From 6eb23353d70d0c92213c5663fe123de6edc3829d Mon Sep 17 00:00:00 2001 From: Rong Ou Date: Wed, 29 Jun 2022 02:58:06 -0700 Subject: [PATCH] Update nvflare demo for release 2.1.2 (#8038) --- demo/nvflare/README.md | 12 +++++------- 1 file changed, 5 insertions(+), 7 deletions(-) diff --git a/demo/nvflare/README.md b/demo/nvflare/README.md index ad5ed9a60cc7..e3f8a3023d9d 100644 --- a/demo/nvflare/README.md +++ b/demo/nvflare/README.md @@ -8,9 +8,10 @@ This directory contains a demo of Federated Learning using To run the demo, first build XGBoost with the federated learning plugin enabled (see the [README](../../plugin/federated/README.md)). -Install NVFlare (note that currently NVFlare only supports Python 3.8): +Install NVFlare (note that currently NVFlare only supports Python 3.8; for NVFlare 2.1.2 we also +need to pin the protobuf package to 3.20.x to avoid protoc errors): ```shell -pip install nvflare +pip install nvflare protobuf==3.20.1 ``` Prepare the data: @@ -38,12 +39,9 @@ Then start the admin CLI, using `admin/admin` as username/password: ./poc/admin/startup/fl_admin.sh ``` -In the admin CLI, run the following commands: +In the admin CLI, run the following command: ```shell -upload_app hello-xgboost -set_run_number 1 -deploy_app hello-xgboost all -start_app all +submit_job hello-xgboost ``` Once the training finishes, the model file should be written into