Album Art classifier

Development

Install dependencies:

$ python -m venv venv
$ source venv/bin/activate
$ pip install -r requirements.txt

Download dataset

$ python scripts/download_data.py

Downloads and unzips into root direcory with following structure:

 data
  ├── sample
  |   ├── metal/
  |   └── not-metal/
  ├── test
  |   ├── metal/
  |   └── not-metal/
  ├── train
  |   ├── metal/
  |   └── not-metal/
  └── validate
      ├── metal/
      └── not-metal/

Running MLflow server

$ mlflow server

With no arguments, will store data and artifacts (e.g. saved weights) on file system

MlFlow tracking servers

Storage

Can set remote locations for storage backend for experiment data (e.g. metrics, training parameters), and artifacts for large files (saved weights, notebooks)

$ mlflow server \  
--default-artifact-root s3://my_bucket/path/to/model \  
--backend-store-uri /postgresql://<db_uri>

If using remote storage, set env variables for authentication e.g

$ export AWS_ACCESS_KEY_ID=<access_key>
$ export AWS_SECRET_ACCESS_KEY=<secret_key>

Setting up authentication for S3

Training

To track to an mlflow server set MLFLOW_TRACKING_URI env variable:
$ export MLFLOW_TRACKING_URI=http://localhost:5000

If not set, will log experiments (data and artifacts) to mlflow folder in root directory

Run training script (with no arguments, will run with defaults shown below):

$ python scripts/train.py \
  --train_dir data/train \
  --val_dir data/validate \
  --epochs 10 \
  --batch_size 32 \
  --exp_name test

Running on data/train will take a long time on CPU (~15 hours for 10 epochs on my machine), to test that everything runs use --train_dir data/sample

• TODO: Add instructions for training on GPU

Run Flask app locally

To ensure app runs in debug mode set FLASK_ENV environment variable before running:
$ export FLASK_ENV=development

Set MODEL_URI env variable
$ export MODEL_URI=s3://my_bucket/path/to/model

Points to an MLFlow model with a valid URI

(if using S3, will also need to set AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY environment variables)

Start Flask app
$ flask run

Deploying To Heroku

1. Create heroku account and install heroku CLI: https://devcenter.heroku.com/articles/heroku-cli#download-and-install
2. $ heroku create <app_name>
3. Set env variables:
   $ heroku config:set MODEL_URI=s3://my_bucket/path/to/model
   (if using S3 will also need to set AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY )
4. $ git push heroku master