# CUDA 8 + VS2015 corecrt.h error

**URL:** <https://forums.developer.nvidia.com/t/cuda-8-vs2015-corecrt-h-error/45263>\
**Category:** CUDA Setup and Installation\
**Created:** [September 30, 2016, 8:37pm UTC](https://forums.developer.nvidia.com/t/cuda-8-vs2015-corecrt-h-error/45263 "2016-09-30T20:37:13Z")\
**Posts on this page:** 1\
**Showing post:** 5

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**Author:** ![PF1](https://sea2.discourse-cdn.com/nvidia/user_avatar/forums.developer.nvidia.com/pf1/32/143626_2.png) [@PF1](https://forums.developer.nvidia.com/u/PF1)\
**Post date:** [October 5, 2016, 11:47pm UTC](https://forums.developer.nvidia.com/t/cuda-8-vs2015-corecrt-h-error/45263/5 "2016-10-05T23:47:45Z")

</div>

Okay. I found a “permanent” solution - feel free to pinch your nose now - by hardcoding two sysenv vars:

```auto
INCLUDE="C:\Program Files (x86)\Windows Kits0\Include0.0.10240.0\ucrt"
LIB="C:\Program Files (x86)\Windows Kits0\Lib0.0.10240.0\um\x64;C:\Program Files (x86)\Windows Kits0\Lib0.0.10240.0\ucrt\x64"

```

Yes, I know…

Gory details for my Keras1.1.0 + Theano0.8.2 + VS2015 + CUDA8.0 + cuDNN5.1 setup for Windows 10, here:

> **[GitHub - philferriere/dlwin: GPU-accelerated Deep Learning on Windows 10 native](https://github.com/philferriere/dlwin)**
>
> GPU-accelerated Deep Learning on Windows 10 native

Tested on the following hardware:

- Dell Precision T7900, 64GB RAM [Intel Xeon E5-2630 v4 @ 2.20 GHz (1 processor, 10 cores total, 20 logical processors)]
- NVIDIA GeForce Titan X, 12GB RAM [Driver version: 372.90 / Win 10 64]

Uses the following tools/libraries:

- Visual Studio 2015 Community Edition Update 3 w. Windows Kit 10.0.10240.0 [Used for its C/C++ compiler (not its IDE) and SDK]
- CUDA 8.0.44 (64-bit) [Used for its GPU math libraries, card driver, and CUDA compiler]
- MinGW-w64 (5.4.0) [Used for its Unix-like compiler and build tools (g++/gcc, make…) for Windows]
- Anaconda (64-bit) w. Python 2.7 (Anaconda2-4.2.0) [A Python distro that gives us NumPy, SciPy, and other scientific libraries]
- Theano 0.8.2 [Used to evaluate mathematical expressions on multi-dimensional arrays]
- Keras 1.1.0 [Used for deep learning on top of Theano]
- OpenBLAS 0.2.14 (Optional) [Used for its CPU-optimized implementation of many linear algebra operations]
- cuDNN v5.1 (August 10, 2016) for CUDA 8.0 (Recommended) [Used to run vastly faster convolution neural networks]

Hope this helps!

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