In Q1 2019, I ran some experiments and I noticed that Colab notebooks with the same Runtime type (None/GPU/TPU) would always share the same Runtime (i.e., the same VM). For example, I could write a file to disk in one Colab notebook and read it in another Colab notebook, as long as both notebooks had the same Runtime type.
However, I tried again today (October 2019) and it now seems that each Colab notebook gets its own dedicated Runtime.
My questions are:
When did this change happen? Was this change announced anywhere?
Is this always true now? Will Runtimes sometimes be shared and sometimes not?
What is the recommended way to communicate between two Colab notebooks? I'm guessing Google Drive?
Thanks
Distinct notebooks are indeed isolated from one another. Isolation isn't configurable.
For file sharing, I think you're right that Drive is the best bet as described in the docs:
https://colab.research.google.com/notebooks/io.ipynb#scrollTo=u22w3BFiOveA
I have found no easy way of running multiple notebooks within the same runtime. That being said, I have no idea how this effects the quota. On my real computer, I'd limit GPU memory per script and run multiple python threads. They don't let you do this, and I think if you do not use the whole amount of RAM, they should not treat that the same as if you had used all of that GPU for 12 or 24 hrs. They can pool your tasks with other users.
Related
I have a neural net that takes about 7-15 days to train on several GPU's.
Google colab disconnects after running the script for a few hours. There are a couple "hacks" that you can do to keep the session alive but this is obviously not the official standard.
Once I have written my script in google colab, how should I go about running the script for a long period of time? I'm connected and using google's deep learning VM's.
There is currently no way of running scripts for such long times (i.e. days) in the free version of Colab; in fact, it is clear from the Resource Limits section of the official FAQ that the maximum running time is 12 hours (emphasis added):
How long can notebooks run in Colab?
Notebooks run by connecting to virtual machines that have maximum
lifetimes that can be as much as 12 hours. Notebooks will also
disconnect from VMs when left idle for too long. Maximum VM lifetime
and idle timeout behavior may vary over time, or based on your usage.
This is necessary for Colab to be able to offer computational
resources for free. Users interested in longer VM lifetimes and more
lenient idle timeout behaviors that don’t vary as much over time may
be interested in Colab Pro.
So, if you really need running times in the order of days, you should consider Colab Pro.
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 was getting started with TalkingData AdTracking, my first entry to Kaggle competitions. The first line was pd.read_csv() and I got this error
Your notebook tried to allocate more memory than is available. It has restarted
I thought my code was run in the cloud and that I don't have to worry about the memory requirements. Can someone help me with that?
Yes, kaggle has a memory limit: Its 8GB per kernel, or 20 Min running. It takes a lot of server juice to host such a thing.
There are various solutions for this problem, notably loading and processing the dataset in chunks.
There is this as well. But I have no experience with it.
And of course you can use another cloud platform such as AWS, GCP, Oracle, etc..
I used files.upload() on three hdf5 data files on Google Colab, in order to train some TensorFlow models, and it took a few minutes to complete.
Everything ran smooth with a few minor modifications from the local Jupyter notebook. However, when I proceed to change the runtime from "None" to "GPU", no files previously uploaded are present in the home folder. I just had to re-upload them. Going back to the "None" runtime showed that the files were still there.
Is there a convenient way to copy+paste the data from one runtime to another?
Thanks a lot.
I don't think there is anyway to directly copy data from a CPU instance to a GPU one.
So, you probably need to copy it to Google Drive (or mount it with ocamlfuse).
Another way is to use git to add, and push from one. Then clone from the other.
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.