I am a university professor trying to learn deep learning for a possible class in the future. I have been using google colab with GPU support for the past couple of months. Just recently, the GPU device is not found. But, I am doing everything that I have done in the past. I can't imagine that I have done anything wrong because I am just working through tutorials from books and the tensorflow 2.0 tutorials site.
tensorflow 2 on Colab GPU was broken recently due to an upgrade from CUDA 10.0 to CUDA 10.1. As of this afternoon, the issue should be resolved for the tensorflow builds bundled with Colab. That is, if you run the following magic command:
%tensorflow_version 2.x
then import tensorflow will import a working, GPU-compatible tensorflow 2.0 version.
Note, however, if you attempt to install a version of tensorflow using pip install tensorflow-gpu or similar, the result may not work in Colab due to system incompatibilities.
See https://colab.research.google.com/notebooks/tensorflow_version.ipynb for more information.
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I'm trying to run the following google colab:
https://colab.research.google.com/gist/zsyzzsoft/5fbb71b9bf9a3217576bebae5de46fc2/data-efficient-gans.ipynb?authuser=1#scrollTo=Re5R6VX8VNgo
colab no longer recognises gpu's with tensorflow 1.x. so is there any way to get this colab working again??
I have tried reinstalling to tensorflow 1.x and also upgrading the code to tensorflow 2 but nothing seems to work.
Google Colab removed support for Tensorflow 1, and it is not possible to use %tensorflow_version 1.x magic anymore. You have to install a specific version of tensorflow 1.x version using
pip install tensorflow==1.x
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
For more details please refer to this link. Thank You.
I bought a new Mac version 12.2 (OS Monterey) and installed Anaconda. Most of the Python packages can be installed correctly. However, Tensorflow (ver 2.8.0) and Keras (ver 2.8.0) have major issues. The Jupyter notebook kernel gets killed when tensorflow and/or keras get imported. I looked up various posts on Stackoverflow and Medium, however, nothing seems helpful. I even tried to convert the .ipynb to .py script, however, the same error occurs.
Is there anything that can be done to resolve this?
I started a new machine learning project.
In according to this document (https://www.tensorflow.org/tensorboard/tensorboard_profiling_keras)
TF with Tensorboard appears to support GPU profiling. So, i used the same code in my Jupyter Notebook for testing.
The sample code generates profiling resulting. However, there is no GPU tracing information in resulting file. (only CPU)
This is my main problem.
I am using two RTX 2080 TI graphic cards.
And also, they were working when running the code.
The sample code does not use MirroredStrategy. So, i could see the one of them was running.
At first, i thought Tensorboard was the problem. But,i realized soon that TF does not generate the GPU tracing information.
The image above is the resulting file (local.trace). There was no GPU data.
It is my system specification.
OS ubuntu 18.04
jupyter-client 5.3.4
jupyter-core 4.6.1
jupyter-tensorboard 0.1.10
tensorflow-gpu 2.0.0
tensorflow-estimator 2.0.1
tensorflow-metadata 0.15.1
tensorboard 2.0.2
nVidia 410.104
CUDA 10.0
anaconda 4.7.12 (with python 3.6)
It looks irrelevant, but there was a warning message like the image below.
I have tested this on other PC and got the same resulting. It could be the GPU profiling is only supporting on Google Colab. (I am still confusing) Recently, I have searched it on google to fix the problem. I could not get still the answer.
Is there someone who is using GPU profiling on your own System instead of Google Colab?
Please give me piece of advices.
I figured out what caused the problem.
It was related with CUPTI(CUDA Profiling Tools Interface)
In contrast to Jupyter Notebook, there was a warning message when the code is running on Ubunto shell.
CUPTI error: CUPTI could not be loaded or symbol could not be found.
TF could not find CUPTI libraries. This is the main reason of the problem.
After adding the path to LD_LABRARY_PATH as below link, the problem is fixed!
https://stackoverflow.com/a/58752904/5553618
I have a problem since few days.
I installed NVIDIA drivers and cuDNN using some step by steph tutoriaI from the Internet.
The installation succeded since tests on CUDA samples passed.
I then installed python 3.7, jupyter and Tensorflow-gpu.
However, Tensorflow don't see my 2 GPUs and see only XLA_GPUs.
I tried recommandations from other posts (such us uninstalling and installing tensorflow) but this did not solve my problem.
Anyone have an idea how to solve this problem ?
How can I report an issue on Tensorflow website? I am not talking about the API, but everything else, e.g. installation instructions and tutorials.
For instance, installation instructions indicate that Tensorflow for Python 3.5 and GPU requires CuDNN v5, but that is incorrect, as with CuDNN v5 it doesn't work, and it requires CuDNN v5.1 instead.
Thanks!
Please open an issue at the TensorFlow GitHub Issues page: https://github.com/tensorflow/tensorflow/issues