I am currently using google colabs to implement CNN.
While I am training my model my RAM is insufficient and my session gets crash.
I saw this video and tried but I am not getting options to increase RAM.
Can someone let me know how to increase it. Video from where I tried to increase RAM
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I am using colab pro. About 4 months ago, I experienced slow learning of the tensorflow model. The learning speed is so slow, and as a result of checking it myself today, I was able to confirm that the gpu was detected normally, but the GPU POWER was off. The volatile GPU Util is also allocated as 0 , but it looks like the GPU is not being utilized for training. When I looked for the cause, there was a saying that the data I/O bottleneck was, so I also modified the DATALOADER, and when I ran the same code and dataset in a different COLAB account, I was able to see that the GPU allocation worked well and the time was also shortened. If there is a problem with the os settings or if there is something I need to fix, please let me know. have a good day
I figured out that the problem was simply a path problem. As we've gotten feedback before, it seems like there's been a bottleneck in loading images through folders.
It was solved by specifying the path of the dataset as content/ .
one of the limitations is that we can get only 12 continuous hours per session. Is there any limitations for the usage for GPU and TPU?
Yes, you can only use 1 GPU with a limited memory of 12GB and TPU has 64 GB High Bandwidth Mmeory.You can read here in this article.
So, if you want to use large dataset then I would recommend you to use tf.data.Dataset for preparing it before training.
If you want to use GPUs you can use any TF version. But for TPU I would recommend using TF1.14.
From Colab's documentation,
In order to be able to offer computational resources for free, Colab needs to maintain the flexibility to adjust usage limits and hardware availability on the fly. Resources available in Colab vary over time to accommodate fluctuations in demand, as well as to accommodate overall growth and other factors.
In a nutshell, Colab has dynamic resource provisioning. So they can change the hardware, it it is being taxed too much automatically.
Google giveth and Google taketh away.
Link
Recently google colab consumes too much of internet data . Approx 4GB in 6 hours of training for single notebook . What can be the issue ?
Yes I have the same issue. It normally works fine but, there is sudden spike in the internet data. Check this. In the process it wasted 700 Mb in just 20 minutes, and I have mobile internet, so this creates a problem sometimes. Didn't find the answer but it seems like there is some kind of synchronization going on between the browser and the colab platform.
One thing you could do is to open the notebook in Playground mode as shown in this link How to remove the autosave option in Colab. This only happens because of the fact that Colab is saving everytime and there is a constant spike in the network. It becomes difficult when you use only mobile data. So, it is a safe option to open the notebook in Playground mode, so that the synchronization doesn't continue as usual.
I am working on APTOS Blindness detection challenge datasets from Kaggle. Post uploading the files; when I try to unzip the train images folder ; I get an error of file size limit saying the limited space available on RAM and Disk. Could any one please suggest an alternative to work with large size of image data.
If you get that error while unzipping the archive, it is a disk space problem. Colab gives you about 80 gb by default, try switching runtime to GPU acceleration, aside from better performance during certain tasks as using tensorflow, you will get about 350 gb of available space.
From Colab go to Runtime -> Change runtime type, and in the hardware acceleration menu select GPU.
If you need more disk space, Colab now offers a Pro version of the service with double disk space available in the free version.
I tried to do Action Recognition using this Kinetics labels in colab. I refer this link
When I gave the input video below 2 MB this model was working fine. But if I give the input video more than 2 MB I got ResourceExhausted Error after few mins I got GPU memory usage is close to the limit.
Even I terminate the notebook and start the new one I got the same error.
As the error says, the physical limitations of your hardware has been reached. It requires more GPU memory than there is available.
You could prevent this by reducing the models' batch size, or resize the resolution of your input video sequence.
Alternatively, you could try to use Google's cloud training to gain additional hardware resources, however it is not free.
https://cloud.google.com/tpu/