# StandardCaffeWindowsVersion1

**Repository Path**: glgoo/StandardCaffeWindowsVersion1

## Basic Information

- **Project Name**: StandardCaffeWindowsVersion1
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-01-02
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

Forked from https://www.github.com/BVLC/caffe master branch in 2016/02/04 . Next update time may be around 2016/05/01 .

I have made a list of some frequently asked questions in [FAQ.md](FAQ.md). If you get confused during configuring, please firstly look up for your question in the [FAQ.md](FAQ.md). This FAQ list is still under construction, I will keep adding questions into it.

Setup step:
======

1. Download third-party libraries from [BaiduYun Disk](http://pan.baidu.com/s/1sk3QyyL) or [OneDrive](http://1drv.ms/23KkbTk) and extract the files to `caffe-windows_root/3rdparty/`. **Please don't forget to add the `./3rdparty/bin` folder to your environment variable `PATH`.**

2. Run `./src/caffe/proto/extract_proto.bat` to create `caffe.pb.h`, `caffe.pb.cc` and `caffe_pb2.py`.

3. Double click ./buildVS2013/MainBuilder.sln to open the solution. If you do not have a Nvidia GPU, please open ./build_cpu_only/MainBuilder.sln.

4. Change the compile mode to Release and X64.

5. Modify the cuda device compute capability defined in the settings (`caffelib properties` -> `CUDA C/C++` -> `Device` -> `Code Generation`) to your GPU's compute capability (such as compute_30,sm_30; etc). You can look up for your GPU's compute capability in https://en.wikipedia.org/wiki/CUDA . Some general GPUs' compute capabilities are listed below.

 - If your GPU's compute capability is below or equal to 2.1, please remove the `USE_CUDNN` macro in the proprocessor definition of all projects.
 - If you are using cpu only solution, just ignore this step.

6. Compile.

| GPU                                         | Compute Capability    |
| ------------------------------------------- |:---------------------:|
| GTX660, 680, 760, 770                       | compute_30,sm_30      |
| GTX780, Titan Z, Titan Black, K20, K40      | compute_35,sm_35      |
| GTX960, 980, Titan X                        | compute_52,sm_52      |


中文安装说明：http://blog.csdn.net/happynear/article/details/45372231

Matlab Wrapper
======
Just replace the Matlab include and library path defined in the settings and compile.
**Don't forget to add `./matlab` to your Matlab path.**

Python Wrapper
======
Similar with Matlab, replace the python include and library path and compile.

Most of the libraries listed in `./python/requirements.txt` can be installed by `pip install`. However, some of them cannot be installed so easily.

For protobuf, you may download the codes from https://github.com/google/protobuf. Copy `caffe-windows-root/src/caffe/proto/protoc.exe` to `protobuf-root/src`. Then run `python setup.py install` in `protobuf-root/python`.

For leveldb, I have created a repository https://github.com/happynear/py-leveldb-windows . Please follow the instructions in `README.md` to install it.

MNIST example
======
Please download the mnist leveldb database from http://pan.baidu.com/s/1mgl9ndu and extract it to `./examples/mnist`. Then double click `./run_mnist.bat` to run the MNIST demo.

Update log
======
2015/11/09 CuDNN v3 works well now.

2015/09/14 Multi-GPU is supported now. 

WARNING: When you are using multiple gpus to train a model, please do not directly close the command window. Instead, please use `Ctrl+C` to avoid the gpu driver from crash.

You can also press `Ctrl+Break` to save a model snapshot whenever you want during training.

Acknowlegement
======
We greatly thank [Yangqing Jia](https://github.com/Yangqing) and [BVLC group](https://www.github.com/BVLC/caffe) for developing Caffe,

[@niuzhiheng](https://github.com/niuzhiheng) for his contribution on the first generation of caffe-windows,

[@ChenglongChen](https://github.com/ChenglongChen/batch_normalization) for his implementation of Batch Normalization,

[@jackculpepper](https://github.com/jackculpepper/caffe) for his implementation of locally-connected layer,

and all people who have contributed to the caffe user group.