Neurophysiology, TensorFlow, and PyTorch Conda Environments

The Neurophysiology 1.0 for CPU healthcare focused conda environment provides the best-in-class tooling for analyzing, visualizing and exploring neurophysiological data. Using tools such as MNE and Gumpy empowers you to create brain computer interfaces (BCI) or visualize near-infrared spectroscopy (NIRS) data to assess tissue oxygenation. The tools also allow for the analysis of many different neurophysiological signals such as electrocorticograms (ECoG), electroencephalograms (EEG), stereoelectroencephalograms (sEEG), magnetoencephalograms (MEG), and much more. The slug name is neurophysiology_p38_cpu_v1.

The TensorFlow 2.7 CPU conda environment is an ecosystem of tools and libraries to create state-of-the-art machine learning models. You can use TensorFlow to train and deploy deep neural networks for image recognition, natural language processing, recurrent neural networks, and other machine learning applications. The slug name is tensorflow27_p37_cpu_v1.

The PyTorch 3.7 for CPU and GPU conda environment is a machine learning library that is used for applications in computer vision and natural language processing. It provides high-level features for tensor computing and deep neural networks. This environment also includes acceleration support on Intel CPUs with the use of daal4py. This library enhances scikit-learn algorithms by using the Intel oneAPI Data Analytics library. You can use ads-lite to speed up your data science workflow using the tools to automate common tasks. The slug names are:

• pytorch110_p37_cpu_v1
• pytorch110_p37_gpu_v1

For more information, see Data Science, ADS SDK, and ocifs SDK. Take a look at our Data Science blog.