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Fix federated learning demos and tests (#9488)

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Sean Yang 2023-08-16 00:25:05 -07:00 committed by GitHub
parent b2e93d2742
commit 12fe2fc06c
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10 changed files with 60 additions and 11 deletions
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1
demo/nvflare/.gitignore vendored Normal file
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@ -0,0 +1 @@
!config

23
demo/nvflare/config/config_fed_client.json Normal file
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@ -0,0 +1,23 @@
{
"format_version": 2,
"executors": [
{
"tasks": [
"train"
],
"executor": {
"path": "trainer.XGBoostTrainer",
"args": {
"server_address": "localhost:9091",
"world_size": 2,
"server_cert_path": "server-cert.pem",
"client_key_path": "client-key.pem",
"client_cert_path": "client-cert.pem",
"use_gpus": false
}
}
}
],
"task_result_filters": [],
"task_data_filters": []
}

22
demo/nvflare/config/config_fed_server.json Normal file
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@ -0,0 +1,22 @@
{
"format_version": 2,
"server": {
"heart_beat_timeout": 600
},
"task_data_filters": [],
"task_result_filters": [],
"workflows": [
{
"id": "server_workflow",
"path": "controller.XGBoostController",
"args": {
"port": 9091,
"world_size": 2,
"server_key_path": "server-key.pem",
"server_cert_path": "server-cert.pem",
"client_cert_path": "client-cert.pem"
}
}
],
"components": []
}

2
demo/nvflare/horizontal/README.md
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@ -6,7 +6,7 @@ This directory contains a demo of Horizontal Federated Learning using
## Training with CPU only
To run the demo, first build XGBoost with the federated learning plugin enabled (see the
[README](../../plugin/federated/README.md)).
[README](../../../plugin/federated/README.md)).
Install NVFlare (note that currently NVFlare only supports Python 3.8):
```shell

2
demo/nvflare/horizontal/prepare_data.sh
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@ -16,7 +16,7 @@ split -n l/${world_size} --numeric-suffixes=1 -a 1 ../../data/agaricus.txt.test
nvflare poc -n 2 --prepare
mkdir -p /tmp/nvflare/poc/admin/transfer/horizontal-xgboost
cp -fr config custom /tmp/nvflare/poc/admin/transfer/horizontal-xgboost
cp -fr ../config custom /tmp/nvflare/poc/admin/transfer/horizontal-xgboost
cp server-*.pem client-cert.pem /tmp/nvflare/poc/server/
for (( site=1; site<=world_size; site++ )); do
cp server-cert.pem client-*.pem /tmp/nvflare/poc/site-"$site"/

2
demo/nvflare/vertical/README.md
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@ -6,7 +6,7 @@ This directory contains a demo of Vertical Federated Learning using
## Training with CPU only
To run the demo, first build XGBoost with the federated learning plugin enabled (see the
[README](../../plugin/federated/README.md)).
[README](../../../plugin/federated/README.md)).
Install NVFlare (note that currently NVFlare only supports Python 3.8):
```shell

5
demo/nvflare/vertical/custom/trainer.py
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@ -16,7 +16,7 @@ class SupportedTasks(object):
class XGBoostTrainer(Executor):
def __init__(self, server_address: str, world_size: int, server_cert_path: str,
client_key_path: str, client_cert_path: str):
client_key_path: str, client_cert_path: str, use_gpus: bool):
"""Trainer for federated XGBoost.
Args:
@ -32,6 +32,7 @@ class XGBoostTrainer(Executor):
self._server_cert_path = server_cert_path
self._client_key_path = client_key_path
self._client_cert_path = client_cert_path
self._use_gpus = use_gpus
def execute(self, task_name: str, shareable: Shareable, fl_ctx: FLContext,
abort_signal: Signal) -> Shareable:
@ -81,6 +82,8 @@ class XGBoostTrainer(Executor):
'objective': 'binary:logistic',
'eval_metric': 'auc',
}
if self._use_gpus:
self.log_info(fl_ctx, 'GPUs are not currently supported by vertical federated XGBoost')
# specify validations set to watch performance
watchlist = [(dtest, "eval"), (dtrain, "train")]

2
demo/nvflare/vertical/prepare_data.sh
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@ -56,7 +56,7 @@ fi
nvflare poc -n 2 --prepare
mkdir -p /tmp/nvflare/poc/admin/transfer/vertical-xgboost
cp -fr config custom /tmp/nvflare/poc/admin/transfer/vertical-xgboost
cp -fr ../config custom /tmp/nvflare/poc/admin/transfer/vertical-xgboost
cp server-*.pem client-cert.pem /tmp/nvflare/poc/server/
for (( site=1; site<=world_size; site++ )); do
cp server-cert.pem client-*.pem /tmp/nvflare/poc/site-"${site}"/

4
tests/test_distributed/test_federated/runtests-federated.sh
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@ -11,7 +11,7 @@ openssl req -x509 -newkey rsa:2048 -days 7 -nodes -keyout server-key.pem -out se
openssl req -x509 -newkey rsa:2048 -days 7 -nodes -keyout client-key.pem -out client-cert.pem -subj "/C=US/CN=localhost"
# Split train and test files manually to simulate a federated environment.
split -n l/"${world_size}" -d ../../demo/data/agaricus.txt.train agaricus.txt.train-
split -n l/"${world_size}" -d ../../demo/data/agaricus.txt.test agaricus.txt.test-
split -n l/"${world_size}" -d ../../../demo/data/agaricus.txt.train agaricus.txt.train-
split -n l/"${world_size}" -d ../../../demo/data/agaricus.txt.test agaricus.txt.test-
python test_federated.py "${world_size}"

8
tests/test_distributed/test_federated/test_federated.py
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@ -35,14 +35,14 @@ def run_worker(port: int, world_size: int, rank: int, with_ssl: bool, with_gpu:
# Always call this before using distributed module
with xgb.collective.CommunicatorContext(**communicator_env):
# Load file, file will not be sharded in federated mode.
dtrain = xgb.DMatrix('agaricus.txt.train-%02d' % rank)
dtest = xgb.DMatrix('agaricus.txt.test-%02d' % rank)
dtrain = xgb.DMatrix('agaricus.txt.train-%02d?format=libsvm' % rank)
dtest = xgb.DMatrix('agaricus.txt.test-%02d?format=libsvm' % rank)
# Specify parameters via map, definition are same as c++ version
param = {'max_depth': 2, 'eta': 1, 'objective': 'binary:logistic'}
if with_gpu:
param['tree_method'] = 'gpu_hist'
param['gpu_id'] = rank
param['tree_method'] = 'hist'
param['device'] = f"cuda:{rank}"
# Specify validations set to watch performance
watchlist = [(dtest, 'eval'), (dtrain, 'train')]