Machine Learning

Quality Assurance for AI

Backend Requirements Frontend Requirements Backend local development Start the stack with Docker Compose: Now you can open your browser and interact with these URLs: Frontend, built with Docker, with routes handled based on the path: http://localhost Backend, JSON based web API based on OpenAPI: http://localhost/api/ Automatic interactive documentation with Swagger UI (from the OpenAPI backend): http://localhost/docs Alternative automatic documentation with ReDoc (from the OpenAPI backend): http://localhost/redoc PGAdmin, PostgreSQL web administration: http://localhost:5050 Note: you can disable (comment) this component in the […]

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Transformer

MetaFormer : A Unified Meta Framework for Fine-Grained Recognition

A repository for the code used to create and train the model defined in “MetaFormer : A Unified Meta Framework for Fine-Grained Recognition” arxiv:2203.02751 Model zoo Usage python module install Pytorch and torchvision pip install torch==1.5.1 torchvision==0.6.1 git clone https://github.com/NVIDIA/apex cd apex pip install -v –disable-pip-version-check –no-cache-dir –global-option=”–cpp_ext” –global-option=”–cuda_ext” ./ install other requirements pip install opencv-python==4.5.1.48 yacs==0.1.8 data preparation Download inat21,18,17,CUB,NABirds,stanfordcars, andaircraft, put them in respective folders (/datasets/) and    

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PyTorch

MTReclib provides a PyTorch implementation of multi-task recommendation models and common datasets

Introduction MTReclib provides a PyTorch implementation of multi-task recommendation models and common datasets. Currently, we implmented 7 multi-task recommendation models to enable fair comparison and boost the development of multi-task recommendation algorithms. The currently supported algorithms include: Datasets For the processed dataset, you should directly put the dataset in ‘./data/’ and unpack it. For the original dataset, you should put it in ‘./data/’ and run ‘python preprocess.py –dataset_name NL’. Requirements Python 3.6 PyTorch > 1.10 pandas numpy tqdm Run Parameter […]

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Python

Python 3.11 Preview: Even Better Error Messages

Python 3.11 will be released in October 2022. Even though October is still months away, you can already preview some of the upcoming features, including how Python 3.11 will offer more readable and actionable error messages. There are many other improvements and features coming in Python 3.11. Keep track of what’s new in the changelog for an up-to-date list. Python 3.11 Alpha A new version of Python is released in October each year. The code is developed and tested over […]

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Monitoring

Pulumi provider for the Checkly Delightful Active Monitoring

🚨 This project is still in very early stages and is not stable, use at your own risk! 🚨 🟪 Pulumi provider for the Checkly Delightful Active Monitoring 🪛 Installing This package is available for several languages/platforms: Node.js (JavaScript/TypeScript) To use from JavaScript or TypeScript in Node.js, install using either npm: npm install @pulumi/checkly or yarn: Python To use from Python, install using pip: pip install pulumi_checkly Go To use from Go, use go get to grab the latest version […]

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Tool

A tool aimed at enhancing the experience when playing the game on linux through proton

A tool aimed at enhancing the experience when playing the game on linux through proton. Warning This tool is based on patching the game executable through hex-edits. However it is done in a safe and non-destructive way, that ensures the patched executable is never run with EAC enabled. Use at your own risk! Features set custom frame time limits (e.g. 30, 90, 165, …) disable black borders when using resolutions with an aspect ratio other than 16:9 (e.g. ultrawide). vigniette […]

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Structure

A simple vault structure using OP_CTV

This repository demonstrates an implementation of simple, “single-hop” vaultsusing the proposed OP_CHECKTEMPLATEVERIFY opcode. OP_CTV allows the vault strategy to be used without the need to maintain criticalpresigned transaction data for the lifetime of the vault, as in the case of earliervault implementations. This approach is much simpler operationally, since all relevantdata aside from key material can be regenerated algorithmically. This makes vaultingmore practical at any scale. The code included here is intended to be approachable and easy to read, thoughit […]

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