Model training stops after validation after 4000 iterations
See original GitHub issueAfter training for 4000 iterations the validation happens and after that the training stops throwing the following error:
raise ChildFailedError(
torch.distributed.elastic.multiprocessing.errors.ChildFailedError:
***************************************
tools/train.py FAILED
=======================================
Root Cause:
[0]:
time: 2021-09-22_05:54:53
rank: 1 (local_rank: 1)
exitcode: 1 (pid: 2210236)
error_file: <N/A>
msg: "Process failed with exitcode 1"
=======================================
Other Failures:
<NO_OTHER_FAILURES>
***************************************
I am training with 2 gpus. Do you have any insight why this error is being thrown?
Issue Analytics
- State:
- Created 2 years ago
- Comments:19
Top Results From Across the Web
Use Early Stopping to Halt the Training of Neural Networks At ...
Early stopping requires that a validation dataset is evaluated during training. This can be achieved by specifying the validation dataset to the ...
Read more >Number of epochs to train on - fast.ai Course Forums
In Keras, it's a callback you can make when fitting a model to stop training once your validation accuracy flattens out, or starts...
Read more >Can one run validation after N training iterations instead of N ...
Currently, I am evaluating my model on a validation set every epoch but one epoch is very long to proceed. I was wondering...
Read more >Is there any way to stop training a model in Keras after a ...
I need to stop training after a certain validation accuracy, say 98%, has been reached. If the accuracy has not been achieved after...
Read more >How to determine the correct number of epoch during neural ...
You should look at the validation and training losses and track their values. if the validation loss going to increase that means overfitting....
Read more >Top Related Medium Post
No results found
Top Related StackOverflow Question
No results found
Troubleshoot Live Code
Lightrun enables developers to add logs, metrics and snapshots to live code - no restarts or redeploys required.
Start FreeTop Related Reddit Thread
No results found
Top Related Hackernoon Post
No results found
Top Related Tweet
No results found
Top Related Dev.to Post
No results found
Top Related Hashnode Post
No results found
Top GitHub Comments
What the script does is just to 1) prepare data split for partial setting on COCO 2) Convert
image_info_unlabeled2017.json
toinstances_unlabeled2017.json
. So it makes no sense to run it while adding any other dataset.Thank you for solving my question