Skip to main content

The goal of this project is to make pytorch easier to use.

This project has been archived.

The maintainers of this project have marked this project as archived. No new releases are expected.

Project description

pstorch

Generate a ready-to-run PyTorch project in seconds.

Install

pip install pstorch

Python

import pstorch

pstorch.generate(
    name="MyClassifier",
    arch="mlp",
    input_dim=784,
    output_dim=10,
    hidden=[256, 128],
    metrics=["accuracy", "loss"],
    prod=False,
)

Then run the boilerplate:

python main.py

CLI

pstorch MyClassifier --arch mlp --input-dim 784 --output-dim 10 --hidden 256,128 --metrics accuracy,loss

Output:

src/
    __init__.py
    config.py
    models.py
    data.py
    train.py
    predict.py
main.py
requirements.txt

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pstorch-0.1.3.tar.gz (6.1 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

pstorch-0.1.3-py3-none-any.whl (7.0 kB view details)

Uploaded Python 3

File details

Details for the file pstorch-0.1.3.tar.gz.

File metadata

  • Download URL: pstorch-0.1.3.tar.gz
  • Upload date:
  • Size: 6.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: uv/0.7.19

File hashes

Hashes for pstorch-0.1.3.tar.gz
Algorithm Hash digest
SHA256 daaf1204d194bbfa5616e95f6204ddf1529816b69b8616c7f403a184bc551f02
MD5 cd5e809e22199895d807ff8088a61f73
BLAKE2b-256 0bcc7b11325a0c3595b64ea63e96441a4d2984a6a176f6c81d3893f22e4992b7

See more details on using hashes here.

File details

Details for the file pstorch-0.1.3-py3-none-any.whl.

File metadata

  • Download URL: pstorch-0.1.3-py3-none-any.whl
  • Upload date:
  • Size: 7.0 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: uv/0.7.19

File hashes

Hashes for pstorch-0.1.3-py3-none-any.whl
Algorithm Hash digest
SHA256 72c40bb6ab09596e751ade942f9e05deab0a6243cfb71db1c006084d4f49e03a
MD5 b9bd8d750486ab0309c673a23d0e2ebd
BLAKE2b-256 5914bc4b1ebd6bdfeacd838a9c6b70a595928443d734d039b1e36faeff91bcde

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page