Enable Rich Console Elements For Operators/Actuators

by Alex Johnson 53 views

Have you ever been in a situation where a long-running experiment makes your console appear to hang, causing confusion and potentially leading to premature termination of processes? This article delves into a solution for this common problem by enabling operators and actuators to write rich console live elements, such as spinners and progress bars. These visual cues can significantly improve the user experience by providing clear feedback on ongoing processes.

The Problem: Confusing Long-Running Experiments

When conducting experiments, especially those involving explore operations, it's not uncommon for the console to appear unresponsive for extended periods. This can be particularly problematic in environments like IBM and ADO, where users might perceive the application as hanging and prematurely terminate the process, thinking something is wrong. Default logging often lacks the necessary granularity to provide sufficient insight into the progress of the task. While increasing the log level might reveal more information, it can also flood the console with unrelated logs, obscuring the crucial details.

Rich live elements offer a promising solution by visually indicating that a task is in progress. However, there are challenges to overcome:

  • Rich elements can only be effectively used from the main process due to the constraints of managing a single console context.
  • Output from actors using rich.Console gets serialized and transmitted to the main process stdout, losing its rich formatting and visual cues.

Proposed Solution: A Centralized Live Context

To address these challenges, we propose a solution centered around a single, grouped Live context within the main process. This approach leverages the existing Live table functionality while adding a second group specifically for actor progress messages. Imagine a live table at the bottom of the console, complemented by progress indicators for active tasks originating from operators and actuators above. This provides a clear and concise overview of ongoing operations.

Key Components of the Solution

  1. RichConsoleQueue Actor: We introduce an actor, RichConsoleQueue, which serves as a message queue for actors to submit messages intended for rendering in the progress segment of the console.
  2. Main Process Live Context Manager: The main process takes on the responsibility of managing the Live context. It periodically pulls messages from the RichConsoleQueue and performs actions based on the message content:
    • Adding Elements: Creates new visual elements (spinners, progress bars) in the progress group, providing immediate feedback for new tasks.
    • Progressing Elements: Updates the state of existing elements, such as incrementing a progress bar or changing the spinner animation.
    • Removing Elements: Removes elements from the group when their corresponding tasks are completed, maintaining a clean and informative display.

Each message includes an identifier, enabling the Live context manager to accurately determine the appropriate action to take.

This approach centralizes the rendering of rich console elements, ensuring consistent and accurate output while allowing actors to contribute progress information seamlessly.

Benefits of This Approach

  • Improved User Experience: Clear visual cues prevent user confusion and reduce the likelihood of premature process termination.
  • Enhanced Debugging: Real-time progress indicators provide valuable insights into the execution flow of experiments.
  • Simplified Logging: Reduces the need for verbose logging, making it easier to focus on relevant information.
  • Scalability: The actor-based approach allows for efficient handling of progress messages from multiple operators and actuators.

Alternatives Considered

Alternative implementations for consuming messages were considered, but the core limitation remains: only one Live context can effectively manage the console output for correct rendering. This constraint necessitates a centralized approach, making the proposed solution the most viable option.

Technical Implementation Details

Let's dive deeper into the technical aspects of implementing this solution. We'll explore the structure of the RichConsoleQueue actor, the message format, and the logic within the main process Live context manager.

1. The RichConsoleQueue Actor

The RichConsoleQueue actor acts as a central message buffer. Operators and actuators can enqueue messages to this actor, which will then be processed by the main process. The actor can be implemented using a simple queue data structure.

import ray
import queue

@ray.remote
class RichConsoleQueue:
    def __init__(self):
        self.queue = queue.Queue()

    def put(self, message):
        self.queue.put(message)

    def get(self, timeout=None):
        try:
            return self.queue.get(timeout=timeout)
        except queue.Empty:
            return None

2. Message Format

The messages sent to the RichConsoleQueue should follow a defined format to allow the main process to interpret them correctly. A typical message might include:

  • identifier: A unique identifier for the task or element (e.g., a task ID).
  • action: The action to be performed (add, update, remove).
  • type: The type of element (e.g., spinner, progress_bar).
  • data: Data specific to the element and action (e.g., progress percentage, spinner style).

Example message:

{
    "identifier": "task_123",
    "action": "update",
    "type": "progress_bar",
    "data": {"percentage": 50}
}

3. Main Process Live Context Manager

The main process is responsible for creating and managing the Live context. It continuously polls the RichConsoleQueue for messages and updates the console display accordingly. This involves creating, updating, and removing rich elements within the Live context.

from rich.console import Console
from rich.live import Live
from rich.table import Table
from rich.spinner import Spinner
from rich.progress import Progress, BarColumn, TextColumn, TimeElapsedColumn
import time

def main():
    console = Console()
    progress_group = Table.grid().add_column()
    live_table = Table()
    live_table.add_column("Task", style="bold magenta")
    live_table.add_column("Status", style="bold blue")
    live_table.add_row("Initial Task", "Running...")

    with Live(Table.grid().add_row(progress_group).add_row(live_table), console=console, screen=False, redirect_stderr=False, redirect_stdout=False) as live:
        task_spinners = {}
        while True:
            message = ray.get(rich_console_queue.get.remote(timeout=0.1))
            if message:
                identifier = message["identifier"]
                action = message["action"]
                element_type = message["type"]
                data = message["data"]

                if action == "add":
                    if element_type == "spinner":
                        spinner = Spinner("dots", text=f"{identifier} Running...")
                        task_spinners[identifier] = spinner
                        progress_group.add_row(spinner)
                    elif element_type == "progress_bar":
                        progress = Progress(
                            TextColumn("[bold blue]{task.description}"),
                            BarColumn(),
                            TextColumn("[progress.percentage]{task.percentage:>3.0f}%[/]" ),
                            TimeElapsedColumn(),
                            console=console
                        )
                        task_spinners[identifier] = progress
                        progress_group.add_row(progress)

                elif action == "update":
                    if element_type == "progress_bar":
                         task_spinners[identifier].update(data["task_id"], advance=data["advance"])

                elif action == "remove":
                    if identifier in task_spinners:
                        progress_group.remove_row(task_spinners[identifier])
                        del task_spinners[identifier]

            #Update main process table
            live_table.rows[0].cells[1] = f"Still running at {time.strftime('%H:%M:%S')}"

            live.update(Table.grid().add_row(progress_group).add_row(live_table))

            time.sleep(0.01)

if __name__ == "__main__":
    ray.init()
    rich_console_queue = RichConsoleQueue.remote()
    main()
    ray.shutdown()

This snippet demonstrates how to create a Live context, manage spinners, and integrate them into a console display using the rich library.

Conclusion

Enabling operators and actuators to write rich console live elements offers a significant improvement in user experience and debugging capabilities. By centralizing the management of rich elements within a main process Live context and utilizing an actor-based message queue, we can effectively provide real-time feedback on long-running experiments. This approach reduces user confusion, minimizes premature process termination, and ultimately streamlines the development and experimentation workflow.

For further exploration of the rich library and its capabilities, consider visiting the official Rich documentation. This will provide a deeper understanding of the library's features and how they can be applied to enhance console output.