semvec/pypi-packages huggingface dataset:
runcrawler 0.0.2
AVBP API
Keywords: AVBP API

# RUNCRAWLER

**Runcrawler** is a monitoring tool on software-usage based on data extraction.

Parallel computing program such as CFD-solver often run on different clusters, by different users. It is hard for managers to get a big picture on how the program is used. **run crawler** is a tool to monitor their usage and allow managers to get a various type of useful information by extracting data related to the execution of the program.

Here are some examples of questions which managers often have. </br>
- What are the typical errors repeated by the users of my team ? </br>
- Are our runs efficient in terms of CPU time ? </br>
- If so, are they related to a certain parameter setting ? </br>

As an example, this chart pie shows the percentage of runs that are converged versus the runs
that did not converge and on the side, the code associated to the reason it 
crashed (0 if it exited fine, 300 if not in that case)


<p align="center"> 
    <img src=https://cerfacs.fr/coop/images/runcrawler/error_log.png width=300>
</p>



**Runcrawler** can return the error codes associated to crashes and improve 
your understanding.


## Installation
**runcrawler** is available by simple execution of

pip install runcrawler

## How to use

Use the CLI 

**runcrawler scan-log path/to-your-file**.

This will return a code corresponding to the reason why your run crashed.

## To go further

Read [further](https://cerfacs.fr/coop/runcrawler)
yamlflow 0.0.9
Yet Another ML flow
Requires Python: >=3.7,<4.0

# yamlflow
Yet Another ML flow

We follow `convention over configuration` (also known as coding by convention) software design paradigm.

Here are some of the features the `yamlflow` provides.


1. Build and publish your ML solution as a RESTful Web Service `with yaml`.
    
    + You don't need to write web realated code, or dockerfiles.
    
    + You don't need to benchmark which python web server or framework is best in terms of performance.
    
    + WE do it for you. All the best, packed in.


## Metrics for inference

+ Throughput
 (How many requests can server process in some interval of time)

+ Latency
 (How long does it take to get a prediction for a single request)

## Python REST API

```
async
    - web-server(ASGI): Uvicorn, Hypercorn, Daphne
    - web-(micro)framework: Starlete, ...
    - API-framework: FastAPI, ...

sync
    - web-server(WSGI): gunicorn, uWSGI, Gevent, Twisted Web
    - web-(micro)framework: Flask
    - API-framework: None
```


### Project structure 
```
.
├── model
│   ├── ...
│   ├── pipeline.py
│   └── requirements.txt
├── service
│   ├── objects
│   ├── config.py
│   └── predictor.py
├── train
│   ├── ...
│   ├── train.py
│   └── requirements.txt
├── README.md
└── yamlflow.yaml
```

#### example `yamlflow.yaml`
```yaml
kind: Service
meta:
  registry: your.docker.registry
  user: dockerusername
project:
  name: ml-project
  version: 0.1.0
backend:
  runtime: torch
  device: cpu
port: 8002
```

### Installation guide
```bash
pip install yamlflow
```

### User guide
```bash
yamlflow init
yamlflow build
yamlflow run
```

### Developer guide
```
pyenv install 3.8.6
poetry env use ~/.pyenv/versions/3.8.6/bin/python
poetry shell
poetry install
```