Add model manager that automatically manage model across processes

[AMOOOMA]

Added Model Manager as a util class that offers managed access to models, the client can request models without having to worry about managing GPU OOMs.
Also added various tests that checks the functions of all classes.

Classes

GPUMonitor

• start(): Begins background memory polling.
• stop(): Stops polling.
• reset_peak(): Resets peak usage tracking.
• get_stats() -> (current, peak, total): Returns memory stats.

ResourceEstimator

• is_unknown(model_tag: str) -> bool: Checks if model needs profiling.
• get_estimate(model_tag: str, default_mb: float) -> float: Returns memory cost.
• set_initial_estimate(model_tag: str, cost: float): Manually sets cost.
• add_observation(active_snapshot, peak_memory): Updates cost model via NNLS solver.

ModelManager

• acquire_model(tag: str, loader_func: Callable) -> Any: Gets model instance (handles isolation/concurrency).
• release_model(tag: str, instance: Any): Returns model to pool.
• force_reset(): Clears all models and caches.
• shutdown(): Cleans up resources.

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[gemini-code-assist]

Summary of Changes

Hello @AMOOOMA, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request introduces a sophisticated model management system for Apache Beam's ML inference capabilities. The core "ModelManager" class, supported by "GPUMonitor" and "ResourceEstimator", intelligently handles the lifecycle of machine learning models, particularly on GPU-accelerated environments. It aims to prevent out-of-memory errors by dynamically estimating model memory requirements, isolating unknown models for profiling, and implementing a demand-aware eviction strategy. This system ensures efficient and concurrent execution of diverse ML models within Beam pipelines, optimizing GPU resource utilization and improving overall stability.

Highlights

• ModelManager Introduction: A new "ModelManager" class is added to provide managed access to ML models, handling GPU OOMs and optimizing resource usage.
• GPUMonitor for Memory Tracking: Implements "GPUMonitor" to continuously poll and track GPU memory usage, including current, peak, and total memory.
• ResourceEstimator for Cost Estimation: Introduces "ResourceEstimator" which uses Non-Negative Least Squares (NNLS) to dynamically estimate the memory cost of models and adapt to fluctuating usage.
• Intelligent Model Eviction: The "ModelManager" includes an eviction strategy that prioritizes models based on demand, age, and surplus copies to free up GPU memory when needed.
• Isolation Mode for Unknown Models: Unknown models are loaded in an isolated environment to accurately profile their memory footprint without affecting other active models.
• Comprehensive Testing: New unit tests are added to validate the functionality of "GPUMonitor", "ResourceEstimator", and "ModelManager", covering capacity checks, isolation, concurrent execution, OOM recovery, and eviction logic.

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[AMOOOMA]

R: @damccorm

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