semvec/pypi-packages huggingface dataset:
pyvacy 0.0.32
Privacy preserving deep learning for PyTorch

<div align="center">
    <img src="assets/logo.png" width="150px"><br>
    <h1>PyVacy: Privacy Algorithms for PyTorch</h1>
</div>

PyVacy provides custom PyTorch opimizers for conducting deep learning in a differentially private manner. Basically <a href="https://github.com/tensorflow/privacy">TensorFlow Privacy</a>, but for PyTorch.

## Getting Started

```bash
pip install pyvacy
```

## Example Usage

```python
import torch
from pyvacy import optim, analysis

model = torch.nn.Sequential(...)

optimizer = optim.DPSGD(
    l2_norm_clip=...,
    noise_multiplier=...,
    batch_size=...,
    lr=...,
    momentum=...,
)

epsilon = analysis.moments_accountant(
    N=...,
    batch_size=...
    noise_multiplier=...,
    epochs=...,
    delta=...,
)

for epoch in range(epochs):
    # do training as usual...
```

## Tutorials

```python
python tutorials/mnist.py

Training procedure achieves (3.0, 0.00001)-DP
[Epoch 1/60] [Batch 0/235] [Loss: 2.321049]
[Epoch 1/60] [Batch 10/235] [Loss: 0.952795]
[Epoch 1/60] [Batch 20/235] [Loss: 1.040896]
...
```
starlette-jsonrpc 0.2.0
JSON-RPC implementation for Starlette framework

# starlette-jsonrpc

[![Build Status](https://travis-ci.com/kdebowski/starlette-jsonrpc.svg?token=JXg8SCx8Y9Ybz183mTgo&branch=master)](https://travis-ci.com/kdebowski/starlette-jsonrpc)
[![codecov](https://codecov.io/gh/kdebowski/starlette-jsonrpc/branch/master/graph/badge.svg?token=3DkWshhv8x)](https://codecov.io/gh/kdebowski/starlette-jsonrpc)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black)

## Installation

```bash
pip install starlette-jsonrpc
```


## Examples

Code:
```python
import uvicorn
from starlette.applications import Starlette

from starlette_jsonrpc import dispatcher
from starlette_jsonrpc.endpoint import JSONRPCEndpoint


app = Starlette()


@dispatcher.add_method
async def subtract(params):
    return params["x"] - params["y"]


@dispatcher.add_method(name="SubtractMethod")
async def seconds_subtract(params):
    return params["x"] - params["y"]


@dispatcher.add_method
async def subtract_positional(x, y):
    return x - y


app.mount("/api", JSONRPCEndpoint)

if __name__ == "__main__":
    uvicorn.run(app)
```

Example of requests:

```json
{
  "jsonrpc": "2.0",
  "method": "subtract",
  "params": {"x": 42, "y": 23},
  "id": "1"
}
```

```json
{
  "jsonrpc": "2.0",
  "method": "SubtractMethod",
  "params": {"x": 42, "y": 23},
  "id": "1"
}
```

```json
{
    "jsonrpc": "2.0",
    "method": "subtract_positional",
    "params": [42, 23],
    "id": "1"
}
```

Example of response:

```json
{
  "jsonrpc": "2.0",
  "id": "1",
  "result": 19
}
```

## Contributing

Thank you for your interest in contributing. Everyone is welcome to take part in developting this package. Please fFollow contributing guide in [CONTRIBUTING.md](https://github.com/kdebowski/starlette-jsonrpc/blob/master/CONTRIBUTING.md).

## Support

If you like this project and find it useful and want to say "Thank you", please consider to [buy me a coffee](https://buymeacoff.ee/zRWISPHo0) ;)
cal-event-search 1.2.0
Calendar Event Search

# calendar_api
This is an app that is connected to Google Calendar, the main purpose of this application is 
to collect the events of a calendar search them by date, name and color and to receive and analysis of the duration 
of the events and dates. 

### Installation ###

    pip install cal_event_search

Note: after installing you will need a Google API credentials, 
      they are included inside the cal_event_search folder 
      or you can use your own 

## Prerequisites
    python >= 3.7
    qt>=5.9.7
    qtpy>=1.5.2
    google-api-python-client>=1.7.8
    google-auth>=1.6.3
    google-auth-httplib2>=0.0.3
    google-auth-oauthlib>=0.3.0
    google-pasta>=0.1.4
    numpy>=1.15.4 

### Who do I talk to? ###

Pol Monroig
dvrip 0.0.3
DVRIP (Xiongmai, XMEye, Sofia protocol) library and tools

Home-page: https://github.com/alexshpilkin/dvrip
Author: Alexander Shpilkin
Author-email: ashpilkin@gmail.com
License: UNKNOWN
Description: UNKNOWN
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: System Administrators
Classifier: License :: CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Communications
Classifier: Topic :: Internet
Classifier: Topic :: Multimedia :: Sound/Audio :: Capture/Recording
Classifier: Topic :: Multimedia :: Video :: Capture
Classifier: Topic :: Office/Business
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Utilities
Requires-Python: >=3.7, <4
Description-Content-Type: text/x-rst
Provides-Extra: dvr-find
Provides-Extra: dvr-time