example-get-started
Get started DVC project (by iterative)
Sacred
Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA. (by IDSIA)
Our great sponsors
example-get-started | Sacred | |
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2 | 6 | |
167 | 4,157 | |
0.0% | 0.4% | |
0.0 | 3.5 | |
11 days ago | 2 months ago | |
Python | Python | |
- | 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.
example-get-started
Posts with mentions or reviews of example-get-started.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-06-27.
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VS Code extension to track ML experiments
Or open this project https://github.com/iterative/example-get-started in GitHub Codespaces as an example. It will run the extension in Codespaces automatically.
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Tuning Hyperparameters with Reproducible Experiments
We're going to be working with an existing NLP project. You can get the code we're working with in this repo. It already has DVC set up, but you can check out the Get Started docs if you want to know how the DVC pipeline was created.
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.