Installation

Quick install

In Python3.10 environment, in a conda environment:

pip install -U pip wheel
pip install "deepview[complete]"

The preceding command will install the main DeepView package, TensorFlow2 and PyTorch compatibility, and requirements to run the notebook examples.

Note: For dev installation or to install from a specific branch, refer to the Contributor’s Guide.

Python Support

DeepView currently supports Python version 3.10 or greater for macOS or Linux. Python 3.10 is recommended.

To install Python version 3.10 (recommended):

MacOS: The Python installer package can be downloaded from the Python.org website. During installation, deselecting GUI applications, UNIX command-line tools, and Python documentation will reduce the size of what is installed.

Ubuntu:

sudo apt install -y python3.10-dev python3.10-venv python3.10-tk
sudo apt-get install -y libsm6 libxext6 libxrender-dev libgl1-mesa-glx

Conda Environment Creation

It’s recommended to use a conda environment to manage all dependencies:

conda create -n deepview python=3.10
conda activate deepview

And update pip and wheel:

pip install --upgrade pip wheel

Installation with pip

The base DeepView is installed with pip as follows:

pip install deepview

DeepView has additional installation options, which are installed in square brackets, using quotes:

pip install "deepview[dataset-report,tensorflow,...]"

Here are the options currently available:

Module	Description
deepview	Always installed, base DeepView, with Familiarity, PFA, INA, and DimensionReduction.
-> [canvas]	Installs canvas_ux and widgets for visualization. canvas_duplicates, canvas_summary_, canvas_familiarity_, …
-> [data]	Installs deepview_data: utility functions for image data analysis.
-> [notebook]	Installs dependencies to run and visualize the jupyter notebook tutorials, including jupyter, matplotlib, pandas, …
-> [image]	Installs opencv (headless) and Pillow to enable image processing capabilities.
-> [dimreduction]	Installs umap_learn and pacmap for dimensionality reduction.
-> [dataset-report]	Installs all requirements to run the Dataset Report.
-> [tensorflow]	Installs deepview_tensorflow and TF2 to load & run TF models within DeepView.
-> [torch]	Installs deepview_pytorch: convert between PyTorch Dataset and DeepView Producer.
-> [complete]	Installs notebook, image, dimreduction, dataset-report, tensorflow, canvas , & torch options.

Running the Jupyter Notebooks Examples

First, install the notebook dependencies:

pip install "deepview[notebook]"

Next, download the DeepView notebooks directly or use them via cloning the deepview repository.

Finally, launch jupyter to open the notebooks:

jupyter notebook

Installation for developers

Check out the Development Installation page to install DeepView for development.

Issues with installation?

Please file an issue in the GitHub repository.