cascade
Sacred
| cascade | Sacred | |
|---|---|---|
| 9 | 6 | |
| 16 | 4,160 | |
| - | 0.2% | |
| 9.3 | 3.5 | |
| 7 days ago | 3 months ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
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.
cascade
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modeldb VS cascade - a user suggested alternative
2 projects | 12 Dec 2023
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Sacred VS cascade - a user suggested alternative
2 projects | 5 Dec 2023
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keepsake VS cascade - a user suggested alternative
2 projects | 5 Dec 2023
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aim VS cascade - a user suggested alternative
2 projects | 5 Dec 2023
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guildai VS cascade - a user suggested alternative
2 projects | 5 Dec 2023
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metaflow VS cascade - a user suggested alternative
2 projects | 5 Dec 2023
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clearml VS cascade - a user suggested alternative
2 projects | 5 Dec 2023
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cascade alternatives - clearml and MLflow
3 projects | 1 Nov 2023
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Announcing Cascade
This is Cascade - very lightweight MLE solution for individuals and small teams
Sacred
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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?
deeplake - Database for AI. Store Vectors, Images, Texts, Videos, etc. Use with LLMs/LangChain. Store, query, version, & visualize any AI data. Stream data in real-time to PyTorch/TensorFlow. https://activeloop.ai
MLflow - Open source platform for the machine learning lifecycle
NVTabular - NVTabular is a feature engineering and preprocessing library for tabular data designed to quickly and easily manipulate terabyte scale datasets used to train deep learning based recommender systems.
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]
powershap - A power-full Shapley feature selection method.
tensorflow - An Open Source Machine Learning Framework for Everyone
zenml - ZenML 🙏: Build portable, production-ready MLOps pipelines. https://zenml.io.
Keras - Deep Learning for humans
ds2 - Easiest way to use AI models without coding (Web UI & API support)
Clairvoyant - Software designed to identify and monitor social/historical cues for short term stock movement
FeatureHub - The most comprehensive library of AI/ML features across multiple domains. Our goal is to create a dataset that serves as a valuable resource for researchers and data scientists worldwide
scikit-learn - scikit-learn: machine learning in Python