I often start a training run before going to bed and I'd like to release the runtime from within the last cell of my notebook. How can I do this?
My motivation is simply to save the extra 90 minutes of usage before the idle timeout kills the runtime anyway, so I accumulate fewer GPU hours and stay at a higher priority... (Maybe that's a pointless goal...)
kill won't delete the runtime. And you'll still be charged for it. To disconnect and delete use:
from google.colab import runtime
runtime.unassign()
Source (and also checked): https://www.reddit.com/r/GoogleColab/comments/xtj2be/dear_googlecolab_boss_i_want/
To post the answer mentioned in the comments above (taken from here):
Run !kill -9 -1 from within a cell.
Related
In the last week or two I have seen frequent disconnects while trying to run a lengthy training run. A month or two ago this seemed to be working pretty reliably. My code has definitely changed but those internal details seem unrelated to the operation of Colab.
(On the other hand, I did switch my local machine from an Intel MacBook Pro running Big Sur to an M1 (Apple Silicon) MacBook Pro running Monterey. I assume that does not matter to Colab running in the cloud, via a Chrome browser.)
I see two kinds of disconnects:
There are “faux disconnects” which seem like false positives from
the disconnect detector. These last less than a second, then the
computation continues apparently unscathed. A black notification
slides up from the lower left corner of then window, then slides
back. See a link to a video of this below.
Then there are “real disconnects.” I start a computation that I
expect to run for several hours. I see “faux disconnects” happen
frequently. But less than an hour into the computation, I find
the Colab window idle, no status information, and a Reconnect button
in the upper right corner.
Link to video. I started this session around 1:03 pm. This video was recorded at 1:35 pm. Normally the training session should have run for several hours. Instead it died at 1:52 pm (~50 minutes into the run). See some additional comments in an issue at GitHub.
Can anyone help me understand how to get past this? I am currently unable to make progress in my work because I cannot complete a training run before my Colab runtime decides to disconnect.
Edit:
FYI: since once a “real disconnect” happens it is too late to look at the (no longer connected) runtime's log, and since this seems to run for about an hour before disconnecting, I saved a log file when a run was about 10 minutes in.
Edit on August 1, 2022:
My real problem is the “real disconnect” on my real Colab notebook. But my notebook is overly complicated, so not a good test case. I tried to make a small test case, see Colab notebook: DisconnectTest.ipynb. It contains a generic NIST-based Keras/TensorFlow benchmark from the innertubes. I made a screen grab video of the first 2.5 minutes of a run. While this run completes OK — that is, there are no “real disconnects” — it had several “faux disconnects.” The first one is at 1:36. These seem fairly benign, but they do disrupt the Resources panel on the right. This makes it hard to know if the source of the “real disconnect” has anything to do with exhausting resources.
As I described in a parallel post on Colab's Issue #2965 on Github, this appears to be “some interaction between Colab and Chrome (and perhaps macOS Monterey (Version 12.5.1), and perhaps M1 Apple Silicon). Yet Colab seems to work fine on M1/Monterey/Safari.”
As described there, a trivial Colab example fails on Chrome browser but works fine on Safari.
I am running a deep learning training program on my colab notebook which will cost about 10hours. If i close my browser, will it be shutdown by google before it ends as expected? Or will the last output be saved coorectly in my Drive?
I suggest you to look here and here. Basically, the code should keep running, but after some time (around 90 minutes) of idle activity, the notebook should be cut off, so I assume that what you suggest is not viable. Maybe you could try to launch the script in the morning and interact with it every 20-30 minutes to prevent it going to idle. Also, consider using Google Colab pro (faster GPUs and longer runtimes, but never longer that 24 hours)
The simple answer to that question is a solid no. Your session will go ahead and continue executing or will stay idle, as stated in the #SilentCloud 's Answer above it will go for about
90 Minutes [With CPU]
30 Minutes [With GPU]
The reason I say 30 Minutes with GPU is that I have personally tested that and it appears to be this number, as do use on a rather regular basis.
You can make a simple bot on Your Machine using pyautogui in order to go ahead and do some random stuff if for some reason it makes more economical sense, or you are not interested in Google Colab Pro Subscription.
Run with Browser Closed
If you want a seamless experience with the browser window effectively closed and having access to GPU's that are much more better and faster, I would recommend the Colab Pro + Subscription.
But the Scripting Idea is there, and your mileage may vary.
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.
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 have only one project (an ordinary SpringFramework project) opened. And the IDE is crazy using CPU:
JVisualVM CPU sample:
Note this happened just recently
Any idea?
The correct answer was posted by #matt-helliwell if you're coming from a version older than 2016.2.
File -> Invalidate Caches and Restart
If the above doesn't fix your problem, track this issue:
https://youtrack.jetbrains.com/issue/IDEA-157837
I invalidated the caches and it solved the problem for some time. But after a couple days Idea (my version is 2017.1.3) started to work slow with some freezing delays again. Finally I increased maximum available memory to 2 Gb (parameter -Xmx in idea.exe.vmoptions/idea64.exe.vmoptions file) and now it works perfect
I solved the problem by running idea64 bits :
JetBrains\IntelliJ IDEA 2016.2.4\bin\idea64.exe
Another possible solution, my IDEA was very slow because of a huge sql file open which was consuming all my CPU.
It took me a long time to notice this was happening only when opening a specific utility class with more than 1000 lines of code.
This class had maybe 50 public static methods (the reason why it is a utility class...), all pure.
At first, I thought it was stuck on a loop of "Performing code analysis" because that was the thing running heavily on the background as shown when hovering the mouse above a green check on top of the window of the offending class:
, but in reality, it was slowly scanning each instance in which the code was being executed in the entire source code.
It took like 45 minutes to scan the entirety of the class, the entire time the CPU usage at max (100%).
Once the class is closed the usage stops.
The issue (at least with AS Dolphin 2022-23) is that the analysis is never memorized, so if the window is closed, and opened later, the analysis begins from 0. So, it never gets cached...