hopsproject.com

What It Is

HOPS (Heterogeneous Optimized Pipeline Simulator) is a Python-based discrete-event simulator for pipeline-parallel training scenarios. It is not a framework for running model training directly; instead, it simulates the training process under configurable hardware topologies, communication latency, failure modes, and scheduling strategies, then outputs performance metrics and visualizations.

Core Capabilities

Functionally, HOPS focuses on “controllable simulation.” Its event engine uses a priority queue to process timestamped events, emphasizing deterministic simulation. Random seeds can be set in the configuration, and np.random.Generator is used across random components, making experiments easier to reproduce. On the scheduling side, it includes GPipe and 1F1B, and custom strategies can be registered via register_scheduler(), making it suitable for comparing different pipeline schedules. The hardware layer supports defining GPU/CPU devices, link bandwidth, base latency, activation size, and jitter. Its latency models support constant, normal, Pareto heavy-tailed, and Poisson distributions. Failure injection covers device and link failure probabilities, check intervals, and recovery time.

Configuration, API, and Documentation

HOPS uses YAML-driven experiments, with declarative configuration for pipeline, simulation, scheduler, hardware, failure, and other components. At the API level, it mainly exposes a scheduler plugin interface: developers can inherit from Scheduler and implement next_tasks. The documentation covers architecture, directory structure, configuration examples, latency distributions, metric explanations, visualization, and quick start, making the onboarding information fairly complete. However, the main text does not explain the license, release method, compatibility with deep learning frameworks, performance limits, or more complex use cases.

Pricing and Adoption Barrier

The main text does not disclose any pricing, paid editions, or commercial service information. Installation requires first installing uv, then cloning the repository and running uv sync, with Python 3.13+ required. This requirement may be relatively new for some existing research or production environments, and may require additional runtime version management.

Pros, Cons, and Best-Fit Users

Its strengths are detailed modeling dimensions, reproducible experiments, rich metrics, and support for a Gantt timeline and a four-panel dashboard. Its drawbacks are limited information disclosure: there is no visible explanation of licensing, cloud hosting, enterprise support, or ecosystem integrations. It is better suited to distributed training researchers, ML infrastructure engineers, and scheduling algorithm developers for offline evaluation of topologies and scheduling strategies, rather than as a direct replacement for a training framework.

Access from China

Based only on the main text, it is not possible to determine how stable access to hopsproject.com is from mainland China, and payment is not discussed. Since the tool can run locally, once the code is obtained, typical experiments should not depend on an online service. No alternatives are provided in the main text; users will need to choose separately among training simulators, distributed systems simulators, or deep learning parallelism frameworks based on their specific research direction.