mljet
Sacred
| mljet | Sacred | |
|---|---|---|
| - | 6 | |
| 68 | 4,160 | |
| - | 0.2% | |
| 1.0 | 3.5 | |
| 19 days ago | 3 months ago | |
| Python | Python | |
| MIT License | MIT License |
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mljet
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Tracking mentions began in Dec 2020.
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?
MindsDB - The platform for customizing AI from enterprise data
MLflow - Open source platform for the machine learning lifecycle
Metrics - Machine learning evaluation metrics, implemented in Python, R, Haskell, and MATLAB / Octave
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]
H2O - H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
tensorflow - An Open Source Machine Learning Framework for Everyone
bodywork - ML pipeline orchestration and model deployments on Kubernetes.
Keras - Deep Learning for humans
python-recsys - A python library for implementing a recommender system
Clairvoyant - Software designed to identify and monitor social/historical cues for short term stock movement
NuPIC - Numenta Platform for Intelligent Computing is an implementation of Hierarchical Temporal Memory (HTM), a theory of intelligence based strictly on the neuroscience of the neocortex.
scikit-learn - scikit-learn: machine learning in Python