Any help or tips for Neural Networks on Computer Clusters

This page summarizes the projects mentioned and recommended in the original post on /r/fortran

InfluxDB - Power Real-Time Data Analytics at Scale
Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
www.influxdata.com
featured
SaaSHub - Software Alternatives and Reviews
SaaSHub helps you find the best software and product alternatives
www.saashub.com
featured
  • neural-fortran

    A parallel framework for deep learning

  • check out this, it's not really a framework but good enough to start with

  • Flux.jl

    Relax! Flux is the ML library that doesn't make you tensor

  • I would suggest you to look into Julia ecosystem instead of C++. Julia is almost identical to Python in terms of how you use it but it's still very fast. You should look into flux.jl package for Julia.

  • InfluxDB

    Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

    InfluxDB logo
  • f90wrap

    F90 to Python interface generator with derived type support

  • However, if you are writing numerical code in Fortran and want to be able to better interface it it with machine learning tools and methods, the number one thing I can recommend is to look into Fortran-based automatic differentiation tools. This will enable you to calculate exact derivatives of your code, which are useful to have for training and optimization loops. You can also look into f2py and f90wrap for generating Python wrappers for your Fortran codes, as well as Julia for hooking into it directly.

  • julia

    The Julia Programming Language

  • However, if you are writing numerical code in Fortran and want to be able to better interface it it with machine learning tools and methods, the number one thing I can recommend is to look into Fortran-based automatic differentiation tools. This will enable you to calculate exact derivatives of your code, which are useful to have for training and optimization loops. You can also look into f2py and f90wrap for generating Python wrappers for your Fortran codes, as well as Julia for hooking into it directly.

  • Fortran-code-on-GitHub

    Directory of Fortran codes on GitHub, arranged by topic

  • The hints in place ("there is more infrastructure already available outside Fortran, consider using them instead"). Beliavsky's compilation Fortran code on GitHub with its section about neural networks and machine learning still may be worth a visit e.g. how let Fortran reach out for the implementations in other languages.

  • SaaSHub

    SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives

    SaaSHub logo
NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

Suggest a related project

Related posts

  • What Apple hardware do I need for CUDA-based deep learning tasks?

    3 projects | /r/macbook | 27 May 2023
  • Yann Lecun: ML would have advanced if other lang had been adopted versus Python

    9 projects | news.ycombinator.com | 22 Feb 2023
  • AI enthusiasm #9 - A multilingual chatbot📣🈸

    6 projects | dev.to | 1 May 2024
  • What’s the Difference Between Fine-tuning, Retraining, and RAG?

    1 project | dev.to | 8 Apr 2024
  • How to Forecast Air Temperatures with AI + IoT Sensor Data

    3 projects | dev.to | 24 Mar 2024