I have been setting up multiple Python machine learning environments based on NVIDIA GeForce GPUs. Starting with a fresh Ubuntu installation, you will need to go through the installation of multiple NVIDIA packages to get your machine ready to communicate with the GPU(s) properly. The steps here are an overview of the installation process.
Specifications
Here are my system specifications:
- Ubuntu 22.04 LTS
- NVIDIA GeForce RTX 3090 x2
Installation
Since drivers and toolkits follow different versioning schemes, I have added the latest versions as well just to make it easier to know what is what!
1. NVIDIA Drivers
It is recommended to install the drivers via the OS package manager. You can follow the steps here.
2. CUDA Toolkit
You can find the official CUDA Toolkit installation instructions on Linux here. One-time skimming through the entire documentation is recommended as there are a variety of dependencies and settings depending on your OS and the expected use case. In a high-level view, the process at this stage can be broken down into three main steps: 1. pre-installation actions, 2. main installation, and 3. post-installation actions
2.1 Pre-Installation Actions
As long as you are having a typical setup, there is no special step to take here.
2.2 Package Manager Installation
I prefer the Network Repo (over the Local Repo) installation as it gives you the ability to update through the APT package manager in the future. Follow the instructions for Ubuntu here. In summary, you need to install the cuda-keyring and then the CUDA SDK. Once you finish, you need to reboot your system.
2.3 Post-Installation Actions
Some finishing touches can be found here. Make sure you have updated the PATH variable. Adding the export shell command to your shell configuration (e.g. bashrc or zshrc) file is recommended to keep the PATH variable consistent throughout a shell session.
3. cuDNN
The steps here are similar to the ones from the previous step. Install the package according to the documentation here.
Finale
Once you have all the packages installed, you can verify your installation. In the case of machine learning you can follow the quick checks with the PyTorch and TensorFlow libraries from my other post.
I have been recently dealing with projects requiring both the popular automatic differentiation frameworks together.
Installing both TensorFlow and PyTorch in a single environment might be a challenge of its own kind.
I'm, here, writing down the recipe I figured out to get these two frameworks installed.
As we later show, both libraries can have access to GPU from a single Python interpreter instance.
Specifications
The OS and library versions are as follows:
* Ubuntu 20.04.3
* Python 3.8.11
* Tensorflow 2.4.1 (tensorflow-gpu)
* PyTorch 1.9.0
Installation
First, let's create a Conda environment.
I choose to install TensorFlow along with the creation of the environment.
Having a GPU installed on the machine, so I opt for the GPU-ready binaries tensorflow-gpu.
Conda makes your life much easier by handling the installation of the right binaries compatible with your CUDA device(s):
$ conda create --name tfpt8 python=3.8 tensorflow-gpu
Once the env is created, you need to activate it by:
Then we install PyTorch. PyPI can do the job with less clutter:
Sanity check
Now it's time to test the recipe!
Let's run an interpreter instance to see if everything is working properly:
Python 3.8.11 (default, Aug 3 2021, 15:09:35)
[GCC 7.5.0] :: Anaconda, Inc. on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
2021-08-30 00:17:34.532729: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.10.1
>>> import torch
>>>
Both libraries are imported correctly.
We want to find out more about GPU access:
>>> tf.config.list_physical_devices('GPU')
2021-08-30 00:25:16.374593: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-08-30 00:25:16.375472: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcuda.so.1
2021-08-30 00:25:16.401750: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce RTX 2060 SUPER computeCapability: 7.5
coreClock: 1.68GHz coreCount: 34 deviceMemorySize: 7.76GiB deviceMemoryBandwidth: 417.29GiB/s
2021-08-30 00:25:16.401779: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.10.1
2021-08-30 00:25:16.403444: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.10
2021-08-30 00:25:16.403481: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.10
2021-08-30 00:25:16.405139: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10
2021-08-30 00:25:16.405372: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10
2021-08-30 00:25:16.406924: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10
2021-08-30 00:25:16.407862: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.10
2021-08-30 00:25:16.411216: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.7
2021-08-30 00:25:16.411943: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
>>> torch.cuda.is_available()
True
>>> torch.cuda.get_device_name(0)
'GeForce RTX 2060 SUPER'
Voilà!! As you can see both tensorflow and torch have recognized the GPU on the machine.