semvec/pypi-packages huggingface dataset:
BuilT 0.0.4
Easily build your trainer for DNNs.
Requires Python: >=3.7

[![Build Status](https://travis-ci.com/UoA-CARES/BuilT.svg?branch=master)](https://travis-ci.com/UoA-CARES/BuilT)
[![codecov](https://codecov.io/gh/UoA-CARES/BuilT/branch/master/graph/badge.svg)](https://codecov.io/gh/UoA-CARES/BuilT)

# BuilT(Build a Trainer)
Easily build a trainer for your Depp Neural Network model and experiment as many as you want to find optimal combination of components(model, optimizer, scheduler) and hyper-parameters in a well-organized manner. 
- No more boilerplate code to train and evaluate your DNN model. just focus on your model. 
- Simply swap your dataset, model, optimizer and scheduler in the configuration file to find optimal combination. Your code doesn't need to be changed!!!. 
- Support Cross Validation, OOF(Out of Fold) Prediction 
- Support WandB(https://wandb.ai/) or tensorboard logging.
- Support checkpoint management(Save and load a model. Resume the previous training)
- BuilT easily integrates with Kaggle(https://www.kaggle.com/) notebook. (todo: add notebook link)

## Installation
Please follow the instruction below to install BuilT. 

### Installation of BuilT package from the source code
```
git clone https://github.com/UoA-CARES/BuilT.git
cd BuilT
python setup.py install
```

### Installation of BuilT package using pip
BuilT can be installed using pip(https://pypi.org/project/BuilT/). 
```
pip install built
```

## Usage

### Configuration
### Builder

### Trainer
### Dataset

### Model

### Loss
### Optimizer

### Scheduler

### Logger

### Metric

### Inference

### Ensemble



## Examples
### MNIST hand-written image classification
(todo)

### Sentiment Classification
(todo)


## Developer Guide
(todo)
```
conda create -n conda_BuilT python=3.7
conda activate conda_BuilT
pip install -r requirements.txt
```

# Reference
https://packaging.python.org/tutorials/packaging-projects/
jupy5-mcintyre 0.0.1
A simple way to sketch with Python and p5.js in Jupyter
Requires Python: >=3.8

# jupy5
> A simple way to sketch with Python and p5.js in Jupyter

This little experiment aims to simplify the process of teaching creative coding with Jupyter notebooks. It uses the [Skulpt](https://github.com/skulpt/skulpt/) Python implementation to create a wrapper for [p5.js](https://github.com/processing/p5.js).
falcon-ratelimit 1.2
A rate limiter plugin for Falcon

falcon-ratelimit

================





Rate limiter for Falcon. Install with:



```bash

pip install falcon-ratelimit

```



Use like:



```python

import falcon

from falconratelimit import rate_limit





class NoRedisResource(object):

    @falcon.before(rate_limit(per_second=5, window_size=30,

    resource='resource_name'))

    def on_post(self, req, resp):

        ...



class RedisResource(object):

   @falcon.before(rate_limit(redis_url='localhost:6379', per_second=1,

   window_size=10))

   def on_post(self, req, resp):

       ...

```



This package works by limiting the number of requests using two variables

`per_second` and `window_size`. In the first example above, the

`NoRedisResource` class is restricted to 5 requests per second over a 30 second

window meaning that there is a limit of 150 requests over 30 seconds. The

default storage of calling the `rate_limit` decorator is an in memory list to

store the number of requests for the given user using the resource

`resource_name`.



The second example implements the optional storage to use Redis by passing in a

`redis_url` to store user request data. Using Redis allows for the rate

limiting to be implemented across multiple instances of a particular

application. In the `RedisResource` class example the user is allowed to make

1 request per second over 10 seconds meaning that there is a limit of 10

requests over 10 seconds. Since this example didn't pass in a `resource` it

uses `default` as the name for request storage.



If you do not have the `redis` package installed, Redis-related functionality

 will not work.