Numérique Responsable

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Yellow Lab Tools - Page Speed audit
Yellow Lab Tools - Page Speed audit
Yellow Lab Tools is a free online web performance analyzer. It audits a webpage for performance and front-end quality issues. And it's open-source!
·yellowlab.tools·
Yellow Lab Tools - Page Speed audit
Reporting requirements on the energy performance and sustainability of data centres for the Energy Efficiency Directive. Task A report, Options for a reporting scheme for data centres - Publications Office of the EU
Reporting requirements on the energy performance and sustainability of data centres for the Energy Efficiency Directive. Task A report, Options for a reporting scheme for data centres - Publications Office of the EU
Details of the publication
·op.europa.eu·
Reporting requirements on the energy performance and sustainability of data centres for the Energy Efficiency Directive. Task A report, Options for a reporting scheme for data centres - Publications Office of the EU
Tailwind vs Semantic CSS
Tailwind vs Semantic CSS
Comparing two identically designed websites, their weight, amount of HTML and CSS, rendering speed, and best practices.
·nuejs.org·
Tailwind vs Semantic CSS
Tu devrais faire de la WebPerf
Tu devrais faire de la WebPerf
24 jours de web : Le calendrier de l'avent des gens qui font le web d'après.
Front-End Performance Checklist
·24joursdeweb.fr·
Tu devrais faire de la WebPerf
[TeleCoop] Article blog + Déclaration d’écoconception - HackMD
[TeleCoop] Article blog + Déclaration d’écoconception - HackMD
L’écoconception numérique continue à se développer en France, notamment portée par le nouveau réglement général d’écoconception de services numériques (RGESN) développé au sein de l’État. Cette pratique consiste à se demander quoi faire pour réduire la charge environnementale (émissions de gaz à effet de serre, consommation d’énergie, d’eau, etc.) d’un site web et de son usage. Nous avons maintenant une certaine expérience du sujet puisque nous explorons à la fois en théorie et en pratique depuis 5 ans toutes ces questions. La refonte de Telecoop s’inscrit d’ailleurs dans la continuité de celle pour Commown avec Derek Salmon. Dans cette étude de cas, nous allons tâcher d’appliquer ce fameux RGESN qui se distingue par 8 grands axes : la stratégie, les spécifications, l’architecture, l’UX/UI, les contenus, le front-end, le back-end, l’hébergement. Nous allons reprendre point par point chacun de ces éléments.
·hackmd.io·
[TeleCoop] Article blog + Déclaration d’écoconception - HackMD
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