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/
Related
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 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
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.
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 following the Tensorflow Object Detection API tutorial to train a Faster R-CNN model on my own dataset on Google Cloud. But the following "ran out-of-memory" error kept happening.
The replica master 0 ran out-of-memory and exited with a non-zero status of 247.
And according to the logs, a non-zero exit status -9 was returned. As described in the official documentation, a code of -9 might means the training is using more memory than allocated.
However, the memory utilization is lower than 0.2. So why I am having the memory problem? If it helps, the memory utilization graph is here.
The memory utilization graph is an average across all workers. In the case of an out of memory error, it's also not guaranteed that the final data points are successfully exported (e.g., a huge sudden spike in memory). We are taking steps to make the memory utilization graphs more useful.
If you are using the Master to also do evaluation (as exemplified in most of the samples), then the Master uses ~2x the RAM relative to a normal worker. You might consider using the large_model machine type.
The running_pets tutorial uses the BASIC_GPU tier, so perhaps the GPU has ran out of memory.
The graphs on ML engine currently only show CPU memory utilization.
If this is the case, changing your tier to larger GPUs will solve the problem. Here is some information about the different tiers.
On the same page, you will find an example of yaml file on how to configure it.
Looking at your error, it seems that your ML code is consuming more memory that it is originally allocated.
Try with a machine type that allows you more memory such as "large_model" or "complex_model_l". Use a config.yaml to define it as follows:
trainingInput:
scaleTier: CUSTOM
# 'large_model' for bigger model with lots of data
masterType: large_model
runtimeVersion: "1.4"
There is a similar question Google Cloud machine learning out of memory. Please refer to that link for actual solution.