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authorEvan Shelhamer <shelhamer@imaginarynumber.net>2016-04-13 23:40:30 (GMT)
committerEvan Shelhamer <shelhamer@imaginarynumber.net>2016-04-14 02:09:59 (GMT)
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parent6ed3b2ac99fb0c792208c981fe7bb2b096f63e28 (diff)
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[docs] install: CUDA 7+ and cuDNN v4 compatible
Latest CUDA versions are all compatible, and Caffe has been compatible with cuDNN v4 since PR #3439
Diffstat (limited to 'docs')
-rw-r--r--docs/installation.md6
1 files changed, 3 insertions, 3 deletions
diff --git a/docs/installation.md b/docs/installation.md
index 8931645..e6c6886 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -20,7 +20,7 @@ When updating Caffe, it's best to `make clean` before re-compiling.
Caffe has several dependencies:
* [CUDA](https://developer.nvidia.com/cuda-zone) is required for GPU mode.
- * library version 7.0 and the latest driver version are recommended, but 6.* is fine too
+ * library version 7+ and the latest driver version are recommended, but 6.* is fine too
* 5.5, and 5.0 are compatible but considered legacy
* [BLAS](http://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms) via ATLAS, MKL, or OpenBLAS.
* [Boost](http://www.boost.org/) >= 1.55
@@ -30,14 +30,14 @@ Optional dependencies:
* [OpenCV](http://opencv.org/) >= 2.4 including 3.0
* IO libraries: `lmdb`, `leveldb` (note: leveldb requires `snappy`)
-* cuDNN for GPU acceleration (v3)
+* cuDNN for GPU acceleration (v4)
Pycaffe and Matcaffe interfaces have their own natural needs.
* For Python Caffe: `Python 2.7` or `Python 3.3+`, `numpy (>= 1.7)`, boost-provided `boost.python`
* For MATLAB Caffe: MATLAB with the `mex` compiler.
-**cuDNN Caffe**: for fastest operation Caffe is accelerated by drop-in integration of [NVIDIA cuDNN](https://developer.nvidia.com/cudnn). To speed up your Caffe models, install cuDNN then uncomment the `USE_CUDNN := 1` flag in `Makefile.config` when installing Caffe. Acceleration is automatic. The current version is cuDNN v3; older versions are supported in older Caffe.
+**cuDNN Caffe**: for fastest operation Caffe is accelerated by drop-in integration of [NVIDIA cuDNN](https://developer.nvidia.com/cudnn). To speed up your Caffe models, install cuDNN then uncomment the `USE_CUDNN := 1` flag in `Makefile.config` when installing Caffe. Acceleration is automatic. The current version is cuDNN v4; older versions are supported in older Caffe.
**CPU-only Caffe**: for cold-brewed CPU-only Caffe uncomment the `CPU_ONLY := 1` flag in `Makefile.config` to configure and build Caffe without CUDA. This is helpful for cloud or cluster deployment.