updates to PR comments

examples/advanced/sklearn-kmeans/README.md

@@ -127,5 +127,4 @@ The resulting curve for `homogeneity_score` is
 It can be visualized using
 ```commandline
 tensorboard --logdir /tmp/nvflare/workspace/works/kmeans/sklearn_kmeans_uniform_3_clients
-```
-Note that there will be certain amount of randomness in the results.
+```

examples/advanced/sklearn-kmeans/kmeans_job_clientapi.py

@@ -14,11 +14,9 @@
 
 import argparse
 import os
-from enum import Enum
-from typing import List
 
-import numpy as np
 from src.kmeans_assembler import KMeansAssembler
+from utils.split_data import split_data
 
 from nvflare import FedJob
 from nvflare.app_common.aggregators.collect_and_assemble_aggregator import CollectAndAssembleAggregator
@@ -28,92 +26,6 @@
 from nvflare.job_config.script_runner import ScriptRunner
 
 
-class SplitMethod(Enum):
-    UNIFORM = "uniform"
-    LINEAR = "linear"
-    SQUARE = "square"
-    EXPONENTIAL = "exponential"
-
-
-def get_split_ratios(site_num: int, split_method: SplitMethod):
-    if split_method == SplitMethod.UNIFORM:
-        ratio_vec = np.ones(site_num)
-    elif split_method == SplitMethod.LINEAR:
-        ratio_vec = np.linspace(1, site_num, num=site_num)
-    elif split_method == SplitMethod.SQUARE:
-        ratio_vec = np.square(np.linspace(1, site_num, num=site_num))
-    elif split_method == SplitMethod.EXPONENTIAL:
-        ratio_vec = np.exp(np.linspace(1, site_num, num=site_num))
-    else:
-        raise ValueError(f"Split method {split_method.name} not implemented!")
-
-    return ratio_vec
-
-
-def split_num_proportion(n, site_num, split_method: SplitMethod) -> List[int]:
-    split = []
-    ratio_vec = get_split_ratios(site_num, split_method)
-    total = sum(ratio_vec)
-    left = n
-    for site in range(site_num - 1):
-        x = int(n * ratio_vec[site] / total)
-        left = left - x
-        split.append(x)
-    split.append(left)
-    return split
-
-
-def assign_data_index_to_sites(
-    data_size: int,
-    valid_fraction: float,
-    num_sites: int,
-    split_method: SplitMethod = SplitMethod.UNIFORM,
-) -> dict:
-    if valid_fraction > 1.0:
-        raise ValueError("validation percent should be less than or equal to 100% of the total data")
-    elif valid_fraction < 1.0:
-        valid_size = int(round(data_size * valid_fraction, 0))
-        train_size = data_size - valid_size
-    else:
-        valid_size = data_size
-        train_size = data_size
-
-    site_sizes = split_num_proportion(train_size, num_sites, split_method)
-    split_data_indices = {
-        "valid": {"start": 0, "end": valid_size},
-    }
-    for site in range(num_sites):
-        site_id = site + 1
-        if valid_fraction < 1.0:
-            idx_start = valid_size + sum(site_sizes[:site])
-            idx_end = valid_size + sum(site_sizes[: site + 1])
-        else:
-            idx_start = sum(site_sizes[:site])
-            idx_end = sum(site_sizes[: site + 1])
-        split_data_indices[site_id] = {"start": idx_start, "end": idx_end}
-
-    return split_data_indices
-
-
-def get_file_line_count(input_path: str) -> int:
-    count = 0
-    with open(input_path, "r") as fp:
-        for i, _ in enumerate(fp):
-            count += 1
-    return count
-
-
-def split_data(
-    data_path: str,
-    num_clients: int,
-    valid_frac: float,
-    split_method: SplitMethod = SplitMethod.UNIFORM,
-):
-    size_total_file = get_file_line_count(data_path)
-    site_indices = assign_data_index_to_sites(size_total_file, valid_frac, num_clients, split_method)
-    return site_indices
-
-
 def define_parser():
     parser = argparse.ArgumentParser()
     parser.add_argument(
@@ -171,7 +83,7 @@ def main():
     split_mode = args.split_mode
     valid_frac = args.valid_frac
     job_name = f"sklearn_kmeans_{split_mode}_{num_clients}_clients"
-    train_script = "src/kmeans_trainer.py"
+    train_script = "src/kmeans_fl.py"
 
     # Set the output workspace and job directories
     workspace_dir = os.path.join(args.workspace_dir, job_name)
@@ -209,7 +121,6 @@ def main():
         data_path,
         num_clients,
         valid_frac,
-        SplitMethod(split_mode),
     )
 
     for i in range(1, num_clients + 1):

examples/advanced/sklearn-kmeans/prepare_data.sh

@@ -8,6 +8,7 @@ if [ -f "$DATASET_PATH" ]; then
 else
     python3 "${script_dir}"/utils/prepare_data.py \
         --dataset_name iris \
+        --randomize 0 \
         --out_path ${DATASET_PATH}
     echo "Data loaded and saved in ${DATASET_PATH}"
 fi

examples/advanced/sklearn-kmeans/src/kmeans_trainer.py → examples/advanced/sklearn-kmeans/src/kmeans_fl.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2023, NVIDIA CORPORATION.  All rights reserved.
+# Copyright (c) 2025, NVIDIA CORPORATION.  All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

examples/advanced/sklearn-kmeans/utils/prepare_data.py

@@ -43,7 +43,8 @@ def prepare_data(
     x = dataset.data
     y = dataset.target
     if randomize:
-        np.random.seed(0)
+        print("Randomizing data sequence")
+
         idx_random = np.random.permutation(len(y))
         x = x[idx_random, :]
         y = y[idx_random]

examples/advanced/sklearn-kmeans/utils/split_data.py

@@ -0,0 +1,104 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from enum import Enum
+from typing import List
+
+import numpy as np
+
+
+class SplitMethod(Enum):
+    UNIFORM = "uniform"
+    LINEAR = "linear"
+    SQUARE = "square"
+    EXPONENTIAL = "exponential"
+
+
+def get_split_ratios(site_num: int, split_method: SplitMethod):
+    if split_method == SplitMethod.UNIFORM:
+        ratio_vec = np.ones(site_num)
+    elif split_method == SplitMethod.LINEAR:
+        ratio_vec = np.linspace(1, site_num, num=site_num)
+    elif split_method == SplitMethod.SQUARE:
+        ratio_vec = np.square(np.linspace(1, site_num, num=site_num))
+    elif split_method == SplitMethod.EXPONENTIAL:
+        ratio_vec = np.exp(np.linspace(1, site_num, num=site_num))
+    else:
+        raise ValueError(f"Split method {split_method.name} not implemented!")
+
+    return ratio_vec
+
+
+def split_num_proportion(n, site_num, split_method: SplitMethod) -> List[int]:
+    split = []
+    ratio_vec = get_split_ratios(site_num, split_method)
+    total = sum(ratio_vec)
+    left = n
+    for site in range(site_num - 1):
+        x = int(n * ratio_vec[site] / total)
+        left = left - x
+        split.append(x)
+    split.append(left)
+    return split
+
+
+def assign_data_index_to_sites(
+    data_size: int,
+    valid_fraction: float,
+    num_sites: int,
+    split_method: SplitMethod = SplitMethod.UNIFORM,
+) -> dict:
+    if valid_fraction > 1.0:
+        raise ValueError("validation percent should be less than or equal to 100% of the total data")
+    elif valid_fraction < 1.0:
+        valid_size = int(round(data_size * valid_fraction, 0))
+        train_size = data_size - valid_size
+    else:
+        valid_size = data_size
+        train_size = data_size
+
+    site_sizes = split_num_proportion(train_size, num_sites, split_method)
+    split_data_indices = {
+        "valid": {"start": 0, "end": valid_size},
+    }
+    for site in range(num_sites):
+        site_id = site + 1
+        if valid_fraction < 1.0:
+            idx_start = valid_size + sum(site_sizes[:site])
+            idx_end = valid_size + sum(site_sizes[: site + 1])
+        else:
+            idx_start = sum(site_sizes[:site])
+            idx_end = sum(site_sizes[: site + 1])
+        split_data_indices[site_id] = {"start": idx_start, "end": idx_end}
+
+    return split_data_indices
+
+
+def get_file_line_count(input_path: str) -> int:
+    count = 0
+    with open(input_path, "r") as fp:
+        for i, _ in enumerate(fp):
+            count += 1
+    return count
+
+
+def split_data(
+    data_path: str,
+    num_clients: int,
+    valid_frac: float,
+    split_method: SplitMethod = SplitMethod.UNIFORM,
+):
+    size_total_file = get_file_line_count(data_path)
+    site_indices = assign_data_index_to_sites(size_total_file, valid_frac, num_clients, split_method)
+    return site_indices

examples/tutorials/self-paced-training/part-1_federated_learning_introduction/chapter-2_develop_federated_learning_applications/02.3_convert_machine_learning_to_federated_learning/02.3.2_convert_kmeans_to_federated_learning/code/kmeans_job.py

@@ -14,11 +14,9 @@
 
 import argparse
 import os
-from enum import Enum
-from typing import List
 
-import numpy as np
 from src.kmeans_assembler import KMeansAssembler
+from utils.split_data import split_data
 
 from nvflare import FedJob
 from nvflare.app_common.aggregators.collect_and_assemble_aggregator import CollectAndAssembleAggregator
@@ -28,92 +26,6 @@
 from nvflare.job_config.script_runner import ScriptRunner
 
 
-class SplitMethod(Enum):
-    UNIFORM = "uniform"
-    LINEAR = "linear"
-    SQUARE = "square"
-    EXPONENTIAL = "exponential"
-
-
-def get_split_ratios(site_num: int, split_method: SplitMethod):
-    if split_method == SplitMethod.UNIFORM:
-        ratio_vec = np.ones(site_num)
-    elif split_method == SplitMethod.LINEAR:
-        ratio_vec = np.linspace(1, site_num, num=site_num)
-    elif split_method == SplitMethod.SQUARE:
-        ratio_vec = np.square(np.linspace(1, site_num, num=site_num))
-    elif split_method == SplitMethod.EXPONENTIAL:
-        ratio_vec = np.exp(np.linspace(1, site_num, num=site_num))
-    else:
-        raise ValueError(f"Split method {split_method.name} not implemented!")
-
-    return ratio_vec
-
-
-def split_num_proportion(n, site_num, split_method: SplitMethod) -> List[int]:
-    split = []
-    ratio_vec = get_split_ratios(site_num, split_method)
-    total = sum(ratio_vec)
-    left = n
-    for site in range(site_num - 1):
-        x = int(n * ratio_vec[site] / total)
-        left = left - x
-        split.append(x)
-    split.append(left)
-    return split
-
-
-def assign_data_index_to_sites(
-    data_size: int,
-    valid_fraction: float,
-    num_sites: int,
-    split_method: SplitMethod = SplitMethod.UNIFORM,
-) -> dict:
-    if valid_fraction > 1.0:
-        raise ValueError("validation percent should be less than or equal to 100% of the total data")
-    elif valid_fraction < 1.0:
-        valid_size = int(round(data_size * valid_fraction, 0))
-        train_size = data_size - valid_size
-    else:
-        valid_size = data_size
-        train_size = data_size
-
-    site_sizes = split_num_proportion(train_size, num_sites, split_method)
-    split_data_indices = {
-        "valid": {"start": 0, "end": valid_size},
-    }
-    for site in range(num_sites):
-        site_id = site + 1
-        if valid_fraction < 1.0:
-            idx_start = valid_size + sum(site_sizes[:site])
-            idx_end = valid_size + sum(site_sizes[: site + 1])
-        else:
-            idx_start = sum(site_sizes[:site])
-            idx_end = sum(site_sizes[: site + 1])
-        split_data_indices[site_id] = {"start": idx_start, "end": idx_end}
-
-    return split_data_indices
-
-
-def get_file_line_count(input_path: str) -> int:
-    count = 0
-    with open(input_path, "r") as fp:
-        for i, _ in enumerate(fp):
-            count += 1
-    return count
-
-
-def split_data(
-    data_path: str,
-    num_clients: int,
-    valid_frac: float,
-    split_method: SplitMethod = SplitMethod.UNIFORM,
-):
-    size_total_file = get_file_line_count(data_path)
-    site_indices = assign_data_index_to_sites(size_total_file, valid_frac, num_clients, split_method)
-    return site_indices
-
-
 def define_parser():
     parser = argparse.ArgumentParser()
     parser.add_argument(
@@ -171,7 +83,7 @@ def main():
     split_mode = args.split_mode
     valid_frac = args.valid_frac
     job_name = f"sklearn_kmeans_{split_mode}_{num_clients}_clients"
-    train_script = "src/kmeans_trainer.py"
+    train_script = "src/kmeans_fl.py"
 
     # Set the output workspace and job directories
     workspace_dir = os.path.join(args.workspace_dir, job_name)
@@ -209,7 +121,6 @@ def main():
         data_path,
         num_clients,
         valid_frac,
-        SplitMethod(split_mode),
     )
 
     for i in range(1, num_clients + 1):

examples/tutorials/self-paced-training/part-1_federated_learning_introduction/chapter-2_develop_federated_learning_applications/02.3_convert_machine_learning_to_federated_learning/02.3.2_convert_kmeans_to_federated_learning/code/src/kmeans_trainer.py → examples/tutorials/self-paced-training/part-1_federated_learning_introduction/chapter-2_develop_federated_learning_applications/02.3_convert_machine_learning_to_federated_learning/02.3.2_convert_kmeans_to_federated_learning/code/src/kmeans_fl.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2023, NVIDIA CORPORATION.  All rights reserved.
+# Copyright (c) 2025, NVIDIA CORPORATION.  All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.