Cuda toolkit 10

Manufactured by NVIDIA

Sourced in United States

The NVIDIA CUDA Toolkit 10.1 is a software development kit that provides a programming model and tools for developing, deploying, and optimizing parallel computing applications on NVIDIA GPUs. The toolkit includes a compiler, libraries, and tools for creating GPU-accelerated applications.

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Lab products found in correlation

Xeon w 2145, by NVIDIA (1 mentions) Quadro rtx 6000, by NVIDIA (1 mentions) Titan rtx, by NVIDIA (1 mentions) R version 4, by R Project for Statistical Computing (1 mentions) I7 8700, by NVIDIA (1 mentions)

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CUDA toolkit (3 citations)

3 protocols using cuda toolkit 10

High-Performance LSTree Analysis Pipeline

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The LSTree analysis tasks have been trained and used on a workstation with following specifications: 16 core Intel Xeon W-2145, 64 GB 2666 MHz DDR4 RAM equipped with a Nvidia Quadro RTX 6000 GPU with 24 GB VRAM and using Ubuntu 18.04.6 LTS. All code runs with Nvidia cudatoolkit 10.1, and cuDNN 7.
Minimally, one would need 16 GB of RAM and a Tensorflow compatible GPU with at least 8 GB of VRAM. Since many of the steps of the pipeline run in parallel, a higher number of CPUs is also desirable.
A step-by-step guide on installation and on how to run the example data provided can be found in the repository (www.github.com/fmi-basel/LSTree).

de Medeiros G., Ortiz R., Strnad P., Boni A., Moos F., Repina N., Challet Meylan L., Maurer F, & Liberali P. (2022). Multiscale light-sheet organoid imaging framework. Nature Communications, 13, 4864.

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Deep Learning Workflow for Survival Analysis

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Training and operation of the deep feature model in this experiment were both conducted using a Linux Ubuntu 16.04 (Canonical Ltd., London, UK) system environment. The adopted deep learning framework was the PyTorch deep learning library (Facebook’s AI Research lab (FAIR), NY), based on Python 3.8 software (Python Software Foundation, Wilmington, DE). The graphics processing unit (GPU) used was an NVIDIA TITAN RTX with 64G VRAM and 32G RAM, and CUDA Toolkit 10.1 (NVIDIA Corporation, Santa Clara, CA, USA). The Cox proportional hazard regression model was established using R version 4.1.0 (The R Foundation, Vienna, Austria), for programming in a 64-bit Windows system.

Yin Z., Chen T., Shu Y., Li Q., Yuan Z., Zhang Y., Xu X, & Liu Y. (2022). A Gallbladder Cancer Survival Prediction Model Based on Multimodal Fusion Analysis. Digestive Diseases and Sciences, 68(5), 1762-1776.

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Optimized GPU-Accelerated Deep Learning

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The computations were performed on a Lenovo computer with a Windows 10 (64-bit) operating system, an Intel (R) Core (TM) I7-8700 @3.20 GHz CPU, an NVIDIA GeForce RTX2060 graphics card with 16.0 GB of RAM. For GPU acceleration, a computing platform (NVIDIA CUDA Toolkit 10.1) and a deep neural network acceleration library (NVIDIA cuDNN v7.6.5) were used. All models were implemented on TensorFlow 2.1.0 framework and deep learning library Keras 2.3.1 using Python 3.7.3 in Spyder IDE (v. 3.3.3).

Yu G., Ma B., Li H., Hu Y, & Li Y. (2022). Discrimination of Pesticide Residue Levels on the Hami Melon Surface Using Multiscale Convolution. Foods, 11(23), 3881.

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