πΉοΈ SimulationsΒΆ
P2PFL leverages Ray, a powerful distributed computing framework, to enable efficient simulations of large-scale federated learning scenarios. This allows you to train and evaluate models across a cluster of machines or multiple processes on a single machine, significantly accelerating the process and overcoming the limitations of single-machine setups.
π Ray Integration for ScalabilityΒΆ
P2PFL seamlessly integrates with Ray to distribute the learning process. When Ray is installed, P2PFL automatically creates a pool of actors, which are independent Python processes that can be distributed across your cluster. Each actor hosts a Learner
instance, allowing for parallel training and evaluation.
𧩠Actor Pool¢
The core of P2PFLβs simulation capabilities is the SuperActorPool
. This pool manages the lifecycle of VirtualNodeLearner
actors. Each VirtualNodeLearner
wraps a standard Learner
, enabling it to be executed remotely by Ray. This means that each node in your federated learning simulation can be run as an independent actor, managed by the pool.
π Benefits of Using RayΒΆ
Scalability: Distribute the learning process across multiple machines or processes, enabling larger-scale simulations.
Efficiency: Parallelize training and evaluation, significantly reducing overall experiment time.
Fault Tolerance: Rayβs actor model provides fault tolerance. If an actor fails, Ray can automatically restart it.
Resource Management: Ray intelligently manages the allocation of resources (CPUs, GPUs) to actors.
βοΈ Setting Up a Ray ClusterΒΆ
To disable ray (even if installed), set
Settings.DISABLE_RAY=True
.
To run P2PFL simulations with Ray, you need to set up a Ray cluster. This can be done on a single machine (for smaller simulations) or across multiple machines (for larger simulations).
Single Machine SetupΒΆ
For simulations on a single machine, you donβt need to explicitly start a Ray cluster. P2PFL will automatically initialize Ray in local mode when you start your experiment.
Multi-Machine SetupΒΆ
For larger simulations, youβll need to set up a Ray cluster across multiple machines:
Start the head node: On the machine designated as the head node, run:
ray start --head --port=6379
Start worker nodes: On each additional machine, run:
ray start --address='<head_node_ip>:6379'
Replace
<head_node_ip>
with the IP address of the head node.Verify the cluster: Check the status of your Ray cluster using:
ray status
Once the cluster is set up, you can run your P2PFL experiment as usual. P2PFL will automatically distribute the VirtualNodeLearner
actors across the available nodes in the cluster.