Quickstart PyTorch Lightning

In this federated learning tutorial we will learn how to train an AutoEncoder model on MNIST using Flower and PyTorch Lightning. It is recommended to create a virtual environment and run everything within a virtualenv.

Then, clone the code example directly from GitHub:

git clone --depth=1 https://github.com/adap/flower.git _tmp \
             && mv _tmp/examples/quickstart-pytorch-lightning . \
             && rm -rf _tmp && cd quickstart-pytorch-lightning

This will create a new directory called quickstart-pytorch-lightning containing the following files:

quickstart-pytorch-lightning
├── pytorchlightning_example
│   ├── client_app.py   # Defines your ClientApp
│   ├── server_app.py   # Defines your ServerApp
│   └── task.py         # Defines your model, training and data loading
├── pyproject.toml      # Project metadata like dependencies and configs
└── README.md

Next, activate your environment, then run:

# Navigate to the example directory
$ cd path/to/quickstart-pytorch-lightning

# Install project and dependencies
$ pip install -e .

By default, Flower Simulation Engine will be started and it will create a federation of 4 nodes using FedAvg as the aggregation strategy. The dataset will be partitioned using Flower Dataset’s IidPartitioner. To run the project, do:

# Run with default arguments
$ flwr run .

With default arguments you will see an output like this one:

Loading project configuration...
Success
INFO :      Starting FedAvg strategy:
INFO :          ├── Number of rounds: 3
INFO :          ├── ArrayRecord (0.39 MB)
INFO :          ├── ConfigRecord (train): (empty!)
INFO :          ├── ConfigRecord (evaluate): (empty!)
INFO :          ├──> Sampling:
INFO :          │       ├──Fraction: train (0.50) | evaluate ( 0.50)
INFO :          │       ├──Minimum nodes: train (2) | evaluate (2)
INFO :          │       └──Minimum available nodes: 2
INFO :          └──> Keys in records:
INFO :                  ├── Weighted by: 'num-examples'
INFO :                  ├── ArrayRecord key: 'arrays'
INFO :                  └── ConfigRecord key: 'config'
INFO :
INFO :
INFO :      [ROUND 1/3]
INFO :      configure_train: Sampled 2 nodes (out of 4)
INFO :      aggregate_train: Received 2 results and 0 failures
INFO :          └──> Aggregated MetricRecord: {'train_loss': 0.0487}
INFO :      configure_evaluate: Sampled 2 nodes (out of 4)
INFO :      aggregate_evaluate: Received 2 results and 0 failures
INFO :          └──> Aggregated MetricRecord: {'eval_loss': 0.0495}
INFO :
INFO :      [ROUND 2/3]
INFO :      configure_train: Sampled 2 nodes (out of 4)
INFO :      aggregate_train: Received 2 results and 0 failures
INFO :          └──> Aggregated MetricRecord: {'train_loss': 0.0420}
INFO :      configure_evaluate: Sampled 2 nodes (out of 4)
INFO :      aggregate_evaluate: Received 2 results and 0 failures
INFO :          └──> Aggregated MetricRecord: {'eval_loss': 0.0455}
INFO :
INFO :      [ROUND 3/3]
INFO :      configure_train: Sampled 2 nodes (out of 4)
INFO :      aggregate_train: Received 2 results and 0 failures
INFO :          └──> Aggregated MetricRecord: {'train_loss': 0.05082}
INFO :      configure_evaluate: Sampled 2 nodes (out of 4)
INFO :      aggregate_evaluate: Received 2 results and 0 failures
INFO :          └──> Aggregated MetricRecord: {'eval_loss': 0.0441}
INFO :
INFO :      Strategy execution finished in 159.24s
INFO :
INFO :      Final results:
INFO :
INFO :          Global Arrays:
INFO :                  ArrayRecord (0.389 MB)
INFO :
INFO :          Aggregated ClientApp-side Train Metrics:
INFO :          { 1: {'train_loss': '4.8696e-02'},
INFO :            2: {'train_loss': '4.1957e-02'},
INFO :            3: {'train_loss': '5.0818e-02'}}
INFO :
INFO :          Aggregated ClientApp-side Evaluate Metrics:
INFO :          { 1: {'eval_loss': '4.9516e-02'},
INFO :            2: {'eval_loss': '4.5510e-02'},
INFO :            3: {'eval_loss': '4.4052e-02'}}
INFO :
INFO :          ServerApp-side Evaluate Metrics:
INFO :          {}
INFO :

Each simulated ClientApp (two per round) will also log a summary of their local training process. Expect this output to be similar to:

# The left part indicates the process ID running the `ClientApp`
(ClientAppActor pid=38155) ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
(ClientAppActor pid=38155) ┃        Test metric        ┃       DataLoader 0        ┃
(ClientAppActor pid=38155) ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
(ClientAppActor pid=38155) │         test_loss         │   0.045175597071647644    │
(ClientAppActor pid=38155) └───────────────────────────┴───────────────────────────┘

You can also override the parameters defined in the [tool.flwr.app.config] section in pyproject.toml like this:

# Override some arguments
$ flwr run . --run-config num-server-rounds=5

팁

Check the 시뮬레이션 실행 documentation to learn more about how to configure and run Flower simulations.

참고

Check the source code of this tutorial in examples/quickstart-pytorch-lightning in the Flower GitHub repository.