ray_template/README.md

# Dask: How to execute python workloads using a Dask cluster on Vulcan

Motivation: This document aims to show users how to launch a Dask cluster in our compute platforms and perform a simple workload using it.

Structure:
- [ ] [Tutorial](https://diataxis.fr/tutorials/)
- [x] [How-to guide](https://diataxis.fr/how-to-guides/)
- [ ] [Reference](https://diataxis.fr/reference/)
- [ ] [Explanation](https://diataxis.fr/explanation/)

To do:
- [x] Made scripts for environment creation and deployment in the folder `local_scripts`
- [x] Changed scripts to `deployment_scripts`
- [x] Added step about sending python file

---

This repository looks at a deployment of a Dask cluster on Vulcan, and executing your programs using this cluster.

## Table of Contents
- [Prerequisites](#prerequisites)
- [Getting Started](#getting-started)
- [Usage](#usage)
- [Notes](#notes)

## Prerequisites

Before running the application, make sure you have the following prerequisites installed in a conda environment:
- [Conda Installation](https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html): Ensure that Conda is installed on your local system. For more information on, look at the documentation for Conda on [HLRS HPC systems](https://kb.hlrs.de/platforms/index.php/How_to_move_local_conda_environments_to_the_clusters).
- [Dask](https://dask.org/): Install Dask using conda.
- [Conda Pack](https://conda.github.io/conda-pack/): Conda pack is used to package the Conda environment into a single tarball. This is used to transfer the environment to Vulcan. 

## Getting Started

### 1. Build and transfer the Conda environment to Hawk:

Only the `main` and `r` channels are available using the Conda module on the clusters. To use custom packages, we need to move the local Conda environment to Hawk. 

Follow the instructions in [the Conda environment builder repository](https://code.hlrs.de/SiVeGCS/conda-env-builder). The YAML file to create a test environment is available in the `deployment_scripts` directory.

### 2. Allocate workspace on Hawk:

Proceed to the next step if you have already configured your workspace. Use the following command to create a workspace on the high-performance filesystem, which will expire in 10 days. For more information, such as how to enable reminder emails, refer to the [workspace mechanism](https://kb.hlrs.de/platforms/index.php/Workspace_mechanism) guide.

```bash
ws_allocate dask_workspace 10
ws_find dask_workspace # find the path to workspace, which is the destination directory in the next step
```

### 3. Clone the repository on Hawk to use the deployment scripts and project structure:

```bash
cd <workspace_directory>
git clone <repository_url>
```

### 4. Send all the code to the appropriate directory on Vulcan using `scp`:

```bash
scp <your_script>.py <destination_host>:<destination_directory>
```

### 5. SSH into Vulcan and start a job interactively using:

```bash
qsub -I -N DaskJob -l select=1:node_type=clx-21 -l walltime=02:00:00
```
Note: For multiple nodes, it is recommended to write a `.pbs` script and submit it using `qsub`.

### 6. Go into the directory with all code:

```bash
cd <destination_directory>
```

### 7. Initialize the Dask cluster:

```bash
source deploy-dask.sh "$(pwd)"
```
   Note: At the moment, the deployment is verbose, and there is no implementation to silence the logs.
   Note: Make sure all permissions are set using `chmod +x` for all scripts.

## Usage

To run the application interactively, execute the following command after all the cluster's nodes are up and running:

```bash
python
```

Or to run a full script:
```bash
python <your-script>.py
```

Note: If you don't see your environment in the python interpretor, then manually activate it using:
```bash
conda activate <your-env>
```
Do this before using the python interpretor.

## Notes

Note: Dask Cluster is set to verbose, add the following to your code while connecting to the Dask cluster:
```python
client = Client(..., silence_logs='error')
```

Note: Replace all filenames within `<>` with the actual values applicable to your project.