taskcore-lib 0.0.2

Package for basic task queue in the filesystem.

TaskCore

A lightweight, filesystem-based task queue system for Python. TaskCore provides a simple way to distribute and manage tasks across multiple processes or machines using the filesystem as the backend.

Features

• Simple Setup: No external dependencies - uses only Python standard library
• Filesystem-based: Tasks are stored as JSON files on disk
• Multi-process Support: Built-in support for distributed task processing
• Fault Tolerant: Automatic task recovery and stale task reclamation
• Atomic Operations: Safe concurrent access using atomic file operations
• SLURM Integration: Works seamlessly with SLURM job schedulers

Installation

pip install taskcore

Or install from source:

git clone https://github.com/goncalorafaria/taskcore
cd taskcore
pip install -e .

Quick Start

Basic Usage

from taskcore import FileSystemTaskQueueClient

# Initialize the task queue
queue = FileSystemTaskQueueClient("/path/to/task/directory")

# Add tasks to the queue
task_config = {
    "learning_rate": 1e-4,
    "batch_size": 32,
    "epochs": 100,
    "model_name": "bert-base-uncased"
}

task_id = queue.add_task(task_config)
print(f"Added task with ID: {task_id}")

# Process tasks
def init_function(**config):
    # Initialize your model/trainer here
    trainer = create_trainer(**config)
    return trainer, {"status": "initialized"}

def run_function(trainer):
    # Run your training/processing here
    trainer.train()

# Fetch and run tasks
while queue.fetch_and_run_task(init_func=init_function, func=run_function):
    pass

Multi-Process Example

import os
from taskcore import FileSystemTaskQueueClient

def main():
    # Use LOCAL_RANK for multi-process setups (e.g., with torch.distributed)
    rank = int(os.environ.get("LOCAL_RANK", 0))
    queue = FileSystemTaskQueueClient("/path/to/tasks", rank=rank)
    
    def init_func(**config):
        # Initialize your distributed training setup
        trainer = create_distributed_trainer(**config)
        return trainer, {"wandb_link": trainer.wandb_link}
    
    def run_func(trainer):
        trainer.train()
    
    # Process tasks until none are available
    while queue.fetch_and_run_task(init_func=init_func, func=run_func):
        pass

if __name__ == "__main__":
    main()

Task Generation Example

from taskcore import FileSystemTaskQueueClient

# Initialize queue
queue = FileSystemTaskQueueClient("/path/to/task/directory")

# Define hyperparameter sweep
learning_rates = [1e-4, 1e-5, 1e-6]
batch_sizes = [16, 32, 64]
model_names = ["bert-base-uncased", "roberta-base"]

# Generate all combinations
for lr in learning_rates:
    for batch_size in batch_sizes:
        for model_name in model_names:
            config = {
                "learning_rate": lr,
                "batch_size": batch_size,
                "model_name": model_name,
                "epochs": 100,
                "wandb_project": "my-experiment"
            }
            
            task_id = queue.add_task(config)
            print(f"Added task {task_id}: {config}")

API Reference

FileSystemTaskQueueClient

The main client class for interacting with the task queue.

Constructor

FileSystemTaskQueueClient(base_dir: str, rank: int = 0)

• base_dir: Directory where task files are stored
• rank: Process rank for multi-process setups (default: 0)

Methods

add_task(task_dict: Dict) -> str

Add a new task to the queue.

task_id = queue.add_task({"param": "value"})

fetch_and_run_task(init_func: Callable, func: Callable) -> bool

Fetch a task, initialize it with init_func, and run it with func.

def init_func(**config):
    return trainer, extra_info

def run_func(trainer):
    trainer.train()

success = queue.fetch_and_run_task(init_func, run_func)

fetch_task() -> Tuple[str, Dict]

Fetch the next available task.

task_id, config = queue.fetch_task()

finish_current_task()

Mark the current task as completed.

release_current_task()

Release the current task back to the pending queue.

edit_current_task(new_dict: Dict)

Update the current task's configuration.

get_current_task() -> Dict

Get the configuration of the current task.

has_task() -> bool

Check if a task is currently being processed.

Directory Structure

TaskCore creates the following directory structure:

base_dir/
├── pending/     # Tasks waiting to be processed
├── running/     # Tasks currently being processed
└── finished/    # Completed tasks

Environment Variables

TaskCore uses these environment variables for distributed processing:

• SLURM_JOB_ID: Job ID from SLURM scheduler
• MY_JOB_ID: Fallback job ID if SLURM_JOB_ID is not available
• LOCAL_RANK: Process rank in multi-process setups

Error Handling

Tasks that fail during execution are automatically released back to the pending queue:

try:
    queue.fetch_and_run_task(init_func, run_func)
except Exception as e:
    print(f"Task failed: {e}")
    # Task is automatically released back to pending queue

Stale Task Recovery

TaskCore automatically reclaims stale tasks (default: 4 hours timeout):

# Custom timeout in seconds
queue.queue.reclaim_stale_tasks(timeout_seconds=3600)  # 1 hour

License

MIT License - see LICENSE file for details.