https://www.nas.nasa.gov/hecc/support/kb/173/ https://www.nas.nasa.gov/hecc/support/kb/cabeus-gpu-nodes_298.html Cabeus, named after a lunar crater in the south polar region of the Moon, is a cluster system that supports HPC and machine learning/artificial intelligence (ML/AI) applications that can benefit from the GPU technology. GPU Hardware Summary The... Machine Learning/Machine Learning at NAS Tue, 13 Oct 2026 11:32:01 -0700 https://www.nas.nasa.gov/hecc/support/kb/adding-conda-kernels-to-jupyterlab-on-nas-systems_711.html When you work in JupyterLab, you may find it convenient to customize the JupyterLab interface by adding a conda environment to the list of available kernels. To accomplish this, complete the procedures described below. Note: Some of the command... Machine Learning/Machine Learning at NAS Mon, 17 Aug 2026 13:16:21 -0700 https://www.nas.nasa.gov/hecc/support/kb/using-jupyter-notebook-for-machine-learning-development-on-nas-systems_576.html Jupyter Notebook, an open-source application based on Interactive Python (IPython), is a useful tool for interactively exploring science data. WARNING: You must access Jupyter through a Unix socket; accessing Jupyter using the default web server... Machine Learning/Machine Learning at NAS Mon, 17 Aug 2026 13:09:30 -0700 https://www.nas.nasa.gov/hecc/support/kb/using-conda-environments-on-nas-systems_557.html WARNING: This article was recently updated with information about using conda environments on Grace Hopper nodes. Please note that because these nodes are fundamentally different from the other NAS node types, due to their Arm architecture, conda... Machine Learning/Machine Learning at NAS Mon, 17 Aug 2026 13:01:31 -0700 https://www.nas.nasa.gov/hecc/support/kb/how-to-set-up-r-kernel-in-jupyterlab_685.html NAS currently supports three versions of the R machine learning platform: R Versions 3.6 and 4.2 are provided in the r3_6 and r4_2 NAS conda environments, respectively. R Version 4.1 and 4.2 are provided as a NAS software package. The... Machine Learning/Machine Learning at NAS Wed, 25 Mar 2026 10:25:39 -0700 https://www.nas.nasa.gov/hecc/support/kb/using-the-what-if-tool-for-data-and-model-visualization_655.html The What-If Tool (WIT) is a helpful visualization tool that aids in analyzing models and datasets. The tool automatically visualizes datasets and allows you to compare different models and see how specific inputs change the output. WIT can... Machine Learning/Machine Learning at NAS Fri, 19 Dec 2025 09:56:47 -0800 https://www.nas.nasa.gov/hecc/support/kb/is-jupyter-notebook-available_629.html Jupyter Notebook, an open-source application based on Interactive Python, is a useful tool for interactively exploring science data. You cannot run Jupyter on a front-end system (such as a PFE); instead, you must run it on a compute note dedicated... Machine Learning/Machine Learning FAQs Thu, 18 Dec 2025 16:16:00 -0800 https://www.nas.nasa.gov/hecc/support/kb/managing-and-installing-python-packages-in-conda-environments_627.html You have two options to install your own Python packages in our machine learning environment: Use the pip tool to install them directly Build your own conda environment Consider the benefits and disadvantages of each method, described below,... Machine Learning/Machine Learning at NAS Thu, 18 Dec 2025 16:14:41 -0800 https://www.nas.nasa.gov/hecc/support/kb/secure-setup-for-using-jupyter-notebook-on-nas-systems_622.html The typical Jupyter installation includes more than 125 packages. If any one of these packages has been compromised, any account running Jupyter could become compromised. Keep in mind that even if the initial installation does not include any... Machine Learning/Machine Learning at NAS Thu, 18 Dec 2025 16:12:58 -0800 https://www.nas.nasa.gov/hecc/support/kb/machine-learning-overview_572.html Machine learning, deep learning, and artificial intelligence have become essential tools for handling and gaining insight from the enormous amounts of data that are being generated via high-performance computing, modern modeling and simulation, and... Machine Learning/Machine Learning at NAS Fri, 22 Aug 2025 13:36:50 -0700 https://www.nas.nasa.gov/hecc/support/kb/using-tensorboard-for-machine-learning-development-on-nas-systems_650.html TensorBoard is a tool for providing the measurements and visualizations needed during the machine learning workflow. It enables tracking of experiment metrics such as loss and accuracy, visualizing the model graph, projecting embeddings to a lower... Machine Learning/Machine Learning at NAS Mon, 18 Aug 2025 14:45:13 -0700 https://www.nas.nasa.gov/hecc/support/kb/using-large-language-models-at-nas_710.html Introduction Large language models (LLMs) are an AI/Ml innovation that has the potential to enhance scientific discovery. One way to use LLMs in the NAS environment is through the Hugging Face application programming interface (API). Hugging Face... Machine Learning/Machine Learning at NAS Mon, 21 Jul 2025 14:09:51 -0700 https://www.nas.nasa.gov/hecc/support/kb/deep-learning-using-multiple-gpus_656.html The two deep learning frameworks supported on NAS systems, TensorFlow and PyTorch, are capable of training using multiple GPUs on the same node. In TensorFlow, the MirroredStrategy function is the best option for training using multiple GPUs on a... Machine Learning/Machine Learning at NAS Tue, 01 Apr 2025 18:42:48 -0700 https://www.nas.nasa.gov/hecc/support/kb/using-dask-at-nas_648.html Dask is a Pythonic distributed programming framework that enables development of scalable Python code for big data. You can think of Dask as a Python library for parallel computing, and also as a tool to scale computational libraries such as Numpy,... Machine Learning/Machine Learning at NAS Mon, 27 Nov 2023 15:45:31 -0800 https://www.nas.nasa.gov/hecc/support/kb/which-environments-can-i-use-to-complete-step-2-in-the-secure-setup-for-jupyter-notebooks_664.html All of our provided environments have Jupyter installed, so you can choose any of them, besides the base environment. You can find the available environments by using the conda info –envs command. We suggest using the latest TensorFlow environment... Machine Learning/Machine Learning FAQs Mon, 25 Sep 2023 19:00:28 -0700