🌴 dl-schema

A deep learning training template constructed as a minimal working MNIST example. Utilizes dataclasses as flexible train configs and mlflow for analytics and artifact logging.

Install

# create `dl` conda environment
conda create -n dl python=3.10 pip
conda activate dl

# install torch and dependencies, assumes cuda version >= 11.0
pip install torch==2.1.0 torchvision==0.16.0
pip install mlflow==2.8.1 pyrallis==0.3.1 
pip install pandas tqdm pillow matplotlib 

# install hyperparameter search dependencies
pip install ray[tune] hyperopt

# install dl-schema repo
git clone https://github.com/phelps-matthew/dl-schema.git
cd dl-schema
pip install -e .

Usage

• Download and extract the MNIST dataset

cd dl-schema
python data/create_mnist_dataset.py

• Train small CNN model (ResNet-18)

python train.py

• View train configuration options

python train.py --help

• Train from yaml configuration, with CLI override

python train.py --config_path configs/resnet.yaml --lr 0.001 --gpus [7]

• Start mlflow ui to visualize results

# navgiate to dl_schema root directory containing `mlruns`
mlflow ui
# to set host and port
mlflow ui --host 0.0.0.0 --port 8080

• Serialize dataclass train config to yaml, outputting configs/train_cfg.yaml

python cfg.py

Hyperparameter Experiments

• Use ray tune to perform multi-gpu hyperparameter search

CUDA_VISIBLE_DEVICES=0,1,2,3 python tune.py --exp_name hyper_search