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https://github.com/kohulan/tensorflow-2.0-installation-with-cuda-support
A detailed step by step guide to install Tensorflow-2.0-gpu with CUDA Drivers on Ubuntu Server/ Desktop LTS
https://github.com/kohulan/tensorflow-2.0-installation-with-cuda-support
cuda gpu nvidia ubuntu
Last synced: 2 months ago
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A detailed step by step guide to install Tensorflow-2.0-gpu with CUDA Drivers on Ubuntu Server/ Desktop LTS
- Host: GitHub
- URL: https://github.com/kohulan/tensorflow-2.0-installation-with-cuda-support
- Owner: Kohulan
- License: mit
- Created: 2019-07-23T12:56:41.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2022-05-24T08:14:42.000Z (over 2 years ago)
- Last Synced: 2023-03-06T23:39:39.063Z (almost 2 years ago)
- Topics: cuda, gpu, nvidia, ubuntu
- Homepage:
- Size: 12.7 KB
- Stars: 2
- Watchers: 0
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# CUDA-11-with-Tensoflow2.0-Installation-Guide
Installing Nvidia Drivers, Installing CUDA drivers with cuDNN on a Ubuntu machine is not straightforward. Where many tutorials give a detail step-by-step guide to install Tensorflow-1.0, There is no proper tutorial which explains the steps a beginner should take when installing Tensorflow-2.0. This is a more elaborative guide on installing All the necessary drivers and kick off your first machine learning algorithm.
## First, remove all previous CUDA and NVIDIA installation.
```shell
sudo apt-get --purge remove "*cublas*" "cuda*" "nsight*" "*nvidia*"
sudo nano /etc/apt/sources.list #comment nvidia dev
sudo apt --fix-broken install
```
## Second, add NVIDIA package repositories:
```shell
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-ubuntu1804.pin
sudo mv cuda-ubuntu1804.pin /etc/apt/preferences.d/cuda-repository-pin-600
sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub
```
You might need to check the https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/ link to check for the latest keys pub file and modify the previous line.
```shell
sudo add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/ /"
sudo apt-get update
wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb
sudo apt install ./nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb
sudo apt-get update
wget --no-check-certificate https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/libnvinfer7_7.1.3-1+cuda11.0_amd64.deb
sudo apt install ./libnvinfer7_7.1.3-1+cuda11.0_amd64.deb
sudo apt-get update
```
## Third, install development and runtime libraries (~4GB)
```shell
sudo apt-get install --no-install-recommends cuda-11-3 libcudnn8=8.2.1.32-1+cuda11.3 libcudnn8-dev=8.2.1.32-1+cuda11.3 #cuda-runtime-11-3 cuda-demo-suite-11-3 cuda-drivers-510 nvidia-driver-510 libnvidia-extra-510
sudo apt-get update
```
## Finally, reboot the PC and check the installation
```shell
sudo reboot
nvidia-smi
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 515.43.04 Driver Version: 515.43.04 CUDA Version: 11.7 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 NVIDIA GeForce ... On | 00000000:3B:00.0 Off | N/A |
| 23% 27C P8 16W / 250W | 1MiB / 11264MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
```
## Installing pip3 and Tensorflow-2.x-GPU
- The support for python v2.7 ended officially in 2020, So it's better if we can stick with python version 3.6
### Step 1 (Installing pip3):
- Use the following commend to install pip3 in your PC,
```shell
$ sudo apt-get install python3-pip
$ sudo pip3 --upgrade pip
```
### Step 2 (Installing Tensorflow):
- Now let's install Tensorflow 2.x
```shell
$ pip3 install tensorflow-gpu==2.x.0
```
### Step 3 (Verifying the installation):
- Run the following inside python3 terminal to verify the installation
```shell
$ python3
```
```python
>>> import tensorflow as tf
>>> hello = tf.constant('hello tensorflow')
>>> x = [[2.]]
>>> print('hello, {}'.format(tf.matmul(x, x)))
2019-00-00 16:04:38.589080: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0
hello, [[4.]]
>>> exit()
```
### That's it you have successfully Tensorflow-2.x-GPU with CUDA 11.0.