https://github.com/mlh-fellowship/torchblaze
A CLI-based python package that provides a suite of functionalities to perform end-to-end ML using PyTorch.
https://github.com/mlh-fellowship/torchblaze
cli deep-learning docker docker-image docusaurus2 flask-api flask-restful mlops pypi-package python python3 pytorch torchblaze
Last synced: 8 days ago
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A CLI-based python package that provides a suite of functionalities to perform end-to-end ML using PyTorch.
- Host: GitHub
- URL: https://github.com/mlh-fellowship/torchblaze
- Owner: MLH-Fellowship
- License: mit
- Created: 2021-02-11T13:09:32.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2023-02-15T07:44:36.000Z (over 2 years ago)
- Last Synced: 2025-06-13T10:16:04.952Z (13 days ago)
- Topics: cli, deep-learning, docker, docker-image, docusaurus2, flask-api, flask-restful, mlops, pypi-package, python, python3, pytorch, torchblaze
- Language: Python
- Homepage: https://mlh-fellowship.github.io/torchblaze/
- Size: 799 KB
- Stars: 10
- Watchers: 5
- Forks: 3
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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.)
```