827d0e8edb
- Bump the Python requirement. - Fix type hints. - Use loky to avoid deadlock. - Workaround cupy-numpy compatibility issue on Windows caused by the `safe` casting rule. - Simplify the repartitioning logic to avoid dask errors.
65 lines
1.7 KiB
Markdown
65 lines
1.7 KiB
Markdown
# Experimental Support of Vertical Federated XGBoost using NVFlare
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This directory contains a demo of Vertical 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:
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```shell
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pip install nvflare
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```
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Prepare the data (note that this step will download the HIGGS dataset, which is 2.6GB compressed, and 7.5GB
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uncompressed, so make sure you have enough disk space and are on a fast internet connection):
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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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/tmp/nvflare/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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/tmp/nvflare/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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/tmp/nvflare/poc/site-2/startup/start.sh
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```
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Then start the admin CLI:
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```shell
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/tmp/nvflare/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 vertical-xgboost
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```
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Once the training finishes, the model file should be written into
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`/tmp/nvlfare/poc/site-1/run_1/test.model.json` and `/tmp/nvflare/poc/site-2/run_1/test.model.json`
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respectively.
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Finally, shutdown everything from the admin CLI, using `admin` as password:
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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 Vertical 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
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(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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