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https://github.com/kengz/mnist-classifier
Simple MNIST classifier example using PyTorch Lightning.
https://github.com/kengz/mnist-classifier
Last synced: 21 days ago
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Simple MNIST classifier example using PyTorch Lightning.
- Host: GitHub
- URL: https://github.com/kengz/mnist-classifier
- Owner: kengz
- License: mit
- Created: 2022-05-21T18:08:42.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-05-13T20:20:15.000Z (over 1 year ago)
- Last Synced: 2024-10-03T10:33:39.953Z (about 1 month ago)
- Language: Python
- Size: 39.1 KB
- Stars: 1
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# mnist-classifier
Simple MNIST classifier example using PyTorch Lightning.
## Installation
[Install Miniconda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html) if you haven't already.
Create a Conda environment and install dependencies. This MNIST example uses:
- [PyTorch Lightning](https://pytorch-lightning.readthedocs.io/en/stable/notebooks/lightning_examples/mnist-hello-world.html#Introduction-to-Pytorch-Lightning) for constructing data module and model that can work across machines and utilize accelerator devices (GPUs/TPUs) if available.
- [Hydra](https://hydra.cc) config for cleanly specifying config
### Using Conda (recommended)
```bash
conda env create --file environment.yml
```
Before running any commands below, activate the environment:
```bash
conda activate mnist
```
### Using pip (for demo completeness)
Pip-based installation is not recommended as it provides less control on the dependencies, can be less optimal, and some packages are also only released through Conda. However a `requirements.txt` file is provided here for demo of a pip setup.
```bash
pip install -r requirements.txt
```
## Usage
Inspect/modify the Hydra config at `config.yaml`. Then run:
```bash
# train
python mnist/train.py
# to change configs
python mnist/train.py datamodule.batch_size=64 model.hidden_size=128
# resume training from last checkpoint
python mnist/train.py resume=true
```
This run training with validation at epoch-end, and test when training is done. Metrics will be logged from torchmetrics.
### Hyperparameter Search
By using config-based approach, any variant to the run can be specified as parameter overrides to Hydra configs - hence we can tune hyperparameters without any code changes.
The entrypoint `train.py` returns a float to be used for optimization; the logged metrics in trainer can be accessed via `trainer.callback_metrics`, and the config `cfg.metric` specifies which field.
Hydra has an [Optuna sweeper plugin](https://hydra.cc/docs/plugins/optuna_sweeper/). To run hyperparameter tuning, simply specify the parameter override and search space/details in `config/optuna/`, and run the folllowing:
```bash
# hyperparameter search using Optuna + Hydra. Configure in config/optuna.yaml
# view Optuna sweeper config
python mnist/train.py hydra/sweeper=optuna +optuna=tune -c hydra -p hydra.sweeper
# run Optuna sweeper using optuna/tune.yaml to search over tune and other hyperparams
python mnist/train.py hydra/sweeper=optuna +optuna=tune --multirun
```
### dstack usage
For [dstack](https://docs.dstack.ai) usage, including interactive development, see workflows defined in `.dstack/workflows/*.yaml`.
First, start dstack hub:
```bash
# setup dstack
pip install -U dstack
# start dstack hub
dstack start
```
Then in a new shell, init dstack project and run workflow:
```bash
# initialize project
dstack init
# run training workflow: pip-train
dstack run pip-train
# or run hyperparameter search workflow: pip-tune
dstack run pip-tune
```