semvec/pypi-packages huggingface dataset:
tensor-dash 1.8.1
A python package that lets you remotely monitor your deep learning training metrics through it's companion app.
License: MIT

# TensorDash
<img src="readme_resources/cover_image.jpeg">
TensorDash is an application that lets you remotely monitor your deep learning model's metrics and notifies you when your model training is completed or crashed.

#### [Go to docs](https://cleanpegasus.github.io/TensorDash/)

## Why Tensordash?

1. Watch your model train in real-time.
2. Supports all major deep learning frameworks.
3. Remotely get details on the training and validation metrics.
4. Get notified when your model has completed trainng or when it has crashed.
5. Get detailed graphs on your model’s metrics.


## Installation ##

### Installing the Python Package ###

There are two ways to install tensordash:

- **Install tensordash from PyPI (recommended):**

Note: These installation steps assume that you are on a Linux or Mac environment.
If you are on Windows, you will need to remove `sudo` to run the commands below.

```sh
sudo pip install tensor-dash
```

If you are using a virtualenv, you may want to avoid using sudo:

```sh
pip install tensor-dash
```

- **Alternatively: install tensordash from the GitHub source:**

First, clone TensorDash using `git`:

```sh
git clone https://github.com/CleanPegasus/TensorDash.git
```

 Then, `cd` to the TensorDash folder and run the install command:
```sh
cd TensorDash
sudo python setup.py install
```


### Installing the Android App ###

Install the android app from the play store.<br>
[<img src="https://play.google.com/intl/en_us/badges/static/images/badges/en_badge_web_generic.png" height="30%" width="30%">](https://play.google.com/store/apps/details?id=tech.tensordash.tensordash)

## [How to use](https://cleanpegasus.github.io/TensorDash/usage/)


<table border="0">
<tr>
	<td><a href="https://cleanpegasus.github.io/TensorDash/usage/#tensorflow"><img src="readme_resources/tensorflow.png" width="" height="100px"><br><p align="center">Tensorflow</p></a></td>
	<td><a href="https://cleanpegasus.github.io/TensorDash/usage/#keras"> <img src="readme_resources/keras.png" width="" height="100px"><br><p align="center">Keras</p></a></td>
	<td><a href="https://cleanpegasus.github.io/TensorDash/usage/#pytorch"> <img src="readme_resources/pytorch.png" width="" height="100px"><br><p align="center">PyTorch</p></a></td>
	<td><a href="https://cleanpegasus.github.io/TensorDash/usage/#fastai"> <img src="readme_resources/fastai.png" width="" height="100px"><br><p align="center">Fast.ai</a></p>
</tr>
</table>
motephat 0.0.3
Python library for driving Pimoroni Mote pHAT
License: MIT
Keywords: Raspberry Pi LED

|Mote| https://shop.pimoroni.com/products/mote-phat

Drive four channels of APA102 pixels from your Raspberry Pi or Pi Zero
with Mote pHAT

Installing
----------

Full install (recommended):
~~~~~~~~~~~~~~~~~~~~~~~~~~~

We've created an easy installation script that will install all
pre-requisites and get your Mote pHAT up and running with minimal
efforts. To run it, fire up Terminal which you'll find in Menu ->
Accessories -> Terminal on your Raspberry Pi desktop, as illustrated
below:

.. figure:: http://get.pimoroni.com/resources/github-repo-terminal.png
   :alt: Finding the terminal

In the new terminal window type the command exactly as it appears below
(check for typos) and follow the on-screen instructions:

.. code:: bash

    curl https://get.pimoroni.com/motephat | bash

Alternatively, on Raspbian, you can download the ``pimoroni-dashboard``
and install your product by browsing to the relevant entry:

.. code:: bash

    sudo apt-get install pimoroni

(you will find the Dashboard under 'Accessories' too, in the Pi menu -
or just run ``pimoroni-dashboard`` at the command line)

If you choose to download examples you'll find them in
``/home/pi/Pimoroni/motephat/``.

Manual install:
~~~~~~~~~~~~~~~

Library install for Python 3:
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

on Raspbian:

.. code:: bash

    sudo apt-get install python3-motephat

other environments:

.. code:: bash

    sudo pip3 install motephat

Library install for Python 2:
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

on Raspbian:

.. code:: bash

    sudo apt-get install python-motephat

other environments:

.. code:: bash

    sudo pip2 install motephat

Development:
~~~~~~~~~~~~

If you want to contribute, or like living on the edge of your seat by
having the latest code, you should clone this repository, ``cd`` to the
library directory, and run:

.. code:: bash

    sudo python3 setup.py install

(or ``sudo python setup.py install`` whichever your primary Python
environment may be)

In all cases you will have to enable the i2c bus.

Documentation & Support
-----------------------

-  Guides and tutorials - https://learn.pimoroni.com/mote-phat
-  Function reference - http://docs.pimoroni.com/motephat/
-  GPIO Pinout - https://pinout.xyz/pinout/mote\_phat
-  Get help - http://forums.pimoroni.com/c/support

.. |Mote| image:: mote-logo.png


0.0.3
-----

* Fix EOF packet to 42 clocks
* Add gamma adjustment
* Switch from "exit" to "raise ImportError" when checking required modules
* Add short sleep to guarantee that pin state is set when toggled

0.0.2
-----

* Initial release to Raspbian apt repository

0.0.1
-----

* Mote-compatible library for Mote pHAT