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joblib avatar

joblib/joblib

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Joblib

Joblib is a suite of utilities for parallelizing computational workloads and optimizing the storage of large numerical datasets and function results. It functions as a parallel computing library and multiprocessing wrapper that distributes function execution across multiple CPU cores to accelerate independent tasks and computational loops.

The project provides a disk caching framework that persists expensive function outputs to the filesystem, re-evaluating them only when input arguments change. It further specializes in the serialization of large numerical arrays, utilizing efficient compression and memory mapping to optimize the storage and retrieval of massive datasets.

The toolkit includes capabilities for parallel function mapping and the use of pluggable execution backends to control how tasks are distributed across available hardware. Its storage layer covers complex object persistence and transparent compression for serialized data.

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Features

  • Parallel Processing - Distributes independent Python function calls across multiple CPU cores to accelerate data-heavy computational tasks.
  • Parallel Loop Transformers - Enables the transformation of standard sequential loops into parallel versions for execution across multiple CPU cores.
  • Scientific Array Serialization - Provides specialized serialization for massive numerical arrays using efficient compression and binary formats.
  • Function Result Caching - Provides a framework for persisting expensive function outputs to the filesystem to avoid redundant computations.
  • Computational Result Caching - Implements disk-based caching of expensive function results to avoid redundant computations via input hash verification.
  • Compressed NumPy Persistence - Implements fast compressed persistence for numerical arrays to optimize disk storage and retrieval speed.
  • NumPy Array Compression - Provides a high-performance utility for reading and writing compressed large NumPy arrays to disk.
  • Memory-Mapped Arrays - Provides memory-mapping for large numerical arrays to allow efficient disk-based random access without consuming full RAM.
  • Object Persistence - Provides mechanisms for saving and loading complex in-memory Python objects and arrays to persistent storage.
  • Object Serialization - Utilizes pickle-based serialization to convert complex Python objects into byte streams for process distribution.
  • Parallel Function Execution - Provides a mapping interface to distribute the evaluation of functions across multiple processes or threads.
  • Parallel Execution Backends - Provides pluggable execution backends to switch between multiprocessing and threading for parallel computation.
  • Memoization Caches - Provides a disk-based caching framework that avoids redundant computations by persisting function outputs based on input hash verification.
  • Local Multiprocessing - Acts as a simplified wrapper for the multiprocessing module to distribute compute-intensive tasks across CPU cores.
  • Parallel Task Execution - Implements a mechanism for executing multiple asynchronous computations simultaneously and aggregating their results.
  • Process Pools - Manages worker process pools to distribute independent function calls across multiple CPU cores.
  • Computational Parallelization - Implements a toolkit for distributing function execution across multiple cores to accelerate computational loops.
  • Transparent Compression Interfaces - Implements a transparent compression layer that wraps file I/O to reduce the disk footprint of serialized arrays.
  • Numerical Engine Backends - Allows switching between different parallel execution engines to optimize task distribution across available hardware.
  • Data Pipelines - Tools for lightweight pipelining and parallel computing with Python functions.
  • Specialized Data Utilities - Lightweight pipelining for large NumPy arrays.
  • Distributed Computing - Lightweight pipelining and parallel execution.
4,366 stars·459 forks·Python·BSD-3-Clause·21 views

Star history

Star history chart for joblib/joblibStar history chart for joblib/joblib

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

Projects sharing features with Joblib

These projects share indexed features with Joblib. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • dask/daskdask avatar

    dask/dask

    13,746View on GitHub↗

    Dask is a parallel computing framework and distributed task scheduler designed to scale Python data science workflows from single machines to large clusters. It functions as a cluster resource manager that orchestrates computational logic by representing tasks and their dependencies as directed acyclic graphs. This architecture allows the system to automate the distribution of workloads across available hardware while managing complex execution requirements. The project distinguishes itself through a lazy evaluation engine that defers data operations until they are explicitly requested, enabl

    Pythondasknumpypandas
    View on GitHub↗13,746
  • rayon-rs/rayonrayon-rs avatar

    rayon-rs/rayon

    13,071View on GitHub↗

    Rayon is a data parallelism library for Rust that provides a framework for converting sequential computations into parallel operations. It enables the transformation of standard data structures and loops into parallel iterators, allowing workloads to be distributed across multiple processor cores. By utilizing a work-stealing scheduler, the library dynamically balances tasks to maximize throughput and minimize execution time. The library distinguishes itself through its focus on safe, scoped task synchronization, which ensures that all spawned operations complete before a scope exits to preve

    Rust
    View on GitHub↗13,071
  • nalepae/pandarallelnalepae avatar

    nalepae/pandarallel

    3,802View on GitHub↗

    Pandarallel is a Python library designed to accelerate data manipulation workflows by distributing computationally intensive tasks across all available CPU cores. It functions as a parallel processing utility that integrates directly into standard data analysis pipelines to reduce the execution time of large-scale operations. The library employs a wrapper pattern that intercepts standard data processing calls, redirecting them from sequential execution to a parallelized architecture. It manages this distribution through a multiprocessing-based task model, which utilizes shared-memory serializ

    Python
    View on GitHub↗3,802
  • higherorderco/hvm2HigherOrderCO avatar

    HigherOrderCO/HVM2

    11,290View on GitHub↗

    HVM2 is a high-performance execution environment for pure functional programs, implemented as a systems-level runtime in Rust. It functions as a massively parallel functional runtime that uses interaction combinators to achieve automatic parallelism across multi-core CPUs and GPUs. The project distinguishes itself by using a graph-rewriting computational model to execute programs via local reduction rules, which eliminates the need for manual locks or atomic operations. It employs beta-optimal reduction and lazy evaluation to optimize higher-order functions and eliminate redundant computation

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Frequently asked questions

What does joblib/joblib do?

Joblib is a suite of utilities for parallelizing computational workloads and optimizing the storage of large numerical datasets and function results. It functions as a parallel computing library and multiprocessing wrapper that distributes function execution across multiple CPU cores to accelerate independent tasks and computational loops.

What are the main features of joblib/joblib?

The main features of joblib/joblib are: Parallel Processing, Parallel Loop Transformers, Scientific Array Serialization, Function Result Caching, Computational Result Caching, Compressed NumPy Persistence, NumPy Array Compression, Memory-Mapped Arrays.

Which projects share features with joblib/joblib?

Projects with overlapping indexed features include: dask/dask — Dask is a parallel computing framework and distributed task scheduler designed to scale Python data science workflows… rayon-rs/rayon — Rayon is a data parallelism library for Rust that provides a framework for converting sequential computations into… nalepae/pandarallel — Pandarallel is a Python library designed to accelerate data manipulation workflows by distributing computationally… higherorderco/hvm2 — HVM2 is a high-performance execution environment for pure functional programs, implemented as a systems-level runtime… vaexio/vaex — Vaex is a high-performance Apache Arrow DataFrame library and out-of-core data processing engine designed to handle… torch/torch7 — Torch7 is a scientific computing environment and tensor computation library used for deep learning research and…