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Pratik raj b113da5d19 chore: Use --no-cache-dir flag to pip in Dockerfiles, to save space Using "--no-cache-dir" flag in pip install ,make sure dowloaded packages by pip don't cached on system . This is a best practise which make sure to fetch ftom repo instead of using local cached one . Further , in case of Docker Containers , by restricing caching , we can reduce image size. In term of stats , it depends upon the number of python packages multiplied by their respective size . e.g for heavy packages with a lot of dependencies it reduce a lot by don't caching pip packages. Further , more detail information can be found at https://medium.com/sciforce/strategies-of-docker-images-optimization-2ca9cc5719b6 Signed-off-by: Pratik Raj <rajpratik71@gmail.com>		2021-07-02 01:02:49 +05:30
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data	add ludwig	2019-12-07 04:38:30 +08:00
Dockerfile	chore: Use --no-cache-dir flag to pip in Dockerfiles, to save space	2021-07-02 01:02:49 +05:30
README.md	update ludwig	2019-12-09 08:33:02 +08:00
docker-compose.yml	update ludwig	2019-12-09 08:33:02 +08:00

README.md

ludwig

Ludwig is a toolbox that allows to train and test deep learning models without the need to write code.

up and running

$ mkdir -p data
$ vim data/model.yaml

$ wget http://boston.lti.cs.cmu.edu/classes/95-865-K/HW/HW2/epinions.zip
$ unzip epinions.zip
$ mv epinions/epinions-1.csv data/train.csv
$ mv epinions/epinions-2.csv data/predict.csv

$ tree data
├── model.yaml
├── predict.csv
└── train.csv

$ docker-compose run --rm train
$ docker-compose run --rm visualize
$ docker-compose run --rm predict
$ docker-compose up -d serve

$ curl http://127.0.0.1:8000/predict -X POST -F 'text=taking photos and recording videos'
{
  "class_predictions": "Camera",
  "class_probabilities_<UNK>": 9.438252263072044e-11,
  "class_probabilities_Auto": 0.32920214533805847,
  "class_probabilities_Camera": 0.6707978248596191,
  "class_probability": 0.6707978248596191
}

$ curl http://127.0.0.1:8000/predict -X POST -F 'text=looking to buy a new sports car'
{
  "class_predictions": "Auto",
  "class_probabilities_<UNK>": 1.900043131457165e-15,
  "class_probabilities_Auto": 0.9999126195907593,
  "class_probabilities_Camera": 8.738834003452212e-05,
  "class_probability": 0.9999126195907593
}

$ tree -L 3 data
├── model.yaml
├── predict.csv
├── train.csv
├── results
│   └── experiment_example
│       ├── description.json
│       ├── model
│       └── training_statistics.json
├── results_0
│   ├── class_predictions.csv
│   ├── class_predictions.npy
│   ├── class_probabilities.csv
│   ├── class_probabilities.npy
│   ├── class_probability.csv
│   └── class_probability.npy
└── visualize
    ├── learning_curves_class_accuracy.png
    ├── learning_curves_class_hits_at_k.png
    ├── learning_curves_class_loss.png
    ├── learning_curves_combined_accuracy.png
    └── learning_curves_combined_loss.png