2019-12-06 22:38:30 +02:00
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ludwig
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======
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[Ludwig][1] is a toolbox that allows to train and test deep learning models
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without the need to write code.
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## up and running
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```bash
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$ mkdir -p data
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$ vim data/model.yaml
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$ wget http://boston.lti.cs.cmu.edu/classes/95-865-K/HW/HW2/epinions.zip
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$ unzip epinions.zip
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$ mv epinions/epinions-1.csv data/train.csv
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$ mv epinions/epinions-2.csv data/predict.csv
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$ tree data
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├── model.yaml
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├── predict.csv
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└── train.csv
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$ docker-compose run --rm train
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$ docker-compose run --rm visualize
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$ docker-compose run --rm predict
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$ docker-compose up -d serve
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$ curl http://127.0.0.1:8000/predict -X POST -F 'text=taking photos and recording videos'
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{
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"class_predictions": "Camera",
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"class_probabilities_<UNK>": 9.438252263072044e-11,
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"class_probabilities_Auto": 0.32920214533805847,
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"class_probabilities_Camera": 0.6707978248596191,
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"class_probability": 0.6707978248596191
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}
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$ curl http://127.0.0.1:8000/predict -X POST -F 'text=looking to buy a new sports car'
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{
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"class_predictions": "Auto",
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"class_probabilities_<UNK>": 1.900043131457165e-15,
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"class_probabilities_Auto": 0.9999126195907593,
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"class_probabilities_Camera": 8.738834003452212e-05,
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"class_probability": 0.9999126195907593
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}
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$ tree -L 3 data
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├── model.yaml
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├── predict.csv
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├── train.csv
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├── results
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2019-12-09 02:33:02 +02:00
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│ └── experiment_example
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2019-12-06 22:38:30 +02:00
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│ ├── description.json
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│ ├── model
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│ └── training_statistics.json
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├── results_0
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│ ├── class_predictions.csv
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│ ├── class_predictions.npy
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│ ├── class_probabilities.csv
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│ ├── class_probabilities.npy
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│ ├── class_probability.csv
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│ └── class_probability.npy
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└── visualize
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├── learning_curves_class_accuracy.png
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├── learning_curves_class_hits_at_k.png
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├── learning_curves_class_loss.png
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├── learning_curves_combined_accuracy.png
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└── learning_curves_combined_loss.png
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```
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[1]: https://uber.github.io/ludwig/
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