Simple PyTorch AutoEncoder to play with.

Set Up

Google Colab

Check out simple examples in the Notebooks.

Elsewhere

As an example:

python -m venv
source .venv/bin/activate
pip install git+https://github.com/ghsanti/torch_practice

One then can run it:

python -m torch_practice.simple_train

For custom configurations, write a simple script:

from torch_practice.simple_train import train
from torch_practice.default_config import default_config

config = default_config()
config["n_workers"] = 3

# then train it.
train(config)

This package installs torch+cpu by default. For other hardware please install torch from the matrix versions.

Configuration

The "blueprint" is in the DAEConfig, in this file.

Reproducibility

basic practices

From the [docs](https://pytorch.org/docs/stable/notes/randomness.html):

Completely reproducible results are not guaranteed across PyTorch releases, individual commits, or different platforms.

To control the sources of randomness one can pass a seed to the configuration dictionary. This controls some ops and dataloading.

Dev

simple steps here

1. Fork 2. Clone your fork and run ```bash pip install uv uv venv source .venv/bin/activate uv sync --all-extras # non-cpu users need extra torch installs. ```

Checking out to a Codespace it installs everything. Activate the venv using:

source .venv/bin/activate

• In both cases, remember to select the .venv python-interpreter in VSCode.
• Use absolute imports.

Build

uv pip install --upgrade build
uv build