semvec/pypi-packages huggingface dataset:
sdee 0.0.1
A Python Package for Structural Dynamics & Earthquake Engineering
License: MIT
Requires Python: >=3.8

# SDEE
A Python Package for Structural Dynamics & Earthquake Engineering

## Installation
```pip install sdee```

## How to use it?
Open terminal and type ```sdee```.

## License

© 2021 Mohsen Azimi

This repository is licensed under the MIT license. See LICENSE for details.
PyExecTime 0.0.5
PyExecTime is a python module which can be used to find the execution time of a complete or partial python code.
License: MIT

# PyExecTime

PyExecTime is a python module which can be used to find the execution time of a complete or partial python code. 

## Version

The current version of this module is 0.0.5.

Check it by below command,

```bash
python -m pyexectime.version
```

## Installation

You can use `pip` to install the module or you clone the repository from github and install it manually.

```bash
pip install pyexectime
```

or

```bash
git clone https://github.com/antaripchatterjee/PyExecTime.git
cd PyExecTime
python setup.py install
```

## Usage and Application

You can use the module in two different ways.

### 1. Using contextlib manager `PyExecTime`

```python
# % API Reference % #

class PyExecTime():
    def __init__(self, file=sys.stdout):
        ...
    
    def __exit__(self, exc_type, exc_val, exc_tb):
        ...
    
    def __enter__(self):
        ...
```

Follow the below code to understand the usage of the contextlib manager class `PyExecTime`.

```python
from pyexectime.inspector import PyExecTime

with PyExecTime():
    for i in range(10000):
        print(i, end = ' ')
```

The above code will generate the below output.

```output
0 1 2 3 ....
....
....
.... 9998 9999
[Sat Mar  6 12:41:44 2021 PyExecTime(main.py) <3:5>] -> Execution took 0.000039 seconds
```

### 2. Using decorator function `py_exec_time`
```python
# % API Reference % #

def py_exec_time(file=sys.stdout):
    def wrapper(fn):
        @wraps(fn)
        def inner(*argv, **kwargv):
            ...
        return inner
    return wrapper
```
The same objective can be done using a following code

```python
from pyexectime.inspector import py_exec_time

@py_exec_time()
def write_number(r):
    for i in range(r):
        print(i, end = ' ')

write_number(10000)
```
And the output will be,

```output
0 1 2 3 ....
....
....
.... 9998 9999
[Sat Mar  6 12:41:44 2021 PyExecTime(main.py) <@write_number:8>] -> Execution took 0.000035 seconds
```

## License

The module has been licensed under [MIT](https://github.com/antaripchatterjee/PyExecTime/blob/master/LICENSE) license.

## Development

Currently this python module is in BETA stage but it can be used safely.
torchblaze 1.0.6
A CLI-based python package that provides a suite of functionalities to perform end-to-end ML using PyTorch.
License: MIT
Requires Python: >=3.7.0

![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>)
```