A high-performance distributed training framework for Reinforcement Learning
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Updated
Jul 30, 2024 - Python
A high-performance distributed training framework for Reinforcement Learning
Open-source, free, self-hosted alternative to Cypress Dashboard
A package which efficiently applies any function to a pandas dataframe or series in the fastest available manner
Extract Transform Load for Python 3.5+
🚀 R package: future: Unified Parallel and Distributed Processing in R for Everyone
Pythonic tool for orchestrating machine-learning/high performance/quantum-computing workflows in heterogeneous compute environments.
Software rendering engine with PBR. Built from scratch on C++.
for mass exploiting
Autoscalable Programming Language
FastFlow pattern-based parallel programming framework (formerly on sourceforge)
Cuneiform distributed programming language
Enables the parallelization of Symfony Console commands.
🚀 R package: future.apply - Apply Function to Elements in Parallel using Futures
ClusterRunner makes it easy to parallelize test suites across your infrastructure in the fastest and most efficient way possible.
Easy to use map and starmap python equivalents
Modified version of Alphafold to divide CPU part (MSA and template searching) and GPU part. This can accelerate Alphafold when predicting multiple structures
A Tool for Automatic Parallelization of Deep Learning Training in Distributed Multi-GPU Environments.
PARALLEL: Stata module for parallel computing
Maven plugin that simplifies running Cucumber scenarios in parallel.
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