TorchBlaze

Link to Documentation

A CLI-based python package that provides a suite of functionalities to perform end-to-end ML using PyTorch.

The following are the set of functionalities provided by the tool:

• Flask-API Template: Set up the basic PyTorch project sturcture and an easily tweakable flask-RESTful API with a single CLI command. Deploying your ML models has never been so easy.

• Test ML API: Once you have set up your API, test all the API end-points to ensure you get the expected results before pushing your API to deployment.

• Dockerizing: A simplified, single-command, easy dockerization for your ML API.

• ML Model Test Suite: The package comes with a built-in test suite that evaluates your PyTorch models over a set of tests to look for any errors that otherwise might not be traceable easily.

Here are the available list of commands:

• Setting-up the Template Project:

foo@bar:~$ torchblaze generate_template --project_name example

• Building Docker Image (Requires Docker Installed):

First cd to the root project directory containing app.py file.

foo@bar:~$ torchblaze generate_docker --image_name example_image

• Run Docker Image (Requires Docker Installed):

foo@bar:~$ torchblaze run_docker --image_name example

• Performing API Tests:

First cd to the root project directory containing app.py file.

foo@bar:~$ torchblaze api_tests

• Performing Model Testing:

Import the mltests package

import torchblaze.mltests as mls

Then use the variety of testing methods available in the mltests package. Run the following command to get the list of available methods.

dir(mls)

To check the documentation for any of the available tests, use the help method:

help(mls.<method_name>)