Metadata-Version: 2.1
Name: ClusterMonitor
Version: 0.0.7
Summary: Simple script which can be used to monitor and log CPU and RAM usage of submitted cluster jobs
Home-page: UNKNOWN
Author: nickhir
License: UNKNOWN
Keywords: python,cluster,monitor,CPU,RAM,usage,SLURM
Platform: UNKNOWN
Classifier: Operating System :: Unix
Description-Content-Type: text/markdown
Requires-Dist: matplotlib

# ClusterMonitor
A very simple python script which monitors and records the CPU and RAM consumption of submitted cluster jobs.

## Installation
Simplest option is to install via [PyPi](https://pypi.org/):
```bash
pip install ClusterMonitor
```
Alternatively you can also clone this repository and use the scripts inside the `ClusterMonitor` directory. 
## Usage
To start recording use the `cpu_ram_log.py` script. It requires 3 arguments:
- `-u` which corresponds to your username.
- `-o` which specifies the output file (in tsv format)
- `--interval` which specifies the time (in seconds) over which the CPU and RAM usage gets averaged

Example:
```python
python ClusterMonitor/cpu_ram_log.py -u nickhir -o cpu_ram.log --interval 5
```

The script can simply be included in your cluster job submittion like this:
```bash
#!/bin/bash
#SBATCH --job-name=Example_run

sambamba sort -t 3 some_bam.bam &
MainScript=$!
python cpu_ram_log.py -u nickhir -o cpu_ram.log --interval 5 &
wait $MainScript
pkill -P $$
```
***
If you used `pip` to install the package, `cpu_ram_log` and `visualize_log` will be added to your `PATH` variable, thus you can simple use:
```bash
cpu_ram_log -u nickhir --interval 5 -o cpu_ram.log
```
***
Afterwards the resulting log file can be plotted (for that `matplotlib` is needed).
For that use the `visualize_log.py` script which needs 2 arguments: 
- `-i` which specifies the path to the log file which was created with `cpu_ram_log.py`
- `-o` which specifies the path to the resulting PDF which contains a plot for the CPU and RAM usage.


Example:
```python
python visualize_log.py -i cpu_ram.log -o cpu_ram_visualization.pdf
```

The resulting plots will look somewhat like this:
![exampleImage](https://github.com/nickhir/ClusterMonitor/raw/main/ClusterMonitor/example/example_image.png)