Skip to main content

Machine Learning tool allowing plug-and-play training for pytorch models

Project description

pyroml

🔥 Machine Learning framework allowing plug-and-play training for pytorch models

Installation

$ git clone https://github.com/peacefulotter/pyroml.git
$ cd pyroml
$ sudo apt install python3.10-venv # check you python version and change it here if !=
$ sudo apt install python3-virtualenv
$ python3 -m venv venv
$ source ./venv/bin/activate
$ pip install -r requirements.txt

Running tests

$ cd tests
$ python main.py # this will launch the training, follow the wandb link to access the plots
$ python pretrain.py # will load the last checkpoint and compute mse on a small part of the dataset, outputs True if model predicts correctly!

Done

  • Metrics, with support for custom metrics
  • WandB
  • Checkpoints
  • Load pretrained models from checkpoints

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

pyroml-0.0.8.tar.gz (8.9 kB view details)

Uploaded Source

Built Distribution

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

pyroml-0.0.8-py3-none-any.whl (10.1 kB view details)

Uploaded Python 3

File details

Details for the file pyroml-0.0.8.tar.gz.

File metadata

  • Download URL: pyroml-0.0.8.tar.gz
  • Upload date:
  • Size: 8.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.10.12

File hashes

Hashes for pyroml-0.0.8.tar.gz
Algorithm Hash digest
SHA256 964dd4f0ff93ac311c4e92834fce06479f56c358575389d93a7e322b6ed5bd1c
MD5 ee12f71461c2c6f94a309bff39ee54f5
BLAKE2b-256 787f60ce8b491823b5e2540c223a4f14ca7d040ffaf755bc6b2a315c1ec9853b

See more details on using hashes here.

File details

Details for the file pyroml-0.0.8-py3-none-any.whl.

File metadata

  • Download URL: pyroml-0.0.8-py3-none-any.whl
  • Upload date:
  • Size: 10.1 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.10.12

File hashes

Hashes for pyroml-0.0.8-py3-none-any.whl
Algorithm Hash digest
SHA256 80cab67f05193ba9d2eba26b6bb2646ea495dce023dd49558cab41bbd4064111
MD5 c899729caca66b604762daf4fc194502
BLAKE2b-256 c01be42b9977614150ab2d7e3e0916d74ede51ceaca39b715b415608400e372b

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