Difference between revisions of "Neural net software notes"
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* if looking to experiment, probably look at keras instead | * if looking to experiment, probably look at keras instead | ||
* GPU: CUDA | * GPU: CUDA | ||
| + | * CPU: yes | ||
Tensorflow | Tensorflow | ||
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* varying frontends | * varying frontends | ||
* GPU: CUDA | * GPU: CUDA | ||
| + | * CPU: yes | ||
* multi-node: yes | * multi-node: yes | ||
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* not always tweakable | * not always tweakable | ||
* GPU: yes (see backends; CUDA) | * GPU: yes (see backends; CUDA) | ||
| + | * CPU: yes | ||
* usable as API/frontend to TensorFlow, Theano, CNTK | * usable as API/frontend to TensorFlow, Theano, CNTK | ||
* creator/backer: google | * creator/backer: google | ||
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* can be annoying to compile/install | * can be annoying to compile/install | ||
* GPU: yes, but a little work | * GPU: yes, but a little work | ||
| + | * CPU: yes | ||
* multi-node: yes (MPI) | * multi-node: yes (MPI) | ||
* C++ | * C++ | ||
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Torch | Torch | ||
* itself C, with lua wrapper | * itself C, with lua wrapper | ||
| + | * CPU: yes{{verify}} | ||
Pytorch wraps the torch binaries with python wrapper (and also something of a successor{{verify}}) | Pytorch wraps the torch binaries with python wrapper (and also something of a successor{{verify}}) | ||
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* caffe2 is merging into pytorch | * caffe2 is merging into pytorch | ||
| + | |||
| + | darknet | ||
| + | * GPU: CUDA | ||
| + | * CPU: yes | ||
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* creator/backer: microsoft | * creator/backer: microsoft | ||
* GPU: CUDA | * GPU: CUDA | ||
| − | + | * CPU: yes | |
MXNet | MXNet | ||
* creator/backer: Microsoft/Amazon | * creator/backer: Microsoft/Amazon | ||
* language/wrapper: R, C, python, Js | * language/wrapper: R, C, python, Js | ||
| + | * GPU: CUDA | ||
| + | * CPU: yes | ||
Chainer | Chainer | ||
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* easy an fast, apparently | * easy an fast, apparently | ||
* GPU: CUDA | * GPU: CUDA | ||
| + | * CPU: yes | ||
* smaller community | * smaller community | ||
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* smaller community, though | * smaller community, though | ||
* GPU: yes | * GPU: yes | ||
| + | * CPU: yes | ||
* scaling: yes | * scaling: yes | ||
Static versus dynamic graphs: | Static versus dynamic graphs: | ||
| − | + | : Static means you have to define the graph before you run it, which is perfectly good for pre-set jobs, and sometimes faster. | |
| − | Static means you have to define the graph before you run it, which is good for pre-set jobs | + | : Dynamic means it can change during execution, which can be more powerful for e.g. unstructured data and RNNs - while they're not necessary for e.g. most image / CNN work. |
| − | + | ||
| − | Dynamic means it can change during execution, which can be more powerful for e.g. unstructured data and RNNs - while they're not necessary for most image / CNN work. | + | |
| − | + | ||
| − | + | ||
