Row 56268
Content Data
This page contains data entry 56268 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.
I am training a model and I heard gradient checkpointing is good because it saves GPU memory which is limited on Kaggle. Currently I have it set up that the checkpoints are saved to /kaggle/working/ but that is limited to 20gb which fills up fast even with <5 epochs on a small dataset. Is it okay to use /kaggle/temp/ instead? Or are there any workarounds to this?
EDIT: Looks like temp isn't unlimited after all!
https://preview.redd.it/0hgexxxdku2d1.png?width=565&format=png&auto=webp&s=d568c49b1e59aa4b3c97bf8983af9cfc66aed6a9
| Field | Value |
|---|---|
| text | I am training a model and I heard gradient checkpointing is good because it saves GPU memory which is limited on Kaggle. Currently I have it set up that the checkpoints are saved to /kaggle/working/ but that is limited to 20gb which fills up fast even with <5 epochs on a small dataset. Is it okay to use /kaggle/temp/ instead? Or are there any workarounds to this? EDIT: Looks like temp isn't unlimited after all! https://preview.redd.it/0hgexxxdku2d1.png?width=565&format=png&auto=webp&s=d568c49b… |
| label | r/deeplearning |
| dataType | post |
| communityName | r/deeplearning |
| datetime | 2024-05-23 |
| username_encoded | Z0FBQUFBQm5Lak1XUGJVZEd1WU8za2RzSktHMk5wVXVwR05VYW1vZE9pRGJKR1Fld3V3LVVRYUVMZ2k4QVdmc2V5R1JwZnlJZFZFc1d4VTB6TU13aGdnVkdBTDlGNjlSaHc9PQ== |
| url_encoded | Z0FBQUFBQm5Lak9teThHUFhJbzdpcV9MNGRDNjFvWjZEc2Q1bkF1WE1nYndpb3dkc0VfWVJJLTFNRWNUaFIzb0VpWll6NGwzUkc4YW8yU09iR3U0MHRUczBWVk9ZeWVDU0c2RzRBYlR1ZUNMczhacUxqS0Z1ZG82V183b2pFcy1nTk9SWGZ0Wl9zTmZGT0J0VzAxZkZKaEJXWmlRTTByRGdPb3hDWjdRTTRYSjA5YlVNblkwWERMdVlaYUVFb0lGeEw1QWF5SUQ4dVBRTER1OFNFNE9PNnR6Yk9uazdpZVkzdz09 |
Raw Record
{
"text": "I am training a model and I heard gradient checkpointing is good because it saves GPU memory which is limited on Kaggle. Currently I have it set up that the checkpoints are saved to /kaggle/working/ but that is limited to 20gb which fills up fast even with <5 epochs on a small dataset. Is it okay to use /kaggle/temp/ instead? Or are there any workarounds to this?\n\nEDIT: Looks like temp isn't unlimited after all!\n\nhttps://preview.redd.it/0hgexxxdku2d1.png?width=565&format=png&auto=webp&s=d568c49b1e59aa4b3c97bf8983af9cfc66aed6a9",
"label": "r/deeplearning",
"dataType": "post",
"communityName": "r/deeplearning",
"datetime": "2024-05-23",
"username_encoded": "Z0FBQUFBQm5Lak1XUGJVZEd1WU8za2RzSktHMk5wVXVwR05VYW1vZE9pRGJKR1Fld3V3LVVRYUVMZ2k4QVdmc2V5R1JwZnlJZFZFc1d4VTB6TU13aGdnVkdBTDlGNjlSaHc9PQ==",
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}
Entry Information
- Entry ID: 56268
- Repository: Axioma AXP
- Dataset: arrmlet/reddit_dataset_36
- Total Entries: 100,000