m*****n
发帖数: 1631 | 1 Our software does deep learning training fully synchronously with very low
communication overhead. As a result, when we scaled to a large cluster with
100s of NVIDAI GPUs, it yielded record image recognition accuracy of 33.8%
on 7.5M images from the ImageNet-22k dataset vs the previous best published
result of 29.8% by Microsoft. A 4% increase in accuracy is a big leap
forward; typical improvements in the past have been less than 1%. Our
innovative distributed deep learning (DDL) approach enabled us to not just
improve accuracy, but also to train a ResNet-101 neural network model in
just 7 hours, by leveraging the power of 10s of servers, equipped with 100s
of NVIDIA GPUs; Microsoft took 10 days to train the same model. This
achievement required we create the DDL code and algorithms to overcome
issues inherent to scaling these otherwise powerful deep learning frameworks.
https://www.ibm.com/blogs/research/2017/08/distributed-deep-learning/ | a*****g
发帖数: 19398 | 2 不错啊。拿来练习围棋。
with
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【在 m*****n 的大作中提到】
: Our software does deep learning training fully synchronously with very low
: communication overhead. As a result, when we scaled to a large cluster with
: 100s of NVIDAI GPUs, it yielded record image recognition accuracy of 33.8%
: on 7.5M images from the ImageNet-22k dataset vs the previous best published
: result of 29.8% by Microsoft. A 4% increase in accuracy is a big leap
: forward; typical improvements in the past have been less than 1%. Our
: innovative distributed deep learning (DDL) approach enabled us to not just
: improve accuracy, but also to train a ResNet-101 neural network model in
: just 7 hours, by leveraging the power of 10s of servers, equipped with 100s
: of NVIDIA GPUs; Microsoft took 10 days to train the same model. This
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