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This repository has been archived by the owner on Jun 14, 2024. It is now read-only.
I'm a member of Deep Numpy (a new MXNet frontend API with numpy like interface: https://numpy.mxnet.io/index.html) dev team and I'm currently working on the random module for Deep Numpy, which, as its name, has behavior exactly like Numpy, including broadcastable parameters and output shape, for example: apache/mxnet#15858
Also, sampling backend for rejection sampling is entirely rewritten to remove while loop from GPU Kernel. The new version, according to my profiling, performs ten times faster than nd.random on GPU at the cost of tiny increase in memory usage. (not merged yet) apache/mxnet#15928
We could have some further discussion if you have interests in switching to deep numpy's random sampling backend, or, migrating MXFusion to Deep Numpy API :-)
The text was updated successfully, but these errors were encountered:
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Hi,
I'm a member of Deep Numpy (a new MXNet frontend API with numpy like interface: https://numpy.mxnet.io/index.html) dev team and I'm currently working on the random module for Deep Numpy, which, as its name, has behavior exactly like Numpy, including broadcastable parameters and output shape, for example:
apache/mxnet#15858
Also, sampling backend for rejection sampling is entirely rewritten to remove while loop from GPU Kernel. The new version, according to my profiling, performs ten times faster than
nd.random
on GPU at the cost of tiny increase in memory usage. (not merged yet)apache/mxnet#15928
We could have some further discussion if you have interests in switching to deep numpy's random sampling backend, or, migrating MXFusion to Deep Numpy API :-)
The text was updated successfully, but these errors were encountered: