semvec/pypi-packages huggingface dataset:
aimaxcli 4.5.1.2021072201
AIMax python commands client

=================
 Running demoapp
=================

Setup
-----

First, you need to create a virtual environment and activate it.

::

  $ pip install virtualenv
  $ virtualenv .venv
  $ . .venv/bin/activate
  (.venv)$ 

Next, install ``cliff`` in the environment.

::

  (.venv)$ python setup.py install

Now, install the demo application into the virtual environment.

::

  (.venv)$ cd demoapp
  (.venv)$ python setup.py install

Usage
-----

With cliff and the demo setup up, you can now play with it.

To see a list of commands available, run::

  (.venv)$ cliffdemo --help

One of the available commands is "simple" and running it

::

  (.venv)$ cliffdemo simple

produces the following

::

  sending greeting
  hi!


To see help for an individual command, include the command name on the
command line::

  (.venv)$ cliffdemo files --help

Cleaning Up
-----------

Finally, when done, deactivate your virtual environment::

  (.venv)$ deactivate
  $
sonu-s-diabetes-secret-pdf-free-download 1
sonu's diabetes secret pdf free download
Requires Python: >=3.6

<h1></h1>
<p>
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<br>

# sonu's diabetes secret pdf free download
```bash
pip3 sonu's diabetes secret pdf free download
qdatalib 0.1.0
Library for Quantum Data file Organization
Requires Python: >=3.7

# qdatalib 
### Note this project is under development, and will undergo a massive number of breaking changes 
###TODO

- better naming
- export to csv
- better handling of not existing files 
# Description

QDataLib is a library of wrappers around some of the most useful ”data”-functions in QCoDeS. The Idea of QDataLib is to keep track of your data files using a MongoDB database, and ease the export to other file formats than SQLite

# Installation
To install QDataLib from source do the following:
```bash
$ git clone https://github.com/qdev-dk/QDataLib.git
$ cd QDataLib
$ pip install .
```
# Usage
 see [here](https://qdev-dk.github.io/QDataLib/)
## Running the tests

If you have gotten 'qdatalib' from source, you may run the tests locally.

Install `qdatalib` along with its test dependencies into your virtual environment by executing the following in the root folder

```bash
$ pip install .
$ pip install -r test_requirements.txt
```

Then run `pytest` in the `tests` folder.

## Building the documentation

If you have gotten `qdatalib` from source, you may build the docs locally.

Install `qdatalib` along with its documentation dependencies into your virtual environment by executing the following in the root folder

```bash
$ pip install .
$ pip install -r docs_requirements.txt
```

You also need to install `pandoc`. If you are using `conda`, that can be achieved by

```bash
$ conda install pandoc
```
else, see [here](https://pandoc.org/installing.html) for pandoc's installation instructions.

Then run `make html` in the `docs` folder. The next time you build the documentation, remember to run `make clean` before you run `make html`.
mlp-tracking 0.0.3
mlp_tracking
Keywords: kafka issue tracking

# REQUIREMENTS
```
pip install mlp_tracking
```
# USAGE EXAMPLE

```python
from mlp_tracking import MlpTracker, PipeType, IssueModule
import os


# initialize 
tracker = MlpTracker(
    pipe_type=PipeType.FAQ_PREDIC_PIPE.value,
    issue_module=IssueModule.ML.value,
    # you may get kafka_servers from environments
    kafka_servers=os.environ.get('KAFKA_SERVERS', ''),
    kafka_username=os.environ.get('KAFKA_USERNAME', ''),
    kafka_password=os.environ.get('KAFKA_PASSWORD', ''),
)


# send event
tracker.send(
    message='predict error',
    reason='model not found',
)
```