DeeFunia
DeeFunia is an open-source deep learning platform for creating, training and deploying AI models. It provides a user-friendly interface and supports all major frameworks like TensorFlow, PyTorch and Keras.
deep-learning
ai-models
open-source
DeeFunia: Open-Source Deep Learning Platform
DeeFunia is an open-source deep learning platform for creating, training and deploying AI models. It provides a user-friendly interface and supports all major frameworks like TensorFlow, PyTorch and Keras.
What is DeeFunia?
DeeFunia is an open-source deep learning platform aimed at making AI more accessible. It provides a graphical user interface that allows anyone to easily build, train and deploy deep learning models without coding.
Some key capabilities and benefits of DeeFunia include:
- Supports all popular AI frameworks like TensorFlow, PyTorch, Keras, etc. Models can be built visually via drag-and-drop or imported.
- Includes pre-built templates and examples for common tasks like image classification, object detection, text generation etc.
- Built-in datasetLoaders, preprocessors and augmentation tools to easily prepare data for training models.
- Flexibility to train models locally or leverage GPU/TPU clouds like Colab, Kaggle and Google Cloud.
- Models can be exported and productionized for real-time serving via APIs or batch processing.
- Community-updated model zoo containing thousands of pre-trained models across domains.
- Completely free and open-source under Apache 2.0 license. Can be self-hosted on-premise as well.
Overall, DeeFunia simplifies and accelerates deep learning application development for coders and non-coders alike. Its visual interface and full lifecycle support from data prep to deployment make AI more understandable and accessible.
DeeFunia Features
Features
- User-friendly interface
- Supports major frameworks like TensorFlow, PyTorch and Keras
- Tools for creating, training and deploying AI models
- Visual workflow editor
- Model catalog to manage different versions
- Monitoring and logging
- Supports distributed training across GPUs and TPUs
- AutoML functionality for automating model development
Pricing
- Open Source
Pros
Open source and free to use
Simplifies the model development process
Can scale to large datasets and models
Reduces time spent on coding and infrastructure
Cons
Limited community support compared to mature frameworks
Less flexibility than coding models directly in Python
Currently only supports Python, unlike frameworks with APIs in multiple languages
Official Links
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