Virtual Environments with GPU support


Anaconda configuration for CPU and GPU

Deploying a Machine Learning and Deep Learning project is not always straight forward particularly when we need to run the model in the GPU if there is one in our machine. First of all, we need to have a GPU that supports the CUDA library. The list of the CUDA supported GPUs can be found here. The next step is to specify which framework we intend to use for our deployment, it will be TensorFlow, Pytorch, MXNET etc, for this example, we will use the TensorFlow.

Before installing anything it is the best practice to use virtual environments to install the specific versions that we want and keep the main system intact, otherwise by installing different versions for different projects we will face many difficulties with the conflicting libraries. Here we'll use Anaconda to set up our virtual environments.

Installing Anaconda

The cool thing about using Anaconda is that allows us to install the required from the GPU drivers and not bother to install the drivers locally to the machine, which could be rather a messy procedure. The other best thing when using Anaconda environments is that we can have different environments with different CUDA and TensorFlow versions. Also, when installing Anaconda Python is included.

I am using the Miniconda version which is much lighter than the full Anaconda version, and the Linux version can be downloaded fromhere. After the installation has finished we need to exit the terminal and open it again in order for the installation of Anaconda to take effect.

Before we setup the environment need to find the correct versions of CUDA, cuDNN, etc required by TensorFlow.

TensorFlow Requirements

To istall TensorFlow 2.4 and TensorFlow 2.4 GPU we need:

  1. Python 3.5 - 3.8
  2. For Linux, Ubuntu 16.04 or later (we can use other Ubuntu based distros as well)
  3. NVIDIA® GPU drivers —CUDA® 11.0 requires 450.x or higher.
  4. CUDA® Toolkit —TensorFlow supports CUDA® 11 (TensorFlow >= 2.4.0)
  5. CUPTI ships with the CUDA® Toolkit.
  6. cuDNN SDK 8.0.4 cuDNN versions.
  7. 7. (Optional) TensorRT 6.0 to improve latency and throughput for inference on some models.

Search Anaconda repos for the needed packages

Since we'll use conda environments to install all the necessary drivers, only we need to ensure that the machine has the correct Nvidia GPU drivers (450.x or higher). Then we need to make check the Anaconda repositories to find if the above drivers exist. Fortunately, we can make use of the conda-forge channel, as well as th Nvidia channel to install these drivers. We can do that as follows:

                conda search cuda
                Loading channels: done
                    No match found for: cuda. Search: *cuda*
                    # Name                       Version           Build  Channel             
                    cudatoolkit                      9.0      h13b8566_0  pkgs/main           
                    cudatoolkit                      9.2               0  pkgs/main           
                    cudatoolkit                 10.0.130               0  pkgs/main           
                    cudatoolkit                 10.1.168               0  pkgs/main           
                    cudatoolkit                 10.1.243      h6bb024c_0  pkgs/main           
                    cudatoolkit                  10.2.89      hfd86e86_0  pkgs/main           
                    cudatoolkit                  10.2.89      hfd86e86_1  pkgs/main           
                    cudatoolkit                 11.0.221      h6bb024c_0  pkgs/main

For cudnn will use the Nvidia channel:

                conda search -c nvidia cudnn
                Loading channels: done
                    # Name                       Version           Build  Channel                       
                    cudnn                          8.0.0      cuda10.2_0  nvidia              
                    cudnn                          8.0.0      cuda11.0_0  nvidia              
                    cudnn                          8.0.4      cuda10.1_0  nvidia              
                    cudnn                          8.0.4      cuda10.2_0  nvidia              
                    cudnn                          8.0.4      cuda11.0_0  nvidia              
                    cudnn                          8.0.4      cuda11.1_0  nvidia

So, for TF-2.4 we'll install:

  1. Python 3.8
  2. cudatoolkit 11.0
  3. cudnn 8.0.4
  4. tensorflow-gpu 2.4
Now that we have found the correct versions of the necessary libraries and drivers for the TF-2.4, the next step is to create the virtual environment, as the official Anaconda documentation describes, that will host the packages and libraries required by TensorFlow with the following command:

conda create -n tf24-cuda11 python=3.8

After creating the tf24-cuda11 environment, environment, we install the above packages plus the jupyter lab:

conda install cudatoolkit
conda install -c nvidia cudnn=8
pip install tensorflow-gpu

The pip install tensorflow-gpu command will install the Tensorflow GPU version, Tensorflow estimator, and Tensorflow base. We don't use conda to install TensorFlow-GPU because the latest TensorFlow version in the conda repo is 2.2 for Linux and 2.3 for Windows. If we do useconda install tnsorflow-gpu, it will install also CUDA 10.2 and cuDNN 7.

The Jupyter Lab can be installed from the conda-forge channel:

conda install-c conda-forge jupyterlab

In the new version of jupyter lab 3.x the tab completion does not work, and we need to downgrade the jedi library to 0.17.2. So, actually, we install:

                conda install -c conda-forge jupyterlab
                conda install -c conda-forge jedi=0.17.2

Additionally, I have created a requiremets.txt to install some extra frameworks and libraries such as:

  1. Matplotlib
  2. OpenCV
  3. Scikit-learn
  4. Pillow, etc
with the following command: pip install -r requiremets.txt

Register the environment

It is also a good practice to register the ipykernel for the given environment. After activating the conda environment, in this case, is conda activate tf24-cuda11 we use the following command to link the kernel with the environment using ipykernel install --user --name myenv --display-name "my_environment_name":

ipykernel install --user --name tf24-cuda11 --display-name "Python 3.8.5 (tf24-cuda11)"
Test the environment

Test the environment

After completing the above steps we can test the installation.

    import sys

    import tensorflow.keras
    import pandas as pd
    import sklearn as sk
    import tensorflow as tf
    print(f"Tensor Flow Version: {tf.__version__}")
    print(f"Keras Version: {tensorflow.keras.__version__}")
    print(f"Python {sys.version}")
    print(f"Pandas {pd.__version__}")
    print(f"Scikit-Learn {sk.__version__}")
    logical_gpus = tf.config.experimental.list_logical_devices('GPU')
    print(len(logical_gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
    print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))
    Tensor Flow Version: 2.4.1
    Keras Version: 2.4.0
    Python 3.8.5 (default, Sep  4 2020, 07:30:14) 
    [GCC 7.3.0]
    Pandas 1.2.1
    Scikit-Learn 0.24.1
    1 Physical GPUs, 1 Logical GPUs
    Num GPUs Available:  1

Managing the Environment

So far we have created the environment for TensorFlow 2.4 with the appropriate CUDA and cuDNN drivers installed through conda repos. Also, to make our life easier, the different libraries are installed using the requirements.txt file. Now, it's a good opportunity to export the environment by creating a .yml file using the command:

                conda env export > tf24-cuda11.yml

Finally, with this way, of using Anaconda environments to install CUDA drivers, we can install multiple versions of TensorFlow as well as other ML/DL frameworks, without mess the systems libraries. Also, another way is to use Docker containers, more details can be found: