I am trying to set up a deep learning VM on Google Cloud but I keep running into the same issue over and over again.
I will follow all the steps, set up a N1-highmem-8 (8 vCPU, 52gb Memory) instance, add a single T4 GPU and select the Deep Learning Image: TensorFlow 2.4 m69 CUDA 110 image. That's it.
After that, I will ssh into the vm, run the script that installs all the NVIDIA drivers and... when I begin using it, by simply running
from tensorflow.keras.layers import Input, Dense
i = Input((100,))
x = Dense(500)(i)
I keep getting failed call to cuInit: CUDA_ERROR_UNKNOWN: unknown error. By that point I haven't installed anything and haven't done anything custom, just the vanilla image from GCP.
What is more concerning is that, even if I delete the vm and then create a new one with the same config, some times the error won't happen immediately and sometimes it's present off the bat.
Has anyone encountered this? I've googled around to see if anyone has faced this issue and while I came across suggestions, all of them are old and have not worked for me. More over, suggestions on NVIDIA support forums tell me to re-install everything and the whole point of me using a pre-built GCP image specifically for deep learning is so that I don't have to enter the hell of installing and resolving issues with NVIDIA drivers.
The issue is fixed with the M74 image, but you are using M69. So follow one of the two fixes provided in the Google Cloud public forum.
we can mitigate the issue by:
Fix #1: Use the latest DLVM image (M74 or later) in a new VM instance: They have released a fix for the newest DLVM image in M74 so you will no longer be affected by this issue.
Fix #2: Patch your existing instance running images older than M74.
Run the following via an SSH session on the affected instance:
gsutil cp gs://dl-platform-public-nvidia/b191551132/restart_patch.sh /tmp/restart_patch.sh
chmod +x /tmp/restart_patch.sh
sudo /tmp/restart_patch.sh
sudo service jupyter restart
This only needs to be done once, and does not need to be rerun each time the instance is rebooted.
Related
I wish someone could help me to connect local runtime on this specific notebook on Google Colab at this link:
https://colab.research.google.com/github/ANonEntity/WhisperWithVAD/blob/main/WhisperWithVAD.ipynb
Basically it is a modified version of OpenAI's Whisper for Speech-to-Text for my videos to learn languages, with this model it take use of GPU accellation acceleration
I have been using the free version but it has been restricted due to the fact that it is not unlimited to use this model through Google's host. Since I am not really a dev, I have difficulties with the setup. It would be also if you can give me a clear instruction.
Corresponding with error in Colab: Unable to connect to runtime
You can follow these instructions to install Jupyter on your local machine and then connect the WhisperWithVAD notebook to your local runtime: https://research.google.com/colaboratory/local-runtimes.html
I was able to get this up running, but had to comment out some code in the Run Whisper block where the notebook was getting hung up:
#from google.colab import files (near the top of the block)
and
#files.download(out_path) (at the bottom of the block)
I am using the pre-built deep learning VM instances offered by google cloud, with an Nvidia tesla K80 GPU attached. I choose to have Tensorflow 2.5 and CUDA 11.0 automatically installed. When I start the instance, everything works great - I can run:
Import tensorflow as tf
tf.config.list_physical_devices()
And my function returns the CPU, accelerated CPU, and the GPU. Similarly, if I run tf.test.is_gpu_available(), the function returns True.
However, if I log out, stop the instance, and then restart the instance, running the same exact code only sees the CPU and tf.test.is_gpu_available() results in False. I get an error that looks like the driver initialization is failing:
E tensorflow/stream_executor/cuda/cuda_driver.cc:355] failed call to cuInit: CUDA_ERROR_UNKNOWN: unknown error
Running nvidia-smi shows that the computer still sees the GPU, but my tensorflow can’t see it.
Does anyone know what could be causing this? I don’t want to have to reinstall everything when I’m restarting the instance.
Some people (sadly not me) are able to resolve this by setting the following at the beginning of their script/main:
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
I had to reinstall CUDA drivers and from then on it worked even after restarting the instance. You can configure your system settings on NVIDIAs website and it will provide you the commands you need to follow to install cuda. It also asks you if you want to uninstall the previous cuda version (yes!).This is luckily also very fast.
I fixed the same issue with the commands below, taken from https://issuetracker.google.com/issues/191612865?pli=1
gsutil cp gs://dl-platform-public-nvidia/b191551132/restart_patch.sh /tmp/restart_patch.sh
chmod +x /tmp/restart_patch.sh
sudo /tmp/restart_patch.sh
sudo service jupyter restart
Option-1:
Upgrade a Notebooks instance's environment. Refer the link to upgrade.
Notebooks instances that can be upgraded are dual-disk, with one boot disk and one data disk. The upgrade process upgrades the boot disk to a new image while preserving your data on the data disk.
Option-2:
Connect to the notebook VM via SSH and run the commands link.
After execution of the commands, the cuda version will update to 11.3 and the nvidia driver version to 465.19.01.
Restart the notebook VM.
Note: Issue has been solved in gpu images. New notebooks will be created with image version M74. About new image version is not yet updated in google-public-issue-tracker but you can find the new image version M74 in console.
Doing a course in Machine Learning and can't get Tensorboard to work. I have saved runs from running a DQN and I write:
tensorboard -logdir runs
With the folliwng result:
2019-12-28 18:32:04.265065: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
TensorBoard 1.7.0 at http://david-linux:6006 (Press CTRL+C to quit)
So I click the link and get:
No dashboards are active for the current data set.
Probable causes:
You haven’t written any data to your event files
TensorBoard can’t find your event files.
I also get this result after having the code running for a while:
"W1228 18:34:34.186506 Thread-2 application.py:272] path /[[_dataImageSrc]] not found, sending 404
W1228 18:34:34.205581 Thread-2 application.py:272] path /[[_imageURL]] not found, sending 404"
Running this on Linux using Anaconda Python version 3.6 because that is what the course book uses. Have no idea what the above errors means, quite new to coding in general and reinforment learning in particular.
It could be caused if the browser isn't updated. You could also try installing the latest version of Tensorboard:
pip uninstall tensorflow-tensorboard
pip install tensorboard
Also try using different browsers.
Can you just try going to http://localhost:6006 instead? It looks like your hostname is not one that actually resolves in DNS.
I am trying to run tensorflow on a remote machine's GPU through Jupyter notebook. However, if I print the available devices using tf, I only get CPUs. I have never used a GPU before and am relatively new at using conda / jupyter notebook remotely as well, so I am not sure how to set up using the GPU in jupyter notebook.
I am using an environment set up by someone else who already executed the same code on the same GPU, but they did it via python script, not in a jupyter notebook.
this is the only code in the other person's file that had to do with the GPU
config = tf.ConfigProto()
config.gpu_options.allow_growth=True
set_session(tf.Session(config=config))
I think the problem was that I had tensorflow in my environment instead of tensorflow-gpu. But now I get this message "cudaGetDevice() failed. Status: CUDA driver version is insufficient for CUDA runtime version" and I don't know how to update the driver through terminal
How is your environment set up? Specifically, what is your remote environment, and what is your local environment? Sounds like your CUDA drivers are out of date, but it could be more than just that. If you are just getting started, I would recommend finding an environment that requires little to no configuration work on your part, so you can get started more easily/quickly.
For example, you can run GPUs on the cloud, and connect to them via local terminal. You also have your "local" frontend be Colab by connecting it to a local runtime. (This video explains that particular setup, but there's lots of other options)
You may also want to try running nvidia-smi on the remote machine to see if the GPUs are visible.
Here is another solution, that describes how to set up a GPU-Jupyterlab instance with Docker.
To update your drivers via terminal, run:
ubuntu-drivers devices
sudo ubuntu-drivers autoinstall
sudo reboot
Are your CUDA paths set appropriately? Like that?
export PATH=/usr/local/cuda/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
I can't be able to run the tensorflow code with GPU when I ran it from a jupyter notebook. Same code runs no problem, if I ran in a python script.
I followed the main installation link:
https://www.tensorflow.org/install/install_windows
Also tried:
http://bailiwick.io/2017/11/05/tensorflow-gpu-windows-and-jupyter/
No problems outside notebook when I ran in a python script file.
Most likely the problem is similar to this:
Tensorflow not running on GPU in jupyter notebook
More specifically my test:
I can see both devices CPU and GPU via python a script
I can see only CPU via notebook
Thanks a lot for any help in advance!
Very late, but short answer:
Here is a Tutorial on how to set up a GPU-based Jupyterlab instance with Docker (which makes the installation faster).
I hope this helps!
I removed all existing environments and created a new one, which resolved the issue.
(Also, I had to apply the following to get around an issue caused by removed environments:
https://github.com/jupyter/notebook/issues/2301
)