📌   Introduction

This template tries to be as general as possible - you can easily delete any unwanted features from the pipeline or rewire the configuration, by modifying behavior in src/train.py.

Effective usage of this template requires learning of a couple of technologies: PyTorch, PyTorch Lightning and Hydra. Knowledge of some experiment logging framework like Weights&Biases, Neptune or MLFlow is also recommended.

Why you should use it: it allows you to rapidly iterate over new models/datasets and scale your projects from small single experiments to hyperparameter searches on computing clusters, without writing any boilerplate code. To my knowledge, it's one of the most convenient all-in-one technology stack for Deep Learning research. Good starting point for reproducing papers, kaggle competitions or small-team research projects. It's also a collection of best practices for efficient workflow and reproducibility.

Why you shouldn't use it: this template is not fitted to be a production environment, should be used more as a fast experimentation tool. Also, even though Lightning is very flexible, it's not well suited for every possible deep learning task. See #Limitations for more.

Why PyTorch Lightning?

PyTorch Lightning is a lightweight PyTorch wrapper for high-performance AI research. It makes your code neatly organized and provides lots of useful features, like ability to run model on CPU, GPU, multi-GPU cluster and TPU.

Why Hydra?

Hydra is an open-source Python framework that simplifies the development of research and other complex applications. The key feature is the ability to dynamically create a hierarchical configuration by composition and override it through config files and the command line. It allows you to conveniently manage experiments and provides many useful plugins, like Optuna Sweeper for hyperparameter search, or Ray Launcher for running jobs on a cluster.

Main Ideas Of This Template

• Predefined Structure: clean and scalable so that work can easily be extended and replicated | #Project Structure
• Rapid Experimentation: thanks to automating pipeline with config files and hydra command line superpowers | #Your Superpowers
• Reproducibility: obtaining similar results is supported in multiple ways | #Reproducibility
• Little Boilerplate: so pipeline can be easily modified | #How It Works
• Main Configuration: main config file specifies default training configuration | #Main Project Configuration
• Experiment Configurations: can be composed out of smaller configs and override chosen hyperparameters | #Experiment Configuration
• Workflow: comes down to 4 simple steps | #Workflow
• Experiment Tracking: many logging frameworks can be easily integrated, like Tensorboard, MLFlow or W&B | #Experiment Tracking
• Logs: all logs (checkpoints, data from loggers, hparams, etc.) are stored in a convenient folder structure imposed by Hydra | #Logs
• Hyperparameter Search: made easier with Hydra built-in plugins like Optuna Sweeper | #Hyperparameter Search
• Tests: unit tests and shell/command based tests for speeding up the development | #Tests
• Best Practices: a couple of recommended tools, practices and standards for efficient workflow and reproducibility | #Best Practices

Project Structure

The directory structure of new project looks like this:

├── bash                    <- Bash scripts
│   └── schedule.sh             <- Schedule execution of many runs
│
├── configs                 <- Hydra configuration files
│   ├── callbacks               <- Callbacks configs
│   ├── datamodule              <- Datamodule configs
│   ├── experiment              <- Experiment configs
│   ├── hparams_search          <- Hyperparameter search configs
│   ├── local                   <- Local configs
│   ├── logger                  <- Logger configs
│   ├── mode                    <- Running mode configs
│   ├── model                   <- Model configs
│   ├── trainer                 <- Trainer configs
│   │
│   └── config.yaml             <- Main project configuration file
│
├── data                    <- Project data
│
├── logs                    <- Logs generated by Hydra and PyTorch Lightning loggers
│
├── notebooks               <- Jupyter notebooks. Naming convention is a number (for ordering),
│                              the creator's initials, and a short `-` delimited description, e.g.
│                              `1.0-jqp-initial-data-exploration.ipynb`.
│
├── tests                   <- Tests of any kind
│   ├── helpers                 <- A couple of testing utilities
│   ├── shell                   <- Shell/command based tests
│   └── unit                    <- Unit tests
│
├── src
│   ├── callbacks               <- Lightning callbacks
│   ├── datamodules             <- Lightning datamodules
│   ├── models                  <- Lightning models
│   ├── utils                   <- Utility scripts
│   ├── vendor                  <- Third party code that cannot be installed using PIP/Conda
│   │
│   └── train.py                <- Training pipeline
│
├── run.py                  <- Run pipeline with chosen experiment configuration
│
├── .env.example            <- Template of the file for storing private environment variables
├── .gitignore              <- List of files/folders ignored by git
├── .pre-commit-config.yaml <- Configuration of automatic code formatting
├── setup.cfg               <- Configurations of linters and pytest
├── requirements.txt        <- File for installing python dependencies
└── README.md

🚀   Quickstart

# clone project
git clone https://github.com/ashleve/lightning-hydra-template
cd lightning-hydra-template

# [OPTIONAL] create conda environment
conda create -n myenv python=3.8
conda activate myenv

# install pytorch according to instructions
# https://pytorch.org/get-started/

# install requirements
pip install -r requirements.txt

Template contains example with MNIST classification.
When running python run.py you should see something like this:

⚡   Your Superpowers

(click to expand)

python run.py trainer.max_epochs=20 model.lr=1e-4

python run.py +model.new_param="uwu"

# train on CPU
python run.py trainer.gpus=0

# train on 1 GPU
python run.py trainer.gpus=1

# train on TPU
python run.py +trainer.tpu_cores=8

# train with DDP (Distributed Data Parallel) (4 GPUs)
python run.py trainer.gpus=4 +trainer.strategy=ddp

# train with DDP (Distributed Data Parallel) (8 GPUs, 2 nodes)
python run.py trainer.gpus=4 +trainer.num_nodes=2 +trainer.strategy=ddp

# train with pytorch native automatic mixed precision (AMP)
python run.py trainer.gpus=1 +trainer.precision=16

# set project and entity names in `configs/logger/wandb`
wandb:
  project: "your_project_name"
  entity: "your_wandb_team_name"

# train model with Weights&Biases (link to wandb dashboard should appear in the terminal)
python run.py logger=wandb

# debug mode changes logging folder to `logs/debug/`
# also enables default trainer debugging options from `configs/trainer/debug.yaml`
# also sets level of all command line loggers to 'DEBUG'
python run.py mode=debug

# experiment mode changes logging folder to `logs/experiments/name_of_your_experiment/`
# name is also used by loggers
python run.py mode=exp name='my_new_experiment_253'

python run.py experiment=example_simple

python run.py callbacks=default

# gradient clipping may be enabled to avoid exploding gradients
python run.py +trainer.gradient_clip_val=0.5

# stochastic weight averaging can make your models generalize better
python run.py +trainer.stochastic_weight_avg=true

# run validation loop 4 times during a training epoch
python run.py +trainer.val_check_interval=0.25

# accumulate gradients
python run.py +trainer.accumulate_grad_batches=10

# terminate training after 12 hours
python run.py +trainer.max_time="00:12:00:00"

# run in debug mode
# changes logging folder to `logs/debug/...`
# enables trainer debugging options specified in `configs/trainer/debug.yaml`
# sets level of all command line loggers to 'DEBUG'
python run.py mode=debug

# enable trainer debugging options specified in `configs/trainer/debug.yaml`
python run.py trainer=debug

# run 1 train, val and test loop, using only 1 batch
python run.py +trainer.fast_dev_run=true

# raise exception if there are any numerical anomalies in tensors, like NaN or +/-inf
python run.py +trainer.detect_anomaly=true

# print execution time profiling after training ends
python run.py +trainer.profiler="simple"

# try overfitting to 1 batch
python run.py +trainer.overfit_batches=1 trainer.max_epochs=20

# use only 20% of the data
python run.py +trainer.limit_train_batches=0.2 \
+trainer.limit_val_batches=0.2 +trainer.limit_test_batches=0.2

# log second gradient norm of the model
python run.py +trainer.track_grad_norm=2

python run.py +trainer.resume_from_checkpoint="/absolute/path/to/ckpt/name.ckpt"

# this will run 6 experiments one after the other,
# each with different combination of batch_size and learning rate
python run.py -m datamodule.batch_size=32,64,128 model.lr=0.001,0.0005

# this will run hyperparameter search defined in `configs/hparams_search/mnist_optuna.yaml`
# over chosen experiment config
python run.py -m hparams_search=mnist_optuna experiment=example_simple

python run.py -m 'experiment=glob(*)'

🐳   Docker

First you will need to install Nvidia Container Toolkit to enable GPU support.

The template Dockerfile is provided on branch dockerfiles. Copy it to the template root folder.

To build the container use:

docker build -t <project_name> .

To mount the project to the container use:

docker run -v $(pwd):/workspace/project --gpus all -it --rm <project_name>

❤️   Contributions

Have a question? Found a bug? Missing a specific feature? Ran into a problem? Feel free to file a new issue or PR with respective title and description. If you already found a solution to your problem, don't hesitate to share it. Suggestions for new best practices and tricks are always welcome!

ℹ️   Guide

How To Get Started

• First, you should probably get familiar with PyTorch Lightning
• Next, go through Hydra quick start guide and basic Hydra tutorial

How It Works

Every run is initialized by run.py file. All PyTorch Lightning modules are dynamically instantiated from module paths specified in config. Example model config:

_target_: src.models.mnist_model.MNISTLitModel
input_size: 784
lin1_size: 256
lin2_size: 256
lin3_size: 256
output_size: 10
lr: 0.001

Using this config we can instantiate the object with the following line:

model = hydra.utils.instantiate(config.model)

This allows you to easily iterate over new models!
Every time you create a new one, just specify its module path and parameters in appriopriate config file.
The whole pipeline managing the instantiation logic is placed in src/train.py.

Main Project Configuration

Location: configs/config.yaml
Main project config contains default training configuration.
It determines how config is composed when simply executing command python run.py.
It also specifies everything that shouldn't be managed by experiment configurations.

# specify here default training configuration
defaults:
  - trainer: default.yaml
  - model: mnist_model.yaml
  - datamodule: mnist_datamodule.yaml
  - callbacks: default.yaml # set this to null if you don't want to use callbacks
  - logger: null # set logger here or use command line (e.g. `python run.py logger=wandb`)

  - mode: default.yaml

  - experiment: null
  - hparams_search: null

# path to original working directory
# hydra hijacks working directory by changing it to the current log directory,
# so it's useful to have this path as a special variable
# https://hydra.cc/docs/next/tutorials/basic/running_your_app/working_directory
work_dir: ${hydra:runtime.cwd}

# path to folder with data
data_dir: ${work_dir}/data/

# pretty print config at the start of the run using Rich library
print_config: True

# disable python warnings if they annoy you
ignore_warnings: True

Experiment Configuration

Location: configs/experiment
You should store all your experiment configurations in this folder.
Experiment configurations allow you to overwrite parameters from main project configuration.

Simple example

# to execute this experiment run:
# python run.py experiment=example_simple

defaults:
  - override /mode: exp.yaml
  - override /trainer: default.yaml
  - override /model: mnist_model.yaml
  - override /datamodule: mnist_datamodule.yaml
  - override /callbacks: default.yaml
  - override /logger: null

# all parameters below will be merged with parameters from default configurations set above
# this allows you to overwrite only specified parameters

# name of the run determines folder name in logs and is accessed by loggers
name: "example_simple"

seed: 12345

trainer:
  max_epochs: 10
  gradient_clip_val: 0.5

model:
  lin1_size: 128
  lin2_size: 256
  lin3_size: 64
  lr: 0.005

datamodule:
  train_val_test_split: [55_000, 5_000, 10_000]
  batch_size: 64

# to execute this experiment run:
# python run.py experiment=example_full

defaults:
  - override /mode: exp.yaml
  - override /trainer: null
  - override /model: null
  - override /datamodule: null
  - override /callbacks: null
  - override /logger: null

# we override default configurations with nulls to prevent them from loading at all
# instead we define all modules and their paths directly in this config,
# so everything is stored in one place

name: "example_full"

seed: 12345

trainer:
  _target_: pytorch_lightning.Trainer
  gpus: 0
  min_epochs: 1
  max_epochs: 10
  gradient_clip_val: 0.5

model:
  _target_: src.models.mnist_model.MNISTLitModel
  lr: 0.001
  weight_decay: 0.00005
  input_size: 784
  lin1_size: 256
  lin2_size: 256
  lin3_size: 128
  output_size: 10

datamodule:
  _target_: src.datamodules.mnist_datamodule.MNISTDataModule
  data_dir: ${data_dir}
  train_val_test_split: [55_000, 5_000, 10_000]
  batch_size: 64
  num_workers: 0
  pin_memory: False

logger:
  wandb:
    _target_: pytorch_lightning.loggers.wandb.WandbLogger
    project: "lightning-hydra-template"
    name: ${name}
    tags: ["best_model", "mnist"]
    notes: "Description of this model."

Local Configuration

Location: configs/local
Some configurations are user/machine/installation specific (e.g. configuration of local cluster, or harddrive paths on a specific machine). For such scenarios, a file configs/local/default.yaml can be created which is automatically loaded but not tracked by Git.

# @package _global_

defaults:
  - override /hydra/launcher@_here_: submitit_slurm

data_dir: /mnt/scratch/data/

hydra:
  launcher:
    timeout_min: 1440
    gpus_per_task: 1
    gres: gpu:1
  job:
    env_set:
      MY_VAR: /home/user/my/system/path
      MY_KEY: asdgjhawi8y23ihsghsueity23ihwd

Workflow

1. Write your PyTorch Lightning model (see mnist_model.py for example)
2. Write your PyTorch Lightning datamodule (see mnist_datamodule.py for example)
3. Write your experiment config, containing paths to your model and datamodule
4. Run training with chosen experiment config: python run.py experiment=experiment_name

Logs

Hydra creates new working directory for every executed run.
This means your working directory is different for every run, which might not be compatible with some libraries and workflows. By default, logs have the following structure:

├── logs
│   ├── experiments           # Folder for logs generated by experiments
│   │   ├── experiment_name     # Name of the experiment
│   │   │   ├── runs
│   │   │   │   ├── YYYY-MM-DD        	# Date of execution
│   │   │   │   │   ├── HH-MM-SS          # Time of execution
│   │   │   │   │   │   ├── .hydra          # Hydra logs
│   │   │   │   │   │   ├── wandb           # Weights&Biases logs
│   │   │   │   │   │   ├── checkpoints     # Training checkpoints
│   │   │   │   │   │   └── ...             # Any other thing saved during training
│   │   │   │   │   └── ...
│   │   │   │   └── ...
│   │   │   │
│   │   │   └── multiruns
│   │   │       ├── YYYY-MM-DD
│   │   │       │   ├── HH-MM-SS
│   │   │       │   │   ├── 1               # Multirun job number
│   │   │       │   │   │   └── ...
│   │   │       │   │   ├── 2
│   │   │       │   │   └── ...
│   │   │       │   └── ...
│   │   │       └── ...
│   │   └── ...
│   │
│   ├── debugs                  # Folder for logs generated during debugging
│   │ 	├── runs
│   │   │   ├── YYYY-MM-DD
│   │   │   │   ├── HH-MM-SS
│   │   │   │   └── ...
│   │   │   └── ...
│   │   └── multiruns
│   │       ├── YYYY-MM-DD
│   │       │   ├── HH-MM-SS
│   │       │   └── ...
│   │       └─ ...
│   │
│   ├── runs                    # Folder for logs generated by normal runs
│   │   ├── YYYY-MM-DD
│   │   │   ├── HH-MM-SS
│   │   │   └── ...
│   │   └── ...
│   │
│   └── multiruns               # Folder for logs generated by normal multiruns (sweeps)
│       ├── YYYY-MM-DD
│       |   ├── HH-MM-SS
│       |   └── ...
│       └── ...
│

You can change this structure by modifying paths in hydra configuration.

Experiment Tracking

PyTorch Lightning supports the most popular logging frameworks:
Weights&Biases · Neptune · Comet · MLFlow · Tensorboard

These tools help you keep track of hyperparameters and output metrics and allow you to compare and visualize results. To use one of them simply complete its configuration in configs/logger and run:

python run.py logger=logger_name

You can use many of them at once (see configs/logger/many_loggers.yaml for example).

You can also write your own logger.

Lightning provides convenient method for logging custom metrics from inside LightningModule. Read the docs here or take a look at MNIST example.

Hyperparameter Search

Defining hyperparameter optimization is as easy as adding new config file to configs/hparams_search.

defaults:
  - override /hydra/sweeper: optuna

# choose metric which will be optimized by Optuna
optimized_metric: "val/acc_best"

hydra:
  # here we define Optuna hyperparameter search
  # it optimizes for value returned from function with @hydra.main decorator
  # learn more here: https://hydra.cc/docs/next/plugins/optuna_sweeper
  sweeper:
    _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper
    storage: null
    study_name: null
    n_jobs: 1

    # 'minimize' or 'maximize' the objective
    direction: maximize

    # number of experiments that will be executed
    n_trials: 20

    # choose Optuna hyperparameter sampler
    # learn more here: https://optuna.readthedocs.io/en/stable/reference/samplers.html
    sampler:
      _target_: optuna.samplers.TPESampler
      seed: 12345
      consider_prior: true
      prior_weight: 1.0
      consider_magic_clip: true
      consider_endpoints: false
      n_startup_trials: 10
      n_ei_candidates: 24
      multivariate: false
      warn_independent_sampling: true

    # define range of hyperparameters
    search_space:
      datamodule.batch_size:
        type: categorical
        choices: [32, 64, 128]
      model.lr:
        type: float
        low: 0.0001
        high: 0.2
      model.lin1_size:
        type: categorical
        choices: [32, 64, 128, 256, 512]
      model.lin2_size:
        type: categorical
        choices: [32, 64, 128, 256, 512]
      model.lin3_size:
        type: categorical
        choices: [32, 64, 128, 256, 512]

Next, you can execute it with: python run.py -m hparams_search=mnist_optuna
Using this approach doesn't require you to add any boilerplate into your pipeline, everything is defined in a single config file. You can use different optimization frameworks integrated with Hydra, like Optuna, Ax or Nevergrad. The optimization_results.yaml will be available under logs/multirun folder.

Inference

The following code is an example of loading model from checkpoint and running predictions.

from PIL import Image
from torchvision import transforms

from src.models.mnist_model import MNISTLitModel


def predict():
    """Example of inference with trained model.
    It loads trained image classification model from checkpoint.
    Then it loads example image and predicts its label.
    """

    # ckpt can be also a URL!
    CKPT_PATH = "last.ckpt"

    # load model from checkpoint
    # model __init__ parameters will be loaded from ckpt automatically
    # you can also pass some parameter explicitly to override it
    trained_model = MNISTLitModel.load_from_checkpoint(checkpoint_path=CKPT_PATH)

    # print model hyperparameters
    print(trained_model.hparams)

    # switch to evaluation mode
    trained_model.eval()
    trained_model.freeze()

    # load data
    img = Image.open("data/example_img.png").convert("L")  # convert to black and white
    # img = Image.open("data/example_img.png").convert("RGB")  # convert to RGB

    # preprocess
    mnist_transforms = transforms.Compose(
        [
            transforms.ToTensor(),
            transforms.Resize((28, 28)),
            transforms.Normalize((0.1307,), (0.3081,)),
        ]
    )
    img = mnist_transforms(img)
    img = img.reshape((1, *img.size()))  # reshape to form batch of size 1

    # inference
    output = trained_model(img)
    print(output)


if __name__ == "__main__":
    predict()

Tests

Template comes with example tests implemented with pytest library.
To execute them simply run:

# run all tests
pytest

# run tests from specific file
pytest tests/shell/test_basic_commands.py

# run all tests except the ones marked as slow
pytest -k "not slow"

To speed up the development, you can once in a while execute tests that run a couple of quick experiments, like training 1 epoch on 25% of data, executing single train/val/test step, etc. Those kind of tests don't check for any specific output - they exist to simply verify that executing some bash commands doesn't end up in throwing exceptions. You can find them implemented in tests/shell folder.

You can easily modify the commands in the scripts for your use case. If 1 epoch is too much for your model, then make it run for a couple of batches instead (by using the right trainer flags).

Callbacks

Template contains example callbacks enabling better Weights&Biases integration, which you can use as a reference for writing your own callbacks (see wandb_callbacks.py).
To support reproducibility:

• WatchModel
• UploadCodeAsArtifact
• UploadCheckpointsAsArtifact

To provide examples of logging custom visualisations with callbacks only:

• LogConfusionMatrix
• LogF1PrecRecHeatmap
• LogImagePredictions

To try all of the callbacks at once, use:

python run.py logger=wandb callbacks=wandb

To see the result of all the callbacks attached, take a look at this experiment dashboard.

Multi-GPU Training

Lightning supports multiple ways of doing distributed training.
The most common one is DDP, which spawns separate process for each GPU and averages gradients between them. To learn about other approaches read lightning docs.

You can run DDP on mnist example with 4 GPUs like this:

python run.py trainer.gpus=4 +trainer.strategy=ddp

⚠️ When using DDP you have to be careful how you write your models - learn more here.

Reproducibility

What provides reproducibility:

• Hydra manages your configs
• Hydra manages your logging paths and makes every executed run store its hyperparameters and config overrides in a separate file in logs
• Single seed for random number generators in pytorch, numpy and python.random
• LightningDataModule allows you to encapsulate data split, transformations and default parameters in a single, clean abstraction
• LightningModule separates your research code from engineering code in a clean way
• Experiment tracking frameworks take care of logging metrics and hparams, some can also store results and artifacts in cloud
• Pytorch Lightning takes care of creating training checkpoints
• Example callbacks for wandb show how you can save and upload a snapshot of codebase every time the run is executed, as well as upload ckpts and track model gradients

You can load the config of previous run using:

python run.py --config-path /logs/runs/.../.hydra/ --config-name config.yaml

The config.yaml from .hydra folder contains all overriden parameters and sections. This approach however is not officially supported by Hydra and doesn't override the hydra/ part of the config, meaning logging paths will revert to default!

Limitations

• Currently, template doesn't support k-fold cross validation, but it's possible to achieve it with Lightning Loop interface. See the official example. Implementing it requires rewriting the training pipeline.
• Pytorch Lightning might not be the best choice for scalable reinforcement learning, it's probably better to use something like Ray.
• Currently hyperparameter search with Hydra Optuna Plugin doesn't support prunning.
• Hydra changes working directory to new logging folder for every executed run, which might not be compatible with the way some libraries work.

Useful Tricks

Best Practices

wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh

conda create -n myenv python=3.8
conda activate myenv

pip install pre-commit

pre-commit install

pre-commit run -a

MY_VAR=/home/user/my_system_path

path_to_data: ${oc.env:MY_VAR}

self.log("train/loss", loss)

from torchmetrics.classification.accuracy import Accuracy


class LitModel(LightningModule):
    def __init__(self)
        self.train_acc = Accuracy()
        self.val_acc = Accuracy()

    def training_step(self, batch, batch_idx):
        ...
        acc = self.train_acc(predictions, targets)
        self.log("train/acc", acc)
        ...

    def validation_step(self, batch, batch_idx):
        ...
        acc = self.val_acc(predictions, targets)
        self.log("val/acc", acc)
        ...

dvc init

dvc add data/MNIST

git add data/MNIST.dvc data/.gitignore
git commit -m "Add raw data"

from setuptools import find_packages, setup


setup(
    name="src",  # change "src" folder name to your project name
    version="0.0.0",
    description="Describe Your Cool Project",
    author="...",
    author_email="...",
    url="https://github.com/ashleve/lightning-hydra-template",  # replace with your own github project link
    install_requires=[
        "pytorch>=1.10.0",
        "pytorch-lightning>=1.4.0",
        "hydra-core>=1.1.0",
    ],
    packages=find_packages(),
)

pip install -e .

pip install git+git://github.com/YourGithubName/your-repo-name.git --upgrade

from project_name.models.mnist_model import MNISTLitModel
from project_name.datamodules.mnist_datamodule import MNISTDataModule

Other Repositories

License

This project is licensed under the MIT License.

MIT License

Copyright (c) 2021 ashleve

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
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Description

What it does

How to run

Install dependencies

# clone project
git clone https://github.com/YourGithubName/your-repo-name
cd your-repo-name

# [OPTIONAL] create conda environment
conda create -n myenv python=3.8
conda activate myenv

# install pytorch according to instructions
# https://pytorch.org/get-started/

# install requirements
pip install -r requirements.txt

Train model with default configuration

# train on CPU
python run.py trainer.gpus=0

# train on GPU
python run.py trainer.gpus=1

Train model with chosen experiment configuration from configs/experiment/

python run.py experiment=experiment_name.yaml

You can override any parameter from command line like this

python run.py trainer.max_epochs=20 datamodule.batch_size=64