使用redis-rq作为远程任务运行器的架构合理性及代码同步方案咨询

Hey there! Let's tackle your two questions head-on, since you're working with Python and looking to optimize your experiment workflow across idle laptops.

1. Is Your Current Redis-RQ Architecture Reasonable? Are There Better Alternatives? #

First off: your Redis-RQ setup is totally reasonable for a small-scale cluster (2 idle laptops). It's lightweight, easy to configure, and gets the job done for basic task queuing. That said, it does have limitations—like the code sync issue you're facing, since it doesn't natively handle code distribution to workers.

If you want to streamline things or scale later, here are some Python-friendly alternatives worth considering:

• Ray: This is my top pick for your use case. Ray is built for distributed Python workloads and automatically serializes your task code/functions and sends them to workers—no manual code sync required. It's great for parameter sweep experiments (your exact scenario) and has a simple API that feels like regular Python.
• Dask: Ideal if your experiments are data-heavy or involve parallel computing. Dask has a distributed scheduler that can manage workers across your laptops, and it can handle code distribution for most tasks. It integrates well with Python data tools like Pandas/Numpy too.
• Celery: A more mature (but slightly more complex) distributed task queue. While it still requires code consistency across workers, it has richer features like task retries, result backends, and monitoring tools. It's a good fit if you need advanced task orchestration later.

2. How to Ensure Remote Machines Have the Latest Code (Python-Based Solutions) #

If you want to stick with Redis-RQ, here are two solid ways to automate code sync in Python:

Option 1: Auto-Sync Code via SCP (Using Paramiko) #

You can use the paramiko and scp libraries to write a Python function that syncs your local code directory to remote laptops before submitting tasks. This ensures workers always have the latest version.

First, install the dependencies:

pip install paramiko scp

Then, the sync function:

import paramiko
from scp import SCPClient
from typing import List

def sync_code_to_remotes(remote_hosts: List[dict], local_code_dir: str, remote_code_dir: str):
    """
    Sync local code directory to multiple remote machines.
    
    Args:
        remote_hosts: List of dicts with 'host', 'username', 'password' keys
        local_code_dir: Path to your local experiment code
        remote_code_dir: Path where code should live on remote machines
    """
    for host in remote_hosts:
        ssh_client = paramiko.SSHClient()
        ssh_client.set_missing_host_key_policy(paramiko.AutoAddPolicy())
        try:
            ssh_client.connect(
                host["host"],
                username=host["username"],
                password=host["password"]
            )
            # Recursively copy the local directory to remote
            with SCPClient(ssh_client.get_transport()) as scp:
                scp.put(local_code_dir, remote_path=remote_code_dir, recursive=True)
            print(f"✅ Synced code to {host['host']}")
        except Exception as e:
            print(f"❌ Failed to sync {host['host']}: {str(e)}")
        finally:
            ssh_client.close()

# Example usage
remote_hosts = [
    {"host": "192.168.1.101", "username": "your_user", "password": "your_pass"},
    {"host": "192.168.1.102", "username": "your_user", "password": "your_pass"}
]
sync_code_to_remotes(remote_hosts, "./my_experiment_code", "/home/your_user/my_experiment_code")

Call this function right before you submit tasks to Redis-RQ, and you'll avoid version mismatches.

Option 2: Pull Latest Code from Git (Automated via SSH) #

If your code is stored in a Git repo, you can automate a git pull on remote machines instead of syncing the entire directory. This is more efficient for large codebases since it only pulls changes.

Using paramiko again, here's a function to run git pull remotely:

def pull_latest_git_code(remote_hosts: List[dict], remote_repo_dir: str):
    for host in remote_hosts:
        ssh_client = paramiko.SSHClient()
        ssh_client.set_missing_host_key_policy(paramiko.AutoAddPolicy())
        try:
            ssh_client.connect(
                host["host"],
                username=host["username"],
                password=host["password"]
            )
            # Run git pull command
            stdin, stdout, stderr = ssh_client.exec_command(f"cd {remote_repo_dir} && git pull")
            output = stdout.read().decode().strip()
            error = stderr.read().decode().strip()
            
            if error:
                print(f"⚠️ {host['host']} pull error: {error}")
            else:
                print(f"✅ {host['host']} updated: {output}")
        except Exception as e:
            print(f"❌ Failed to update {host['host']}: {str(e)}")
        finally:
            ssh_client.close()

# Example usage
pull_latest_git_code(remote_hosts, "/home/your_user/my_experiment_repo")

Bonus: Switch to Ray for Zero Code Sync #

If you're open to changing your setup, Ray eliminates the code sync problem entirely. As mentioned earlier, it automatically sends your task code to workers. Here's a quick example of how you'd run your parameter sweep:

1. Install Ray on all machines:

pip install ray

2. On each remote laptop, start a Ray worker:

ray start --address='YOUR_MAIN_MACHINE_IP:6379' --num-cpus=2

3. On your main machine, submit tasks:

import ray

# Connect to the Ray cluster
ray.init(address='auto')

# Define your experiment task (no need to push this to remotes!)
@ray.remote
def run_experiment(param_a, param_b):
    # Your experiment logic here
    result = param_a + param_b  # Replace with your actual code
    return result

# Generate your parameter combinations
param_pairs = [(1, 2), (3, 4), (5, 6), (7, 8)]

# Submit all tasks to the cluster
futures = [run_experiment.remote(p[0], p[1]) for p in param_pairs]

# Get all results
results = ray.get(futures)
print("Experiment results:", results)

This setup lets you iterate on your code locally and run it remotely without any manual sync steps.

内容的提问来源于stack exchange，提问作者alexandregg