Description
OpenMOLE (Open MOdeL Experiment) is a workflow engine designed to leverage the computing power of distributed execution environments for naturally parallel processes. A process is told naturally parallel if the same computation runs many times for a set of different inputs, such as model experiment or data processing. OpenMOLE can be used as a desktop application and embedded in web applications. It is a free software distributed under the AGPLv3 free software license.
OpenMOLE alternatives and similar packages
Based on the "Science and Data Analysis" category.
Alternatively, view OpenMOLE alternatives based on common mentions on social networks and blogs.
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MLLib
Apache Spark - A unified analytics engine for large-scale data processing -
PredictionIO
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Zeppelin
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BigDL
Accelerate local LLM inference and finetuning (LLaMA, Mistral, ChatGLM, Qwen, Baichuan, Mixtral, Gemma, etc.) on Intel CPU and GPU (e.g., local PC with iGPU, discrete GPU such as Arc, Flex and Max). A PyTorch LLM library that seamlessly integrates with llama.cpp, HuggingFace, LangChain, LlamaIndex, DeepSpeed, vLLM, FastChat, ModelScope, etc. -
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Spark Notebook
Interactive and Reactive Data Science using Scala and Spark. -
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Tensorflow_scala
TensorFlow API for the Scala Programming Language -
Squants
The Scala API for Quantities, Units of Measure and Dimensional Analysis -
FACTORIE
FACTORIE is a toolkit for deployable probabilistic modeling, implemented as a software library in Scala. It provides its users with a succinct language for creating relational factor graphs, estimating parameters and performing inference. -
ND4S
ND4S: N-Dimensional Arrays for Scala. Scientific Computing a la Numpy. Based on ND4J. -
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Optimus * 96
Optimus is a mathematical programming library for Scala. -
Clustering4Ever
C4E, a JVM friendly library written in Scala for both local and distributed (Spark) Clustering. -
rscala
The Scala interpreter is embedded in R and callbacks to R from the embedded interpreter are supported. Conversely, the R interpreter is embedded in Scala. -
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Synapses
A group of neural-network libraries for functional and mainstream languages -
Axle
Axle Domain Specific Language for Scientific Cloud Computing and Visualization
InfluxDB - Power Real-Time Data Analytics at Scale
* Code Quality Rankings and insights are calculated and provided by Lumnify.
They vary from L1 to L5 with "L5" being the highest.
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Popular Comparisons
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README
OpenMOLE (Open MOdeL Experiment) has been developed since 2008 as a free and open-source platform. It offers tools to run, explore, diagnose and optimize your numerical model, taking advantage of distributed computing environments. With OpenMOLE you can explore your already developed model, in any language (Java, Binary exe, NetLogo, R, SciLab, Python, C++...).
- The stable version is available on openmole.org.
- A fresh build of the developement version is available on next.openmole.org.
OpenMOLE is distributed under the AGPLv3 free software license.
OpenMOLE by example
Before you use OpenMOLE, you need:
- a program you want to study
- to be able to run this program using a command line
- to be able to set some inputs of the program
- to be able to get some outputs variable or some output files out of this program
Then use OpenMOLE:
- embed the executable of your program in OpenMOLE using (5 minutes)
- use one of the distributed exploration algorithms provided by OpenMOLE (5 minutes)
- launch the exploration indeferently on your laptop (10 seconds)
- or on a distributed execution environment with thousands of machines (1 minute).
To summarize, you can model exploration processes at scale reusing legacy code and advanced numeric methods in approximately 10 minutes.
Try it!
To checkout OpenMOLE you can play with to the demo site (this site is wiped out every few hours). You should click on the little cart and try out some of the market place examples.
OpenMOLE Features:
- Expressive syntax – A Domain Specific Language to describe your exploration processes,
- Transparent distributed computing – Zero-deployment (no installation step) approach to distribute the workload transparently on your multi-core machines, desktop-grids, clusters, grids, ...
- Works with your programs – Embed user’s executables (Java, Binary exe, NetLogo, R, Scilab, Python, C++, ...),
- Scalable – Handles millions of tasks and TB of data,
- Advanced methods – Advanced numerical experiments (design of experiments, optimization, calibration, sensitivity analysis, ...).
OpenMOLE Avanced Features:
- Workflow plateform – Design scientific workflows that may use legacy code,
- Distributed genetic algorithms - Distribute the computation of your fitness functions,
- Distributed computing - A high level aproach to distributed computing.
*Note that all licence references and agreements mentioned in the OpenMOLE README section above
are relevant to that project's source code only.