Installation¶

Quick install

In Python3.9 environment, in a virtualenv:

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 3.7 or greater for macOS or Linux. Python 3.10 is recommended. Note: to run TensorFlow 1, install Python 3.7 (Though not tested)

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 python3.9-dev python3.9-venv python3.9-tk
sudo apt-get install -y libsm6 libxext6 libxrender-dev libgl1-mesa-glx

Virtualenv Creation¶

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

python3.9 -m venv .venv39
source .venv37/bin/activate

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.
-> [tensorflow1]	Installs deepview_tensorflow and TF1 to load & run TF models within DeepView. Must have Python <=3.7 due to TF 1.
-> [tensorflow1-gpu]	Same as preceding row, but with TensorFlow GPU. Must have Python <=3.7 due to TF 1 constraints.
-> [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.