BIDMach alternatives and similar packages
Based on the "Big Data" category.
Alternatively, view BIDMach alternatives based on common mentions on social networks and blogs.
-
Deeplearning4J
Suite of tools for deploying and training deep learning models using the JVM. Highlights include model import for keras, tensorflow, and onnx/pytorch, a modular and tiny c++ library for running math code and a java based math library on top of the core c++ library. Also includes samediff: a pytorch/tensorflow like library for running deep learn... -
Reactive-kafka
Alpakka Kafka connector - Alpakka is a Reactive Enterprise Integration library for Java and Scala, based on Reactive Streams and Akka. -
Schemer
Schema registry for CSV, TSV, JSON, AVRO and Parquet schema. Supports schema inference and GraphQL API. -
GridScale
Scala library for accessing various file, batch systems, job schedulers and grid middlewares. -
Spark Utils
Basic framework utilities to quickly start writing production ready Apache Spark applications
CodeRabbit: AI Code Reviews for Developers
* Code Quality Rankings and insights are calculated and provided by Lumnify.
They vary from L1 to L5 with "L5" being the highest.
Do you think we are missing an alternative of BIDMach or a related project?
README
BIDMach is a very fast machine learning library. Check the latest benchmarks
The github distribution contains source code only. You also need a jdk 8, an installation of NVIDIA CUDA 8.0 (if you want to use a GPU) and CUDNN 5 if you plan to use deep networks. For building you need maven 3.X.
After doing git clone, cd to the BIDMach directory, and build and install the jars with mvn install. You can then run bidmach with ./bidmach
. More details on installing and running are available here.
The main project page is here.
Documentation is here in the wiki
New BIDMach has a discussion group on Google Groups.
BIDMach is a sister project of BIDMat, a matrix library, which is also on github
BIDData also has a project for deep reinforcement learning. BIDMach_RL contains state-of-the-art implementations of several reinforcement learning algorithms.