Installation of SDK on Linux Ubuntu

This section describes how to install Metavision SDK on Linux Ubuntu 20.04 and 22.04 64-bit.


Metavision SDK comes as pre-built Ubuntu packages of our whole software offer. If you want to work from source (for example to compile on other platforms than those we officially support), you should use OpenEB and follow the specific guide on the Installation of OpenEB. Note that OpenEB contains only the Open modules of our SDK. Additionally, beware that pre-built SDK packages and OpenEB should not be installed simultaneously to avoid conflicts.

Supported Cameras

Metavision SDK comes with a Prophesee camera plugin, enabling to stream data from our event-based cameras and to read recordings of event-based data. The supported cameras are:

If you want to use a Prophesee EVK that is not in the list above, it might have been discontinued. Refer to the Prophesee EVKs Support section to find out which previous version of the SDK is supporting it.

If you own a third-party vendor event-based camera, refer to the Camera Plugin Installation page to see how it can be deployed and configured.

Required Configuration

Please note the base system requirements for using Metavision SDK:

  • Operating system: Linux Ubuntu 20.04 or 22.04 64-bit

  • Architecture: amd64 (a.k.a. x64)

  • Graphic card: must support OpenGL 3.0 minimum

  • CPU: must support AVX2

To install the SDK on Windows, see the page SDK installation on Windows. For other operating systems and architectures, you must clone our open source project OpenEB and contact us if you run into compatibility issues.


If you want install Metavision SDK packages on an older version of Ubuntu that we don’t support anymore, you can refer to the Prophesee Operating System Support page to find out which previous version of the SDK is supporting it.

Required Artifact

Whether you are installing the SDK for the first time or are currently using a previous version, to install Metavision SDK on Linux, you need our APT repository configuration file metavision.list.


If you are looking for a previous version of the SDK, please find your version in the Knowledge Center Download section (request an account if you don’t have one yet).

Upgrading Metavision

If you are upgrading Metavision from a previous version, you should first read carefully the Release Notes as some changes may impact your usage of our SDK (e.g. API updates) and cameras (e.g. firmware update might be necessary).

Then, you will need to remove the previously installed Prophesee software:

sudo apt remove '~nprophesee-.*' '~nmetavision-.*' '~nhdf5-ecf-*' '~nhdf5-plugin-ecf-*' --purge


The “apt upgrade” mechanism is currently not supported by our SDK. Hence, removing the old version of Metavision SDK is mandatory even for patch upgrade (e.g. from x.y.0 to x.y.1)

Installing Dependencies

Install additional packages required to manage videos and diagnose OpenGL drivers:

sudo apt -y install libcanberra-gtk-module mesa-utils ffmpeg

To execute our Python Samples and use the Python API, you will need Python and some additional libraries. We support Python 3.8 and 3.9 on Ubuntu 20.04 and Python 3.9 and 3.10 on Ubuntu 22.04. If Python is not available on your system, install it:

sudo apt -y install python3-pip
sudo apt -y install python3.X-dev  # where X is 8, 9 or 10 depending on your Python version (3.8, 3.9 or 3.10)

Then install some extra libraries (note that you can skip this step if you don’t plan to use our Python samples/API and are willing to use Metavision Studio only for now):

python3 -m pip install pip --upgrade
python3 -m pip install "opencv-python==" "sk-video==1.1.10" "fire==0.4.0" "numpy==1.23.4" "h5py==3.7.0" pandas scipy
python3 -m pip install matplotlib "ipywidgets==7.6.5"


You can use anaconda to install Python and conda to manage your Python packages, but be sure to use a Python version that we support. You will need to adapt the library installation steps accordingly and use conda whenever we use pip.

In addition, check that conda PYTHONPATH contains /usr/lib/python3/dist-packages/. If not, add it. For example with sys.path.append() method of python module sys:

import sys
# Add /usr/lib/python3/dist-packages/ to PYTHONPATH if the output of print(sys.path) does not mention it.

In this documentation, we chose to use pip as a package manager. We recommend using it with virtualenv to avoid conflicts with other installed Python packages.

To compile the C++ code samples, you need to install CMake and some libraries:

sudo apt -y install cmake libboost-program-options-dev libeigen3-dev libprotobuf-dev protobuf-compiler


Start by configuring the APT software repository of your system:

  • First, clean your sources list and preferences directories from potential earlier installation of Prophesee software:

    • remove any prophesee file (e.g. essentials.list, prophesee.list, sdk.list, metavision.list or designer.list) from the folder /etc/apt/sources.list.d

      cd /etc/apt/sources.list.d
      sudo rm prophesee.list essentials.list metavision.list designer.list
    • Remove any Prophesee file (e.g. prophesee) from the folder /etc/apt/preferences.d

      sudo rm /etc/apt/preferences.d/prophesee
    • Remove any Prophesee Server listed in the file /etc/apt/sources.list

  • Then, add Prophesee’s servers to the list of APT repositories by copying the metavision.list file downloaded in the previous section to the folder /etc/apt/sources.list.d

    sudo cp metavision.list /etc/apt/sources.list.d


    In addition to a link to Prophesee’s APT server, the metavision.list file for Ubuntu 20 also contains a link to the repository for the Ogre3D library, a graphic engine for 3D rendering, required by the metavision_xyt sample.

  • Update the list of repositories and packages:

    sudo apt update

Now you are ready to install Metavision SDK:

sudo apt -y install metavision-sdk


If you are looking for a specific version of the SDK which is not the last one, in addition to downloading the proper .list file as mentioned in the Required Artifact section, check the Release Notes to know the exact version number to install and pin this version in the APT server configuration by launching this command:

sudo sh -c "echo 'Package: metavision-*
Pin: version 4.1.0
Pin-Priority: 1001' > /etc/apt/preferences.d/prophesee"

And then update the list of repositories and install Metavision SDK:

sudo apt update
sudo apt -y install metavision-sdk


If the above command returns the error E: unable to locate package metavision-sdk, it might mean that you did not properly install the APT server list. More probably, you tried to install Metavision SDK on a platform that we don’t support. In that case, ensure that your OS is Ubuntu 20.04 or 22.04 on amd64/x64. For example, if you are trying to install on a NVidia Jetson or a Raspberry Pi, you will get this error as both are not amd64-based but arm-based. If you want to work on a non-supported platform, you should compile OpenEB, our open-source project.

Regarding our Python API, the previous command installs the Python bindings of our C++ libraries for the default version of Python of your Ubuntu version (Python 3.8 on Ubuntu 20.04 and 3.10 on Ubuntu 22.04). If you want to use another supported Python version (namely 3.9), you should install an extra package:

sudo apt -y install metavision-sdk-python3.9

To use our HDF5 ECF codec plugin with some HDF5 tools (for example h5py), you have to install our HDF5 plugin and may have to modify the HDF5_PLUGIN_PATH:

  • For Ubuntu 20.04:

    sudo apt install hdf5-plugin-ecf hdf5-plugin-ecf-dev
    export HDF5_PLUGIN_PATH=$HDF5_PLUGIN_PATH:/usr/lib/x86_64-linux-gnu/hdf5/plugins
  • For Ubuntu 22.04:

    sudo apt install hdf5-plugin-ecf hdf5-plugin-ecf-dev
    export HDF5_PLUGIN_PATH=$HDF5_PLUGIN_PATH:/usr/lib/x86_64-linux-gnu/hdf5/serial/plugins

Get started!

You are now ready to use Metavision SDK. The best way to start getting familiar with the event-based technology is to open an event-based camera with Metavision Studio to begin data collection and visualization. You can choose to dive directly in the SDK by following a getting started guide or looking at a Code Sample.

If you want to use the Machine Learning modules, please follow the next section that describes the required additional dependencies.

Additional Dependencies for Machine Learning Module

To use Machine Learning features, you need to install some additional dependencies. If you have some Nvidia hardware with GPUs, you can optionally install CUDA (11.6 or 11.7) and cuDNN to leverage them with PyTorch and LibTorch. Make sure that you install a version of CUDA that is compatible with your GPUs by checking Nvidia compatibility page.


At the moment, we don’t support OpenCL and AMD GPUs.

Python Packages

You need to install PyTorch 1.13.1. Retrieve and execute the pip command of version 1.13.1 from the previous versions install guide section.

Then install some extra Python libraries:

python3 -m pip install "numba==0.56.3" "profilehooks==1.12.0" "pytorch_lightning==1.8.6" "tqdm==4.63.0" "kornia==0.6.8"
python3 -m pip install "llvmlite==0.39.1" "pycocotools==2.0.7" "seaborn==0.11.2" "torchmetrics==0.7.2" "pillow==9.3.0"

LibTorch for C++

To compile and run the neural network inference in a C++ pipeline, you need LibTorch (PyTorch’s C++ frontend)

  • Download the LibTorch version corresponding to your CUDA version (or take the CPU version if you don’t have CUDA):

  • Unzip the archive to a new folder LIBTORCH_DIR_PATH that you will reference when compiling the C++ inference sample. For example, for the CPU version:

    unzip -d LIBTORCH_DIR_PATH
  • LibTorch is delivered with a copy of libgtest libraries. Those should be removed to avoid conflicts:

    rm LIBTORCH_DIR_PATH/libtorch/lib/libgtest.a
    rm LIBTORCH_DIR_PATH/libtorch/lib/libgtest_main.a

Pre-trained Models

We also provide pre-trained models for inference in automotive applications. They can be downloaded during the sign-up. (if you are a Prophesee customer, retrieve the link to the model by accessing to your SDK version in the Knowledge Center Download section).

Get Started with Machine Learning

To see the different tools, samples and turorials available for our ML module, check out our Machine Learning guide.