How do I use TensorFlow GPU?

Thank you for visiting the Q&A section on Magenaut. Please note that all the answers may not help you solve the issue immediately. So please treat them as advisements. If you found the post helpful (or not), leave a comment & I’ll get back to you as soon as possible.

Method 1

Follow this tutorial Tensorflow GPU I did it and it works perfect.

Attention! – install version 9.0! newer version is not supported by Tensorflow-gpu

Steps:

Uninstall your old tensorflow
Install tensorflow-gpu pip install tensorflow-gpu
Install Nvidia Graphics Card & Drivers (you probably already have)
Download & Install CUDA
Download & Install cuDNN
Verify by simple program

from tensorflow.python.client import device_lib 
print(device_lib.list_local_devices())

Method 2

The ‘new’ way to install tensorflow GPU if you have Nvidia, is with Anaconda. Works on Windows too. With 1 line.

conda create --name tf_gpu tensorflow-gpu

This is a shortcut for 3 commands, which you can execute separately if you want or if you already have a conda environment and do not need to create one.

Create an anaconda environment conda create --name tf_gpu
Activate the environment conda activate tf_gpu
Install tensorflow-GPU conda install tensorflow-gpu

You can use the conda environment.

Method 3

Follow the steps in the latest version of the documentation. Note: GPU and CPU functionality is now combined in a single tensorflow package

pip install tensorflow

# OLDER VERSIONS pip install tensorflow-gpu

https://www.tensorflow.org/install/gpu

This is a great guide for installing drivers and CUDA if needed:
https://www.quantstart.com/articles/installing-tensorflow-22-on-ubuntu-1804-with-an-nvidia-gpu/

Method 4

First you need to install tensorflow-gpu, because this package is responsible for gpu computations. Also remember to run your code with environment variable CUDA_VISIBLE_DEVICES = 0 (or if you have multiple gpus, put their indices with comma). There might be some issues related to using gpu. if your tensorflow does not use gpu anyway, try this

Method 5

I tried following the above tutorial. Thing is tensorflow changes a lot and so do the NVIDIA versions needed for running on a GPU. The next issue is that your driver version determines your toolkit version etc. As of today this information about the software requirements should shed some light on how they interplay:

NVIDIA® GPU drivers —CUDA 9.0 requires 384.x or higher.
CUDA® Toolkit —TensorFlow supports CUDA 9.0.
CUPTI ships with the CUDA Toolkit.
cuDNN SDK (>= 7.2) Note: Make sure your GPU has compute compatibility >3.0
(Optional) NCCL 2.2 for multiple GPU support.
(Optional) TensorRT 4.0 to improve latency and throughput for inference on some models.

And here you’ll find the up-to-date requirements stated by tensorflow (which will hopefully be updated by them on a regular basis).

Method 6

For conda environment.

conda search tensorflow to search the available versions of tensorflow. The ones that have mkl are optimized for CPU. You can choose the ones with gpu.
Then check the version of your cuda using nvcc --version and find the proper version of tensorflow in this page, according to your version of cuda.
For example, for cuda/10.1,and python3.8, you can use
- conda install tensorflow=2.2.0=gpu_py38hb782248_0

Method 7

Strangely, even though the tensorflow website 1 mentions that CUDA 10.1 is compatible with tensorflow-gpu-1.13.1, it doesn’t work so far. tensorflow-gpu gets installed properly though but it throws out weird errors when running.

So far, the best configuration to run tensorflow with GPU is CUDA 9.0 with tensorflow_gpu-1.12.0 under python3.6.

Following this configuration with the steps mentioned in https://stackoverflow.com/a/51307381/2562870 (the answer above), worked for me 🙂

Method 8

Uninstall tensorflow and install only tensorflow-gpu; this should be sufficient. By default, this should run on the GPU and not the CPU. However, further you can do the following to specify which GPU you want it to run on.

If you have an nvidia GPU, find out your GPU id using the command nvidia-smi on the terminal. After that, add these lines in your script:

os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = #GPU_ID from earlier

config = tf.ConfigProto()
sess = tf.Session(config=config)

For the functions where you wish to use GPUs, write something like the following:

with tf.device(tf.DeviceSpec(device_type="GPU", device_index=gpu_id)):

Method 9

There are 2 steps:

Update your graphics driver to latest and proprietary.(not open source)
Run the simple script I have created to Create conda virtual environment and Download all necessary requirements Script here

    echo 'Name of the TENSORFLOW ENVIRONMENT:'
    read ENVNAME

    #CREATING THE ENV
    conda create --name $ENVNAME -y

    #ACTIVATE THE eNV
    conda activate $ENVNAME6

    # INSTALLING CUDA DRIVERS
    conda install -c conda-forge cudatoolkit=11.2 cudnn=8.1.0 -y

    # INSTALLING TENSORFLOW
    conda install tensorflow-gpu -y
    conda install -c anaconda ipykernel -y
    conda install ipykernel -y

    # ADDING ENV TO JUPYTER LIST
    python3 -m ipykernel install --user --name=$ENVNAME

    # 'VERIFY GPU SUPPORT'
    python3 -c "import tensorflow as tf; 
    print(tf.config.list_physical_devices('GPU'))"

All methods was sourced from stackoverflow.com or stackexchange.com, is licensed under cc by-sa 2.5, cc by-sa 3.0 and cc by-sa 4.0

0 0 votes

Article Rating