Skip to content

Latest commit

 

History

History
52 lines (34 loc) · 3.13 KB

README.md

File metadata and controls

52 lines (34 loc) · 3.13 KB
Raw

DengAI: Predicting Disease Spread

This repo contains the work realized during my participation to a DrivenData Challenge: DengAI: Predicting Disease Spread.

This repository also serve as a submission repository for the Udacity Machine Learning Engineer - Capstone Project

The project was done on AWS SageMaker. Although some notebooks can be run on any machine with Jupyter installed, the training and hypertuning of the model can only be done on AWS platform.

Files

.
├── data/*.csv            # Scores and hyperparameters of trained models and submissions
├── data/figures/         # Comparison on validation data set
├── data/input/           # Copy of the original data set
├── data/submissions/     # Some of the submitted files to the competition
├── report/               # Udacity Machine Learning Engineer Capstone proposal and final report
├── src/preprocessing.py  # Preprocessing script used on SageMaker
└── *.ipynb               # Notebooks used for EDA , model training, validation and submission creation , hypertuning job creation, hypertuning analysis, comparison of imputation method, and scores and submissions analysis

Setup

Setup you SageMaker environment (nothing fancy here)

Custom docker image for SageMaker

To be able to use a newer version of scikit-learn (0.22.2) than the one provided on SageMaker (0.20.0), we create a docker container with updated version and uploaded it to an AWS ECR repository.

All the information to create the container as well as ready-to-use Dockerfile with scikit-learn v0.22.0 can be found on this repository (fork of sagemaker-scikit-learn-container).

Create a repository in Amazon ECR.

Once created you can access it from the AWS console and select View push commands to easily be able to push your created container to the repository.

Once uploaded you can reference it in 1-deepAr.ipynb for the uri parameter while creating the ScriptProcessor:

sklearn_processor = ScriptProcessor(image_uri='AWS-ID.dkr.ecr.eu-central-1.amazonaws.com/ECS-REPOSITORY:IMAGE-TAG',
                                     role=role,
                                     instance_type='ml.m4.xlarge',
                                     instance_count=1,
                                     command = ["python3"], # default required using the same as in SKLearnProcessor
                                     volume_size_in_gb=30, # default required using the same as in SKLearnProcessor
                                     base_job_name=f'{prefix}-{tag}-pprocess'
                                    )

PDF generation

PDF files were generated from Markdown files using pandoc:

pandoc -o output.pdf input.md -V geometry:margin=1in