An abstraction to work with iterables of asyncIterables
Split an iterable into evenly sized chunks
Get the first fulfilled promise that satisfies the provided testing function
A tiny, zero-dependency yet spec-compliant asynchronous iterator polyfill/ponyfill for ReadableStreams.
[](http://www.typescriptlang.org/) [](https://www.npmjs.com/package/@n1ru4l/push-pull-async
Extended iterable class, providing lazy array-like methods with automatic async and return/throw forwarding
Set of classes used for async prefetching with backpressure (IterableMapper) and async flushing with backpressure (IterableQueueMapper, IterableQueueMapperSimple)
Abstraction for DynamoDB batch reads and writes for that handles batch splitting and partial retries with exponential backoff
Process (async)iterable values as functions with concurrency control
A set of utils for faster development of GraphQL tools
Convert a string/promise/array/iterable/asynciterable/buffer/typedarray/arraybuffer/object into a stream
Simple async batch with concurrency control and progress reporting.
Iterable wrapper that add methods to read ahead or behind current item.
Deques are a generalization of stacks and queues
Iterable SDK for React Native.
The Interactive Extensions for JavaScript
Abstraction for DynamoDB batch reads and writes for that handles batch splitting and partial retries with exponential backoff
Array manipulation, ordering, searching, summarizing, etc.
Asynchronous batched iterable for (mongo) cursors
A tiny but capable push & pull stream library for TypeScript and Flow
Convert streaming iterables to Node.js streams
Checks if a given object is iterable
Multipart and Tar utilities for the Web Streams API
AWS SDK for JavaScript Batch Client for Node.js, Browser and React Native
A library to iterate over entire Mongo collections or large queries exposing an API to control things like batch size, order and limit.
`knife batch` is a wonderful little plugin for executing commands a la `knife ssh`, but doing it in groups of `n` with a sleep between execution iterations.
This is a weak deduper to make things like bulk email run safer. It is not a lock safe for financial/security needs because it uses a weak redis locking pattern that can have race conditions. However, imagine a bulk email job that loops over 100 users, and enqueues a background email for each user. If the job fails at iteration 50, a retry would enqueue all the users again and many will receive dupes. This would continue multiple times as the parent job continued to rerun. By marking that a subjob has been enqueued, we can let that isolated job handle its own failures, and the batch enqueue job can run multiple times without re-enqueueing the same subjobs.
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.