Sacred
coddx-alpha
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| Sacred | coddx-alpha | |
|---|---|---|
| 6 | 2 | |
| 4,157 | 198 | |
| 0.4% | 0.5% | |
| 3.5 | 0.0 | |
| 2 months ago | over 1 year ago | |
| Python | TypeScript | |
| MIT License | MIT License |
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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.
coddx-alpha
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I tried many Todo apps and ended up creating an extension to manage my Todo list in Vscode.
Github repo: https://github.com/coddx-hq/coddx-alpha
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[D] How to be more productive while doing Deep Learning experiments?
Yes for deciding the order of experiments, I also like a Kanban board, like the other commenter suggested. There is a VSCode plugin that displays the content of a TODO.md as kanban board: https://github.com/coddx-hq/coddx-alpha
What are some alternatives?
MLflow - Open source platform for the machine learning lifecycle
todo.md - TODO.md file format - todomd.org
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]
TaskBoard - A Kanban-inspired app for keeping track of things that need to get done. (Don't forget to read the Wiki page!)
tensorflow - An Open Source Machine Learning Framework for Everyone
vscode-highlight - Advanced text highlighter based on regexes. Useful for todos, annotations etc.
Keras - Deep Learning for humans
detectron2 - Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.
Clairvoyant - Software designed to identify and monitor social/historical cues for short term stock movement
obsidian-toggle-list - This is a simple plugin for Obsidian to overwrite the default behavior of toggle checkbox status. Also, it offers a simple way to toggle through frequently used attributes: task states, task tags, highlighted list, etc.
scikit-learn - scikit-learn: machine learning in Python
Kolan - A collaborative Kanban manager with nested boards