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System Requirements
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-------------------
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CEBRA is written in Python (3.8+) and PyTorch. CEBRA is most effective when used with a GPU, but CPU-only support is provided. We provide instructions to run CEBRA on your system directly using Anaconda or Docker. The instructions below were tested on different compute setups with Ubuntu 18.04 or 20.04, using Nvidia GTX 2080, A4000, and V100 cards. Other setups are possible (including Windows), as long as CUDA 10.2+ support is guaranteed.
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CEBRA is written in Python (3.8+) and PyTorch. CEBRA is most effective when used with a GPU, but CPU-only support is provided. We provide instructions to run CEBRA on your system directly. The instructions below were tested on different compute setups with Ubuntu 18.04 or 20.04, using Nvidia GTX 2080, A4000, and V100 cards. Other setups are possible (including Windows), as long as CUDA 10.2+ support is guaranteed.
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- Software dependencies and operating systems:
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- Linux or MacOS
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We outline installation instructions for different systems.
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CEBRA will be installed via ``pip install cebra``.
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Its dependencies can be installed using ``pip`` or ``conda`` and
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CEBRA's dependencies can be installed using ``pip`` or ``conda`` and
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we outline different options below.
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Most users can only install the **minimal install**. 🚀 For more advanced users, CEBRA has different extra install options that you can select based on your usecase:
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* ``[integrations]``: This will install (experimental) support for our streamlit and jupyter integrations.
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* ``[docs]``: This will install additional dependencies for building the package documentation.
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* ``[dev]``: This will install additional dependencies for development, unit and integration testing,
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code formatting, etc. Install this extension if you want to work on a pull request.
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* ``[demos]``: This will install additional dependencies for running our demo notebooks.
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* ``[datasets]``: This extension will install additional dependencies to use the pre-installed datasets
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in ``cebra.datasets``. Note that installing this extension *will not* download the data, which is available on `FigShare <https://figshare.com/s/60adb075234c2cc51fa3>`_.
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.. tabs::
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.. tab:: Google Colab
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CEBRA can also be installed and run on Google colaboratory.
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Please see the ``open in colab`` button at the top of each demo notebook for examples.
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CEBRA can also be installed and run on Google Colaboratory.
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Please see the ``open in colab`` button at the top of each `demo notebook<https://cebra.ai/docs/demos.html>`_ for examples.
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If you are starting with a new notebook, simply run
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$ conda env create -f conda/cebra.yml
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.. tab:: Supplied conda (paper reproduction)
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We provide a ``conda`` environment with the full requirements needed to reproduce the first CEBRA paper (although we
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recommend using Docker). Namely, you can run CEBRA, piVAE, tSNE and UMAP within this conda env. It is *NOT* needed if you only want to use CEBRA.
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* For all platforms except MacOS with M1/2 chipsets, create the full environment using ``cebra_paper.yml``, by running the following from the CEBRA repo root directory:
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.. code:: bash
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$ conda env create -f conda/cebra_paper.yml
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* If you are a MacOS M1 or M2 user and want to reproduce the paper, use the ``cebra_paper_m1.yml`` instead. You'll need to install tensorflow. For that, use `miniconda3 <https://docs.conda.io/projects/conda/en/latest/user-guide/install/macos.html>`_ and follow the setup instructions for tensorflow listed in the `Apple developer docs <https://developer.apple.com/metal/tensorflow-plugin/>`_. In the Terminal, run the following commands:
Then, you can build the full environment from the root directory:
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.. code:: bash
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$ conda env create -f conda/cebra_paper_m1.yml
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.. tab:: conda
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Conda users should currently use ``pip`` for installation. The missing dependencies will be installed in the install process. A fresh conda environment can be created using
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$ pip install cebra
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* 🚀 For more advanced users, CEBRA has different extra install options that you can select based on your usecase:
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* ``[integrations]``: This will install (experimental) support for our streamlit and jupyter integrations.
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* ``[docs]``: This will install additional dependencies for building the package documentation.
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* ``[dev]``: This will install additional dependencies for development, unit and integration testing,
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code formatting, etc. Install this extension if you want to work on a pull request.
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* ``[demos]``: This will install additional dependencies for running our demo notebooks.
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* ``[datasets]``: This extension will install additional dependencies to use the pre-installed datasets
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in ``cebra.datasets``.
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* Inference and development tools only
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.. code:: bash
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If yopu have issues installing CEBRA, we recommend carefully checking the `traceback`_ which can help you look on `stackoverflow`_ or the popular-in-life-sciences, `Image Forum`_ for similar issues. If you cannot find a solution, please do post an issue on GitHub!
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Advanced Installation for Schneider, Lee, Mathis 2023 paper experiments
If you want to install the additional dependencies required to run comparisons with other algorithms, please see the following:
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.. tabs::
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.. tab:: Supplied conda (paper reproduction)
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We provide a ``conda`` environment with the full requirements needed to reproduce the first CEBRA paper (although we
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recommend using Docker). Namely, you can run CEBRA, piVAE, tSNE and UMAP within this conda env. It is *NOT* needed if you only want to use CEBRA.
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* For all platforms except MacOS with M1/2 chipsets, create the full environment using ``cebra_paper.yml``, by running the following from the CEBRA repo root directory:
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.. code:: bash
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$ conda env create -f conda/cebra_paper.yml
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* If you are a MacOS M1 or M2 user and want to reproduce the paper, use the ``cebra_paper_m1.yml`` instead. You'll need to install tensorflow. For that, use `miniconda3 <https://docs.conda.io/projects/conda/en/latest/user-guide/install/macos.html>`_ and follow the setup instructions for tensorflow listed in the `Apple developer docs <https://developer.apple.com/metal/tensorflow-plugin/>`_. In the Terminal, run the following commands:
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