semvec/pypi-packages huggingface dataset:
sclbl 0.0.1
Python package for uploading sklearn and onnx models to Scailable toolchain.
Requires Python: >=3.7

# The sclbl CLI tools.

Main usage:
```shell script
$ sclbl upload -f file.onnx -n "Model name"
```

The package exposes the following commands:

* `upload` : Upload an onnx file to the Scaailable Platform. Options:
* `update` : Update an existing model using its cfid
* `models` : List all models associated with the current user ID
* `devices` : List all registered devices associated with the current user ID
* `assignments` : List all assignments associated with the current user ID
* `assign` : Create a new assignment
* `delete` : Delete a model, assignment, or id.

Each of the commands above requires logging in to the Scailable platform using a valid username and
password combination. See www.scailable.net for details. Upon first login, you will be asked to store your
username and password locally. You can remove the stored local details using:

* - `sclbl reset` : Reset you user details.

See `sclbl command --help` for additional information and the required arguments/options.

Note that the sclbl CLI depends on the `sclblpy` package. See https://pypi.org/project/sclblpy/ for details. 
The CLI needs at least version 0.1.6 of the `sclblpy` package to work.
If you would like to upload fitted `sklearn` models, please use the `sclblpy` package directly from python.
Pyevals4 1.0
Evaluation

<!-- PROJECT SHIELDS -->
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<!-- TABLE OF CONTENTS -->
## Table of Contents

* [About the Project](#about-the-project)
  * [Built With](#built-with)
* [Getting Started](#getting-started)
  * [Installation](#installation)
* [Usage](#usage)
* [Contact](#contact)


<!-- ABOUT THE PROJECT -->
## About The Project

A very elegant and simple library to evaluate models. In this version we are only providing the reports,
but soon we will be adding plots too.

### Built With
This section should list any major frameworks that you built your project using. Leave any add-ons/plugins for the acknowledgements section. Here are a few examples.


<!-- GETTING STARTED -->
## Getting Started

### Installation

1. Clone the repo
```sh
git clone https://github.com/https://github.com/Anand-gokul/Pyevals/.git
```

2. Install using pip or pip3
```commandline
pip3 install Pyevals==1.0
```



<!-- USAGE EXAMPLES -->
## Usage

```python
import pyevals

# For classification
pyevals.build(x_train,x_test,y_train,y_test, method = 'Classification')
  
```


<!-- CONTACT -->
## Contact

Anirudh Palaparthi - [@anirudh8889](https://twitter.com/anirudh8889) - aniruddhapnbb@gmail.com

Sai Gokul Krishna Reddy Talla 

Ananda Datta Sai Phanindra Tangirala

Project Link: [https://github.com/anirudhpnbb/Pyevals](https://github.com/Anand-Gokul/Pyevals)