visionmedia/batch, but with support for sync and generator functions, along with arguments
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A set of utils for faster development of GraphQL tools
Simple async batch with concurrency control and progress reporting.
Better Queue for NodeJS
Better Stack Typescript types (formerly Logtail)
AWS SDK for JavaScript Batch Client for Node.js, Browser and React Native
A library to work around hard limit for Firestore Batched Writes
JSON.parse with context information on error
JSON.parse with context information on error
Batch processing in JS
The most comprehensive authentication framework for TypeScript.
collection of cryptographic functions that support every js runtime for ES5+
The fastest and simplest library for SQLite in Node.js.
A better opn. Reuse the same tab on Chrome for 👨💻.
Specialized Promise Extensions
A better path.resolve() that normalizes paths on Windows
Manage a cluster of child processes
Human-friendly JSON Schema validation for APIs
Library modules used by contentful batch utility CLI tools.
Telemetry package for Better Auth
A set of utils for faster development of GraphQL tools
Advanced fetch wrapper for typescript with zod schema validations, pre-defined routes, hooks, plugins and more. Works on the browser, node (version 18+), workers, deno and bun.
The most comprehensive authentication framework for TypeScript.
A simple SQL query builder for better batch operations.
Better batch operations.
Batch up your ActiveRecord "touch" operations for better performance. ActiveRecord::Base.delay_touching do ... end. When "end" is reached, all accumulated "touch" calls will be consolidated into as few database round trips as possible.
Batch up your ActiveRecord "touch" operations for better performance. All accumulated "touch" calls will be consolidated into as few database round trips as possible.
Batch up your ActiveRecord "touch" operations for better performance. ActiveRecord::Base.delay_touching do ... end. When "end" is reached, all accumulated "touch" calls will be consolidated into as few database round trips as possible.
Makes neography-batches better composable (for neography-batches see https://github.com/maxdemarzi/neography/wiki/Batch)
Hydrangea provides better flow control for batch operations
ActiveRecord::Batches#find_in_batches has some gotchas. This library provides alternate algorithms that may better suit you, in certain circumstances. Specifically: you can order your results other than by primary key, and you can limit your batches to just a certain range of results not only all records.
ComputedModel is a helper for building a read-only model (sometimes called a view) from multiple sources of models. It comes with batch loading and dependency resolution for better performance. It is designed to be universal. It's as easy as pie to pull data from both ActiveRecord and remote server (such as ActiveResource).
A collection of things that I do with jira. Many of which are either big batch operations, or need bits of info from the command line. All of which turn out to be better handled as a command line app. This very specifically does not try to be a generic jira tool; those exist already. Rather this is specific to things I need.
GRYDRA v2.0 is a complete, modular Ruby library for building, training, and deploying neural networks. NEW in v2.0: - Complete modular architecture with 29 organized files - Keyword arguments API for better readability - Full implementations (no more "simplified" versions) - 8 loss functions (MSE, MAE, Huber, Cross-Entropy, Hinge, Log-Cosh, Quantile) - 5 optimizers (Adam, SGD, RMSprop, AdaGrad, AdamW) - 6 training callbacks (EarlyStopping, LearningRateScheduler, ReduceLROnPlateau, ModelCheckpoint, CSVLogger, ProgressBar) - Complete LSTM implementation with backpropagation - Complete 2D Convolutional layer with padding and stride - Real PCA with eigenvalue decomposition using Power Iteration - Multiple activation functions (Tanh, ReLU, Leaky ReLU, Sigmoid, Swish, GELU, Softmax) - Regularization (Dropout, L1, L2) - Weight initialization (Xavier, He) - Data normalization (Z-Score, Min-Max) - Comprehensive metrics (MSE, MAE, Accuracy, Precision, Recall, F1, Confusion Matrix, AUC-ROC) - Advanced training (mini-batch, early stopping, learning rate decay, validation split) - Cross-validation and hyperparameter search - Text processing (vocabulary, binary vectorization, TF-IDF) - Model persistence (save/load with Marshal) - Network visualization and gradient analysis - Simplified EasyNetwork interface - 100% backward compatibility with v1.x Perfect for machine learning projects, research, and education in Ruby.
No description provided.
No description provided.