10",
      "content": "<p>It is hard to debug your situation without seeing the training code or model definition.  If you are using TPU instead of GPU then your device is going to go to CPU because cuda available will be false on TPU resulting in no speed up.  I recommend switching to a GPU if you can for an easier implementation or look at using TPU and pytorch together (torch_xla.core.xla_model for example).</p>\n<p>As for 0 loss it depends on your dataset definition and loss function but if I had to guess it is because the target is very sparse in this dataset so the model is predicting background for everything resulting in a near 0 loss but not actually correctly identifying the target.</p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "3156778": "The issue of zero loss but long training time\nThe comparison between TPU and CPU training\nThe broader goal of optimizing training speed\n\nMy model detects everything, and the loss is 0, but the training time is too long. I don’t know how it figured it out. Can anyone give me a suggestion?![](https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F8486392%2Fdb144815b67c11ef3c6855095dc5a539%2FScreenshot%202025-03-22%20194449.png?generation=1742652915034510&alt=media) .                                                                                                   I don't know how this model trains so fast. How is it possible? pls anyone explain this ??  ![](https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F8486392%2F751ee235b3f967c96bffdccbe79027aa%2FScreenshot%202025-03-22%20195100.png?generation=1742653283685342&alt=media)",
    "3158773": "It is hard to debug your situation without seeing the training code or model definition.  If you are using TPU instead of GPU then your device is going to go to CPU because cuda available will be false on TPU resulting in no speed up.  I recommend switching to a GPU if you can for an easier implementation or look at using TPU and pytorch together (torch_xla.core.xla_model for example).\n\nAs for 0 loss it depends on your dataset definition and loss function but if I had to guess it is because the target is very sparse in this dataset so the model is predicting background for everything resulting in a near 0 loss but not actually correctly identifying the target."
  },
  "source": "meta"
}