snakemake-executor-plugin-donau 0.1.3

A Snakemake executor plugin for Huawei Donau scheduler

Snakemake Executor Plugin for Huawei Donau Scheduler

中文文档 (Chinese Documentation)

This is a Snakemake executor plugin designed specifically for the Huawei Donau High Performance Computing (HPC) scheduler. It enables Snakemake to interact directly with the Donau scheduling system, handling job submission, status monitoring, and resource management automatically.

✨ Key Features

• Native Adaptation: deeply integrated with dsub, djob, and dkill commands.
• Smart Resource Mapping: Automatically translates Snakemake resources (threads, mem_mb, runtime, account, mpi) into Donau resource request parameters (e.g., -R cpu=X,mem=YMB, -T, -A, --mpi).
• Robust Status Checking: Implements a dual-query mechanism ("Active Queue" + "History Database") to prevent false status judgments caused by jobs finishing instantly or rapid scheduler cleanup.
• Seamless Logging: Integrated with Snakemake's global logging system. Automatically adapts to --logger rich-loguru for high-quality terminal output while maintaining detailed local debug logs.
• Safe Cancellation: Supports batch, forced, and non-interactive job cancellation via Ctrl+C.
• Async Performance: Utilizes asyncio for non-blocking status polling, suitable for large-scale workflows.

🛠️ Installation

Ensure you have Python 3.8+ and Snakemake 8.0+ installed.

pip Installation (Preferred)

This is the easiest way to install the plugin directly from PyPI:

pip install snakemake-executor-plugin-donau

Source Installation (Development)

Since Snakemake is often used with Conda & Mamba, it is recommended to install the plugin via pip after activating your Snakemake environment:

git clone https://github.com/xsx123123/snakemake_executor_donau.git
cd snakemake_executor_donau
pip install -e .

🚀 Quick Start

1. Basic Usage

Once installed, use the --executor donau argument to enable this plugin:

snakemake --executor donau --jobs 100

🧪 Testing

A test environment is provided in the Test/ directory. You can verify the plugin's functionality using the following command:

# Run from the project root
snakemake --snakefile Test/snakefile --executor donau --jobs 10 --latency-wait 60

• --jobs 10: Limits the maximum number of concurrent jobs to 10.
• --latency-wait 60: Wait up to 60 seconds for output files to appear on the filesystem (recommended for HPC shared filesystems).

2. Snakefile Example

Define resources in your Snakefile, and the plugin will automatically convert them to scheduler parameters:

rule complex_task:
    input:
        "data/raw.txt"
    output:
        "results/final.txt"
    # 1. Set Job Priority (Maps to dsub -p)
    priority: 9999
    # 2. Set Resources
    resources:
        queue = "fat_node",       # -q fat_node
        mem_mb = 8192,            # -R mem=8192MB
        runtime = 120,            # -T 7200 (120 min -> seconds)
        nodes = 2,                # -N 2 (Replicas/Nodes)
        exclusive = True,         # -x (Exclusive mode)
        tag = "group=bio",        # --tag group=bio
        account = "proj_01",      # -A proj_01
        mpi = "openmpi"           # --mpi openmpi
    threads: 8                    # -R cpu=8
    shell:
        "echo 'Running on Donau' > {output}"

⚙️ Resource Mapping Details

The plugin maps Snakemake resource definitions to dsub parameters as follows:

Snakemake Keyword	Meaning	Donau Parameter	Notes
threads	CPU Cores	-R cpu=<threads>	Defaults to 1
priority	Priority	-p <int>	Maps Snakemake priority (1-9999)
resources.mem_mb	Memory (MB)	-R mem=<mem_mb>MB	Defaults to 1024MB
resources.queue	Queue Name	-q <queue>	partition is also supported
resources.runtime	Runtime (min)	-T <seconds>	Converted to seconds. time_min is also supported
resources.nodes	Replicas/Nodes	-N <count>	replica is also supported
resources.exclusive	Exclusive	-x job	Set to True or 1 to enable
resources.tag	Custom Tag	--tag <string>	e.g. "key=value"
resources.account	Account	-A <account>	For billing/permissions
resources.mpi	MPI Type	--mpi <type>	e.g., openmpi, intelmpi

📝 Logging & Troubleshooting

1. Unified Terminal Output

The plugin automatically inherits Snakemake's global logger. If you use --logger rich-loguru, plugin logs (submission, success, etc.) will be rendered with the same high-quality formatting.

2. Local Debug Log (Workdir)

For detailed troubleshooting, the executor writes a persistent, high-quality log to your working directory:

• Path: Created in the workflow's current working directory (respects Snakefile workdir or --directory).
• Content: Modern formatting with colors, precise timestamps, and source code location (module:function:line) for easier debugging. It records UUIDs, full shell commands (dsub), and raw scheduler responses.

3. Job Standard Output (Per Rule)

The stdout and stderr of each specific job are redirected to:

• Path: .snakemake/donau_logs/rule_<name>/<wildcards>/<jobid>.log

🔧 Underlying Logic

This plugin relies on the following Donau commands (ensure they are available in $PATH):

1. Job Submission (dsub)

   • Uses -n to specify the job name.
   • Uses -oo to capture both stdout and stderr.
   • Uses --cwd to lock the working directory.
   • Includes automatic retry logic for network stability.

2. Status Query (djob)

   • Command: djob -o "jobid state" --no-header <id_list>
   • Logic: Prioritizes querying the active list. If an ID is missing, it automatically appends the -D flag to query the completed/history database, ensuring accurate status retrieval.

3. Job Cancellation (dkill)

   • Command: dkill -y --force <id_list>
   • Logic: Uses -y to skip interactive confirmation and --force to ensure jobs are thoroughly cleaned up.

📂 Project Structure

Following the official Snakemake plugin conventions:

snakemake_executor_donau/
├── pyproject.toml                     # Poetry configuration (deps & entry points)
├── README.md                          # Documentation (English)
├── docs/
│   └── README_zh.md                   # Documentation (Chinese)
└── snakemake_executor_plugin_donau/   # Core code directory (must follow strict naming)
    ├── __init__.py                    # Plugin entry point
    ├── executor.py                    # Core logic (submit/query/cancel)
    └── logging.py                     # Logging configuration

📦 Development & Building Guide

If you intend to develop your own Snakemake plugin or contribute to this project, please adhere to the following standards:

1. Naming Convention (Strict)

Snakemake's plugin discovery mechanism enforces strict naming:

• Code Directory: Must be named snakemake_executor_plugin_<name> (e.g., snakemake_executor_plugin_donau).
• Project Name (PyPI): Recommended to be snakemake-executor-plugin-<name>.

2. Configuration (pyproject.toml)

This project uses the Poetry standard format, which is recommended by Snakemake. The key configuration is:

[tool.poetry.plugins."snakemake.executors"]
donau = "snakemake_executor_plugin_donau:Executor"

This line tells Snakemake: "When the user specifies --executor donau, load the Executor class from the snakemake_executor_plugin_donau module."

3. Local Development Flow

1. Clone the Repository:

   git clone https://github.com/xsx123123/snakemake_executor_donau.git
   cd snakemake_executor_donau
   

2. Install in Editable Mode: Within your Snakemake environment, run:

   pip install -e .
   

   Note: You do not need to install poetry explicitly; pip handles the build via pyproject.toml.
3. Verify:

   snakemake --help | grep donau
   

   If donau appears in the output, the plugin is successfully registered.

⚠️ Notes

• Runtime Configuration: It is not recommended to set runtime or time_min in your resources unless strictly necessary. Setting a hard limit might cause the scheduler to kill long-running jobs prematurely, or if misconfigured, might affect Snakemake's status polling behavior (though the plugin handles timeouts gracefully). Let the scheduler determine the default walltime when possible.
• Queue Names: Ensure the queue specified in your Snakefile actually exists in your cluster.
• Memory Units: The plugin enforces MB as the unit when interacting with the scheduler.
• Shared Filesystem: The default configuration assumes all compute nodes share a filesystem. If not, storage plugins need to be configured.