I have been using Google Colab pro to train a yolov4 model. Previously, I was able to run training for more than 12-15 hours easily, but off late, the cell executing the training stops after one or two hours with a "^C" .
Any ideas how I can avoid this or why this might be happening?
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I took the colab pro plus offer and last night I wanted to train a model for 15 hours. But the notebook is disconnected after 13 hours. So I lost all my checkpoints and I have to start training again. I took the colab pro plus offer to avoid these inconveniences. How is it that this happened despite the colab pro plus offer? What can I do to prevent this from happening again?
I'm currently trying to train tiny yolo weights.
I've already trained normal yolov3 weights but I want to make a live detector on a raspberry pi so I need the tiny ones.
The training of the normal ones went great no hiccups whatsoever, but the tiny weights just won't work.
I've tried like 4 different tutorials but the outcome is the same everytime.
Google colab just stops.
I also tried to train the normals again to test but also there it immediately stops.
-clear 1 after the command doesn't work and I've tried to modify the cfg in different ways but nothing. I don't know what to do anymore. Does anyone have an idea or a tip. That would be great.
I am trying to train a model on around 4500 text sentences. The embedding is however heavy. The session crashes for number of training clauses > 350. It works fine and displays results till 350 sentences though.
Error message:
Your session crashed after using all available RAM.
Runtime type - GPU
I have hereby attached the screenshot of logs.
I am considering training model by batches, but I am a newbie and finding it difficult to have my way around it. Any help will be appreciated. session logs attached
This is basically because of out of memory on Google colab.
Google colab provides ~12GB Free RAM, to extend it 25 GB follow the instruction mentioned here.
I am training a TF ML job on cloud-ml, and it seems the job is stuck after a few iterations (900 iterations). Surprisingly, when I run the code locally it works fine, and also hyper tuning on GCP continues training but runs slower than my local laptop which has a 1060GTX GPU.
I am also using the runtime version 1.6.
I changed the scale-tier and it doesn't help. What can be the issue?
I am trying to train a model on google colaboratoy but the problem is that the GPU gets disconnected after 12 hours and hence I am not able to train my model beyond a certain point. Is there a way to keep GPU connected for longer times?