Metadata-Version: 2.1
Name: torchblaze
Version: 1.0.5
Summary: A CLI-based python package that provides a suite of functionalities to perform end-to-end ML using PyTorch.
Home-page: https://github.com/MLH-Fellowship/torchblaze
Author: Sai Durga Kamesh Kota
Author-email: ksdkamesh99@gmail.com
License: MIT
Description: ![TorchBlaze](https://raw.githubusercontent.com/MLH-Fellowship/torchblaze/v1.0.2/documentation/static/img/torchblaze.svg?token=AK7ZFSTALFJP2BHI2SUTJPDAIJPX6)
        
        # TorchBlaze 
        [Link to Documentation](https://mlh-fellowship.github.io/torchblaze/)
        ---
        
        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:
        
        ```console
        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.
        
        ```console
        foo@bar:~$ torchblaze generate_docker --image_name example_image
        ```
        
        * Run Docker Image (Requires Docker Installed):
        
        ```console
        foo@bar:~$ torchblaze run_docker --image_name example
        ```
        
        * Performing API Tests:
        
        > First cd to the root project directory containing app.py file.
        ```console
        foo@bar:~$ torchblaze api_tests
        ```
        
        * Performing Model Testing:
        
        
        > Import the mltests package
        ```py
        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.
        ```py
        dir(mls)
        ```
        > To check the documentation for any of the available tests, use the help method:
        ```py
        help(mls.<method_name>)
        ```
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Requires-Python: >=3.7.0
Description-Content-Type: text/markdown
