saving VAE graph NN

README.md

@@ -11,17 +11,46 @@ Website: [Nextflow Graph Machine Learning](https://jbris.github.io/nextflow-grap
 - [Nextflow Graph Machine Learning](#nextflow-graph-machine-learning)
 - [Table of contents](#table-of-contents)
 - [Introduction](#introduction)
-- [The pipeline](#the-pipeline)
+- [The Nextflow pipeline](#the-nextflow-pipeline)
+- [Python Environment](#python-environment)
+  - [MLOps](#mlops)
+- [ArangoDB](#arangodb)
 
 # Introduction
 
-The purpose of this project is to provide a simple demonstration of how to construct a Nextflow pipeline, with MLOps integration, for performing gene regulatory network (GRN) reconstruction using graph neural networks (GNNs). 
+The purpose of this project is to provide a simple demonstration of how to construct a Nextflow pipeline, with MLOps integration, for performing gene regulatory network (GRN) reconstruction using graph neural networks (GNNs). In practice, GRN reconstruction is an unsupervised link prediction problem.
 
-# The pipeline
+[For developing GNNs, we use PyTorch Geometric.](https://pytorch-geometric.readthedocs.io/en/latest/)
+
+# The Nextflow pipeline
+
+[Nextflow has been included to orchestrate the GRN reconstruction pipeline.](https://www.nextflow.io/)
 
 The pipeline is composed of the following steps:
 
 1. Exploratory data analysis: View the GRN and calculate some summary statistics.
 2. Processing: Process the graph feature matrix and edge list. Remove the disconnected subgraph.
 3. ArangoDB Importing: Import the graph into ArangoDB.
-4. Train a graph neural network using SAGE convolutional layers.
+4. GNN training: Train a GNN using SAGE convolutional layers.
+5. GNN training: Train a variational autoencoder GNN, and save the neural embeddings.
+
+# Python Environment
+
+[Python dependencies are specified in this requirements.txt file.](services/python/requirements.txt). 
+
+These dependencies are installed during the build process for the following Docker image: ghcr.io/jbris/nextflow-graph-machine-learning:1.0.0
+
+Execute the following command to pull the image: *docker pull ghcr.io/jbris/nextflow-graph-machine-learning:1.0.0*
+
+## MLOps
+
+* [A Docker compose file has been provided to launch an MLOps stack.](docker-compose.yml)
+* [See the .env file for Docker environment variables.](.env)
+* [The docker_up.sh script can be executed to launch the Docker services.](scripts/docker_up.sh)
+* [DVC is included for data version control.](https://dvc.org/)
+* [MLFlow is available for experiment tracking.](https://mlflow.org/)
+* [MinIO is available for storing experiment artifacts.](https://min.io/)
+
+# ArangoDB
+
+[This pipeline provides a simple demonstration for saving and retrieving graph data to ArangoDB, combined with NetworkX usage and integration.](https://docs.arangodb.com/3.11/data-science/adapters/arangodb-networkx-adapter/)