autoai
nni
| autoai | nni | |
|---|---|---|
| 3 | 5 | |
| 166 | 13,742 | |
| 2.4% | 0.5% | |
| 5.4 | 6.7 | |
| 3 months ago | about 2 months ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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autoai
- [P] AutoAI – A framework to find the best performing AI/ML model
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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/...
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
What are some alternatives?
adanet - Fast and flexible AutoML with learning guarantees.
optuna - A hyperparameter optimization framework
ds2ai-python - The MLOps platform for innovators 🚀
FLAML - A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
automlbenchmark - OpenML AutoML Benchmarking Framework
autogluon - Fast and Accurate ML in 3 Lines of Code
ds2 - Easiest way to use AI models without coding (Web UI & API support)
AutoML - This is a collection of our NAS and Vision Transformer work. [Moved to: https://github.com/microsoft/Cream]
SAP-HANA-AutoML - Python Automated Machine Learning library for tabular data.
hyperopt - Distributed Asynchronous Hyperparameter Optimization in Python
DeepCamera - Open-Source AI Camera. Empower any camera/CCTV with state-of-the-art AI, including facial recognition, person recognition(RE-ID) car detection, fall detection and more
archai - Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research.