Installation Guide


You need to have the following components to use CuPy.

    • Compute Capability of the GPU must be at least 3.0.
  • CUDA Toolkit
    • Supported Versions: 7.0, 7.5, 8.0, 9.0 and 9.1.
    • If you have multiple versions of CUDA Toolkit installed, CuPy will choose one of the CUDA installations automatically. See Working with Custom CUDA Installation for details.
  • Python
    • Supported Versions: 2.7.6+, 3.4.3+, 3.5.1+ and 3.6.0+.
  • NumPy
    • Supported Versions: 1.9, 1.10, 1.11, 1.12 and 1.13.
    • NumPy will be installed automatically during the installation of CuPy.

Before installing CuPy, we recommend you to upgrade setuptools and pip:

$ pip install -U setuptools pip

Optional Libraries

Some features in CuPy will only be enabled if the corresponding libraries are installed.

  • cuDNN (library to accelerate deep neural network computations)
    • Supported Versions: v4, v5, v5.1, v6, v7
  • NCCL (library to perform collective multi-GPU / multi-node computations)
    • Supported Versions: v1.3.4, v2

Install CuPy

Wheels (precompiled binary packages) are available for the recommended environments above. Package names are different depending on the CUDA version you have installed on your host.

(For CUDA 8.0)
$ pip install cupy-cuda80

(For CUDA 9.0)
$ pip install cupy-cuda90

(For CUDA 9.1)
$ pip install cupy-cuda91


The latest version of cuDNN and NCCL libraries are included in these wheels. You don’t have to install them manually.

When using wheels, please be careful not to install multiple CuPy packages at the same time. Any of these packages and cupy package (source installation) conflict with each other. Please make sure that only one CuPy package (cupy or cupy-cudaXX where XX is a CUDA version) is installed:

$ pip freeze | grep cupy

Install CuPy from Source

It is recommended to use wheels whenever possible. However, if wheels cannot meet your requirements (e.g., you are running non-Linux environment or want to use a version of CUDA / cuDNN / NCCL not supported by wheels), you can also build CuPy from source.

When installing from source, C++ compiler such as g++ is required. You need to install it before installing CuPy. This is typical installation method for each platform:

# Ubuntu 14.04
$ apt-get install g++

# CentOS 7
$ yum install gcc-c++


When installing CuPy from source, features provided by optional libraries (cuDNN and NCCL) will be disabled if these libraries are not available at the time of installation. See Installing cuDNN and NCCL for the instructions.


If you upgrade or downgrade the version of CUDA Toolkit, cuDNN or NCCL, you may need to reinstall CuPy. See Reinstall CuPy for details.

Using pip

You can install CuPy package via pip.

$ pip install cupy

Using Tarball

The tarball of the source tree is available via pip download cupy or from the release notes page. You can install CuPy from the tarball:

$ pip install cupy-x.x.x.tar.gz

You can also install the development version of CuPy from a cloned Git repository:

$ git clone
$ cd cupy
$ pip install .

If you are using source tree downloaded from GitHub, you need to install Cython 0.26.1 or later (pip install cython).

Uninstall CuPy

Use pip to uninstall CuPy:

$ pip uninstall cupy


When you upgrade Chainer, pip sometimes installs the new version without removing the old one in site-packages. In this case, pip uninstall only removes the latest one. To ensure that CuPy is completely removed, run the above command repeatedly until pip returns an error.


If you are using a wheel, cupy shall be replaced with cupy-cudaXX (where XX is a CUDA version number).

Upgrade CuPy

Just use pip install with -U option:

$ pip install -U cupy


If you are using a wheel, cupy shall be replaced with cupy-cudaXX (where XX is a CUDA version number).

Reinstall CuPy

If you want to reinstall CuPy, please uninstall CuPy and then install it. When reinstalling CuPy, we recommend to use --no-cache-dir option as pip caches the previously built binaries:

$ pip uninstall cupy
$ pip install cupy --no-cache-dir


If you are using a wheel, cupy shall be replaced with cupy-cudaXX (where XX is a CUDA version number).

Run CuPy with Docker

We are providing the official Docker image. Use nvidia-docker command to run CuPy image with GPU. You can login to the environment with bash, and run the Python interpreter:

$ nvidia-docker run -it cupy/cupy /bin/bash

Or run the interpreter directly:

$ nvidia-docker run -it cupy/cupy /usr/bin/python


Warning message “cuDNN is not enabled” appears when using Chainer

You failed to build CuPy with cuDNN. If you don’t need cuDNN, ignore this message. Otherwise, retry to install CuPy with cuDNN.

See Installing cuDNN and NCCL and pip fails to install CuPy for details.

pip fails to install CuPy

Please make sure that you are using the latest setuptools and pip:

$ pip install -U setuptools pip

Use -vvvv option with pip command. This will display all logs of installation:

$ pip install cupy -vvvv

If you are using sudo to install CuPy, note that sudo command does not propagate environment variables. If you need to pass environment variable (e.g., CUDA_PATH), you need to specify them inside sudo like this:

$ sudo CUDA_PATH=/opt/nvidia/cuda pip install cupy

If you are using certain versions of conda, it may fail to build CuPy with error g++: error: unrecognized command line option ‘-R’. This is due to a bug in conda (see conda/conda#6030 for details). If you encounter this problem, please downgrade or upgrade it.

Installing cuDNN and NCCL

We recommend installing cuDNN and NCCL using binary packages (i.e., using apt or yum) provided by NVIDIA.

If you want to install tar-gz version of cuDNN and NCCL, we recommend you to install it under CUDA directory. For example, if you are using Ubuntu, copy *.h files to include directory and *.so* files to lib64 directory:

$ cp /path/to/cudnn.h $CUDA_PATH/include
$ cp /path/to/* $CUDA_PATH/lib64

The destination directories depend on your environment.

If you want to use cuDNN or NCCL installed in another directory, please use CFLAGS, LDFLAGS and LD_LIBRARY_PATH environment variables before installing CuPy:

export CFLAGS=-I/path/to/cudnn/include
export LDFLAGS=-L/path/to/cudnn/lib
export LD_LIBRARY_PATH=/path/to/cudnn/lib:$LD_LIBRARY_PATH


Use full paths for the environment variables. distutils that is used in the setup script does not expand the home directory mark ~.

Working with Custom CUDA Installation

If you have installed CUDA on the non-default directory or have multiple CUDA versions installed, you may need to manually specify the CUDA installation directory to be used by CuPy.

CuPy uses the first CUDA installation directory found by the following order.

  1. CUDA_PATH environment variable.
  2. The parent directory of nvcc command. CuPy looks for nvcc command in each directory set in PATH environment variable.
  3. /usr/local/cuda

For example, you can tell CuPy to use non-default CUDA directory by CUDA_PATH environment variable:

$ CUDA_PATH=/opt/nvidia/cuda pip install cupy


CUDA installation discovery is also performed at runtime using the rule above. Depending on your system configuration, you may also need to set LD_LIBRARY_PATH environment variable to $CUDA_PATH/lib64 at runtime.

Using custom nvcc command during installation

If you want to use a custom nvcc compiler (for example, to use ccache) to build CuPy, please set NVCC environment variables before installing CuPy:

export NVCC='ccache nvcc'


During runtime, you don’t need to set this environment variable since CuPy doesn’t use the nvcc command.

Installation for Developers

If you are hacking CuPy source code, we recommend you to use pip with -e option for editable mode:

$ cd /path/to/cupy/source
$ pip install -e .

Please note that even with -e, you will have to rerun pip install -e . to regenerate C++ sources using Cython if you modified Cython source files (e.g., *.pyx files).

CuPy always raises cupy.cuda.compiler.CompileException

If CuPy does not work at all with CompileException, it is possible that CuPy cannot detect CUDA installed on your system correctly. The followings are error messages commonly observed in such cases.

  • nvrtc: error: failed to load builtins
  • catastrophic error: cannot open source file "cuda_fp16.h"
  • error: cannot overload functions distinguished by return type alone
  • error: identifier "__half_raw" is undefined

Please try setting LD_LIBRARY_PATH and CUDA_PATH environment variable. For example, if you have CUDA installed at /usr/local/cuda-9.0:

export CUDA_PATH=/usr/local/cuda-9.0

Also see Working with Custom CUDA Installation.

If you are installing CuPy on Anaconda environment, also make sure that the following packages are not installed.

Use conda uninstall cudatoolkit cudnn nccl to remove these package.