forked from SiVeGCS/dask_template
117 lines
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3.6 KiB
Markdown
117 lines
No EOL
3.6 KiB
Markdown
# Dask: How to execute python workloads using a Dask cluster on Vulcan
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This repository looks at a deployment of a Dask cluster on Vulcan, and executing your programs using this cluster.
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## Table of Contents
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- [Getting Started](#getting-started)
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- [Usage](#usage)
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- [Notes](#notes)
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## Getting Started
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### 1. Build and transfer the Conda environment to Hawk:
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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.
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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.
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### 2. Allocate workspace on Hawk:
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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.
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```bash
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ws_allocate dask_workspace 10
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ws_find dask_workspace # find the path to workspace, which is the destination directory in the next step
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```
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### 3. Clone the repository on Hawk to use the deployment scripts and project structure:
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```bash
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cd <workspace_directory>
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git clone <repository_url>
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```
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### 4. Send all the code to the appropriate directory on Vulcan using `scp`:
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```bash
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scp <your_script>.py <destination_host>:<destination_directory>
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```
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### 5. SSH into Vulcan and start a job interactively using:
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```bash
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qsub -I -N DaskJob -l select=1:node_type=clx-21 -l walltime=02:00:00
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```
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Note: For multiple nodes, it is recommended to write a `.pbs` script and submit it using `qsub`. Follow section [Multiple Nodes](#multiple-nodes) for more information.
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### 6. Go into the directory with all code:
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```bash
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cd <destination_directory>
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```
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### 7. Initialize the Dask cluster:
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```bash
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source deploy-dask.sh "$(pwd)"
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```
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Note: At the moment, the deployment is verbose, and there is no implementation to silence the logs.
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Note: Make sure all permissions are set using `chmod +x` for all scripts.
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## Usage
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### Single Node
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To run the application interactively on a single node, execute the following command after all the cluster's nodes are up and running:
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```bash
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python
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```
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Or to run a full script:
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```bash
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python <your-script>.py
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```
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Note: If you don't see your environment in the python interpretor, then manually activate it using:
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```bash
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conda activate <your-env>
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```
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Do this before using the python interpretor.
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### Multiple Nodes
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To run the application on multiple nodes, you need to write a `.pbs` script and submit it using `qsub`. Follow lines 1-4 from the [Getting Started](#getting-started) section. Write a `submit-dask-job.pbs` script:
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```bash
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#!/bin/bash
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#PBS -N dask-job
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#PBS -l select=3:node_type=rome-ai
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#PBS -l walltime=1:00:00
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#Go to the directory where the code is
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cd <destination_directory>
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#Deploy the Dask cluster
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source deploy-dask.sh "$(pwd)"
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#Run the python script
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python <your-script>.py
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```
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And then execute the following commands to submit the job:
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```bash
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qsub submit-dask-job.pbs
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qstat -anw # Q: Queued, R: Running, E: Ending
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ls -l # list files after the job finishes
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cat dask-job.o... # inspect the output file
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cat dask-job.e... # inspect the error file
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```
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## Notes
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Note: Dask Cluster is set to verbose, add the following to your code while connecting to the Dask cluster:
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```python
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client = Client(..., silence_logs='error')
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```
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Note: Replace all filenames within `<>` with the actual values applicable to your project. |