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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/ .
Related
I need GPU for my project. Till now I had limited use and used Colab free. Now I think I may need as much as 3 hours a day. Now it says GPU is not available because they are already taken. My question is, what effect does upgrading to Colab pro have on GPU availability? How many hours should I expect to have GPU and are these hours arbitrary chosen by me or not?
I referred Here and There but no good detail about GPU availability is given.
In Their website they tell that these limitations vary and depends on previous usage, and a precise answer might not be even available, so even an approximated answer is welcome.
Thanks.
Yeah.I had the same experience that GPU is not available in colab.
Why not try gpushare.com to run 3090 or 2080ti with free credit.
The platform supports the most popular machine learning frameworks,like TensorFlow and PyTorch,users can be fast to instantiate a VM image.
I think it's appropriate to accelerate your model training.
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)
Here it is described how to use gpu with google-colaboratory:
Simply select "GPU" in the Accelerator drop-down in Notebook Settings (either through the Edit menu or the command palette at cmd/ctrl-shift-P).
However, when I select gpu in Notebook Settings I get a popup saying:
Failed to assign a backend
No backend with GPU available. Would you like to use a runtime with no accelerator?
When I run:
import tensorflow as tf
device_name = tf.test.gpu_device_name()
if device_name != '/device:GPU:0':
raise SystemError('GPU device not found')
print('Found GPU at: {}'.format(device_name))
Of course, I get GPU device not found. It seems the description is incomplete. Any ideas what needs to be done?
You need to configure the Notebook with GPU device
Click Edit->notebook settings->hardware accelerator->GPU
You'll need to try again later when a GPU is available. The message indicates that all available GPUs are in use.
The FAQ provides additional info:
How may I use GPUs and why are they sometimes unavailable?
Colaboratory is intended for interactive use. Long-running background
computations, particularly on GPUs, may be stopped. Please do not use
Colaboratory for cryptocurrency mining. Doing so is unsupported and
may result in service unavailability. We encourage users who wish to
run continuous or long-running computations through Colaboratory’s UI
to use a local runtime.
There seems to be a cooldown on continuous training with GPUs. So, if you encounter the error dialog, try again later, and perhaps try to limit long-term training in subsequent sessions.
Add some pictures to make it clearer
My reputation is just slightly too low to comment, but here's a bit of additional info for #Bob Smith's answer re cooldown period.
There seems to be a cooldown on continuous training with GPUs. So, if you encounter the error dialog, try again later, and perhaps try to limit long-term training in subsequent sessions.
Based on my own recent experience, I believe Colab will allocate you at most 12 hours of GPU usage, after which there is roughly an 8 hour cool-down period before you can use compute resources again. In my case, I could not connect to an instance even without a GPU. I'm not entirely sure about this next bit but I think if you run say 3 instances at once, your 12 hours are depleted 3 times as fast. I don't know after what period of time the 12 hour limit resets, but I'd guess maybe a day.
Anyway, still missing a few details but the main takeaway is that if you exceed you'll limit, you'll be locked out from connecting to an instance for 8 hours (which is a great pain if you're actively working on something).
After Reset runtime didn't work, I did:
Runtime -> Reset all runtimes -> Yes
I then got a happy:
Found GPU at: /device:GPU:0
This is the precise answer to your question man.
According to a post from Colab :
overall usage limits, as well as idle timeout periods, maximum VM
lifetime, GPU types available, and other factors, vary over time.
GPUs and TPUs are sometimes prioritized for users who use Colab
interactively rather than for long-running computations, or for users
who have recently used less resources in Colab. As a result, users who
use Colab for long-running computations, or users who have recently
used more resources in Colab, are more likely to run into usage limits
and have their access to GPUs and TPUs temporarily restricted. Users
with high computational needs may be interested in using Colab’s UI
with a local runtime running on their own hardware.
Google Colab has by default tensorflow 2.0, Change it to tensorflow 1. Add the code,
%tensorflow_version 1.x
Use it before any keras or tensorflow code.
I am running TensorFlow for Windows with a Titan X GPU (12 GB memory). When I try to train a network for images of 256X256X1 with mini-batches larger than 50 images, my computer just crashes and restarts automatically. With smaller mini-batches it runs just fine.
Any clues on what might be causing this?
I've seen similar problems being discussed in some gaming forums, where the PC would just shut down when the GPU was under heavy load. The reason was usually that the GPU was drawing more power than the power supply unit could handle. Check e.g. here or here. So may be it's worth investigating whether your PSU is the culprit.
Edit: May be the program SpeedFan can help you debugging this - it is able to show both voltages and readings of temperature sensors, which would also tell you if your PC is overheating (I've never used the tool myself, and I'm not affiliated with it either, just found it online).