Applied Sciences | Free Full-Text | Efficient Use of GPU Memory for Large-Scale Deep Learning Model Training | HTML
Information | Free Full-Text | Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence
Zador Pataki – ETH Zürich – Zürich, Zürich, Schweiz | LinkedIn
Swiss National Supercomputing Centre HPE NVIDIA “ALPS” supercomputer. | sciencesprings
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JetRacer AI Kit, AI Racing Robot Powered by Nvidia Jetson Nano - ShopOfThings
From Research to Production with PyTorch
PyTorch | SpringerLink
KIT-15365 | SparkFun Electronics JetBot AI-Roboter-Kit mit NVIDIA Jetson Nano 5V | Distrelec Schweiz
Performance prediction of deep learning applications training in GPU as a service systems | SpringerLink
The Best GPUs for Deep Learning in 2020 — An In-depth Analysis
GPU Cloud Computing | Exoscale Cloud Provider
From Research to Production with PyTorch
Tutorials | CSCS
The Best GPUs for Deep Learning in 2020 — An In-depth Analysis
GPU Cloud Computing | Exoscale Cloud Provider
DLBench: a comprehensive experimental evaluation of deep learning frameworks | SpringerLink
NVIDIA Pascal Archives | Tech ARP
Video of the course: Distributed Training with PyTorch on Piz Daint | CSCS
GPU-accelerated Sentiment Analysis Using Pytorch and Huggingface on Databricks - The Databricks Blog
GPU-Acceleration in Spark 3 - Why and How? | NVIDIA
Distributed Deep Learning With PyTorch Lightning (Part 1) | by Adrian Wälchli | PyTorch Lightning Developer Blog
