Installation Guide


The following Linux distributions are recommended.

These components must be installed to use CuPy:

  • NVIDIA CUDA GPU with the Compute Capability 3.0 or larger.

  • CUDA Toolkit: v9.0 / v9.2 / v10.0 / v10.1 / v10.2 / v11.0 / v11.1 / v11.2

  • Python: v3.5.1+ / v3.6.0+ / v3.7.0+ / v3.8.0+ / v3.9.0+


On Windows, CuPy only supports Python 3.6.0 or later.

Python Dependencies

NumPy/SciPy-compatible API in CuPy v8 is based on NumPy 1.19 and SciPy 1.5, and has been tested against the following versions:


SciPy and Optuna are optional dependencies and will not be installed automatically.


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

$ python -m pip install -U setuptools pip

Additional CUDA Libraries

Part of the CUDA features in CuPy will be activated only when the corresponding libraries are installed.

  • cuTENSOR: v1.2

  • NCCL: v2.0 / v2.1 / v2.2 / v2.3 / v2.4 / v2.5 / v2.6 / v2.7 / v2.8

    • The library to perform collective multi-GPU / multi-node computations.

  • cuDNN: v7.0 / v7.1 / v7.2 / v7.3 / v7.4 / v7.5 / v7.6 / v8.0 / v8.1

    • The library to accelerate deep neural network computations.

Installing CuPy

Wheels (precompiled binary packages) are available for Linux (x86_64, Python 3.5+) and Windows (amd64, Python 3.6+). Package names are different depending on your CUDA Toolkit version.




$ pip install cupy-cuda90


$ pip install cupy-cuda92


$ pip install cupy-cuda100


$ pip install cupy-cuda101


$ pip install cupy-cuda102


$ pip install cupy-cuda110


$ pip install cupy-cuda111


$ pip install cupy-cuda112 (see #4704 for Linux instructions)


Wheel packages are built with NCCL (Linux only) and cuDNN support enabled.

  • NCCL library is bundled with these packages. You don’t have to install it manually.

  • cuDNN library is bundled with these packages except for CUDA 10.1+. For CUDA 10.1+, you need to manually download and install cuDNN v8.x library to use cuDNN features.


Use pip install --pre cupy-cudaXXX if you want to install prerelease (development) versions.

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

Installing CuPy from Conda-Forge

Conda/Anaconda is a cross-platform package management solution widely used in scientific computing and other fields. The above pip install instruction is compatible with conda environments. Alternatively, for Linux 64 systems once the CUDA driver is correctly set up, you can install CuPy from the conda-forge channel:

$ conda install -c conda-forge cupy

and conda will install pre-built CuPy and most of the optional dependencies for you, including CUDA runtime libraries (cudatoolkit), NCCL, and cuDNN. It is not necessary to install CUDA Toolkit in advance. If you need to enforce the installation of a particular CUDA version (say 10.0) for driver compatibility, you can do:

$ conda install -c conda-forge cupy cudatoolkit=10.0


cuTENSOR is available on conda-forge for CUDA 10.1+ and is an optional dependency. To install CuPy with the cuTENSOR support enabled, you can do:

$ conda install -c conda-forge cupy cutensor cudatoolkit=10.2

Note that cupy and cutensor must be installed at the same time (as shown above) in order for the conda solver to pick up the right package; otherwise, the cuTENSOR support is disabled.


If you encounter any problem with CuPy from conda-forge, please feel free to report to cupy-feedstock, and we will help investigate if it is just a packaging issue in conda-forge’s recipe or a real issue in CuPy.


If you did not install CUDA Toolkit yourselves, the nvcc compiler might not be available. The cudatoolkit package from Anaconda does not have nvcc included.

Installing CuPy from Source

Use of wheel packages is recommended 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.


CuPy source build requires g++-6 or later. For Ubuntu 18.04, run apt-get install g++. For Ubuntu 16.04, CentOS 6 or 7, follow the instructions here.


When installing CuPy from source, features provided by additional CUDA libraries will be disabled if these libraries are not available at the build time. See Installing cuDNN and NCCL for the instructions.


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

You can install the latest stable release version of the CuPy source package via pip.

$ pip install cupy

If you want to install the latest development version of CuPy from a cloned Git repository:

$ git clone --recursive
$ cd cupy
$ pip install .


To build the source tree downloaded from GitHub, you need to install Cython 0.29.22 or later (pip install cython). You don’t have to install Cython to build source packages hosted on PyPI as they include pre-generated C++ source files.

Uninstalling CuPy

Use pip to uninstall CuPy:

$ pip uninstall cupy


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


If CuPy is installed via conda, please do conda uninstall cupy instead.

Upgrading 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).

Reinstalling CuPy

To reinstall CuPy, please uninstall CuPy and then install it. When reinstalling CuPy, we recommend using --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).

Using CuPy inside Docker

We are providing the official Docker images. Use NVIDIA Container Toolkit to run CuPy image with GPU. You can login to the environment with bash, and run the Python interpreter:

$ docker run --gpus all -it cupy/cupy /bin/bash

Or run the interpreter directly:

$ docker run --gpus all -it cupy/cupy /usr/bin/python


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 upgrade your conda.

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 installing it under the CUDA_PATH 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

Working with Custom CUDA Installation

If you have installed CUDA on the non-default directory or multiple CUDA versions on the same host, 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 from PATH environment variable.

  3. /usr/local/cuda

For example, you can build CuPy using 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.

CuPy always raises cupy.cuda.compiler.CompileException

If CuPy raises a CompileException for almost everything, 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.

Build fails on Ubuntu 16.04, CentOS 6 or 7

In order to build CuPy from source on systems with legacy GCC (g++-5 or earlier), you need to manually set up g++-6 or later and configure NVCC environment variable.

On Ubuntu 16.04:

$ sudo add-apt-repository ppa:ubuntu-toolchain-r/test
$ sudo apt update
$ sudo apt install g++-6
$ export NVCC="nvcc --compiler-bindir gcc-6"

On CentOS 6 / 7:

$ sudo yum install centos-release-scl
$ sudo yum install devtoolset-7-gcc-c++
$ source /opt/rh/devtoolset-7/enable
$ export NVCC="nvcc --compiler-bidir gcc-7"