semvec/pypi-packages huggingface dataset:
activate-app 0.0.3
Activate is a free activity log and analysis tool.
Requires Python: >=3.6

# Activate

![Icon](activate/resources/icons/icon_cropped.png)

Activate is a free activity log and analysis tool.

The Python dependencies are:

- `PyQt5`
- `PyQtChart`
- `PyQtWebEngine`
- `pyqtlet`
- `Markdown`
- `fitparse`
- `requests`
- `setuptools`
- `dtw`

`Flask` is also required to host an Activate server.

Run `make` to build the app.

Then, run `./app` to launch Activate, or `python setup.py install` to
install it.

## Basic Usage

When you first open Activate, you will see a mostly empty screen with
your total distance, time, number of activities and climb (which are all
zero). To add your activities, go to File/Import or press Ctrl-I. Select
the activities you wish to import, and wait for the import to finish. If
you switch to the 'Activities' tab, you can see individual activities.
Go to Activity/Analyse to see more detail.

To connect to an Activate server, go to Settings/Servers, press 'Add
Server' and enter the server details. You will need a username and
password given to you by the server admin. You can then access the
'Social' tab to see the activities of members of that server.

---

Activate, a free activity log and analysis tool.
Copyright © 2020–2021 Tom Fryers

This program is free software: you can redistribute it and/or modify it
under the terms of the GNU Affero General Public License as published
by the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero
General Public License for more details.

You should have received a copy of the GNU Affero General Public
License along with this program. If not, see
<https://www.gnu.org/licenses/>.
eazynlp 0.0.7
NLP made easy for researchers
Requires Python: >=3.6

# eazynlp
NLP made easy for researchers

## What is it?

**eazynlp** is a Python package aimed at qualitative researchers, especially in the social sciences, that wish to take advantage of natural language processing to complement traditional qualitative research methods. 
In particular, **eazynlp** is unique in that commonly used entities of qualitative research, such as interview transcripts, are treated as first-class objects.
This gives the researcher the opportunity to directly express NLP procedures in the analysis of such entities.

## Main Features:
  - Construction of data pipelines
  - Feature engineering
  - Cluster analysis, e.g., topic modeling
  - Visualization

## Where to get it
The latest released version is available at the [Python
Package Index (PyPI)](https://pypi.org/project/eazynlp).

```sh
pip install eazynlp
```

## Dependencies
  - [matplotlib](https://matplotlib.org/)
  - [nltk](https://www.nltk.org/)
  - [pandas](https://pandas.pydata.org/)
  - [wordcloud](https://amueller.github.io/word_cloud/)

## License
[GNU GENERAL PUBLIC LICENSE, Version 3](LICENSE)

Note that where the below libraries are used, the corresponding licenses apply as follows:
  - [matplotlib](https://github.com/matplotlib/matplotlib/blob/master/LICENSE/LICENSE)
  - [nltk](https://github.com/nltk/nltk/blob/develop/LICENSE.txt)
  - [pandas](https://github.com/pandas-dev/pandas/blob/master/LICENSE)
  - [wordcloud](https://github.com/amueller/word_cloud/blob/master/LICENSE)
pytorch-nemo 0.0.8
NEural Minimizer for pytOrch
Requires Python: >=3.5

# NEMO (NEural Minimizer for pytOrch)
**NEMO (NEural Minimizer for pytOrch)** is a small library for minimization of Deep Neural Networks developed in PyTorch, aimed at their deployment on ultra-low power, highly memory constrained platforms, in particular (but not exclusively) PULP-based microcontrollers.
NEMO features include:
 - deployment-related transformations such as BatchNorm folding, bias removal, weight equalization 
 - collection of statistics on activations and weights
 - post-training quantization
 - quantization-aware fine-tuning, with partially automated precision relaxation
 - mixed-precision quantization
 - bit-accurate deployment model
 - export to ONNX

NEMO operates on three different "levels" of quantization-aware DNN representations, all built upon `torch.nn.Module` and `torch.autograd.Function`:
 - fake-quantized *FQ*: replaces regular activations (e.g., ReLU) with quantization-aware ones (PACT) and dynamically quantized weights (with linear PACT-like quantization), maintaining full trainability (similar to the native PyTorch support, but not based on it).
 - quantized-deployable *QD*: replaces all function with deployment-equivalent versions, trading off trainability for a more accurate representation of numerical behavior on real hardware.
 - integer-deployable *ID*: replaces all activation and weight tensors used along the network with integer-based ones. It aims at bit-accurate representation of actual hardware behavior.
All the quantized representations support mixed-precision weights (signed and asymmetric) and activations (unsigned). The current version of NEMO targets per-layer quantization; work on per-channel quantization is in progress.

NEMO is organized as a Python library that can be applied with relatively small changes to an existing PyTorch based script or training framework.

# Installation and requirements
The NEMO library currently supports PyTorch >= 1.3.1 and runs on Python >= 3.5.
To install it from PyPI, just run:
```
pip install pytorch-nemo
```
You can also install a development (and editable) version of NEMO by directly downloading this repo:
```
git clone https://github.com/pulp-platform/nemo
cd nemo
pip install -e .
```
Then, you can import it in your script using
```
import nemo
```

# Example
- MNIST post-training quantization: https://colab.research.google.com/drive/1AmcITfN2ELQe07WKQ9szaxq-WSu4hdQb

# Documentation
Full documentation for NEMO is under development (see `doc` folder). You can find a technical report covering the deployment-aware quantization methodology here: https://arxiv.org/abs/2004.05930

# License
NEMO is released under Apache 2.0, see the LICENSE file in the root of this repository for details.

# Acknowledgements
![ALOHA Logo](/var/aloha.png)

NEMO is an outcome of the European Commission [Horizon 2020 ALOHA Project](https://www.aloha-h2020.eu/), funded under the EU's Horizon 2020 Research and Innovation Programme, grant agreement no. 780788.