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| author | Evan Shelhamer <shelhamer@imaginarynumber.net> | 2014-04-02 18:48:45 -0700 |
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| committer | Evan Shelhamer <shelhamer@imaginarynumber.net> | 2014-04-02 18:48:45 -0700 |
| commit | 1406cc8eba3721328e8c0c62f91074b3a0db912d (patch) | |
| tree | 445b7fd5f76e4fa29f5a1c2c11d927856106dbdb /README.md | |
| parent | 8472041c0b9fde733b2a9ba4f1d407f2e7616559 (diff) | |
| download | caffeonacl-1406cc8eba3721328e8c0c62f91074b3a0db912d.tar.gz caffeonacl-1406cc8eba3721328e8c0c62f91074b3a0db912d.tar.bz2 caffeonacl-1406cc8eba3721328e8c0c62f91074b3a0db912d.zip | |
Include k40 images per day benchmark
Diffstat (limited to 'README.md')
| -rw-r--r-- | README.md | 4 |
1 files changed, 2 insertions, 2 deletions
@@ -12,8 +12,8 @@ parameters in the code. Python and Matlab wrappers are provided. At the same time, Caffe fits industry needs, with blazing fast C++/Cuda code for GPU computation. Caffe is currently the fastest GPU CNN implementation publicly -available, and is able to process more than **20 million images per day** on a -single Tesla K20 machine \*. +available, and is able to process more than **40 million images per day** on a +single NVIDIA K40 GPU (or 20 million per day on a K20)\*. Caffe also provides **seamless switching between CPU and GPU**, which allows one to train models with fast GPUs and then deploy them on non-GPU clusters with one |