Train deep learning neural networks with CPU and GPU
TensorFlow using GPUs
Saturn Cloud has a built in GPU image for TensorFlow that has all the required libraries to get started using TensorFlow on a GPU. When creating a new resource, select the
saturn-tensorflow image. Once the resource starts, you TensorFlow code should be ready to run.
If you want to create your own image, you will need to install the GPU version of Tensorflow. In
pip, the library is call
tensorflow-gpu. In conda, look through the list to find a GPU build.
$ conda search tensorflow ... #> tensorflow 2.2.0 eigen_py36h84d285f_0 pkgs/main #> tensorflow 2.2.0 eigen_py37h1b16bb3_0 pkgs/main #> tensorflow 2.2.0 gpu_py37h1a511ff_0 pkgs/main #> tensorflow 2.2.0 gpu_py38hb782248_0 pkgs/main #> tensorflow 2.2.0 mkl_py36h5a57954_0 pkgs/main
Then in the image specifications you can ask for:
dependencies: - tensorflow=2.2.0=gpu_py37h1a511ff_0
TensorFlow using CPUs
If you want to use TensorFlow but on a CPU resource (which may be cheaper depending on which Saturn Cloud plan you are using), you can manually set up TensorFlow yourself by creating a resource with the following settings:
- Hardware: CPU
- Image: saturn
- Extra Packages (Pip): Add the following: