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.
Related
I have been using Google colab for quite sometime. Recently I noticed that 43 GB of disk space is already occupied. Even if I change runtime to TPU 43 GB out of 107 GB remains occupied. I tried factory reset runtime but it doesn't work. Even if I use Google colab from another Google account still 43 GB of disk space is occupied. How to clear the disk space ?
There's some amount of space that's used by the base operating system and libraries. You won't be able to trim this value very much by deleting files since most will be required for normal operation.
If you need a larger disk, consider Colab Pro, which has 2x the disk space.
I recently upgraded to colab pro. I am trying to use GPU resources from colab pro to train my Mask RCNN model. I was allocated around 15 GB of memory when I tried to run the model right after I signed up for Pro. However, for some reason, I was allocated just 1 GB of memory from the next morning. Since then, I haven't been allocated more than 1 GB. I was wondering if I am missing something or I perturbed the VM inherent packages. I understand that the allocation varies from day to day, but it's been like this for almost 3 days now. Following attempts have already made to improve, but none seems to work.
I have made sure that GPU and "High-RAM" option is selected.
I have tried restarting runtimes several times
I have tried running other scripts (just to make sure that problem was not with mask rcnn script)
I would appreciate any suggestions on this issue.
GPU info
The high memory setting in the screen controls the system RAM rather than GPU memory.
The command !nvidia-smi will show GPU memory. For example:
The highlighted output shows the GPU memory utilization: 0 of 16 GB.
When I run this code in google colab
n = 100000000
i = []
while True:
i.append(n * 10**66)
it happens to me all the time. My data is huge. After hitting 12.72 GB RAM, but I don't immediately get to the crash prompt and the option to increase my RAM.
I have just this Your session crashed after using all available RAM. View runtime logs
What is the solution ? Is there another way ?
You either need to upgrade to Colab Pro or if your computer itself has more RAM than the VM for Colab, you can connect to your local runtime instead.
Colab Pro will give you about twice as much memory as you have now. If that’s enough, and you’re willing to pay $10 per month, that’s probably the easiest way.
If instead you want to use a local runtime, you can hit the down arrow next to “Connect” in the top right, and choose “Connect to local runtime
The policy was changed. However, currently, this workaround works for me:
Open and copy this notebook to your drive. Check if you already have 25gb RAM by hovering over the RAM indicator on the top right (this was the case for me). If not, follow the instructions in the colab notebook.
Source: Github
To double the RAM size of Google Colab use this Notebook, it gives a 25GB RAM! Note: set Runtime type to "None" to double RAM, then change it again to GPU or TPU.
https://colab.research.google.com/drive/155S_bb3viIoL0wAwkIyr1r8XQu4ARwA9?usp=sharing
as you said 12GB
this needs a large RAM,
if you need a small increase you can use colab pro
If you need a large increase and using a deep learning framework my advice you should use :
1- the university computer (ACADEMIC & RESEARCH COMPUTING)
2- using a platform like AWS, GCP, etc 3- you may use your very professional computer using GPU (I didn't recommend this)
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/
I have a tar.gz file that contains 77 gigabyte of data and i am trying to load it into my Google.colab. But i get
"Runtime died"
error and then it automaticly restarts. Please can anyone help?
Google colab is only for research purposes or for educational purposes and not for prolonged training. It has limitations most important being memory.
If you run :
!df
You will find that memory that runtime is allocated is about 45-50 GB (47GB, to be precise), you are trying to load 77 GB, don't you expect runtime to die?
If you want to use, try splitting your data into small parts and train on them, delete and reload from g-drive and repeat.
See this answer for more info on runtime hardware
What's the hardware spec for Google Colaboratory?