graph LR
ParameterServer["ParameterServer"]
__init__["__init__"]
start["start"]
stop["stop"]
run["run"]
action_listener["action_listener"]
get_parameters["get_parameters"]
update_parameters["update_parameters"]
ParameterServer -- "defines" --> __init__
ParameterServer -- "defines" --> start
ParameterServer -- "defines" --> stop
ParameterServer -- "defines" --> run
ParameterServer -- "defines" --> action_listener
ParameterServer -- "defines" --> get_parameters
ParameterServer -- "defines" --> update_parameters
start -- "can call" --> stop
run -- "delegates to" --> action_listener
action_listener -- "calls" --> get_parameters
action_listener -- "calls" --> update_parameters
The Parameter Server subsystem is a critical component within Elephas, designed to manage and synchronize model parameters in a distributed deep learning environment. It embodies the central coordination point for data parallelism, ensuring all worker nodes operate with a consistent and up-to-date global model.
The core entity of the subsystem, responsible for centralizing and synchronizing model parameters (weights and gradients) across worker nodes. It acts as the authoritative source for the global model state, enabling data parallelism by providing workers with the current model and integrating their computed updates.
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Initializes the ParameterServer instance. This includes setting up the underlying communication mechanism (e.g., a Flask web service) that will listen for incoming requests from worker nodes, and initializing the model parameters.
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Initiates the server's operation, bringing it online to actively listen for and process parameter requests from worker nodes. It sets up the server to begin accepting connections.
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Halts the server's operation, ensuring a graceful shutdown. This involves releasing resources and closing communication channels to prevent leaks or orphaned processes.
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Executes the server's continuous operation loop. It is responsible for keeping the server alive, constantly listening for incoming requests, and delegating their processing to the appropriate handlers.
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Acts as the primary request handler for incoming HTTP requests from worker nodes. It interprets the request's intent, determining whether it's for retrieving the current model parameters or for updating them with gradients/weights from a worker.
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Provides the current global model parameters (weights) to requesting worker nodes. This ensures that all workers are training with the most up-to-date model state, crucial for synchronized distributed training.
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Incorporates parameter updates (e.g., gradients or updated weights) received from worker nodes into the global model state. This is crucial for synchronizing the model across the distributed training process, typically by applying an aggregation strategy.
Related Classes/Methods: