Metadata-Version: 2.1
Name: pathml
Version: 2.0.dev4
Summary: Tools for computational pathology
Home-page: UNKNOWN
Author: Jacob Rosenthal, Ryan Carelli et al.
Author-email: PathML@dfci.harvard.edu
License: UNKNOWN
Project-URL: Documentation, https://pathml.readthedocs.io/en/stable
Project-URL: Source Code, https://github.com/Dana-Farber-AIOS/pathml
Platform: UNKNOWN
Classifier: License :: OSI Approved :: GNU General Public License v2 (GPLv2)
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Operating System :: OS Independent
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Framework :: Sphinx
Classifier: Framework :: Pytest
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Image Processing
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
License-File: LICENSE

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| Branch | Test status   |
| ------ | ------------- |
| master | ![tests](https://github.com/Dana-Farber-AIOS/pathml/actions/workflows/tests-conda.yml/badge.svg?branch=master) |
| dev    | ![tests](https://github.com/Dana-Farber-AIOS/pathml/actions/workflows/tests-conda.yml/badge.svg?branch=dev) |

A toolkit for computational pathology and machine learning.

**View [documentation](https://pathml.readthedocs.io/en/latest/)**

:construction: the `dev` branch is under active development, with experimental features, bug fixes, and refactors that may happen at any time! 
Stable versions are available as tagged commits on the `master` branch, or as versioned releases on PyPI

# Installation

There are several ways to install `PathML`:

1. pip install (**recommended for users**)
2. clone repo to local machine and install from source (recommended for developers/contributors)
3. Use the PathML Docker container

Options (1) and (2) require that you first install all external dependencies:
* openslide
* JDK 8

We recommend using conda for environment management. 
Download Miniconda [here](https://docs.conda.io/en/latest/miniconda.html)

*Note: these instructions are for Linux. Commands may be different for other platforms.*

## Installation option 1: pip install

Create conda environment
````
conda create --name pathml python=3.8
conda activate pathml
````

Install external dependencies (Linux) with [Apt](https://ubuntu.com/server/docs/package-management)
````
sudo apt-get install openslide-tools g++ gcc libblas-dev liblapack-dev
````

Install external dependencies (MacOS) with [Brew](www.brew.sh)
````
brew install openslide
````

Install [OpenJDK 8](https://openjdk.java.net/)
````
conda install openjdk==8.0.152
````

Optionally install CUDA (instructions [here](#CUDA))

Install `PathML`
````
pip install pathml
````

## Installation option 2: clone repo and install from source

Clone repo
````
git clone https://github.com/Dana-Farber-AIOS/pathml.git
cd pathml
````

Create conda environment
````
conda env create -f environment.yml
conda activate pathml
````

Optionally install CUDA (instructions [here](#CUDA))

Install `PathML`: 
````
pip install -e .
````

## Installation option 3: docker

Build the PathML docker container:
````
docker build -t pathml-analysis .
````

Connect to the container:
````
docker run -it -p 8888:8888 pathml-analysis
````

The above command runs the container, which is configured to spin up a jupyter lab session and expose it on port 8888. 
The terminal should display a URL to the jupyter lab session starting with http://127.0.0.1:8888/lab?token=<...>. 
Navigate to that page and you should connect to the jupyter lab session running on the container with the pathml 
environment fully configured.

Note that the docker container requires extra configurations to use with GPU.  
Note that these instructions assume that there are no other processes using port 8888.

Please refer to the `Docker run` [documentation](https://docs.docker.com/engine/reference/run/) for further instructions
on accessing the container, e.g. for mounting volumes to access files on a local machine from within the container.

## CUDA

To use GPU acceleration for model training or other tasks, you must install CUDA. 
This guide should work, but for the most up-to-date instructions, refer to the [official PyTorch installation instructions](https://pytorch.org/get-started/locally/).

Check the version of CUDA:
````
nvidia-smi
````

Install correct version of `cudatoolkit`:
````
# update this command with your CUDA version number
conda install cudatoolkit=11.0
````

After installing PyTorch, optionally verify successful PyTorch installation with CUDA support: 
````
python -c "import torch; print(torch.cuda.is_available())"
````

# Using with Jupyter

Jupyter notebooks are a convenient way to work interactively. To use `PathML` in Jupyter notebooks: 

## Set JAVA_HOME environment variable

PathML relies on Java to enable support for reading a wide range of file formats.
Before using `PathML` in Jupyter, you may need to manually set the `JAVA_HOME` environment variable 
specifying the path to Java. To do so:

1. Get the path to Java by running `echo $JAVA_HOME` in the terminal in your pathml conda environment (outside of Jupyter)
2. Set that path as the `JAVA_HOME` environment variable in Jupyter:
    ````
    import os
    os.environ["JAVA_HOME"] = "/opt/conda/envs/pathml" # change path as needed
    ````

## Register environment as an IPython kernel
````
conda activate pathml
conda install ipykernel
python -m ipykernel install --user --name=pathml
````
This makes the pathml environment available as a kernel in jupyter lab or notebook.


# Contributing

``PathML`` is an open source project. Consider contributing to benefit the entire community!

There are many ways to contribute to `PathML`, including:

* Submitting bug reports
* Submitting feature requests
* Writing documentation and examples
* Fixing bugs
* Writing code for new features
* Sharing workflows
* Sharing trained model parameters
* Sharing ``PathML`` with colleagues, students, etc.

See [contributing](https://github.com/Dana-Farber-AIOS/pathml/blob/master/CONTRIBUTING.rst) for more details.

# Citing

If you use `PathML` in your work, please cite our paper:

Rosenthal J, Carelli R, Omar M, Brundage D, Halbert E, Nyman J, Hari SN, Van Allen EM, Marchionni L, Umeton R, Loda M. 
Building tools for machine learning and artificial intelligence in cancer research: best practices and a case study 
with the PathML toolkit for computational pathology. *Molecular Cancer Research*, 2021. 
DOI: [10.1158/1541-7786.MCR-21-0665](https://doi.org/10.1158/1541-7786.MCR-21-0665)

# License

The GNU GPL v2 version of PathML is made available via Open Source licensing. 
The user is free to use, modify, and distribute under the terms of the GNU General Public License version 2.

Commercial license options are available also.

# Contact

Questions? Comments? Suggestions? Get in touch!

[PathML@dfci.harvard.edu](mailto:PathML@dfci.harvard.edu)

<img src=https://raw.githubusercontent.com/Dana-Farber-AIOS/pathml/master/docs/source/_static/images/dfci_cornell_joint_logos.png width="750"> 


