olive-oil-ml 0.11

[](https://codeclimate.com/github/mfinzi/pristine-ml) [](https://codeclimate.com/github/mfinzi/pristine-ml “CodeClimate”)

# Olive-Oil-ML

Nuts and bolts deep learning library to make training neural networks easier. Features: * Logging functionality not at set time intervals but as a percentage of the total training time * Convenient specification for Random and Grid hyperparameter search: queued with a single GPU, split over multiple local GPUs, or over a Slurm Cluster * Clean implementation of popular methods/problems in CV such as Vanilla Image Classification, Regression, the PiModel for Semi-Supervised learning, and SN-GAN; all through the common Trainer abstraction

# Installation To install, run pip install git+https://github.com/mfinzi/olive-oil-ml. Dependencies will be checked and installed from the setup.py file.

# Dependencies * Python 3.7+ * [PyTorch](http://pytorch.org/) 1.3.0+ * [torchvision](https://github.com/pytorch/vision/) * [tqdm](https://tqdm.github.io/) 4.40+ * [natsort]() * (optional) [tensorboardX](https://github.com/lanpa/tensorboardX)

# Jump into training a single model

To get a feel for the library, try training a (Classifier, Regressor, PiModel, GAN) model from our recipes. For classification try running * python oil/recipes/simpleTrial.py –dataset CIFAR100 –num_epochs 10

Or, to train a conditional GAN model: * python oil/recipes/simpleCGan.py –dataset SVHN –lr 2e-4

Or train a PiModel semisupervised on CIFAR10 using only 1k labels: * python oil/recipes/simplePi.py –dataset CIFAR10 –train 1000

You can use -h to see the full range of arguments available. Command line arguments and defaults are automatically inferred from the code used to construct the trial, so you can make a new trial (that uses some exotic data augmentation strategy for example) and the command line parser will generated for you, see the example recipes for how this works.

# Perform a hyperparameter search Example: Search over hyperparameters for CNN classifier on Cifar100 * python oil/recipes/exampleHyperSearch.py –dataset CIFAR100 –bs [50,32,64] –k [64,96] –num_epochs 100 See example code for programmatic way of specifying the hyperparameter search. Automatically parallelizes the search over multiple GPUs if available. # Logging Support

# Interfacing with external libraries