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Monk AI Cheatsheet Cheat Sheet by [deleted]

keras     pytorch     gluoncv     mxnet     transfer-learnin

Modes

Quick 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 Frameworks

Pytorch
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 Python

Ubuntu
v16.04, v18.04
Mac-OS
-
Windows
v10
Python
v3.5, v3.6, v3.7

Unique Features

Write 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

Instal­lation

$ git clone https://github.com/Tessellate-Imaging/monk_v1

$ pip install -r monk_v1/installation/requirements_cu10.txt
Choose the right requir­ements file as per platform, OS and cuda support

Setup - Pythonpath

import sys
sys.path.append("monk_v1/monk")
 

Backend selection

from pytorc­h_p­rot­otype import prototype
from gluon_­pro­totype import prototype
from keras_­pro­totype import prototype
Select from pytorch, gluon and keras

Experiment creation

ptf = 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 - Training

ptf.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

Infere­ncing

out = ptf.Infer(img_name="path_to_img");
out = ptf.Infer(img_dir="path_to_dataset");
Infer over image or entire directory of images.

Download the Monk AI Cheatsheet Cheat Sheet

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PDF (recommended)

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