Search Results for "compression"

Requests is the de facto HTTP library for Python—simple, elegant, and human-friendly. It wraps urllib3 to provide intuitive methods for sending HTTP/1.1 requests, handling sessions, cookies, redirects, authentication, proxies, and more.

...QuIC has a mission to help migrate the ecosystem toward fixed-point inference. With this goal, QuIC presents the AI Model Efficiency Toolkit (AIMET) - a library that provides advanced quantization and compression techniques for trained neural network models. AIMET enables neural networks to run more efficiently on fixed-point AI hardware accelerators. Quantized inference is significantly faster than floating point inference. For example, models that we’ve run on the Qualcomm® Hexagon™ DSP rather than on the Qualcomm® Kryo™ CPU have resulted in a 5x to 15x speedup. ...

PyMySQL is a 100% Python implementation of the MySQL client protocol, allowing Python applications to connect to MySQL and MariaDB databases without requiring binary extensions. It supports standard DB‑API 2.0 features, such as cursors, transactions, and parameterized queries. PyMySQL is versatile for web applications, scripts, and tools, offering compatibility with ORMs like SQLAlchemy and frameworks like Django.

Neural Network Intelligence is an open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning. NNI (Neural Network Intelligence) is a lightweight but powerful toolkit to help users automate feature engineering, neural architecture search, hyperparameter tuning and model compression. The tool manages automated machine learning (AutoML) experiments, dispatches and runs experiments' trial jobs generated by tuning algorithms to search the best neural architecture and/or hyper-parameters in different training environments like Local Machine, Remote Servers, OpenPAI, Kubeflow, FrameworkController on K8S (AKS etc.) ...

...To run the examples, please install the package and run the command in the package root directory. Compressing a neural network to speed up inference and minimize memory footprint has been studied widely. One of the popular techniques for model compression is pruning the weights in convnets, is also known as sparse convolutional networks. Such parameter-space sparsity used for model compression compresses networks that operate on dense tensors and all intermediate activations of these networks are also dense tensors.

A lighting-fast prefix tree (trie) and compressed prefix tree (DAWG) implementation. This library can be usefull for longest-prefix matching, code completion, dictionary compression, spell checking.