MMdnn
A comprehensive toolset for converting, visualizing, and managing deep learning models across multiple frameworks like TensorFlow, PyTorch, and Caffe.
What is MMdnn?
MMdnn is a comprehensive toolset for managing deep learning models, focusing on cross-framework interoperability. It solves the problem of model portability by allowing conversion between popular frameworks like TensorFlow, PyTorch, Caffe, and others, enabling developers to train in one environment and deploy in another.
Deep learning researchers, engineers, and developers who work with multiple frameworks and need to convert, visualize, or deploy models across different ecosystems.
It provides a universal, tested converter that supports a wide range of frameworks and models, reducing the complexity and time required for model migration and promoting framework-agnostic development.
Overview
MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML.
Use Cases
Best For
- Converting a TensorFlow model to PyTorch for deployment
- Visualizing the architecture of a Keras or Caffe neural network
- Deploying a trained model to mobile platforms like Android
- Migrating a model from an older framework like Caffe to a newer one like PyTorch
- Accelerating inference of a converted model using TensorRT
- Finding and experimenting with popular pre-trained models from a curated collection
Not Ideal For
- Projects that exclusively use a single deep learning framework with no cross-platform deployment needs
- Real-time systems requiring instantaneous model loading without conversion overhead or manual setup
- Models with novel or non-standard architectures not listed in the tested ImageNet models, such as custom GANs or transformers
Pros & Cons
Pros
Supports conversion between 8+ frameworks including Caffe, TensorFlow, PyTorch, and ONNX, as shown in the compatibility matrix, enabling flexible model portability.
Rigorously tested on popular ImageNet models like VGG19, ResNet variants, and MobileNet, ensuring reliable conversions for common neural network architectures.
Offers model visualization, retraining code generation, and deployment guides for Android and TensorRT, adding practical utilities beyond basic conversion.
Backed by Microsoft with clear contribution guidelines and ongoing updates for frameworks like Torch7, indicating sustained support and community involvement.
Cons
TensorFlow conversion is labeled experimental, and some frameworks like DarkNet are source-only or destination-only, limiting full bidirectional conversions and stability.
Primarily focused on ImageNet classification models; support for other types like object detection or RNNs is listed as ongoing and may be incomplete or unreliable.
Conversion requires specifying parameters like source node names and handling framework-specific quirks, which can be error-prone and time-consuming for complex models.
Frequently Asked Questions
Related Projects
ONNXOpen standard for machine learning interoperability
sklearn-porterTranspile trained scikit-learn estimators to C, Java, JavaScript and others.
treeliteUniversal model exchange and serialization format for decision tree forests
Found a gem we're missing?
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.