Dennis Núñez-Fernández

BSc in Electronic Engineering, Universidad Nacional de Ingeniería (UNI)


Set CUDA and cuDNN for Tensorflow and other AI frameworks


Notes:

- Recommended versions: tensorflow-gpu 1.10, CUDA 9, cuDNN 7.

- Review CUDA versions and Driver versions requirements:

- Review tensorflow versions and CUDA/cuDNN versions requirements:

- All versions of cuDNN only support GPUs with capability >= 3.0, therefore tensorflow-gpu only support GPUs with capability >= 3.0 . Check GPUs and capabilities: [https://developer.nvidia.com/cuda-gpus].


Steps:

0. Check and install the correct Driver.

1. Install CUDA: [https://developer.nvidia.com/cuda-toolkit-archive].

2. Install cuDNN: [https://developer.nvidia.com/rdp/cudnn-archive].

3. Install Ancaconda 3.

4. Create an environment called 'tf-gpu' which uses python 3.5.

5. Install Tensorflow with Conda: (env)$ conda install -c anaconda tensorflow-gpu=1.XX .

6. Verify if tensorflow-gpu works properly and using GPU.


Verify Driver, CUDA and cuDNN:

- Check cuda driver version: ~$ nvidia-smi or ~$ cat /proc/driver/nvidia/version .

- Check local cuda version: ~$ nvcc --version or ~$ cat /usr/local/cuda/version.txt .

- Check local cudnn version: ~$ cat /usr/local/cuda/include/cudnn.h | grep CUDNN_MAJOR -A 2 .


Verify Tensorflow:

- Acticate environment:

$ conda activate tf-gpu

- Open python and test Tensorflow:

(tf-gpu) $ python

>>> import tensorflow as tf >>> sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))

- Then you should obtain:

Device mapping: /job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: GeForce GTX 1050 Ti, pci bus id: 0000:01:00.0 2019-04-27 13:30:50.350374: I tensorflow/core/common_runtime/direct_session.cc:300] Device mapping: /job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: GeForce GTX 1050 Ti, pci bus id: 0000:01:00.0


References:

- Link 1.

- Link 2.

- Link 3.