pytask
Sacred
| pytask | Sacred | |
|---|---|---|
| 1 | 6 | |
| 101 | 4,160 | |
| 5.0% | 0.2% | |
| 9.2 | 3.5 | |
| 7 days ago | 3 months ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | 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.
pytask
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?
EvalAI - :cloud: :rocket: :bar_chart: :chart_with_upwards_trend: Evaluating state of the art in AI
MLflow - Open source platform for the machine learning lifecycle
disentangling-vae - Experiments for understanding disentanglement in VAE latent representations
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]
torch-fidelity - High-fidelity performance metrics for generative models in PyTorch
tensorflow - An Open Source Machine Learning Framework for Everyone
benchmark_VAE - Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)
Keras - Deep Learning for humans
Clairvoyant - Software designed to identify and monitor social/historical cues for short term stock movement
scikit-learn - scikit-learn: machine learning in Python
xgboost - Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
detectron2 - Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.