TensorFlow (Python)

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:

  - 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: tensorflow