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p2pfl 0.5.0 documentation
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  • 📘 Introduction
  • 📥 Installation
  • 🚀 Quickstart
  • 🏛️ Components
    • 🌐 Node
    • 💡 Learning
      • 🧠 Learners
      • 🗂️ Datasets
      • 🧩 Aggregators
    • 🔄 Workflows
    • 🚦 Node State
    • 📝 Logger
    • 📡 Communication Protocols
    • ⌨️ Commands
  • 🌐 P2PFL Web Services
  • ⚠️ Common Errors
    • TensorFlow Data Pipeline Hangs When Used with Ray or OpenDP
    • Segmentation Fault When Using XGBoost with PyTorch (macOS)
  • 👨‍🏫 Tutorials
    • 💻 Using the CLI
    • 🕹️ Simulations
    • 🛡️ Communication Encryption with Mutual TLS
    • 🔀 Network Topologies
  • 👫 Contributing
  • 📚 API
    • p2pfl package
      • p2pfl.communication package
        • p2pfl.communication.commands package
          • p2pfl.communication.commands.message package
            • p2pfl.communication.commands.message.heartbeat_command module
            • p2pfl.communication.commands.message.metrics_command module
            • p2pfl.communication.commands.message.model_initialized_command module
            • p2pfl.communication.commands.message.models_agregated_command module
            • p2pfl.communication.commands.message.models_ready_command module
            • p2pfl.communication.commands.message.pre_send_model_command module
            • p2pfl.communication.commands.message.start_learning_command module
            • p2pfl.communication.commands.message.stop_learning_command module
            • p2pfl.communication.commands.message.vote_train_set_command module
          • p2pfl.communication.commands.weights package
            • p2pfl.communication.commands.weights.full_model_command module
            • p2pfl.communication.commands.weights.init_model_command module
            • p2pfl.communication.commands.weights.partial_model_command module
          • p2pfl.communication.commands.command module
        • p2pfl.communication.protocols package
          • p2pfl.communication.protocols.protobuff package
            • p2pfl.communication.protocols.protobuff.grpc package
              • p2pfl.communication.protocols.protobuff.grpc.address module
              • p2pfl.communication.protocols.protobuff.grpc.client module
              • p2pfl.communication.protocols.protobuff.grpc.server module
            • p2pfl.communication.protocols.protobuff.memory package
              • p2pfl.communication.protocols.protobuff.memory.client module
              • p2pfl.communication.protocols.protobuff.memory.server module
              • p2pfl.communication.protocols.protobuff.memory.singleton_dict module
            • p2pfl.communication.protocols.protobuff.proto package
              • p2pfl.communication.protocols.protobuff.proto.generate_proto module
              • p2pfl.communication.protocols.protobuff.proto.node_pb2 module
              • p2pfl.communication.protocols.protobuff.proto.node_pb2_grpc module
            • p2pfl.communication.protocols.protobuff.client module
            • p2pfl.communication.protocols.protobuff.gossiper module
            • p2pfl.communication.protocols.protobuff.heartbeater module
            • p2pfl.communication.protocols.protobuff.neighbors module
            • p2pfl.communication.protocols.protobuff.protobuff_communication_protocol module
            • p2pfl.communication.protocols.protobuff.server module
          • p2pfl.communication.protocols.communication_protocol module
          • p2pfl.communication.protocols.exceptions module
      • p2pfl.examples package
      • p2pfl.learning package
        • p2pfl.learning.aggregators package
          • p2pfl.learning.aggregators.fedopt package
            • p2pfl.learning.aggregators.fedopt.base module
            • p2pfl.learning.aggregators.fedopt.fedadagrad module
            • p2pfl.learning.aggregators.fedopt.fedadam module
            • p2pfl.learning.aggregators.fedopt.fedyogi module
          • p2pfl.learning.aggregators.aggregator module
          • p2pfl.learning.aggregators.fedavg module
          • p2pfl.learning.aggregators.fedmedian module
          • p2pfl.learning.aggregators.fedprox module
          • p2pfl.learning.aggregators.fedxgbbagging module
          • p2pfl.learning.aggregators.krum module
          • p2pfl.learning.aggregators.scaffold module
          • p2pfl.learning.aggregators.sequential module
        • p2pfl.learning.compression package
          • p2pfl.learning.compression.base_compression_strategy module
          • p2pfl.learning.compression.dp_strategy module
          • p2pfl.learning.compression.lra_strategy module
          • p2pfl.learning.compression.lzma_strategy module
          • p2pfl.learning.compression.manager module
          • p2pfl.learning.compression.quantization_strategy module
          • p2pfl.learning.compression.topk_strategy module
          • p2pfl.learning.compression.zlib_strategy module
        • p2pfl.learning.dataset package
          • p2pfl.learning.dataset.p2pfl_dataset module
          • p2pfl.learning.dataset.partition_strategies module
        • p2pfl.learning.frameworks package
          • p2pfl.learning.frameworks.flax package
            • p2pfl.learning.frameworks.flax.flax_dataset module
            • p2pfl.learning.frameworks.flax.flax_learner module
            • p2pfl.learning.frameworks.flax.flax_model module
          • p2pfl.learning.frameworks.pytorch package
            • p2pfl.learning.frameworks.pytorch.callbacks package
              • p2pfl.learning.frameworks.pytorch.callbacks.fedprox_callback module
              • p2pfl.learning.frameworks.pytorch.callbacks.scaffold_callback module
            • p2pfl.learning.frameworks.pytorch.lightning_dataset module
            • p2pfl.learning.frameworks.pytorch.lightning_learner module
            • p2pfl.learning.frameworks.pytorch.lightning_logger module
            • p2pfl.learning.frameworks.pytorch.lightning_model module
          • p2pfl.learning.frameworks.simulation package
            • p2pfl.learning.frameworks.simulation.actor_pool module
            • p2pfl.learning.frameworks.simulation.virtual_learner module
          • p2pfl.learning.frameworks.tensorflow package
            • p2pfl.learning.frameworks.tensorflow.callbacks package
              • p2pfl.learning.frameworks.tensorflow.callbacks.keras_logger module
              • p2pfl.learning.frameworks.tensorflow.callbacks.scaffold_callback module
            • p2pfl.learning.frameworks.tensorflow.keras_dataset module
            • p2pfl.learning.frameworks.tensorflow.keras_learner module
            • p2pfl.learning.frameworks.tensorflow.keras_model module
          • p2pfl.learning.frameworks.xgboost package
            • p2pfl.learning.frameworks.xgboost.xgboost_dataset module
            • p2pfl.learning.frameworks.xgboost.xgboost_learner module
            • p2pfl.learning.frameworks.xgboost.xgboost_logger module
            • p2pfl.learning.frameworks.xgboost.xgboost_model module
          • p2pfl.learning.frameworks.callback module
          • p2pfl.learning.frameworks.callback_factory module
          • p2pfl.learning.frameworks.exceptions module
          • p2pfl.learning.frameworks.learner module
          • p2pfl.learning.frameworks.learner_factory module
          • p2pfl.learning.frameworks.p2pfl_model module
      • p2pfl.management package
        • p2pfl.management.logger package
          • p2pfl.management.logger.logger module
        • p2pfl.management.cli module
        • p2pfl.management.launch_from_yaml module
        • p2pfl.management.message_storage module
        • p2pfl.management.metric_storage module
        • p2pfl.management.node_monitor module
        • p2pfl.management.p2pfl_web_services module
      • p2pfl.stages package
        • p2pfl.stages.base_node package
          • p2pfl.stages.base_node.gossip_model_stage module
          • p2pfl.stages.base_node.round_finished_stage module
          • p2pfl.stages.base_node.start_learning_stage module
          • p2pfl.stages.base_node.train_stage module
          • p2pfl.stages.base_node.vote_train_set_stage module
          • p2pfl.stages.base_node.wait_agg_models_stage module
        • p2pfl.stages.stage module
        • p2pfl.stages.stage_factory module
        • p2pfl.stages.workflows module
      • p2pfl.exceptions module
      • p2pfl.experiment module
      • p2pfl.node module
      • p2pfl.node_state module
      • p2pfl.settings module
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⚠️ Common Errors¶

This page documents common errors you may encounter when using P2PFL and how to resolve them.

Error

Platform

Frameworks

Issue

TensorFlow Data Pipeline Hangs

macOS

TensorFlow + Ray/OpenDP

ray#59661

XGBoost + PyTorch Segfault

macOS

XGBoost + PyTorch

pytorch#171323
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TensorFlow Data Pipeline Hangs When Used with Ray or OpenDP
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