Getting Started¶
Configuration¶
The first thing to do is to set up the config.yaml. This file is user-specific (it got tracked at some point, so I need to fix this), and will tell cortex everything user-specific regarding data locations, visualation, and outputs.
$ rm -rf ~/.cortex.yml
$ cortex setup
Configuration File Example¶
Located at ~/.cortex.yml
torchvision_data_path: /data/milatmp1/hjelmdev/data/
data_paths: {
Imagenet-12: /data/lisa/data/ImageNet2012_jpeg, CelebA: /tmp/hjelmdev/CelebA}viz: {
font: /usr/share/fonts/truetype/liberation/LiberationSerif-Regular.ttf, server: 'http://132.204.26.180'}
out_path: /data/milatmp1/hjelmdev/outs/
These are as follows:
torchvision_data_path: the path to all torchvision-specific datasets (details can be found in torchvision.datasets)
data_paths: user-specified custom datasets. Currently, only support is for image folders (a la imagenet), but other dataset types (e.g., text) are planned in the near-future.
vis: visdom specific arguments.
- out_path: Out path for experiment outputs
Usage¶
cortex –help
Built-ins¶
| setup: | Setup cortex configuration. |
|---|---|
| GAN: | Generative adversarial network. |
| VAE: | Variational autoencoder. |
| AdversarialAutoencoder: | |
| Adversarial Autoencoder. | |
| ALI: | Adversarially learned inference. |
| ImageClassification: | |
| Basic image classifier. | |
| GAN_MINE: | GAN + MINE. |
Options¶
| -h, --help | show this help message and exit |
| -o OUT_PATH, --out_path OUT_PATH | |
| Output path directory. All model results will go here. If a new directory, a new one will be created, as long as parent exists. | |
| -n NAME, --name NAME | |
| Name of the experiment. If given, base name of output directory will be –name. If not given, name will be the base name of the –out_path | |
| -r RELOAD, --reload RELOAD | |
| Path to model to reload. | |
| -M LOAD_MODELS, --load_models LOAD_MODELS | |
| Path to model to reload. Does not load args, info, etc | |
| -m META, --meta META | |
| TODO | |
| -c CONFIG_FILE, --config_file CONFIG_FILE | |
| Configuration yaml file. See exps/ for examples | |
| -k, --clean | Cleans the output directory. This cannot be undone! |
| -v VERBOSITY, --verbosity VERBOSITY | |
| Verbosity of the logging. (0, 1, 2) | |
| -d DEVICE, --device DEVICE | |
| TODO | |
Custom models¶
It is possible to run experiments with custom models made with Pytorch under the Cortex framework. For doing so, the model has to be added to the demos folder under the root of the project. You can have a look to the given demo autoencoder and classifier already implemented. The main difference is that, rather than registering the plugins, the run function of main.py has to be called. For example,
if __name__ == '__main__':
classifier = MyClassifier()
run(model=classifier)
To run an experiment with a custom model.
python my_model.py --d.source <Dataset> --d.copy_to_local