[D] Facebook Visdom vs Google Tensorboard for Pytorch

This page summarizes the projects mentioned and recommended in the original post on reddit.com/r/MachineLearning

Our great sponsors
  • Scout APM - Less time debugging, more time building
  • OPS - Build and Run Open Source Unikernels
  • SonarQube - Static code analysis for 29 languages.
  • visdom

    A flexible tool for creating, organizing, and sharing visualizations of live, rich data. Supports Torch and Numpy. (by fossasia)

    It seems that some people are still working on it https://github.com/fossasia/visdom

  • clearml

    ClearML - Auto-Magical CI/CD to streamline your ML workflow. Experiment Manager, MLOps and Data-Management

    I'm talking about ClearML😅 trying not to shill for open-source but ~5000 teams have already chosen 💪 https://github.com/allegroai/clearml

  • Scout APM

    Less time debugging, more time building. Scout APM allows you to find and fix performance issues with no hassle. Now with error monitoring and external services monitoring, Scout is a developer's best friend when it comes to application development.

  • omniboard

    Web-based dashboard for Sacred

    I'm using Omniboard (https://github.com/vivekratnavel/omniboard) with Sacred (https://github.com/IDSIA/sacred) for tracking experiments. You can specify custom Observers in Sacred so the model metrics and logs will be saved to a local directory or to a remote DB (e.g., MongoDB). I use a MongoDB database hosted on Atlas. Unlike other suggested options, Sacred and Omniboard are free. Atlas free tier comes with 512MB of free storage which is a huge amount if you're uploading only log files to it. ex = Experiment() ex.observers.append(FileStorageObserver(EXPERIMENTS_ROOT)) ex.observers.append(MongoObserver(url=MONGODB_URL, db_name='sacred'))

  • Sacred

    Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.

    I'm using Omniboard (https://github.com/vivekratnavel/omniboard) with Sacred (https://github.com/IDSIA/sacred) for tracking experiments. You can specify custom Observers in Sacred so the model metrics and logs will be saved to a local directory or to a remote DB (e.g., MongoDB). I use a MongoDB database hosted on Atlas. Unlike other suggested options, Sacred and Omniboard are free. Atlas free tier comes with 512MB of free storage which is a huge amount if you're uploading only log files to it. ex = Experiment() ex.observers.append(FileStorageObserver(EXPERIMENTS_ROOT)) ex.observers.append(MongoObserver(url=MONGODB_URL, db_name='sacred'))

  • MLflow

    Open source platform for the machine learning lifecycle

    Oh I think most of the paid tracking solutions have auto refresh. As for the free ones? At clear.ml we have them for quite a while, for MLflow there is an open feature request. https://github.com/mlflow/mlflow/issues/2099

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