Profiling a program consists in dynamically collecting measures during the program execution : which functions or pieces of code are executed, how many times, the duration of an execution, the call tree, …

During early summer 2020, we benchmarked recent GPU models available on Nef cluster for deep-learning using ResNet-50 model training, with ImageNet 2021 dataset and TensorFlow 2.0 official implementation. Please find here a summary of the results and lessons learnt.

Ecological impact of IT is an increasingly important matter. This is why estimating the impact of a computation is useful, both for assessing the cost of a research activity or designing efficient methods. Power consumption and carbon emissions of a computation are commonly used metrics, as this is a more simple approximation than taking into account the full life cycle of IT infrastructures.

Context

Here is a new series of interesting articles:

Voici un aide-mémoire pour compiler le code C++ d’un projet sous Windows. - The Script Way (le plus simple) - The VS Code Way (<== recommandé) - The Visual Studio Way

You can create a user-friendly installers for Windows and Linux using a dedicated conda package called conda-bundle.

For this month, here is what I found interesting

This is a simple checklist on how to ask for a personal SSL certificate, which will allow to sign your git contribs or your emails. The procedure is described for macOSX platform, but can be easily adapted to other platforms.

Here are this month articles: Hydra A fresh look at configuration for machine learning projects Hydra is a recently released open-source Python framework developed at Facebook AI that simplifies the development of research and other complex applications. Great when you want to launch the same code with different options: python my_app.py --multirun dataset=imagenet,cifar10 optimiser=adam,nesterov. This will launch 4 runs!