Numérique Responsable

L'étude montre que, sans action pour limiter la croissance de l’impact environnemental du numérique, l’empreinte carbone du numérique pourrait tripler entre 2020 et 2050.

Read about our latest project - green handbook that gives practical guidance to community tech practitioners on their path towards digital sustainability.

Learn how digital technologies can drive a deep transformation towards sustainability. Download the full report "Digital Reset" here.

📱 Mobile Ecodesign Best Practices (for developers) - cnumr/best-practices-mobile: 📱 Mobile Ecodesign Best Practices (for developers)

With a record 36 billion tons of carbon dioxide (CO2) emissions produced globally in 2021, the world has no choice but to take action on climate change. Policy reports from the UN’s Intergovernmental Panel on Climate Change (IPCC) make it clear that reducing carbon pollution is essential in mitigating the devastating impacts of a […]

Cet article est l’occasion de mettre en lumière des projets qui font avancer le numérique responsable. Ce mois-ci je suis partie à la rencontre de Simon Cardon.

Google has optimized the browser to prolong browsing and YouTube playback.

A new report from The Register today claims that Google is “paying Apple a portion of search revenue” that comes...

Today's adventure: DIY whole hog data exfiltration

At IBM, our design philosophy is to help guide people so they can do their best work. Our human-centered design practices help us deliver on that goal.

“It's #InternationalBatteryDay! 🔋 In a poll this week, we asked if you knew what #smartphone app drains your battery the most. The correct response: the camera app at 960 mA, and nearly all major internal components of the device from preview to the post-processing are used! 📱”

Hands on labs and real world design scenarios for Well-Architected workloads

Sustainability is an important topic in the tech industry, as well as society as a whole, and defined as the ability to continue to perform a process or function over an extended period of time without depletion of natural resources or the environment. One of the key elements to designing a sustainable workload is software […]

Understand the design principles for building a sustainable application on Microsoft Azure.

Behind many corporate sustainability announcements is a grassroots story of a few employees who were convinced of the opportunity to make a positive impact on both the climate and their business. What started out as a hackathon project three years ago to demonstrate the potential of carbon aware com

The question of how to incorporate sustainability into our lives has long been debated. Can we integrate it at the beginning, before it…

This article attempts to estimate the carbon footprint of the popular OpenAI chatbot called ChatGPT

The web is a major source of carbon emissions. This site provides independent research to help web developers make their products more sustainable.

Want to be true in advertising, earn trust and avoid lawsuits? Steer clear of this lingo (and use other terms with care).

Green Service Design - Pour une conception de services éco-responsables, le 1er livre dédié à l'éco-conception de services

Conception et production du nouveau site web de Nature en Occitanie

The computation demand for machine learning (ML) has grown rapidly recently, which comes with a number of costs. Estimating the energy cost helps measure its environmental impact and finding greener strategies, yet it is challenging without detailed information. We calculate the energy use and carbon footprint of several recent large models-T5, Meena, GShard, Switch Transformer, and GPT-3-and refine earlier estimates for the neural architecture search that found Evolved Transformer. We highlight the following opportunities to improve energy efficiency and CO2 equivalent emissions (CO2e): Large but sparsely activated DNNs can consume 1/10th the energy of large, dense DNNs without sacrificing accuracy despite using as many or even more parameters. Geographic location matters for ML workload scheduling since the fraction of carbon-free energy and resulting CO2e vary ~5X-10X, even within the same country and the same organization. We are now optimizing where and when large models are trained. Specific datacenter infrastructure matters, as Cloud datacenters can be ~1.4-2X more energy efficient than typical datacenters, and the ML-oriented accelerators inside them can be ~2-5X more effective than off-the-shelf systems. Remarkably, the choice of DNN, datacenter, and processor can reduce the carbon footprint up to ~100-1000X. These large factors also make retroactive estimates of energy cost difficult. To avoid miscalculations, we believe ML papers requiring large computational resources should make energy consumption and CO2e explicit when practical. We are working to be more transparent about energy use and CO2e in our future research. To help reduce the carbon footprint of ML, we believe energy usage and CO2e should be a key metric in evaluating models, and we are collaborating with MLPerf developers to include energy usage during training and inference in this industry standard benchmark.

The main training portal of the GSF. Contribute to Green-Software-Foundation/training development by creating an account on GitHub.

“Le conseil #LogicielsLibres, mis en place et animé par la mission logiciels libres de la @_DINUM à @Etalab, émet son premier avis en répondant positivement à cette question:”

Installation