Metadata-Version: 2.1
Name: torchblaze
Version: 1.0.2
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
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
Requires-Dist: fire (==0.4.0)
Requires-Dist: flask
Requires-Dist: requests

![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>)
```


