Discover top open-source software, updated regularly with real-world adoption signals.
AutoML for deep learning that requires no coding expertise
AutoKeras automates model design and training for Keras/TensorFlow, letting users build high‑performing deep‑learning models with just a few lines of code.
AutoKeras is aimed at data scientists, educators, and small teams who want to apply deep learning without spending weeks on model engineering. By abstracting neural architecture search behind a simple API, it lowers the barrier for anyone familiar with Python and Keras.
The library automatically searches for optimal network structures for image, text, and tabular datasets, handling data preprocessing, model selection, and hyper‑parameter tuning. Users launch training with a single line—e.g., ImageClassifier().fit(x_train, y_train)—and obtain a ready‑to‑predict model. Because it builds on the standard Keras/TensorFlow stack, the resulting models integrate seamlessly with existing pipelines and can be exported for deployment on CPU, GPU, or edge devices.
Installation is performed via pip install autokeras, and the project supports Python ≥ 3.7 and TensorFlow ≥ 2.8.0. An active community provides support through GitHub Discussions, and contributions follow a clear guide. Academic backing and an Apache‑2.0 license make AutoKeras suitable for both research and production prototypes.
When teams consider AutoKeras, these hosted platforms usually appear on the same shortlist.
Cloud service for accelerating and managing the machine learning project lifecycle, including training and deployment of models
Automated machine learning platform for building AI models without coding
Unified ML platform for training, tuning, and deploying models
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
Image classification for a startup's product catalog
Trains a high‑accuracy classifier in minutes without writing custom CNN code.
Rapid prototyping of medical imaging models
Enables clinicians to experiment with different architectures and achieve competitive performance quickly.
Educational workshops on deep learning
Students can build and evaluate models using a single API, focusing on concepts rather than boilerplate.
Benchmarking neural architecture search algorithms
Researchers can compare AutoKeras' NAS results against baselines with minimal setup.
Python 3.7 or newer and TensorFlow 2.8.0 or later.
Via pip with the command `pip install autokeras`.
Yes, you can wrap custom models or define custom search spaces.
GPU acceleration is recommended for faster search, but CPU works for small tasks.
Use GitHub Discussions for questions and follow the contributing guide on the repository.
Project at a glance
ActiveLast synced 4 days ago
