Coder Survival Guide

Coder Survival Guide

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Green AI: A Preliminary Empirical Study on Energy Consumption in DL Models Across Different Runtime Infrastructures
Green AI: A Preliminary Empirical Study on Energy Consumption in DL Models Across Different Runtime Infrastructures
Deep Learning (DL) frameworks such as PyTorch and TensorFlow include runtime infrastructures responsible for executing trained models on target hardware, managing memory, data transfers, and multi-accelerator execution, if applicable. Additionally, it is a common practice to deploy pre-trained models on environments distinct from their native development settings. This led to the introduction of interchange formats such as ONNX, which includes its runtime infrastructure, and ONNX Runtime, which work as standard formats that can be used across diverse DL frameworks and languages. Even though these runtime infrastructures have a great impact on inference performance, no previous paper has investigated their energy efficiency. In this study, we monitor the energy consumption and inference time in the runtime infrastructures of three well-known DL frameworks as well as ONNX, using three various DL models. To have nuance in our investigation, we also examine the impact of using different execution providers. We find out that the performance and energy efficiency of DL are difficult to predict. One framework, MXNet, outperforms both PyTorch and TensorFlow for the computer vision models using batch size 1, due to efficient GPU usage and thus low CPU usage. However, batch size 64 makes PyTorch and MXNet practically indistinguishable, while TensorFlow is outperformed consistently. For BERT, PyTorch exhibits the best performance. Converting the models to ONNX usually yields significant performance improvements but the ONNX converted ResNet model with batch size 64 consumes approximately 10% more energy and time than the original PyTorch model.
·arxiv.org·
Green AI: A Preliminary Empirical Study on Energy Consumption in DL Models Across Different Runtime Infrastructures
Sustainability, a surprisingly successful KPI: GreenOps survey results - ClimateAction.Tech
Sustainability, a surprisingly successful KPI: GreenOps survey results - ClimateAction.Tech
If you want to save money in Enterprise IT, it turns out that sustainability as a KPI is more important than cost - this is one of the key findings coming from the recent GreenOps Survey, some research made possible by the recent ClimateAction MiniGrants fund.
·climateaction.tech·
Sustainability, a surprisingly successful KPI: GreenOps survey results - ClimateAction.Tech
How normal am I?
How normal am I?
In this test face detection algorithms will determine how normal you are. 100% privacy friendly.
·hownormalami.eu·
How normal am I?
Qu’est-ce vraiment que l’éco-conception web : frugalité des contenus, designs et fonctionnalités, ou série d'optimisations techniques ?
Qu’est-ce vraiment que l’éco-conception web : frugalité des contenus, designs et fonctionnalités, ou série d'optimisations techniques ?
Récemment, on m’a posé la question de savoir si un site plein de visuels et de grandes images pouvait être considéré comme éco-conçu si on l’optimisait à mort. Alors : éco-conception web = frugalité des contenus ou optimisations techniques ?
·internet2000.net·
Qu’est-ce vraiment que l’éco-conception web : frugalité des contenus, designs et fonctionnalités, ou série d'optimisations techniques ?
How many bytes is "normal" for a web font: a study using Google fonts
How many bytes is "normal" for a web font: a study using Google fonts
TL;DR: If your font file is significantly larger than 20K you may ask yourself "How did I get here?".For images I think we (web developers) have a sense of how many bytes we can expect an image we see on a page to be. A JPEG photo? 100-ish K is ok for a decent quality. Less is nice. How about 200K?
·phpied.com·
How many bytes is "normal" for a web font: a study using Google fonts