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.
93 lines
2.9 KiB
Markdown
93 lines
2.9 KiB
Markdown
# Experimental Support of Horizontal Federated XGBoost using NVFlare
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This directory contains a demo of Horizontal 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:
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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 horizontal-xgboost
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```
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Make a note of the job id:
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```console
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Submitted job: 28309e77-a7c5-45e6-b2bc-c2e3655122d8
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```
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On both workers, you should see train and eval losses printed:
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```console
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[10:45:41] [0] eval-logloss:0.22646 train-logloss:0.23316
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[10:45:41] [1] eval-logloss:0.13776 train-logloss:0.13654
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[10:45:41] [2] eval-logloss:0.08036 train-logloss:0.08243
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[10:45:41] [3] eval-logloss:0.05830 train-logloss:0.05645
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[10:45:41] [4] eval-logloss:0.03825 train-logloss:0.04148
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[10:45:41] [5] eval-logloss:0.02660 train-logloss:0.02958
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[10:45:41] [6] eval-logloss:0.01386 train-logloss:0.01918
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[10:45:41] [7] eval-logloss:0.01018 train-logloss:0.01331
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[10:45:41] [8] eval-logloss:0.00847 train-logloss:0.01112
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[10:45:41] [9] eval-logloss:0.00691 train-logloss:0.00662
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[10:45:41] [10] eval-logloss:0.00543 train-logloss:0.00503
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[10:45:41] [11] eval-logloss:0.00445 train-logloss:0.00420
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[10:45:41] [12] eval-logloss:0.00336 train-logloss:0.00355
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[10:45:41] [13] eval-logloss:0.00277 train-logloss:0.00280
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[10:45:41] [14] eval-logloss:0.00252 train-logloss:0.00244
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[10:45:41] [15] eval-logloss:0.00177 train-logloss:0.00193
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[10:45:41] [16] eval-logloss:0.00156 train-logloss:0.00161
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[10:45:41] [17] eval-logloss:0.00135 train-logloss:0.00142
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[10:45:41] [18] eval-logloss:0.00123 train-logloss:0.00125
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[10:45:41] [19] eval-logloss:0.00106 train-logloss:0.00107
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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/${job_id}/test.model.json` and `/tmp/nvflare/poc/site-2/${job_id}/test.model.json`
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respectively, where `job_id` is the UUID printed out when we ran `submit_job`.
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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 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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