pyro
Deep universal probabilistic programming with Python and PyTorch (by pyro-ppl)
Sacred
Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA. (by IDSIA)
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| pyro | Sacred | |
|---|---|---|
| 9 | 6 | |
| 8,356 | 4,157 | |
| 0.9% | 0.4% | |
| 8.4 | 3.5 | |
| 6 days ago | 2 months ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
pyro
Posts with mentions or reviews of pyro.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-01-11.
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Show HN: Designing Bridges with PyTorch
Mostly I use pytorch for statistical modeling https://pyro.ai . Under the hood that package uses a lot of Monte Carlo integration and variational methods (i.e. integration by optimization). It does support neural nets, but probably >80% of pyro users stick to simpler hierarchical Bayesian models.
- Pyro: The Universal, Probablistic Programming Language
- The Jupyter+Git problem is now solved
- Pyro: Deep universal probabilistic programming with Python and PyTorch
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Computational Bayesian Inference Techniques
Amortized Variational Inference (Like done in pyro.ai with neural networks)
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[P] torchegranate: a PyTorch rewrite of the pomegranate library for probabilistic modeling
Can you compare this to Pyro, which is also built on top of PyTorch?
- [Q] Updated book or review paper on MCMC methods
- Is anyone here working in uncertainty estimation in neural networks?
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[D] Do you train and deploy models using just one framework or multiple frameworks at work?
Using pyod, statmodels, scikit-learn, Tensorflow and pyro.ai (that is using PyTorch as backend). I always use the same framework for training and for production.
Sacred
Posts with mentions or reviews of Sacred.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-12-05.
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Sacred VS cascade - a user suggested alternative
2 projects | 5 Dec 2023
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✨ 7 Best Machine Learning Experiment Logging Tools in 2022 🚀
🔗 https://github.com/IDSIA/sacred
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https://np.reddit.com/r/MachineLearning/comments/pvs8r5/d_facebook_visdom_vs_google_tensorboard_for/hefg131/
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.
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[D] Facebook Visdom vs Google Tensorboard for Pytorch
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'))
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Can someone tell me good libraries you use on a day to day basis that increases your research productivity in ML/AI?
sacred helped me log my experiments. I did setup my environment only once 4 years ago, and since then I have a list of all my training runs with the hyperparameters and results.
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[D] How to be more productive while doing Deep Learning experiments?
For 1, setup an experiment tracking framework. I found Sacred to be helpful https://github.com/IDSIA/sacred.
What are some alternatives?
When comparing pyro and Sacred you can also consider the following projects:
PyMC - Bayesian Modeling and Probabilistic Programming in Python
MLflow - Open source platform for the machine learning lifecycle
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]
scikit-learn - scikit-learn: machine learning in Python
tensorflow - An Open Source Machine Learning Framework for Everyone
trueskill - An implementation of the TrueSkill rating system for Python
Keras - Deep Learning for humans
probability - Probabilistic reasoning and statistical analysis in TensorFlow
Clairvoyant - Software designed to identify and monitor social/historical cues for short term stock movement