Deep Learning Frameworks
There have been many Deep Learning (DL) frameworks, like Theano, CNTK, Caffe2, and MXNet. Nowadays, some of them appear to be dead or dying, as just two frameworks heavily dominate the DL scene: Google TensorFlow (TF) and PyTorch from Meta aka FaceBook.
However, there is no reason to believe such a duopoly will persist forever. Cause new DL frameworks are proposed here and there. So it is hard to say which DL framework will be popular in ten years later..., for example, Google has at least two (perhaps more) competing AI teams: Google Brain and DeepMind. Even in the TensorFlow era, DeepMind used their own layer API called Sonnet (instead of the usual Keras)
As of March'23, someone[1] said that TensorFlow and PyTorch like a wooden bear and teddy bear. There is so much love in teddy bears. PyTorch has logical components that perfectly fit together to form a product.
Popular Deep Learning Framework
| Framework | HPCMATE custom build[2] | Developer | Status as of 2023 | Language | GPU Support | Distributed Computing | Auto-differentiation | Pre-trained models | Visualization | Deployment |
|---|---|---|---|---|---|---|---|---|---|---|
| TensorFlow | Yes | Python | Yes | Yes | Yes | Yes | Yes | Yes | ||
| PyTorch | Yes | Python | Yes | Yes | Yes | Yes | Yes | Yes | ||
| Keras | NA | Merged into TensorFlow | Python | Yes | Yes | Yes | Yes | Yes | Yes | |
| MXNet | Apache | Multiple | Yes | Yes | Yes | Yes | Yes | Yes | ||
| Caffe | NA | Berkeley AI Research | Caffe2 is built on Caffe and is merged into Meta’s PyTorch
Yahoo has also integrated Caffe with Apache Spark to create CaffeOnSpark, which brings deep learning to Hadoop and Spark clusters |
C++ | Yes | No | Yes | Yes | No | Yes |
| Theano | NA | Université de Montréal | Theano is dead now but the other open-source deep libraries which are built on top of Theano are still functioning; these include Keras, Lasagne, and Blocks | Python | Yes | No | Yes | Yes | No | No |
Reference
<references/>
- ↑ https://medium.com/@andreiliphd
- ↑ HPCMATE supports custom build service to optimize binary performance on given system based on customer requirements
- ↑ https://github.com/pytorch/pytorch/releases
- ↑ https://synced.medium.com/caffe2-merges-with-pytorch-a89c70ad9eb7