nni
automlbenchmark
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| nni | automlbenchmark | |
|---|---|---|
| 5 | 3 | |
| 13,726 | 379 | |
| 0.9% | 3.2% | |
| 6.7 | 6.9 | |
| about 2 months ago | 10 days ago | |
| Python | Python | |
| MIT License | 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.
nni
- Filter Pruning for PyTorch
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Automated Machine Learning (AutoML) - 9 Different Ways with Microsoft AI
For a complete tutorial, navigate to this Jupyter Notebook: https://github.com/microsoft/nni/blob/master/examples/notebooks/tabular_data_classification_in_AML.ipynb
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[D] Efficient ways of choosing number of layers/neurons in a neural network
optuna, hyperopt, nni, plenty of less-known tools too.
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Top 10 Developer Trends, Sun Oct 18 2020
microsoft / nni
automlbenchmark
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Show HN: Web App with GUI for AutoML on Tabular Data
Here is benchmark done by independent team of researchers https://openml.github.io/automlbenchmark/
I think most of overfitting is avoided with early stoppoing technique.
The underfitting can be avoidwd with using large training time.
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Show HN: AutoAI
Your list excludes most of well-known open-source AutoML tools such as auto-sklearn, AutoGluon, LightAutoML, MLJarSupervised, etc. These tools have been very extensively benchmarked by the OpenML AutoML Benchmark (https://github.com/openml/automlbenchmark) and have papers published, so they are pretty well-known to the AutoML community.
Regarding H2O.ai: Frankly, you don't seem to understand H2O.ai's AutoML offerings.
I'm the creator of H2O AutoML, which is open source, and there's no "enterprise version" of H2O AutoML. The interface is simple -- all you need to specify is the training data and target. We have included DNNs in our set of models since the first release of the tool in 2017. Read more here: https://docs.h2o.ai/h2o/latest-stable/h2o-docs/automl.html We also offer full explainability for our models: https://docs.h2o.ai/h2o/latest-stable/h2o-docs/explain.html
H2O.ai develops another AutoML tool called Driverless AI, which is proprietary. You might be conflating the two. Neither of these tools need to be used on the H2O AI Cloud. Both tools pre-date our cloud by many years and can be used on a user's own laptop/server very easily.
Your Features & Roadmap list in the README indicates that your tool does not yet offer DNNs, so either you should update your post here or update your README if it's incorrect: https://github.com/blobcity/autoai/blob/main/README.md#featu...
Lastly, I thought I would mention that there's already an AutoML tool called "AutoAI" by IBM. Generally, it's not a good idea to have name collisions in a small space like the AutoML community. https://www.ibm.com/support/producthub/icpdata/docs/content/...
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Show HN: Mljar Automated Machine Learning for Tabular Data (Explanation,AutoDoc)
I'm also curious how does it compare! The package will be included in the newest comparison done by OpenML people https://github.com/openml/automlbenchmark
I have some old comparison of closed-source old system
What are some alternatives?
optuna - A hyperparameter optimization framework
autogluon - AutoGluon: Fast and Accurate ML in 3 Lines of Code
FLAML - A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
autokeras - AutoML library for deep learning
MindsDB - The platform for customizing AI from enterprise data
AutoML - This is a collection of our NAS and Vision Transformer work. [Moved to: https://github.com/microsoft/Cream]
adanet - Fast and flexible AutoML with learning guarantees.
hyperopt - Distributed Asynchronous Hyperparameter Optimization in Python
mljar-supervised - Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
archai - Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research.
Ray - Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.