Deep Learning
With CUDA Hardware Acceleration

Purpose

Please read the Disclaimers before proceeding.

Deep learning with CUDA hardware acceleration.

Open a terminal and type kfocus-001-conda to install and test GPU-accelerated TensorFlow, MXNet, Jupyter Notebook, Keras, and Pytorch in a full Anaconda environment.

Workflow Overview

Alternate packages

None at this time.

Steps

CUDA libraries 10.0 and 10.1 are pre-installed. Run kfocus-001-conda to install and test GPU-accelerated TensorFlow, MXNet, Jupyter Notebook, Keras, and Pytorch in a full Anaconda environment.

Disclaimers

We try hard to provide a useful workflow validated by professionals. However, we cannot anticipate every situation, and therefore cannot guarantee this procedure will work for your needs. Always back up your data and test the workflow to determine the correct procedure for you.

THIS WORKFLOW IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS WORKFLOW, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.