Palma provides a variety of tools to support statistical and machine learning based data analysis on both CPU or accelerated by one or more GPUs.
Below you can find some commonly used frameworks for different application domains that are currently supported by Palma.
For the sake of simplicity, these pages focus on the frameworks and techniques based on the Python programming language. Keep in mind that there are alternative solution in other languages such as R, MatLab or Julia which are not mentioned here.
Available Frameworks
For documentation on how to use the module system, see here.
For simpler (image-) classification task with very small images or simple numerical features, training and inference does not benefit from being executed on a GPU.
In these cases, Palma provides framework installation that can be used on a default compute node in the normal queue
| Toolchain | scikit-learn | TensorFlow | PyTorch |
|---|---|---|---|
palma/2019b → foss | 0.21.3-Python-3.7.4 | 2.1.0-Python-3.7.4 | 1.4.0-Python-3.7.4 |
palma/2020a → foss | 0.23.1-Python-3.8.2 | 2.3.1-Python-3.8.2 | WIP |
| |
|---|---|
| Homepage | https://scikit-learn.org/stable/index.html |
| Documentation | https://scikit-learn.org/stable/modules/classes.html |
| Guide | https://scikit-learn.org/stable/user_guide.html |
| Homepage | https://www.tensorflow.org/ |
| Documentation | https://www.tensorflow.org/versions |
| Guide | https://www.tensorflow.org/overview |
| Homepage | https://pytorch.org/ |
| Documentation | https://pytorch.org/docs/stable/index.html |
| Guide | https://pytorch.org/tutorials/ |
