[fed] Fixes for the encrypted GRPC backend. (#10503)

python-package/xgboost/federated.py

@@ -39,9 +39,9 @@ class FederatedTracker(RabitTracker):
         n_workers: int,
         port: int,
         secure: bool,
-        server_key_path: str = "",
-        server_cert_path: str = "",
-        client_cert_path: str = "",
+        server_key_path: Optional[str] = None,
+        server_cert_path: Optional[str] = None,
+        client_cert_path: Optional[str] = None,
         timeout: int = 300,
     ) -> None:
         handle = ctypes.c_void_p()
@@ -84,7 +84,13 @@ def run_federated_server(  # pylint: disable=too-many-arguments
         for path in [server_key_path, server_cert_path, client_cert_path]
     )
     tracker = FederatedTracker(
-        n_workers=n_workers, port=port, secure=secure, timeout=timeout
+        n_workers=n_workers,
+        port=port,
+        secure=secure,
+        timeout=timeout,
+        server_key_path=server_key_path,
+        server_cert_path=server_cert_path,
+        client_cert_path=client_cert_path,
     )
     tracker.start()
 

python-package/xgboost/testing/federated.py

@@ -0,0 +1,153 @@
+# pylint: disable=unbalanced-tuple-unpacking, too-many-locals
+"""Tests for federated learning."""
+
+import multiprocessing
+import os
+import subprocess
+import tempfile
+import time
+from typing import List, cast
+
+from sklearn.datasets import dump_svmlight_file, load_svmlight_file
+from sklearn.model_selection import train_test_split
+
+import xgboost as xgb
+import xgboost.federated
+from xgboost import testing as tm
+from xgboost.training import TrainingCallback
+
+SERVER_KEY = "server-key.pem"
+SERVER_CERT = "server-cert.pem"
+CLIENT_KEY = "client-key.pem"
+CLIENT_CERT = "client-cert.pem"
+
+
+def run_server(port: int, world_size: int, with_ssl: bool) -> None:
+    """Run federated server for test."""
+    if with_ssl:
+        xgboost.federated.run_federated_server(
+            world_size,
+            port,
+            server_key_path=SERVER_KEY,
+            server_cert_path=SERVER_CERT,
+            client_cert_path=CLIENT_CERT,
+        )
+    else:
+        xgboost.federated.run_federated_server(world_size, port)
+
+
+def run_worker(
+    port: int, world_size: int, rank: int, with_ssl: bool, device: str
+) -> None:
+    """Run federated client worker for test."""
+    communicator_env = {
+        "dmlc_communicator": "federated",
+        "federated_server_address": f"localhost:{port}",
+        "federated_world_size": world_size,
+        "federated_rank": rank,
+    }
+    if with_ssl:
+        communicator_env["federated_server_cert_path"] = SERVER_CERT
+        communicator_env["federated_client_key_path"] = CLIENT_KEY
+        communicator_env["federated_client_cert_path"] = CLIENT_CERT
+
+    cpu_count = os.cpu_count()
+    assert cpu_count is not None
+    n_threads = cpu_count // world_size
+
+    # Always call this before using distributed module
+    with xgb.collective.CommunicatorContext(**communicator_env):
+        # Load file, file will not be sharded in federated mode.
+        X, y = load_svmlight_file(f"agaricus.txt-{rank}.train")
+        dtrain = xgb.DMatrix(X, y)
+        X, y = load_svmlight_file(f"agaricus.txt-{rank}.test")
+        dtest = xgb.DMatrix(X, y)
+
+        # Specify parameters via map, definition are same as c++ version
+        param = {
+            "max_depth": 2,
+            "eta": 1,
+            "objective": "binary:logistic",
+            "nthread": n_threads,
+            "tree_method": "hist",
+            "device": device,
+        }
+
+        # Specify validations set to watch performance
+        watchlist = [(dtest, "eval"), (dtrain, "train")]
+        num_round = 20
+
+        # Run training, all the features in training API is available.
+        results: TrainingCallback.EvalsLog = {}
+        bst = xgb.train(
+            param,
+            dtrain,
+            num_round,
+            evals=watchlist,
+            early_stopping_rounds=2,
+            evals_result=results,
+        )
+        assert tm.non_increasing(cast(List[float], results["train"]["logloss"]))
+        assert tm.non_increasing(cast(List[float], results["eval"]["logloss"]))
+
+        # save the model, only ask process 0 to save the model.
+        if xgb.collective.get_rank() == 0:
+            with tempfile.TemporaryDirectory() as tmpdir:
+                bst.save_model(os.path.join(tmpdir, "model.json"))
+            xgb.collective.communicator_print("Finished training\n")
+
+
+def run_federated(world_size: int, with_ssl: bool, use_gpu: bool) -> None:
+    """Launcher for clients and the server."""
+    port = 9091
+
+    server = multiprocessing.Process(
+        target=run_server, args=(port, world_size, with_ssl)
+    )
+    server.start()
+    time.sleep(1)
+    if not server.is_alive():
+        raise ValueError("Error starting Federated Learning server")
+
+    workers = []
+    for rank in range(world_size):
+        device = f"cuda:{rank}" if use_gpu else "cpu"
+        worker = multiprocessing.Process(
+            target=run_worker, args=(port, world_size, rank, with_ssl, device)
+        )
+        workers.append(worker)
+        worker.start()
+    for worker in workers:
+        worker.join()
+    server.terminate()
+
+
+def run_federated_learning(with_ssl: bool, use_gpu: bool, test_path: str) -> None:
+    """Run federated learning tests."""
+    n_workers = 2
+
+    if with_ssl:
+        command = "openssl req -x509 -newkey rsa:2048 -days 7 -nodes -keyout {part}-key.pem -out {part}-cert.pem -subj /C=US/CN=localhost"  # pylint: disable=line-too-long
+        server_key = command.format(part="server").split()
+        subprocess.check_call(server_key)
+        client_key = command.format(part="client").split()
+        subprocess.check_call(client_key)
+
+    train_path = os.path.join(tm.data_dir(test_path), "agaricus.txt.train")
+    test_path = os.path.join(tm.data_dir(test_path), "agaricus.txt.test")
+
+    X_train, y_train = load_svmlight_file(train_path)
+    X_test, y_test = load_svmlight_file(test_path)
+
+    X0, X1, y0, y1 = train_test_split(X_train, y_train, test_size=0.5)
+    X0_valid, X1_valid, y0_valid, y1_valid = train_test_split(
+        X_test, y_test, test_size=0.5
+    )
+
+    dump_svmlight_file(X0, y0, "agaricus.txt-0.train")
+    dump_svmlight_file(X0_valid, y0_valid, "agaricus.txt-0.test")
+
+    dump_svmlight_file(X1, y1, "agaricus.txt-1.train")
+    dump_svmlight_file(X1_valid, y1_valid, "agaricus.txt-1.test")
+
+    run_federated(world_size=n_workers, with_ssl=with_ssl, use_gpu=use_gpu)