Cheatography
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ModesQuick mode | Use default param settings | Update mode | Update default param settings | Analysis mode | Find hyper params | Expert mode | Set params from scratch | Compare mode | Compare against output metrics |
Backend FrameworksPytorch | v1.2.0, v1.3.0 | Keras | v2.2.5, v2.3.1 | GluonCV | v0.5.0 | Mxnet | v1.5.1 | Cuda - Support | v9.0, v10.0 |
OS and PythonUbuntu | v16.04, v18.04 | Mac-OS | - | Windows | v10 | Python | v3.5, v3.6, v3.7 |
Unique FeaturesWrite syntax invariant code | Resume interrupted experiments | Move workspaces across systems and continue training/inferencing | Compare experiments visually | Auto find hyper params | Run training/inferencing on sub-sample of original datasets |
InstallationChoose the right requirements file as per platform, OS and cuda support Setup - Pythonpathimport sys
sys.path.append("monk_v1/monk") |
| | Backend selectionfrom pytorch_prototype import prototype
from gluon_prototype import prototype
from keras_prototype import prototype
|
Select from pytorch, gluon and keras Experiment creationptf = prototype(verbose=1);
ptf.Prototype("sample-project-1", "sample-experiment-1");
ptf = prototype(verbose=1);
ptf.Prototype("sample-project-1", "sample-experiment-2"); |
Directory sturcture created -
workspace
|----- sample-project-1
|------------sample-experiment-1
|------------sample-experiment-2 | | Quick Mode - Trainingptf.Default(dataset_path="train",
model_name="resnet18",
num_epochs=2);
ptf.Train(); |
1. Set dataset path
2. Set model name
3. Set number of epochs to train Inferencingout = ptf.Infer(img_name="path_to_img");
out = ptf.Infer(img_dir="path_to_dataset"); |
Infer over image or entire directory of images. |
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