Deep Learning Frameworks: Difference between revisions
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!Developer | !Developer | ||
!Language | !Language | ||
!GPU Support | !GPU [[Support]] | ||
!Distributed Computing | !Distributed Computing | ||
!Auto-differentiation | !Auto-differentiation | ||
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Pytorch version 2.0<ref>https://github.com/pytorch/pytorch/releases</ref> released as of 12/2/22 | Pytorch version 2.0<ref>https://github.com/pytorch/pytorch/releases</ref> released as of 12/2/22 | ||
|Facebook | |Facebook | ||
Caffe2 has been merged into PyTorch as of Apr'2018 <ref>https://synced.medium.com/caffe2-merges-with-pytorch-a89c70ad9eb7</ref> | |||
|Python | |Python | ||
|Yes | |Yes | ||
Revision as of 16:54, 27 March 2023
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)
Popular Deep Learning Framework
| Framework | HPCMATE custom build[1] | Developer | Language | GPU Support | Distributed Computing | Auto-differentiation | Pre-trained models | Visualization | Deployment |
|---|---|---|---|---|---|---|---|---|---|
| TensorFlow | Yes | Python | Yes | Yes | Yes | Yes | Yes | Yes | |
| PyTorch | Yes
Pytorch version 2.0[2] released as of 12/2/22 |
Facebook
Caffe2 has been merged into PyTorch as of Apr'2018 [3] |
Python | Yes | Yes | Yes | Yes | Yes | Yes |
| Keras | Python | Yes | Yes | Yes | Yes | Yes | Yes | ||
| MXNet | Apache | Multiple | Yes | Yes | Yes | Yes | Yes | Yes | |
| Caffe | Berkeley AI Research | C++ | Yes | No | Yes | Yes | No | Yes | |
| Theano | Université de Montréal | Python | Yes | No | Yes | Yes | No | No | |
| Torch | Facebook AI Research | Lua | Yes | No | Yes | Yes | No | No |
Reference
<references/>
- ↑ 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