https://djangocongress.jp/#talk-10
OpenTelemetryは、複数のプロセス、システムをまたがってアプリケーションの処理を追跡する分散トレースの仕組みを提供するフレームワークで、2021年春に1.0.0がリリースされました。このライブラリを活用し、Djangoアプリおよび周辺システムの処理を追跡する方法について紹介します。
Google Slide(スライド内のリンクをクリックできます)
https://docs.google.com/presentation/d/e/2PACX-1vRtqRQ6USDeV32_aTPjSaNXpKdn5cbitkmiX9ZfgwXVE-mh74I4eICFOB8rWGz0LPUIEfXn3APRKcrU/pub
コード
https://github.com/shimizukawa/try-otel/tree/20221112-djangocongressjp2022
Let's trace web system processes with opentelemetry djangocongress jp 2022
Deep Learning Demonstration using Tensorflow (7th lecture)Parth Nandedkar
Basic syntax of Tensorflow, Parallel processing in deep learning, Description of MNIST problem, Solving the MNIST problem using Tensorflow(Scikit Learn), Theory of Deep learning(Kullback Leibler Information, Overfitting, Softmax function etc)
https://djangocongress.jp/#talk-10
OpenTelemetryは、複数のプロセス、システムをまたがってアプリケーションの処理を追跡する分散トレースの仕組みを提供するフレームワークで、2021年春に1.0.0がリリースされました。このライブラリを活用し、Djangoアプリおよび周辺システムの処理を追跡する方法について紹介します。
Google Slide(スライド内のリンクをクリックできます)
https://docs.google.com/presentation/d/e/2PACX-1vRtqRQ6USDeV32_aTPjSaNXpKdn5cbitkmiX9ZfgwXVE-mh74I4eICFOB8rWGz0LPUIEfXn3APRKcrU/pub
コード
https://github.com/shimizukawa/try-otel/tree/20221112-djangocongressjp2022
Let's trace web system processes with opentelemetry djangocongress jp 2022
Deep Learning Demonstration using Tensorflow (7th lecture)Parth Nandedkar
Basic syntax of Tensorflow, Parallel processing in deep learning, Description of MNIST problem, Solving the MNIST problem using Tensorflow(Scikit Learn), Theory of Deep learning(Kullback Leibler Information, Overfitting, Softmax function etc)
REALITYアバターを様々なメタバースで活躍させてみた - GREE VR Studio Laboratory インターン研究成果発表gree_tech
The document outlines the research presentations from an internship program at GREE VR Studio Laboratory. The presentations covered topics like motion capture techniques that could be used in metaverses, generating emotive expressions from sound, and representing metaverses using augmented reality. One presentation proposed a way for users to virtually travel the world together by exploring street view locations and taking photos with their avatars.
The document discusses a quality improvement project at Reality Inc. to reduce app startup time. It began with measuring startup sequences on iOS and Android to identify bottlenecks. This showed networking requests and initializing distributions were slow. Performance was then measured in user environments using Firebase and GCP tools. Startup times of over 6 seconds were found. The project aims to parallelize processes, remove unnecessary tasks, and speed up networking to reduce startup time through ongoing measurement and optimization.
This document introduces two operational support tools for making Cloud Spanner more convenient: Spanner Warming Guy and Spanner Up-Down Guy.
Spanner Warming Guy is a tool that performs warmup of Cloud Spanner before releases. It can add load to Spanner through SELECT queries alone and automatically adjusts the number of SELECT bots.
Spanner Up-Down Guy is a tool that auto-scales the number of nodes in Cloud Spanner. It periodically executes Cloud Functions to adjust the node count according to a pre-defined schedule or the load situation. It can gradually decrease nodes to reduce costs.