semvec/pypi-packages huggingface dataset:
orionservermanager-pkg-rioatmadja2018 0.1.1592007190
Engines to manage remote server.
Requires Python: >=3.6

## ORION Server Managers

---
Python wheels for Orion Search Engines that extends Paramiko functionalies

### Technologies 
1. Python 3.6 
2. Fabric 

### LICENSE 
---
MIT License

Copyright (c) 2020 ratmadjads

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
carbonpy 0.52
A Python package which gives IUPAC names of chemical compounds, classifies them and more.
Requires Python: >=3.5

# Carbonpy
A module which names straight/branched chain organic compounds, suggests conversions from one type to another, etc.

## Usage-

### Naming compounds-
Instantiate the class `Namer()` , which takes a string which contains the hydrocarbon (condensed form) and then call it with a method named `analyser()` to get the IUPAC name of the compound.

Example:
```
a = Namer('CH~CH')
a.analyser()
>>> Ethyne
```

Due to limitations in expressing a hydrocarbon easily, we have selected this path  
Single bond:- -  
Double bond:- =  
Triple bond:- ~  
Branches:- -([compound] (more branches...))([another branch from same carbon])- and so on  
(Branches support coming soon)

You can also get the molecular formula of the compound:
```
compound = Namer('CH~C-C~C-CH=C=C=CH2')
compound.molecular_formula()
>>> Câ‚ˆHâ‚„
```
Example- 2-Methylpropane would be expressed as (once branches are supported):
```
a = Namer('CH3-CH(CH3)-CH3').analyser()
>>> 2-Methylpropane
```

Will support naming with functional groups in the future.
eiffel2 0.0.4
Neural Network visualization tool
Requires Python: >=3.6

# Eiffel2
 > current version: 0.0.3  <br />  Next release: 19/11/2020

Neural Network architecture  Visualization tool : [Github](https://github.com/Ale9806/Eiffel2)


## You can use eiffel2, which you can install using pip:
```python
python -m pip install eiffel2
 ```
 to update eiffel:
 ```python
python -m pip  install eiffel2 --upgrade
 ```

Just import builder from eiffel and provide a list of neurons per layer in your network as an input.

## Example:
```python
from eiffel2 import builder
builder([1, 10, 10, 5, 5, 2, 1])
# or the following if you want to have a dark theme
builder([1, 10, 10, 5, 5, 2, 1], bmode="night")
```
**output:** (normal mode)

<a href="url"><img src="https://github.com/Ale9806/Eiffel2/blob/master/eiffel2.PNG" align="left"  width="300"  > </a>

<br />&nbsp;<br />
<br />&nbsp;<br />
<br />&nbsp;<br />

**output:** (with bmode="night')

<a href="url"><img src="https://github.com/Ale9806/Eiffel2/blob/master/eiffel.PNG" align="left"  width="300"  > </a>

<br />&nbsp;<br />
<br />&nbsp;<br />
<br />&nbsp;<br />
<br />&nbsp;<br />

# COLORS:

You can add colors to your network with the net_colors variable in **builder()** , keep in mind that for a n layer network you
must select n-1 colors
Eiffel2 is compatible with vanila python colors ("red","blue") and Hex colors

[Online Color tool](https://www.w3schools.com/colors/colors_picker.asp)

## EXAMPLE
```python
from eiffel2 import builder
builder([1,6,6,5,2],net_colors=["#581845","#900C3F","red","#FF5733"])
```
**output:**

<a href="url"><img src="https://github.com/Ale9806/Eiffel2/blob/master/color.PNG" align="left"  width="300"  > </a>
tasklist 1.2.1
A library to manage lists of commands with re-trial.

# Overview

![coverage](https://gitlab.com/BuddyNS/tasklist/badges/master/coverage.svg)

A simple way to ensure that a list of commands is executed successfully, or re-tried.

Tasklist allows to the following:

- Execute lists of commands (tasks)
- Verify their success
- Re-execute commands that failed, order-preserving
- Control temporary and permanent command statuses
- Discard tasks with temporary failures after a tryout
- Discard tasks with temporary failures after a timeout
- Persist the list of tasks so it's reloaded when your program fails

# Main applications

- DevOps pipelines which require multiple commands to reach a desired state
- Servers which accept operations which may fail and are required to retry them
- Event Sourcing architectures where events are executed with commands
- Manual processes which may be interrupted for a failure, and need to be resumed from there

# Examples

## Ensure complete termination of interrupted process

```python
from tasklist import TaskList, Task

# make sure all of the following commands are executed
commands = [
    "tar cjvf maillog.tbz /var/log/maillog",
    "rm -f /var/log/maillog",
    "service myservice restart"
]

# create a tasklist
tl = TaskList()
for cmd in commands:
    tl.add(Task(cmd=cmd.split()))

if tl.run_all():
    # some task failed. Sleep a while and retry
    tl.run_all()
```

## Manage temporary and permanent failures

```python
import time
from tasklist import TaskList, Task

t = Task(cmd="false")
t.run()                     # False (executed with failure)
t.failed_retry()            # False (failed with non-retriable exit status)
time.sleep(10)
t.age()                     # 10 (created 10 seconds ago)

t = Task(cmd="python", args=["-c", "import sys; sys.exit(2)"])
t.run_all()                 # False
t.failed_retry()            # True (failed with retriable exit status)
```

## Making the queue persistent

```python
from tasklist import TaskList, Task

# automatically persist the tasklist at the given path in the filesystem
tl = TaskList(wrkdir='/tmp/mytasklist', autosync=True)
tl.add(Task(cmd="true"))
tl.add(Task(cmd="false"))
del tl

new_tl = TaskList(wrkdir='/tmp/mytasklist', autosync=True)
len(new_tl)                     # 2 tasks
```

# Support

This is open-source software contributed by the [BuddyNS](https://www.buddyns.com) team. The project is hosted at [https://gitlab.com/BuddyNS/tasklist](https://gitlab.com/BuddyNS/tasklist).

Please do not contact BuddyNS for support with this library. You can get general support on Python from the great people of the `#python` channel on the [freenode IRC network](https://webchat.freenode.net).
mmf 1.0.0rc10
mmf: a modular framework for vision and language multimodal research.
Requires Python: >=3.6

#










---

MMF is a modular framework for vision and language multimodal research from Facebook AI Research. MMF contains reference implementations of state-of-the-art vision and language models and has powered multiple research projects at Facebook AI Research. See full list of project inside or built on MMF [here](https://mmf.sh/docs/notes/projects).

MMF is powered by PyTorch, allows distributed training and is un-opinionated, scalable and fast. Use MMF to **_bootstrap_** for your next vision and language multimodal research project by following the [installation instructions](https://mmf.sh/docs/getting_started/installation). Take a look at list of MMF features [here](https://mmf.sh/docs/getting_started/features).

MMF also acts as **starter codebase** for challenges around vision and
language datasets (The Hateful Memes, TextVQA, TextCaps and VQA challenges). MMF was formerly known as Pythia. The next video shows an overview of how datasets and models work inside MMF. Checkout MMF's [video overview](https://mmf.sh/docs/getting_started/video_overview).


## Installation

Follow installation instructions in the [documentation](https://mmf.sh/docs/getting_started/installation).

## Documentation

Learn more about MMF [here](https://mmf.sh/docs).

## Citation

If you use MMF in your work or use any models published in MMF, please cite:

```
@misc{singh2020mmf,
  author =       {Singh, Amanpreet and Goswami, Vedanuj and Natarajan, Vivek and Jiang, Yu and Chen, Xinlei and Shah, Meet and
                 Rohrbach, Marcus and Batra, Dhruv and Parikh, Devi},
  title =        {MMF: A multimodal framework for vision and language research},
  howpublished = {\url{https://github.com/facebookresearch/mmf}},
  year =         {2020}
}
```

## License

MMF is licensed under BSD license available in [LICENSE](LICENSE) file