ScriptRunner framework option in examples (#2827)

* use framework option in examples * rename files
NVIDIA · Aug 22, 2024 · 9785297 · 9785297
1 parent d7c92cf
commit 9785297
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Showing 13 changed files with 29 additions and 31 deletions.
diff --git a/examples/advanced/job_api/sklearn/kmeans_script_runner_higgs.py b/examples/advanced/job_api/sklearn/kmeans_script_runner_higgs.py
@@ -24,8 +24,7 @@
 from nvflare.app_common.widgets.intime_model_selector import IntimeModelSelector
 from nvflare.app_common.workflows.scatter_and_gather import ScatterAndGather
 from nvflare.app_opt.sklearn.joblib_model_param_persistor import JoblibModelParamPersistor
-from nvflare.client.config import ExchangeFormat
-from nvflare.job_config.script_runner import ScriptRunner
+from nvflare.job_config.script_runner import FrameworkType, ScriptRunner
 
 preprocess = True  # if False, assume data is already preprocessed and split
 
@@ -146,7 +145,7 @@ def split_higgs(input_data_path, input_header_path, output_dir, site_num, sample
         executor = ScriptRunner(
             script=train_script,
             script_args=f"--data_root_dir {data_output_dir}",
-            params_exchange_format=ExchangeFormat.RAW,  # kmeans requires raw values only rather than PyTorch Tensors (the default)
+            framework=FrameworkType.RAW,  # kmeans requires raw values only rather than PyTorch Tensors (the default)
         )
         job.to(executor, f"site-{i+1}")  # HIGGs data splitter assumes site names start from 1
 

diff --git a/examples/advanced/job_api/tf/tf_fl_script_runner_cifar10.py b/examples/advanced/job_api/tf/tf_fl_script_runner_cifar10.py
@@ -23,7 +23,7 @@
 from nvflare import FedJob
 from nvflare.app_common.widgets.intime_model_selector import IntimeModelSelector
 from nvflare.app_opt.tf.job_config.model import TFModel
-from nvflare.job_config.script_runner import ScriptRunner
+from nvflare.job_config.script_runner import FrameworkType, ScriptRunner
 
 gpu_devices = tf.config.experimental.list_physical_devices("GPU")
 for device in gpu_devices:
@@ -159,7 +159,9 @@
     # Add clients
     for i, train_idx_path in enumerate(train_idx_paths):
         curr_task_script_args = task_script_args + f" --train_idx_path {train_idx_path}"
-        executor = ScriptRunner(script=train_script, script_args=curr_task_script_args)
+        executor = ScriptRunner(
+            script=train_script, script_args=curr_task_script_args, framework=FrameworkType.TENSORFLOW
+        )
         job.to(executor, f"site-{i+1}")
 
     # Can export current job to folder.

diff --git a/examples/getting_started/sklearn/kmeans_script_runner_higgs.py b/examples/getting_started/sklearn/kmeans_script_runner_higgs.py
@@ -24,8 +24,7 @@
 from nvflare.app_common.widgets.intime_model_selector import IntimeModelSelector
 from nvflare.app_common.workflows.scatter_and_gather import ScatterAndGather
 from nvflare.app_opt.sklearn.joblib_model_param_persistor import JoblibModelParamPersistor
-from nvflare.client.config import ExchangeFormat
-from nvflare.job_config.script_runner import ScriptRunner
+from nvflare.job_config.script_runner import FrameworkType, ScriptRunner
 
 preprocess = True  # if False, assume data is already preprocessed and split
 
@@ -146,7 +145,7 @@ def split_higgs(input_data_path, input_header_path, output_dir, site_num, sample
         executor = ScriptRunner(
             script=train_script,
             script_args=f"--data_root_dir {data_output_dir}",
-            params_exchange_format=ExchangeFormat.RAW,  # kmeans requires raw values only rather than PyTorch Tensors (the default)
+            framework=FrameworkType.RAW,  # kmeans requires raw values only rather than PyTorch Tensors (the default)
         )
         job.to(executor, f"site-{i+1}")  # HIGGs data splitter assumes site names start from 1
 

diff --git a/examples/getting_started/tf/nvflare_tf_getting_started.ipynb b/examples/getting_started/tf/nvflare_tf_getting_started.ipynb
@@ -262,7 +262,7 @@
    "outputs": [],
    "source": [
     "from nvflare import FedJob\n",
-    "from nvflare.job_config.script_runner import ScriptRunner\n",
+    "from nvflare.job_config.script_runner import FrameworkType, ScriptRunner\n",
     "from nvflare.app_common.workflows.fedavg import FedAvg\n",
     "\n",
     "job = FedJob(name=\"cifar10_tf_fedavg\")"
@@ -363,12 +363,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from nvflare.client.config import ExchangeFormat\n",
-    "\n",
     "for i in range(n_clients):\n",
     "    executor = ScriptRunner(\n",
     "        script=\"src/cifar10_tf_fl.py\", script_args=\"\",  # f\"--batch_size 32 --data_path /tmp/data/site-{i}\"\n",
-    "        params_exchange_format=ExchangeFormat.NUMPY,\n",
+    "        framework=FrameworkType.TENSORFLOW,\n",
     "    )\n",
     "    job.to(executor, f\"site-{i+1}\")"
    ]

diff --git a/examples/getting_started/tf/tf_fl_script_runner_cifar10.py b/examples/getting_started/tf/tf_fl_script_runner_cifar10.py
@@ -23,7 +23,7 @@
 from nvflare import FedJob
 from nvflare.app_common.widgets.intime_model_selector import IntimeModelSelector
 from nvflare.app_opt.tf.job_config.model import TFModel
-from nvflare.job_config.script_runner import ScriptRunner
+from nvflare.job_config.script_runner import FrameworkType, ScriptRunner
 
 gpu_devices = tf.config.experimental.list_physical_devices("GPU")
 for device in gpu_devices:
@@ -159,7 +159,9 @@
     # Add clients
     for i, train_idx_path in enumerate(train_idx_paths):
         curr_task_script_args = task_script_args + f" --train_idx_path {train_idx_path}"
-        executor = ScriptRunner(script=train_script, script_args=curr_task_script_args)
+        executor = ScriptRunner(
+            script=train_script, script_args=curr_task_script_args, framework=FrameworkType.TENSORFLOW
+        )
         job.to(executor, f"site-{i+1}")
 
     # Can export current job to folder.

diff --git a/examples/hello-world/hello-cyclic/README.md b/examples/hello-world/hello-cyclic/README.md
@@ -29,7 +29,7 @@ bash ./prepare_data.sh
 Run the script using the job API to create the job and run it with the simulator:
 
 ```
-python3 cyclic_script_runner_hello-cyclic.py
+python3 cyclic_script_runner.py
 ```
 
 ### 3. Access the logs and results

diff --git a/...clic/cyclic_script_runner_hello-cyclic.py → ...orld/hello-cyclic/cyclic_script_runner.py b/...clic/cyclic_script_runner_hello-cyclic.py → ...orld/hello-cyclic/cyclic_script_runner.py
@@ -16,9 +16,8 @@
 
 from nvflare import FedJob
 from nvflare.app_common.workflows.cyclic import Cyclic
-from nvflare.app_opt.pt.job_config.model import PTModel
-from nvflare.client.config import ExchangeFormat
-from nvflare.job_config.script_runner import ScriptRunner
+from nvflare.app_opt.tf.job_config.model import TFModel
+from nvflare.job_config.script_runner import FrameworkType, ScriptRunner
 
 if __name__ == "__main__":
     n_clients = 2
@@ -35,14 +34,14 @@
     job.to(controller, "server")
 
     # Define the initial global model and send to server
-    job.to(PTModel(Net()), "server")
+    job.to(TFModel(Net()), "server")
 
     # Add clients
     for i in range(n_clients):
         executor = ScriptRunner(
             script=train_script,
             script_args="",  # f"--batch_size 32 --data_path /tmp/data/site-{i}"
-            params_exchange_format=ExchangeFormat.NUMPY,
+            framework=FrameworkType.TENSORFLOW,
         )
         job.to(executor, f"site-{i+1}")
 

diff --git a/examples/hello-world/hello-fedavg-numpy/fedavg_script_runner_hello-numpy.py b/examples/hello-world/hello-fedavg-numpy/fedavg_script_runner_hello-numpy.py
@@ -15,8 +15,7 @@
 from nvflare import FedJob
 from nvflare.app_common.widgets.intime_model_selector import IntimeModelSelector
 from nvflare.app_common.workflows.fedavg import FedAvg
-from nvflare.client.config import ExchangeFormat
-from nvflare.job_config.script_runner import ScriptRunner
+from nvflare.job_config.script_runner import FrameworkType, ScriptRunner
 
 if __name__ == "__main__":
     n_clients = 2
@@ -36,7 +35,7 @@
 
     # Add clients
     for i in range(n_clients):
-        executor = ScriptRunner(script=train_script, script_args="", params_exchange_format=ExchangeFormat.NUMPY)
+        executor = ScriptRunner(script=train_script, script_args="", framework=FrameworkType.NUMPY)
         job.to(executor, f"site-{i+1}")
 
     # job.export_job("/tmp/nvflare/jobs/job_config")

diff --git a/examples/hello-world/hello-fedavg-numpy/hello-fedavg-numpy_getting_started.ipynb b/examples/hello-world/hello-fedavg-numpy/hello-fedavg-numpy_getting_started.ipynb
@@ -194,7 +194,7 @@
    "source": [
     "from nvflare import FedJob\n",
     "from nvflare.app_common.workflows.fedavg import FedAvg\n",
-    "from nvflare.job_config.script_runner import ScriptRunner\n",
+    "from nvflare.job_config.script_runner import FrameworkType, ScriptRunner\n",
     "\n",
     "\n",
     "job = FedJob(name=\"hello-fedavg-numpy\")"
@@ -266,13 +266,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from nvflare.client.config import ExchangeFormat\n",
-    "\n",
     "train_script = \"src/hello-numpy_fl.py\"\n",
     "\n",
     "for i in range(n_clients):\n",
     "    executor = ScriptRunner(\n",
-    "        script=train_script, script_args=\"\", params_exchange_format=ExchangeFormat.NUMPY\n",
+    "        script=train_script, script_args=\"\", framework=FrameworkType.NUMPY\n",
     "    )\n",
     "    job.to(executor, f\"site-{i}\")"
    ]

diff --git a/examples/hello-world/hello-pt/README.md b/examples/hello-world/hello-pt/README.md
@@ -31,7 +31,7 @@ pip3 install -r requirements.txt
 Run the script using the job API to create the job and run it with the simulator:
 
 ```
-python3 fedavg_script_runner_hello-pt.py
+python3 fedavg_script_runner_pt.py
 ```
 
 ### 3. Access the logs and results

diff --git a/...hello-pt/fedavg_script_runner_hello-pt.py → ...world/hello-pt/fedavg_script_runner_pt.py b/...hello-pt/fedavg_script_runner_hello-pt.py → ...world/hello-pt/fedavg_script_runner_pt.py
diff --git a/examples/hello-world/hello-tf/README.md b/examples/hello-world/hello-tf/README.md
@@ -27,7 +27,7 @@ pip3 install tensorflow
 Run the script using the job API to create the job and run it with the simulator:
 
 ```
-python3 fedavg_script_runner_hello-tf.py
+python3 fedavg_script_runner_tf.py
 ```
 
 ### 3. Access the logs and results

diff --git a/...hello-tf/fedavg_script_runner_hello-tf.py → ...world/hello-tf/fedavg_script_runner_tf.py b/...hello-tf/fedavg_script_runner_hello-tf.py → ...world/hello-tf/fedavg_script_runner_tf.py
@@ -15,7 +15,7 @@
 from src.tf_net import TFNet
 
 from nvflare.app_opt.tf.job_config.fed_avg import FedAvgJob
-from nvflare.job_config.script_runner import ScriptRunner
+from nvflare.job_config.script_runner import FrameworkType, ScriptRunner
 
 if __name__ == "__main__":
     n_clients = 2
@@ -27,7 +27,9 @@
     # Add clients
     for i in range(n_clients):
         executor = ScriptRunner(
-            script=train_script, script_args=""  # f"--batch_size 32 --data_path /tmp/data/site-{i}"
+            script=train_script,
+            script_args="",  # f"--batch_size 32 --data_path /tmp/data/site-{i}"
+            framework=FrameworkType.TENSORFLOW,
         )
         job.to(executor, f"site-{i+1}")