semvec/pypi-packages huggingface dataset:
dramatiq-dashboard 0.4.0
A dashboard for Dramatiq (Redis-only!).
Requires Python: >=3.6

# dramatiq_dashboard

A dashboard for [dramatiq], specific to its Redis broker (sorry
RabbitMQ users!).  Very alpha stuff.

It comes in the form of a WSGI middleware, with as few dependencies as
possible (`dramatiq`, `jinja2` and `redis`) so it's super easy to plug
into whatever web application you have.

![screencast](https://media.defn.io/dramatiq-dashboard-screencast.gif)

## Installation

    pip install dramatiq_dashboard

## Quickstart

#### Run the dashboard on top of an existing WSGI app

```python
# Assuming at some point you instantiate your app.
app = create_wsgi_application()

# Import the library, create the middleware and wrap your app with it.
import dramatiq_dashboard

dashboard_middleware = dramatiq_dashboard.make_wsgi_middleware("/drama")
app = dashboard_middleware(app)
```

Run your app, visit `/drama` and you should see the dashboard.

#### Run the dashboard as a standalone webserver

If you don't want to wrap an existing WSGI app, you can also run the
dashboard as a standalone server.  Install the WSGI server of your
choice (e.g. uWSGi, gunicorn, bjoern, etc), setup the Redis broker,
and then start `DashboardApp` directly.

For example, to serve the dashboard on `http://127.0.0.1:8080` using
the `bjoern` WSGI server and a redis server on `17.0.0.1:6379`, run
the following:

```python
import bjoern
import dramatiq
from dramatiq.brokers.redis import RedisBroker
from dramatiq_dashboard import DashboardApp

broker = RedisBroker(host="127.0.0.1", port=6379)
broker.declare_queue("default")
dramatiq.set_broker(broker)
app = DashboardApp(broker=broker, prefix="")
bjoern.run(app, "127.0.0.1", 8080)
```

Then visit http://127.0.0.1:8080/ to see the running dashboard.

*Note that if you use custom queues in your application, they won't be
discovered using this approach.  You'll have to either add each one of
them manually to your broker or import and pass your application's
broker to `DashboardApp`.*

## License

dramatiq_dashboard is licensed under the LGPL.  Please see [COPYING]
and [COPYING.LESSER] for licensing details.


[COPYING.LESSER]: https://github.com/Bogdanp/dramatiq_dashboard/blob/master/COPYING.LESSER
[COPYING]: https://github.com/Bogdanp/dramatiq_dashboard/blob/master/COPYING
[dramatiq]: https://dramatiq.io
piperider-python-sdk 0.0.5
PipeRider Python SDK
Requires Python: >=3.6

# PipeRider SDK For Python

The PipeRiper SDK for Python enables you to access and manipulate project resources in your PipeRider account.

## Installation

```bash
pip install piperider-python-sdk
```

## Configuration

To use the SDK you will need to obtain an API key. This can be done from the **User Settings** page of your account.

[https://app.piperider.io/settings](https://app.piperider.io/settings)

Configure your API key using one of the following methods.

### Store API key in an environment variable

Users can set the API key as an environment variable.

```bash
export PIPERIDER_API_KEY=<user_api_key>
```

### Configure API key using the set_api_key function

Set your API key using the `set_api_key` function.

```python
import PipeRider
PipeRider.set_api_key('api-key')
```

## Usage

Import the PipeRider module.

```python
import PipeRider
```

Create a project.

```python
project = PipeRider.create_project('my-project')
```

Use an existing project.

```python
project = PipeRider.project_by_uid('6f9f6eb9-8657-4761-9e32-116f1b8cfb82')
```

Create a run and save the settings for a machine learning project.

```python
with project.runs.create(name='Awesome Run') as run:
    run.config = {
        'learning_rate': 0.02,
        'architecture': 'CNN',
        'dataset': 'TKNV-users',
    }

    run.params = {
        'batch_size': 64,
        'epoch': 100,
        'learning_rate': 0.005
    }
```

Add a dataset. This will enable you to more easily trace your model lineage.

```python
run.add_dataset('golden-dataset')
```

Comments can also be added to the timeline.

```python
project.comment('it is a good idea.')
```

If the run achieves the desired performance it can be registered as a model by declaring it a `win`.

```python
run.win()
```
oneNeuron-pypi-dushyant-mishra 0.0.3
This project is an implementation of a simple perceptron
Requires Python: >=3.7

# oneNeuron_pypi
## How to use this packages
```python
from oneNeuron.perceptron import Perceptron

##get x and y values than use below commands
model = Perceptron(eta=eta, epochs=epochs)
model.fit(X, y)
```