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Reproducible Deep Learning tools and examples

Project description

PyPi package

dtoolAI is a library for supporting reproducible deep learning.

Quick start

If you’d like to see what dtoolAI can do without installing anything, two of the Jupyter notebooks in this repository highlighting dtoolAI functions can be run through Google Colab without any local software installation:

You’ll need a Google account to run these, and when you load the notebooks, click “Open in playground” to be able to execute code.



dtoolAI is dependent on the following Python packages:

  • pytorch
  • torchvision
  • dtoolcore
  • dtool-http
  • click
  • pillow

If you install dtoolAI with pip or conda as described below, these dependencies will be installed automatically. If you wish to install manually, you’ll need to install these before installing dtoolAI.

For Windows users, we recommend installing pytorch and torchvision through anaconda/conda. See the section below for details.

Through pip

dtoolAI requires Python version 3 and Pytorch.


Install Pytorch before installing dtoolAI. For information on how to install Pytorch this see the Pytorch getting started guide for details.

Once Pytorch has been installed dtoolAI can be installed through pip:

pip install dtoolai

Through conda

You can also install dtoolAI through conda. To optionally create a conda environment in which to install dtoolAI:

conda create -n dtoolai
conda activate dtoolai

Then you can install with:

conda install pytorch==1.4.0 torchvision==0.5.0 -c pytorch
conda install dtoolcore dtool-http dtoolai -c dtool

To install the dtool command line utilities, you’ll need to use pip:

pip install dtool


You can also download this repository and install through:

python install


Primary documentation:

Detailed examples of API use are provided in the notebooks/ directory in this repository.


Running the tests requires pytest.

To run the faster tests in the test suite, use:

pytest tests/ -m "not slow"

The test suite also includes full end-to-end tests that create datasets, train models and evaluate them on those datasets. These are much slower, to run them use:

pytest tests/

Project details

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