edit doc

edit examples

Signed-off-by: Yu Wu <yolandawu131@gmail.com>

doc/2.0/fate/ml/run_launchers.md

@@ -19,7 +19,8 @@ Currently, we provide various ready-to-use launchers for testing mpc protocol an
 modules [here](../../../../launchers).
 
 To write a launcher, first come up with the case to be run with a FATE-module(as in FATE/python/fate/ml) and wrap this
-case into a function. As a demo, we are to analyze a simple [launcher](../../../../launchers/sshe_lr_launcher.py) that
+case into a function. As a demo, we are to
+analyze [a simple launcher](https://raw.githubusercontent.com/FederatedAI/FATE/main/launchers/sshe_lr_launcher.py) that
 trains a SSHE Logistic Regression model using given local data files.
 
 We will use `breast` data set in this demo. Use the following command to download the original data files:
@@ -138,9 +139,12 @@ if __name__ == "__main__":
     launch(run_sshe_lr, extra_args_desc=[SSHEArguments])
 ```
 
+The complete launcher demo may be
+downloaded [here](https://raw.githubusercontent.com/FederatedAI/FATE/main/launchers/sshe_lr_launcher.py).
+
 ### Running A Launcher
 
-As a demo, here we show how to run this Pearson launcher with the following setting from terminal:
+As a demo, here we show how to run this SSHE LR launcher with the following setting from terminal:
 
 - guest: 9999
 - host: 10000

examples/benchmark_performance/coordinated_lr/test_lr.py

@@ -43,9 +43,9 @@ def main(config="../../config.yaml", param="./lr_config.yaml", namespace=""):
     host_train_data = {"name": host_data_table, "namespace": f"experiment{namespace}"}
     pipeline = FateFlowPipeline().set_parties(guest=guest, host=host, arbiter=arbiter)
     if config.task_cores:
-        pipeline.conf.set("task_cores", config.task_cores)
+        pipeline.conf.set("task", dict(engine_run={"cores": config.task_cores}))
     if config.timeout:
-        pipeline.conf.set("timeout", config.timeout)
+        pipeline.conf.set("task", dict(timeout=config.timeout))
 
     reader_0 = Reader("reader_0", runtime_parties=dict(guest=guest, host=host))
     reader_0.guest.task_parameters(namespace=guest_train_data['namespace'], name=guest_train_data['name'])

examples/benchmark_performance/hetero_secureboost/test_sbt.py

@@ -15,15 +15,13 @@
 #
 
 import argparse
-from fate_test.utils import parse_summary_result
+
 from fate_client.pipeline import FateFlowPipeline
-from fate_client.pipeline.components.fate import Evaluation
-from fate_client.pipeline.interface import DataWarehouseChannel
-from fate_client.pipeline.utils import test_utils
 from fate_client.pipeline.components.fate import HeteroSecureBoost, PSI
 from fate_client.pipeline.components.fate.evaluation import Evaluation
-from fate_client.pipeline import FateFlowPipeline
 from fate_client.pipeline.interface import DataWarehouseChannel
+from fate_client.pipeline.utils import test_utils
+from fate_test.utils import parse_summary_result
 
 
 def main(config="../../config.yaml", param="./sbt_breast_config.yaml", namespace=""):
@@ -90,9 +88,9 @@ def main(config="../../config.yaml", param="./sbt_breast_config.yaml", namespace
     pipeline.add_task(evaluation_0)
 
     if config.task_cores:
-        pipeline.conf.set("task_cores", config.task_cores)
+        pipeline.conf.set("task", dict(engine_run={"cores": config.task_cores}))
     if config.timeout:
-        pipeline.conf.set("timeout", config.timeout)
+        pipeline.conf.set("task", dict(timeout=config.timeout))
 
     pipeline.compile()
     pipeline.fit()

examples/benchmark_performance/sshe_lr/test_lr.py

@@ -43,9 +43,9 @@ def main(config="../../config.yaml", param="./lr_config.yaml", namespace=""):
     host_train_data = {"name": host_data_table, "namespace": f"experiment{namespace}"}
     pipeline = FateFlowPipeline().set_parties(guest=guest, host=host, arbiter=arbiter)
     if config.task_cores:
-        pipeline.conf.set("task_cores", config.task_cores)
+        pipeline.conf.set("task", dict(engine_run={"cores": config.task_cores}))
     if config.timeout:
-        pipeline.conf.set("timeout", config.timeout)
+        pipeline.conf.set("task", dict(timeout=config.timeout))
 
     reader_0 = Reader("reader_0", runtime_parties=dict(guest=guest, host=host))
     reader_0.guest.task_parameters(namespace=guest_train_data['namespace'], name=guest_train_data['name'])