快速入门 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 Flower ServerApp, config: num_rounds=3, no round_timeout
INFO :
INFO :      [INIT]
INFO :      Using initial global parameters provided by strategy
INFO :      Starting evaluation of initial global parameters
INFO :      Evaluation returned no results (`None`)
INFO :
INFO :      [ROUND 1]
INFO :      configure_fit: strategy sampled 2 clients (out of 4)
INFO :      aggregate_evaluate: received 2 results and 0 failures
WARNING :   No evaluate_metrics_aggregation_fn provided
INFO :
INFO :      [ROUND 2]
INFO :      configure_fit: strategy sampled 2 clients (out of 4)
INFO :      aggregate_fit: received 2 results and 0 failures
INFO :      configure_evaluate: strategy sampled 2 clients (out of 4)
INFO :      aggregate_evaluate: received 2 results and 0 failures
INFO :
INFO :      [ROUND 3]
INFO :      configure_fit: strategy sampled 2 clients (out of 4)
INFO :      aggregate_fit: received 2 results and 0 failures
INFO :      configure_evaluate: strategy sampled 2 clients (out of 4)
INFO :      aggregate_evaluate: received 2 results and 0 failures
INFO :
INFO :      [SUMMARY]
INFO :      Run finished 3 round(s) in 136.92s
INFO :          History (loss, distributed):
INFO :                  round 1: 0.04982871934771538
INFO :                  round 2: 0.046457378193736076
INFO :                  round 3: 0.04506748169660568
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

Note

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