add readme

Signed-off-by: KumoLiu <[email protected]>

bundle/pythonic_usage_guidance/README.md

@@ -0,0 +1,39 @@
+# Pythonic Bundle Access Tutorial
+
+A MONAI bundle contains the stored weights of a model, training, inference, post-processing transform sequences and other information. This tutorial aims to explore how to access a bundle in Python and use it in your own application. We'll cover the following topics:
+1. Downloading the Bundle.
+2. Creating a `BundleWorkflow`.
+3. Getting Properties from the Bundle.
+4. Updating Properties.
+5. Using Components in Your Own Pipeline.
+6. Utilizing Pretrained Weights from the Bundle.
+7. A Simple Comparison of the Usage between `ConfigParser` and `BundleWorkflow`.
+
+The example training dataset is Task09_Spleen.tar from http://medicaldecathlon.com/.
+
+## Requirements
+
+The script is tested with:
+
+- `Ubuntu 20.04` | `Python 3.8.10` | `CUDA 12.2` | `Pytorch 1.13.1`
+
+- it is tested on 24gb single-gpu machine
+
+## Dependencies and installation
+
+### MONAI
+
+You can conda environments to install the dependencies.
+
+or you can just use MONAI docker.
+```bash
+docker pull projectmonai/monai:latest
+```
+
+For more information please check out [the installation guide](https://docs.monai.io/en/latest/installation.html).
+
+## Questions and bugs
+
+- For questions relating to the use of MONAI, please use our [Discussions tab](https://github.com/Project-MONAI/MONAI/discussions) on the main repository of MONAI.
+- For bugs relating to MONAI functionality, please create an issue on the [main repository](https://github.com/Project-MONAI/MONAI/issues).
+- For bugs relating to the running of a tutorial, please create an issue in [this repository](https://github.com/Project-MONAI/Tutorials/issues).

bundle/pythonic_usage_guidance/pythonic_bundle_access.ipynb

@@ -336,15 +336,15 @@
     "n_splits = 3\n",
     "ensemble_transform = MeanEnsembled(keys=[\"pred\"] * n_splits, output_key=\"pred\")\n",
     "update_postprocessing = Compose((ensemble_transform, train_workflow.val_postprocessing))\n",
-    "\n",
     "print(update_postprocessing.transforms)\n",
     "\n",
+    "device = train_workflow.device\n",
     "train_workflow.add_property(name=\"dataloader\", required=True, config_id=\"train#dataloader\")\n",
     "evaluator = EnsembleEvaluator(\n",
-    "    device=train_workflow.device,\n",
+    "    device=device,\n",
     "    val_data_loader=train_workflow.dataloader,\n",
     "    pred_keys=[\"pred\"] * n_splits,\n",
-    "    networks=[train_workflow.network_def] * n_splits,\n",
+    "    networks=[train_workflow.network_def.to(train_workflow.device)] * n_splits,\n",
     "    inferer=train_workflow.train_inferer,\n",
     "    postprocessing=update_postprocessing,\n",
     ")\n",