semvec/pypi-packages huggingface dataset:
stir 0.0.3
A simple merging package manager for microservices
License: MIT

# stir

![stir header image](stir-800x200.png?raw=true)

stir is a tool for synchronizing files between two or more systems &mdash; a general purpose package manager.

stir is a simple method of sharing source code libraries between microservices,
fetching credential files (like Google's service account `credentials.json`),
or any other files.

## How It Works

1. A package containing some files is created and placed on your private server.
2. The package is then installed via `stir install package-name` wherever the files are needed.

## Features

* **Merging** - Multiple packages can be installed in the same directory.
* **Easy** - Creating and publishing packages is trivial.
* **Language Agnostic** - Python, JavaScript, C source, or any file type you need to package and share.
* **Secure** - Packages can require authentication to update or fetch.
* **Private** - Host your own stir server.
* **Microservices** - No need to duplicate code between services.


## Examples

### Sharing Source Code

Project `bar` depends on libraries maintained in project `foo`'s git repository.

1. From `foo`'s git repository, in a directory that will be the package root, create your package.
    * `stir upsert foolibs -p "**.py"`
    * This will find all files in the current that match `**.py` (`**` is the convention for recursive matching).
2. From `foo`'s git repository, run `stir publish foolibs`. This will bundle the package (`foolibs-0.0.1.zip`) and push it to the server.
3. When working in the `bar` project, fetch the latest `foolibs` package.
    * `stir install foolibs`
    * If `foolibs` is not installed, or an older version, this will pull the most recent `foolibs` package from your stir server and install it in `foolibs/`.
4. Add to your deployment pipeline. For example, in your Dockerfile:
    * `RUN pip install stir && (cd /app/bar && stir -y install foolibs)`
Schedy 0.1.0b6
Python client for Schedy
License: MIT

Welcome to Schedy's documentation!
==================================

**Schedy** is your machine learning assistant. It will help you record your
experiments, your results, visualize them, and it will even suggest new
parameters for your next experiments!

Schedy can do useful things for you:

- Record the hyperparameters and results of all your past models.
- Suggest new hyperparameters for your next models.
- Coordinate a pool of workers (e.g. in a cluster), by making sure they
  stay busy trying to find the best combination of hyperparameters for
  your task.

And all of that in just a few lines of code! Coordinating a cluster of workers
becomes as simple as this:

.. code-block:: python

   import schedy

   db = schedy.SchedyDB()
   exp = db.get_experiment('My Task')
   while True:
      with exp.next_job() as job:
         my_train_function(job)

You can follow the evolution of your experiments thanks to our `online dashboard
<https://schedy.io/>`_.

.. image:: https://raw.githubusercontent.com/incalia/schedy-client/master/docs/images/dashboard.png
  :align: center

We also provide a command line tool, that will help you with the most
repetitive tasks.

.. _setup:

Installation and setup
----------------------

`Sign up here <https://schedy.io>`_, install Schedy & get your API token:

.. code-block:: bash

   pip3 install schedy
   schedy gen-token

You are now ready to `get started <https://schedy.readthedocs.io/en/latest/getting_started.html>`_!

You can also read the `API reference <https://schedy.readthedocs.io/en/latest/reference.html>`_.