semvec/pypi-packages huggingface dataset:
opsbox-azure-output 0.1.4
Azure issues output for opsbox
Requires Python: ==3.11.*

# opsbox-azure-output Plugin

## Overview

The `azure_out` Plugin processes and creates Azure DevOps ticket issues based on the results of checks, helping to manage and track cost-saving recommendations and other tasks.

***Description creation requires LLM***

## Key Features

- **Azure DevOps Integration**: Fetches and processes data from Azure DevOps.
- **Issue Creation**: Creates detailed Azure DevOps issues based on the provided results.
- **Customizable Priority and Tags**: Allows setting priority and tags for the created issues.

## Configuration Parameters

### Azure DevOps Configuration

- **azure_devops_token**: The personal access token for Azure DevOps.
- **azure_devops_organization**: The name of the Azure DevOps organization.
- **azure_devops_project**: The name of the Azure DevOps project.
- **azure_devops_username**: The username for Azure DevOps.
- **azure_devops_priority**: The priority of the work item (default: 4).
- **tags**: The tags to apply to the work item (optional).
- **create_description**: Whether to create a description instead of an issue (default: false).


## Example Configuration

```yaml
azure_devops_token: your_azure_devops_token
azure_devops_organization: your_azure_devops_organization
azure_devops_project: your_azure_devops_project
azure_devops_username: your_azure_devops_username
azure_devops_priority: 4
tags: your_tags
create_description: false
```
elogs 0.3.0
Tracking batch processing tasks and logs on a cluster
Requires Python: >=3.10

# ELOGS

Streamlined batch processing jobs.

Elogs wraps batch jobs tasks, intercepting errors and logging them to S3
preventing the job to fail.

## Usage

```python
import os
from pathlib import Path

from elogs import Elogs, ElogsTask

# load credentials as env vars
aws_env = Path.home() / 'Private/configs/aws.env'
load_dotenv(aws_env)

app_id = "userx_test_app"
elogs = Elogs(app_id=app_id)

@elogs
def process(x, y):
    return x + y

# decorating a function with an elogs instance transforms it in a function
# that will accept 1 single argument, which should be a ElogsTask, containing
# the task_id and original args/kwargs

tasks = [ElogsTask(f"{x}-{y}-{z}", x, y)
         for x in range(5)
         for y in range(5)]

# to track the app progress in the dashboard use the context
with elogs.start(tasks) as filtered_tasks: # removes done tasks
    for task in filtered_tasks:
        _ = process(task)

```

## Dashboard

Once installed the elogs package, you can run the monitoring dashboard with:

```bash
elogs-dashboard
```

this supports optional arguments `--aws-env /path/to/aws.env` and `-p/--port 18080`
to specify credentials and custom port number (defaults to 18080)

If credentials are not specified they will be searched in the env vars and if not
present an error will be raised.

To automatically start the dashboard as system service to run constantly in background,
you can follow this example:


Create the file `/etc/systemd/system/elogs-dashboard.service` (adapt for your user and environment)
```bash
[Unit]
Description=Elogs Dashboard Service
After=network.target

[Service]
ExecStart=/home/dzanaga/miniconda3/envs/eo/bin/elogs-dashboard --aws-env /home/dzanaga/Private/configs/aws.env
Restart=always
Environment="PATH=/home/dzanaga/miniconda3/envs/eo/bin:/home/dzanaga/miniconda3/condabin:/usr/lib64/qt-3.3/bin:/usr/local/bin:/usr/bin:/usr/lib/maven/bin:/opt/puppetlabs/bin:/usr/local/bin:/home/dzanaga/.local/bin/go/bin:/home/dzanaga/.local/share/coursier/bin:/usr/local/sbin:/usr/sbin:/usr/lib/maven/bin:/usr/lib/maven/bin:/usr/local/bin:/home/dzanaga/.local/bin/go/bin:/home/dzanaga/.local/share/coursier/bin:/home/dzanaga/.local/bin:/home/dzanaga/bin"

[Install]
WantedBy=multi-user.target
```

and then run:
```
sudo systemctl daemon-reload
sudo systemctl start elogs-dashboard
sudo systemctl enable elogs-dashboard
```

to check the dashboard server status:
```
sudo systemctl status elogs-dashboard
```