Difference between revisions of "Caffe"
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If you want to use Caffe, you will have to load its corresponding module first. You can do that with the following command: | If you want to use Caffe, you will have to load its corresponding module first. You can do that with the following command: | ||
module load Caffe | module load Caffe | ||
This takes a few seconds because Caffe has quite a few dependencies. | This takes a few seconds because Caffe has quite a few dependencies, 72 including Caffe itself to be accurate. | ||
== Documentation == | == Documentation == | ||
Serveral different documentations can be found on the [http://caffe.berkeleyvision.org/ official homepage] of Caffe. | Serveral different documentations can be found on the [http://caffe.berkeleyvision.org/ official homepage] of Caffe. | ||
Revision as of 11:17, 8 February 2018
Introduction
Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR) and by community contributors. Yangqing Jia created the project during his PhD at UC Berkeley. Caffe is released under the BSD 2-Clause license.
Installed version
The currently installed version is 1.0. The full module name is Caffe/1.0-intel-2016b-CUDA-8.0.61-Python-2.7.12.
Using Caffe with the HPC cluster
If you want to use Caffe, you will have to load its corresponding module first. You can do that with the following command:
module load Caffe
This takes a few seconds because Caffe has quite a few dependencies, 72 including Caffe itself to be accurate.
Documentation
Serveral different documentations can be found on the official homepage of Caffe.
