build: switch to conda dssp installation

.github/actions/install-python-and-package/action.yml

@@ -1,6 +1,6 @@
-name: "Install Python and deeprank2"
+name: "Install Python and DeepRank2"
 
-description: "Installs Python, updates pip and installs deeprank2 together with its dependencies."
+description: "Installs Python, updates pip and installs DeepRank2 together with its dependencies."
 
 inputs:
   python-version:
@@ -27,8 +27,10 @@ runs:
       with:
         update-conda: true
         python-version: ${{ inputs.python-version }}
-        conda-channels: anaconda
-    - run: conda --version
+        conda-channels: pytorch, pyg, bioconda, defaults, sbl, conda-forge
+    - run: |
+        conda --version
+        conda env list
       shell: bash {0}
     - name: Python info
       shell: bash -e {0}
@@ -41,16 +43,16 @@ runs:
         CMAKE_INSTALL_PREFIX: .local
       if: runner.os == 'Linux'
       run: |
-        # Install dependencies not handled by setuptools
+        # Install deeprank2 conda dependencies
         ## DSSP
-        sudo apt-get install -y dssp
+        conda install -c sbl dssp>=4.2.2.1
         ## MSMS
-        conda install -c bioconda msms
+        conda install -c bioconda msms>=2.6.1
         ## PyTorch, PyG, PyG adds
         ### Installing for CPU only on the CI
-        conda install pytorch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 -c pytorch
-        pip install torch_geometric==2.3.1
-        pip install torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-$(python3 -c "import torch; print(torch.__version__)")+cpu.html
+        conda install pytorch=2.1.1 torchvision=0.16.1 torchaudio=2.1.1 cpuonly=2.0.* -c pytorch
+        conda install pyg=2.4.0 -c pyg
+        pip install torch_scatter==2.1.2 torch_sparse==0.6.18 torch_cluster==1.6.3 torch_spline_conv==1.2.2 -f https://data.pyg.org/whl/torch-2.1.0+cpu.html
     - name: Install dependencies on MacOS
       shell: bash {0}
       env:
@@ -59,26 +61,7 @@ runs:
       run: |
         # Install dependencies not handled by setuptools
         ## DSSP
-        git clone https://github.com/PDB-REDO/libcifpp.git --recurse-submodules
-        cd libcifpp
-        cmake -S . -B build -DCMAKE_INSTALL_PREFIX=$HOME/.local -DCMAKE_BUILD_TYPE=Release
-        cmake --build build
-        cmake --install build
-        #######
-        git clone https://github.com/mhekkel/libmcfp.git
-        cd libmcfp
-        mkdir build
-        cd build
-        cmake ..
-        cmake --build .
-        cmake --install .
-        #######
-        git clone https://github.com/PDB-REDO/dssp.git
-        cd dssp
-        mkdir build
-        cmake -S . -B build -DCMAKE_BUILD_TYPE=Release
-        cmake --build build
-        cmake --install build
+        conda install -c sbl dssp>=4.2.2.1
         ## MSMS
         cd /tmp/
         wget http://mgltools.scripps.edu/downloads/tars/releases/MSMSRELEASE/REL2.6.1/msms_i86Linux2_2.6.1.tar.gz

.github/workflows/coveralls.yml

@@ -46,7 +46,7 @@ jobs:
           python-version: ${{ matrix.python-version }}
           extras-require: test
       - name: Run unit tests with coverage
-        run: pytest --cov --cov-append --cov-report xml --cov-report term --cov-report html
+        run: pytest --cov --cov-append --cov-report xml --cov-fail-under=80 --cov-report term --cov-report html
       - name: Coveralls
         env:
           GITHUB_TOKEN: ${{ secrets.github_token }}

Dockerfile

@@ -0,0 +1,32 @@
+# Pull base image
+FROM --platform=linux/x86_64 condaforge/miniforge3:23.3.1-1
+
+# Add files
+ADD ./tutorials /home/deeprank2/tutorials
+ADD ./env/environment.yml /home/deeprank2
+ADD ./env/requirements.txt /home/deeprank2
+
+# Install
+RUN \
+  apt update -y &&
+  apt install unzip -y &&
+  ## GCC
+  apt install -y gcc &&
+  ## Conda and pip deps
+  mamba env create -f /home/deeprank2/environment.yml &&
+  ## Get the data for running the tutorials
+  if [ -d "/home/deeprank2/tutorials/data_raw" ]; then rm -Rf /home/deeprank2/tutorials/data_raw; fi &&
+  if [ -d "/home/deeprank2/tutorials/data_processed" ]; then rm -Rf /home/deeprank2/tutorials/data_processed; fi &&
+  wget https://zenodo.org/records/8349335/files/data_raw.zip &&
+  unzip data_raw.zip -d data_raw &&
+  mv data_raw /home/deeprank2/tutorials
+
+# Activate the environment
+RUN echo "source activate deeprank2" >~/.bashrc
+ENV PATH /opt/conda/envs/deeprank2/bin:$PATH
+
+# Define working directory
+WORKDIR /home/deeprank2
+
+# Define default command
+CMD ["jupyter", "notebook", "--ip=0.0.0.0", "--NotebookApp.token=''","--NotebookApp.password=''", "--allow-root"]

README.md

@@ -1,4 +1,4 @@
-# Deeprank2
+# DeepRank2
 
 |     Badges     |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
 | :------------: | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
@@ -34,76 +34,126 @@ DeepRank2 extensive documentation can be found [here](https://deeprank2.rtfd.io/
 
 ## Table of contents
 
-- [Deeprank2](#deeprank2)
+- [DeepRank2](#deeprank2)
   - [Overview](#overview)
   - [Table of contents](#table-of-contents)
-  - [Installation](#installation)
-    - [Dependencies](#dependencies)
-  - [Deeprank2 Package](#deeprank2-package)
-    - [Test installation](#test-installation)
-    - [Contributing](#contributing)
-    - [Data generation](#data-generation)
-    - [Datasets](#datasets)
-      - [GraphDataset](#graphdataset)
-      - [GridDataset](#griddataset)
-    - [Training](#training)
+  - [Installations](#installations)
+    - [Containerized Installation](#containerized-installation)
+    - [Local/remote installation](#localremote-installation)
+      - [Non-pythonic dependencies](#non-pythonic-dependencies)
+      - [Pythonic dependencies](#pythonic-dependencies)
+      - [Deeprank2 Package](#deeprank2-package)
+      - [Test installation](#test-installation)
+  - [Contributing](#contributing)
+  - [Data generation](#data-generation)
+  - [Datasets](#datasets)
+    - [GraphDataset](#graphdataset)
+    - [GridDataset](#griddataset)
+  - [Training](#training)
     - [Run a pre-trained model on new data](#run-a-pre-trained-model-on-new-data)
   - [Computational performances](#computational-performances)
   - [Package development](#package-development)
 
 ## Installation
 
-The package officially supports ubuntu-latest OS only, whose functioning is widely tested through the continuous integration workflows.
+There are two ways to install DeepRank2:
 
-### Dependencies
+1. In a [dockerized container](#containerized-installation). This allows you to use DeepRank2, including all the notebooks within the container (a protected virtual space), without worrying about your operating system or installation of dependencies.
+   - We recommend this installation for inexperienced users and to learn to use or test our software, e.g. using the provided [tutorials](tutorials/TUTORIAL.md). However, resources might be limited in this installation and we would not recommend using it for large datasets or on high-performance computing facilities.
+2. [Local installation](#localremote-installation) on your system. This allows you to use the full potential of DeepRank2, but requires a few additional steps during installation.
+   - We recommend this installation for more experienced users, for larger projects, and for (potential) [contributors](#contributing) to the codebase.
 
-Before installing deeprank2 you need to install some dependencies. We advise to use a [conda environment](https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) with Python >= 3.10 installed. The following dependency installation instructions are updated as of 14/09/2023, but in case of issues during installation always refer to the official documentation which is linked below:
+### Containerized Installation
 
-- [MSMS](https://anaconda.org/bioconda/msms): `conda install -c bioconda msms`.
-  - [Here](https://ssbio.readthedocs.io/en/latest/instructions/msms.html) for MacOS with M1 chip users.
-- [PyTorch](https://pytorch.org/get-started/locally/)
-  - We support torch's CPU library as well as CUDA.
-  - Currently, the package is tested using [PyTorch 2.0.1](https://pytorch.org/get-started/previous-versions/#v201).
-- [PyG](https://pytorch-geometric.readthedocs.io/en/latest/install/installation.html) and its optional dependencies: `torch_scatter`, `torch_sparse`, `torch_cluster`, `torch_spline_conv`.
-- [DSSP 4](https://swift.cmbi.umcn.nl/gv/dssp/)
-  - Check if `dssp` is installed: `dssp --version`. If this gives an error or shows a version lower than 4:
-    - on ubuntu 22.04 or newer: `sudo apt-get install dssp`. If the package cannot be located, first run `sudo apt-get update`.
-    - on older versions of ubuntu or on mac or lacking sudo priviliges: install from [here](https://github.com/pdb-redo/dssp), following the instructions listed. Alternatively, follow [this](https://github.com/PDB-REDO/libcifpp/issues/49) thread.
-- [GCC](https://gcc.gnu.org/install/)
-  - Check if gcc is installed: `gcc --version`. If this gives an error, run `sudo apt-get install gcc`.
-- For MacOS with M1 chip users only install [the conda version of PyTables](https://www.pytables.org/usersguide/installation.html).
+In order to try out the package without worrying about your OS and without the need of installing all the required dependencies, we created a `Dockerfile` that can be used for taking care of everything in a suitable container.
+
+For this, you first need to install [Docker](https://docs.docker.com/engine/install/) on your system. Then run the following commands. You may need to have sudo permission for some steps, in which case the commands below can be preceded by `sudo`:
+
+```bash
+# Clone the DeepRank2 repository and enter its root directory
+git clone https://github.com/DeepRank/deeprank2
+cd deeprank2
+
+# Build and run the Docker image
+docker build -t deeprank2 .
+docker run -p 8888:8888 deeprank2
+```
+
+Next, open a browser and go to `http://localhost:8888` to access the application running inside the Docker container. From there you can use DeepRank2, e.g. to run the tutorial notebooks.
+
+More details about the tutorials' contents can be found [here](https://github.com/DeepRank/deeprank2/blob/main/tutorials/TUTORIAL.md). Note that in the docker container only the raw PDB files are downloaded, which needed as a starting point for the tutorials. You can obtain the processed HDF5 files by running the `data_generation_xxx.ipynb` notebooks. Because Docker containers are limited in memory resources, we limit the number of data points processed in the tutorials. Please [install the package locally](#localremote-installation) to fully leverage its capabilities.
+
+If after running the tutorials you want to remove the (quite large) Docker image from your machine, you must first [stop the container](https://docs.docker.com/engine/reference/commandline/stop/) and can then [remove the image](https://docs.docker.com/engine/reference/commandline/image_rm/). More general information about Docker can be found on the [official website docs](https://docs.docker.com/get-started/).
+
+### Local/remote installation
+
+Local installation is formally only supported on the latest stable release of ubuntu, for which widespread automated testing through continuous integration workflows has been set up. However, it is likely that the package runs smoothly on other operating systems as well.
 
-## Deeprank2 Package
+Before installing DeepRank2 please ensure you have [GCC](https://gcc.gnu.org/install/) installed: if running `gcc --version` gives an error, run `sudo apt-get install gcc`.
 
-Once the dependencies are installed, you can install the latest stable release of deeprank2 using the PyPi package manager:
+#### Using the provided YML file
+
+You can use the provided YML file for creating a [conda environment](https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) containing the latest stable release of DeepRank2 and all its dependencies.
+This will install the CPU-only version of DeepRank2 on Python 3.10.
+Note that this will not work for MacOS. Do the [Manual Installation](#manual-installation) instead
 
 ```bash
-pip install deeprank2
+# Clone the DeepRank2 repository and enter its root directory
+git clone https://github.com/DeepRank/deeprank2
+cd deeprank2
+
+# Ensure you are in your base environment
+conda activate
+# Create the environment
+conda env create -f env/environment.yml
+# Activate the environment
+conda activate deeprank2
 ```
 
-Alternatively, get all the new developments by cloning the repo and installing the editable version of the package with:
+See instructions below to [test](#testing-your-deeprank2-installation) that the installation was succesful.
+
+#### Manual installation
+
+If you want to use the GPUs, choose a specific python version, are a MacOS user, or if the YML installation was not succesful, you can install the package manually. We advise to do this inside a [conda virtual environment](https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html).
+If you have any issues during installation of dependencies, please refer to the official documentation for each package (linked below), as our instructions may be out of date (last tested on 19 Jan 2024):
+
+- [DSSP 4](https://anaconda.org/sbl/dssp): `conda install -c sbl dssp`
+- [MSMS](https://anaconda.org/bioconda/msms): `conda install -c bioconda msms`
+  - [Here](https://ssbio.readthedocs.io/en/latest/instructions/msms.html) for MacOS with M1 chip users.
+- [PyTorch](https://pytorch.org/get-started/locally/): `conda install pytorch torchvision torchaudio cpuonly -c pytorch`
+  - Pytorch regularly publishes updates and not all newest versions will work stably with DeepRank2. Currently, the package is tested using [PyTorch 2.1.1](https://pytorch.org/get-started/previous-versions/#v211).
+  - We support torch's CPU library as well as CUDA.
+- [PyG](https://pytorch-geometric.readthedocs.io/en/latest/install/installation.html) and its optional dependencies: `torch_scatter`, `torch_sparse`, `torch_cluster`, `torch_spline_conv`.
+  - The exact command to install pyg will depend on the versiobn of pytorch you are using. Please refer to the source's installation instructions (we recommend using the pip installation for this as it also shows the command for the dependencies).
+- For MacOS with M1 chip users: install [the conda version of PyTables](https://www.pytables.org/usersguide/installation.html).
+
+Finally install deeprank2 itself: `pip install deeprank2`.
+
+Alternatively, get the latest updates by cloning the repo and installing the editable version of the package with:
 
 ```bash
 git clone https://github.com/DeepRank/deeprank2
 cd deeprank2
 pip install -e .'[test]'
 ```
 
-The `test` extra is optional, and can be used to install test-related dependencies useful during the development.
+The `test` extra is optional, and can be used to install test-related dependencies, useful during development.
+
+#### Testing your DeepRank2 installation
 
-### Test installation
+You can check that all components were installed correctly, using pytest. We especially recommend doing this in case you installed DeepRank2 and its dependencies manually (the latter option above).
 
-If you have installed the package from a cloned repository (second option above), you can check that all components were installed correctly, using pytest.
 The quick test should be sufficient to ensure that the software works, while the full test (a few minutes) will cover a much broader range of settings to ensure everything is correct.
 
-Run `pytest tests/test_integration.py` for the quick test or just `pytest` for the full test (expect a few minutes to run).
+First run `pip install pytest`, if you did not install it above. Then run `pytest tests/test_integration.py` for the quick test or just `pytest` for the full test (expect a few minutes to run).
 
-### Contributing
+## Contributing
 
 If you would like to contribute to the package in any way, please see [our guidelines](CONTRIBUTING.rst).
 
-The following section serves as a first guide to start using the package, using protein-protein Interface (PPI) queries
-as example. For an enhanced learning experience, we provide in-depth [tutorial notebooks](https://github.com/DeepRank/deeprank2/tree/main/tutorials) for generating PPI data, generating SVR data, and for the training pipeline.
+## Using DeepRank2
+
+The following section serves as a first guide to start using the package, using protein-protein Interface (PPI) queries as example. For an enhanced learning experience, we provide in-depth [tutorial notebooks](https://github.com/DeepRank/deeprank2/tree/main/tutorials) for generating PPI data, generating SVR data, and for the training pipeline.
 For more details, see the [extended documentation](https://deeprank2.rtfd.io/).
 
 ### Data generation
@@ -313,7 +363,7 @@ trainer.test()
 
 ```
 
-### Run a pre-trained model on new data
+#### Run a pre-trained model on new data
 
 If you want to analyze new PDB files using a pre-trained model, the first step is to process and save them into HDF5 files [as we have done above](#data-generation).