Batch iterator using promises
Abstraction for DynamoDB batch reads and writes for that handles batch splitting and partial retries with exponential backoff
Abstraction for DynamoDB batch reads and writes for that handles batch splitting and partial retries with exponential backoff
An ESnext spec-compliant iterator helpers shim/polyfill/replacement that works as far down as ES3.
Firefox 17-26 iterators throw a StopIteration object to indicate "done". This normalizes it.
Abstraction for DynamoDB batch reads and writes for that handles batch splitting and partial retries with exponential backoff
Get an iterator for any JS language value. Works robustly across all environments, all versions.
Higher order iterator library for JavaScript/TypeScript.
Process (async)iterable values as functions with concurrency control
Iterator abstraction based on ES6 specification
Takes an async iterator that emits things and emits them as fixed size batches
Abstraction for DynamoDB batch reads and writes for that handles batch splitting and partial retries with exponential backoff
Iterate any JS iterator. Works robustly in all environments, all versions.
Maximize the parallel calls of an iterator supporting asyncIterator interface
Abstraction for DynamoDB batch reads and writes for that handles batch splitting and partial retries with exponential backoff
Convert an argument into a valid iterator. Based on the `.makeIterator()` implementation in mout https://github.com/mout/mout.
Iterate over promises serially
Turn an abstract-leveldown iterator into a readable stream
[](http://www.typescriptlang.org/) [](https://www.npmjs.com/package/@n1ru4l/push-pull-async
Creates an async iterator for a variety of inputs in the browser and node. Supports fetch, node-fetch, and cross-fetch
Framework-independent loaders for 3D graphics formats
Run multiple promise-returning & async functions with limited concurrency using native ES9
A finite state machine iterator for JavaScript
Get the default iterator or async iterator for an iterable or async iterable
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.