65 lines
1.6 KiB
Markdown
65 lines
1.6 KiB
Markdown
# Experimental Support of Federated XGBoost using NVFlare
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This directory contains a demo of Federated Learning using
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[NVFlare](https://nvidia.github.io/NVFlare/).
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## Training with CPU only
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To run the demo, first build XGBoost with the federated learning plugin enabled (see the
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[README](../../plugin/federated/README.md)).
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Install NVFlare (note that currently NVFlare only supports Python 3.8; for NVFlare 2.1.2 we also
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need to pin the protobuf package to 3.20.x to avoid protoc errors):
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```shell
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pip install nvflare protobuf==3.20.1
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```
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Prepare the data:
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```shell
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./prepare_data.sh
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```
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Start the NVFlare federated server:
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```shell
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./poc/server/startup/start.sh
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```
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In another terminal, start the first worker:
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```shell
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./poc/site-1/startup/start.sh
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```
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And the second worker:
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```shell
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./poc/site-2/startup/start.sh
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```
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Then start the admin CLI, using `admin/admin` as username/password:
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```shell
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./poc/admin/startup/fl_admin.sh
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```
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In the admin CLI, run the following command:
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```shell
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submit_job hello-xgboost
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```
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Once the training finishes, the model file should be written into
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`./poc/site-1/run_1/test.model.json` and `./poc/site-2/run_1/test.model.json`
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respectively.
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Finally, shutdown everything from the admin CLI:
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```shell
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shutdown client
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shutdown server
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```
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## Training with GPUs
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To demo with Federated Learning using GPUs, make sure your machine has at least 2 GPUs.
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Build XGBoost with the federated learning plugin enabled along with CUDA, but with NCCL
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turned off (see the [README](../../plugin/federated/README.md)).
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Modify `config/config_fed_client.json` and set `use_gpus` to `true`, then repeat the steps
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above.
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