Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

(pytorchbook) C:\> conda install pytorch torchvision cpuonly\
-c pytorch
Installing CUDA
CUDA: Installing drivers for a GeForce graphics card, NVIDIA’s cuDNN, and
CUDA Toolkit can be challenging and is highly dependent on which model
you own and which OS you use.
For installing GeForce’s drivers, go to GeForce’s website
(https://www.geforce.com/drivers), select your OS and the model of your
graphics card, and follow the installation instructions.
For installing NVIDIA’s CUDA Deep Neural Network library (cuDNN), you
need to register at https://developer.nvidia.com/cudnn.
For installing CUDA Toolkit (https://developer.nvidia.com/cuda-toolkit), please
follow instructions for your OS and choose a local installer or executable file.
macOS: If you’re a macOS user, please beware that PyTorch’s binaries DO
NOT support CUDA, meaning you’ll need to install PyTorch from source if
you want to use your GPU. This is a somewhat complicated process (as
described in https://github.com/pytorch/pytorch#from-source), so, if you don’t
feel like doing it, you can choose to proceed without CUDA, and you’ll still be
able to execute the code in this book promptly.
4. TensorBoard
TensorBoard is TensorFlow’s visualization toolkit, and "provides the visualization
and tooling needed for machine learning experimentation." [24]
TensorBoard is a powerful tool, and we can use it even if we are developing models
in PyTorch. Luckily, you don’t need to install the whole TensorFlow to get it; you
can easily install TensorBoard alone using Conda. You just need to run this
command in your terminal or Anaconda prompt (again, after activating the
environment):
(pytorchbook)$ conda install -c conda-forge tensorboard
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