A library of helpers to train, evaluate and visualize deep nets with PyTorch.
Project description
Readme
A library of helpers for PyTorch.
Michaël Gharbi <mgharbi@adobe.com>
Installation
From pip: pip install torch-tools
From source: python setup.py install
Quickstart
To quickly get up and running, use our repo initialization script. In your command line, simple run:
ttools.new
This will prompt you to give a name to your new project, and create the necessary files and folders for you.
Documentation
The documentation webpage can be found here https://torch-tools.readthedocs.io/en/latest/
Demo
For a simple demo, look at the MNIST example in examples/train_mnist.py:
python examples/train_mnist.py data out
Contributors
Dima Smirnov implemented the tensorboard hooks and callbacks.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for torch_tools-0.0.32-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 5269d5d542d48de85fd15bd336a9f47cdef264248d50c63a7d659d699827ba65 |
|
MD5 | efd36442d5dbd91f723bc9de236269c8 |
|
BLAKE2b-256 | 728b21ebb34224fcf6a1fbb4fb92f1e1d0ebef8473347d8741b2e5a1056c539a |