FAQs

1. Can I run GaMorNet on any galaxy image?

   No! Please see our recommendations in the Public Data Release Handbook.

2. I am having difficulty enabling GPU support. What should I do?

   Try using Google Colab like we have done in the Tutorials.

   Note that the underlying package that we use to interact with a GPU is TensorFlow. Look at these detailed instructions for enabling GPU support for TensorFlow. Alternatively, if you are running this on a supercomputer, ask the administrators for detailed instructions on installing TensorFlow.

3. I am getting an import error involving GLIBC or libcudas.so or libm.so.

   In all probability, you are getting these errors because TensorFlow cannot find the appropriate CUDA libraries. Please follow the instructions here. Alternatively if you are running this on a supercomputer, ask the administrators for detailed instructions on installing TensorFlow.

4. Should I use the Keras or TFLearn module if I myself don’t have a preference?

   We recommend using the Keras module as we expect it to be better supported going forward. However, you may wish to take a look at the Public Data Release Handbook for differences between the two modules. It should be noted that the results in the original paper was obtained using TFLearn.

5. Is it worth enabling GPU support?

   We highly recommend running GaMorNet on a GPU if you are going to train your own models.

6. What if my question is not answered here?

   Please send me an e-mail at this aritraghsh09+gamornet@xxxxx.com GMail address. Additionally, if you have spotted a bug in the code/documentation or you want to propose a new feature, please feel free to open an issue/a pull request on GitHub