Update google link to use shared drive (Project-MONAI#1819)

Update google link to use shared drive ### Checks  - [ ] Avoid including large-size files in the PR. - [ ] Clean up long text outputs from code cells in the notebook. - [ ] For security purposes, please check the contents and remove any sensitive info such as user names and private key. - [ ] Ensure (1) hyperlinks and markdown anchors are working (2) use relative paths for tutorial repo files (3) put figure and graphs in the `./figure` folder - [ ] Notebook runs automatically `./runner.sh -t <path to .ipynb file>` --------- Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
juampatronics · Sep 7, 2024 · 3c891ec · 3c891ec
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diff --git a/3d_classification/densenet_training_array.ipynb b/3d_classification/densenet_training_array.ipynb
@@ -200,7 +200,7 @@
    ],
    "source": [
     "if not os.path.isfile(images[0]):\n",
-    "    resource = \"https://drive.google.com/file/d/1f5odq9smadgeJmDeyEy_UOjEtE_pkKc0/view?usp=sharing\"\n",
+    "    resource = \"https://developer.download.nvidia.com/assets/Clara/monai/tutorials/IXI-T1.tar\"\n",
     "    md5 = \"34901a0593b41dd19c1a1f746eac2d58\"\n",
     "\n",
     "    dataset_dir = os.path.join(root_dir, \"ixi\")\n",

diff --git a/3d_regression/densenet_training_array.ipynb b/3d_regression/densenet_training_array.ipynb
@@ -205,7 +205,7 @@
    "outputs": [],
    "source": [
     "if not os.path.isfile(images[0]):\n",
-    "    resource = \"https://drive.google.com/file/d/1f5odq9smadgeJmDeyEy_UOjEtE_pkKc0/view?usp=sharing\"\n",
+    "    resource = \"https://developer.download.nvidia.com/assets/Clara/monai/tutorials/IXI-T1.tar\"\n",
     "    md5 = \"34901a0593b41dd19c1a1f746eac2d58\"\n",
     "\n",
     "    dataset_dir = os.path.join(root_dir, \"ixi\")\n",

diff --git a/3d_segmentation/swin_unetr_brats21_segmentation_3d.ipynb b/3d_segmentation/swin_unetr_brats21_segmentation_3d.ipynb
@@ -45,7 +45,7 @@
     "\n",
     "https://www.synapse.org/#!Synapse:syn27046444/wiki/616992\n",
     "\n",
-    "The JSON file containing training and validation sets (internal split) needs to be downloaded from this [link](https://drive.google.com/file/d/1i-BXYe-wZ8R9Vp3GXoajGyqaJ65Jybg1/view?usp=sharing) and placed in the same folder as the dataset. As discussed in the following, this tutorial uses fold 1 for training a Swin UNETR model on the BraTS 21 challenge.\n",
+    "The JSON file containing training and validation sets (internal split) needs to be downloaded from this [link](https://developer.download.nvidia.com/assets/Clara/monai/tutorials/brats21_folds.json) and placed in the same folder as the dataset. As discussed in the following, this tutorial uses fold 1 for training a Swin UNETR model on the BraTS 21 challenge.\n",
     "\n",
     "### Tumor Characteristics\n",
     "\n",
@@ -114,7 +114,7 @@
     "  \"TrainingData/BraTS2021_01146/BraTS2021_01146_flair.nii.gz\"\n",
     "  \n",
     "\n",
-    "- Download the json file from this [link](https://drive.google.com/file/d/1i-BXYe-wZ8R9Vp3GXoajGyqaJ65Jybg1/view?usp=sharing) and placed in the same folder as the dataset.\n"
+    "- Download the json file from this [link](https://developer.download.nvidia.com/assets/Clara/monai/tutorials/brats21_folds.json) and placed in the same folder as the dataset.\n"
    ]
   },
   {

diff --git a/3d_segmentation/swin_unetr_btcv_segmentation_3d.ipynb b/3d_segmentation/swin_unetr_btcv_segmentation_3d.ipynb
@@ -331,7 +331,7 @@
    "outputs": [],
    "source": [
     "# uncomment this command to download the JSON file directly\n",
-    "# wget -O data/dataset_0.json 'https://drive.google.com/uc?export=download&id=1qcGh41p-rI3H_sQ0JwOAhNiQSXriQqGi'"
+    "# wget -O data/dataset_0.json 'https://developer.download.nvidia.com/assets/Clara/monai/tutorials/swin_unetr_btcv_dataset_0.json'"
    ]
   },
   {

diff --git a/3d_segmentation/vista3d/vista3d_spleen_finetune.ipynb b/3d_segmentation/vista3d/vista3d_spleen_finetune.ipynb
@@ -191,7 +191,7 @@
     }
    ],
    "source": [
-    "resource = \"https://drive.google.com/file/d/1Sbe6GjlgH-GIcXolZzUiwgqR4DBYNLQ3/view?usp=drive_link\"\n",
+    "resource = \"https://developer.download.nvidia.com/assets/Clara/monai/tutorials/model_zoo/model_vista3d.pt\"\n",
     "if not os.path.exists(os.path.join(root_dir, \"model.pt\")):\n",
     "    download_url(url=resource, filepath=os.path.join(root_dir, \"model.pt\"))\n",
     "if os.path.exists(os.path.join(root_dir, \"model.pt\")):\n",

diff --git a/competitions/MICCAI/surgtoolloc/preprocess_to_build_detection_dataset.ipynb b/competitions/MICCAI/surgtoolloc/preprocess_to_build_detection_dataset.ipynb
@@ -71,7 +71,7 @@
     "## Load useful data\n",
     "\n",
     "As described in `readme.md`, we manually labeled 1126 frames in order to build the detection model.\n",
-    "Please download the manually labeled bounding boxes from [google drive](https://drive.google.com/file/d/1iO4bXTGdhRLIoxIKS6P_nNAgI_1Fp_Vg/view?usp=sharing), the uncompressed folder `labels` is saved into `label_14_tools_yolo_640_blur/`."
+    "Please download the manually labeled bounding boxes from [google drive](https://developer.download.nvidia.com/assets/Clara/monai/tutorials/1126_frame_labels.zip), the uncompressed folder `labels` is saved into `label_14_tools_yolo_640_blur/`."
    ]
   },
   {

diff --git a/deployment/ray/mednist_classifier_ray.ipynb b/deployment/ray/mednist_classifier_ray.ipynb
@@ -122,7 +122,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "resource = \"https://drive.google.com/uc?id=1zKRi5FrwEES_J-AUkM7iBJwc__jy6ct6\"\n",
+    "resource = \"https://developer.download.nvidia.com/assets/Clara/monai/tutorials/deployment/classifier.zip\"\n",
     "dst = os.path.join(\"..\", \"bentoml\", \"classifier.zip\")\n",
     "if not os.path.exists(dst):\n",
     "    download_url(resource, dst)"

diff --git a/detection/README.md b/detection/README.md
@@ -46,7 +46,7 @@ Then run the following command and go directly to Sec. 3.2.
 python3 luna16_prepare_env_files.py
 ```
 
-Alternatively, you can download the original data and resample them by yourself with the following steps. Users can either download 1) mhd/raw data from [LUNA16](https://luna16.grand-challenge.org/Home/) or its [copy](https://drive.google.com/drive/folders/1-enN4eNEnKmjltevKg3W2V-Aj0nriQWE?usp=share_link), or 2) DICOM data from [LIDC-IDRI](https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=1966254) with [NBIA Data Retriever](https://wiki.cancerimagingarchive.net/display/NBIA/Downloading+TCIA+Images).
+Alternatively, you can download the original data and resample them by yourself with the following steps. Users can either download 1) mhd/raw data from [LUNA16](https://luna16.grand-challenge.org/Home/), or 2) DICOM data from [LIDC-IDRI](https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=1966254) with [NBIA Data Retriever](https://wiki.cancerimagingarchive.net/display/NBIA/Downloading+TCIA+Images).
 
 The raw CT images in LUNA16 have various voxel sizes. The first step is to resample them to the same voxel size, which is defined in the value of "spacing" in [./config/config_train_luna16_16g.json](./config/config_train_luna16_16g.json).
 

diff --git a/federated_learning/breast_density_challenge/data/README.md b/federated_learning/breast_density_challenge/data/README.md
@@ -1,6 +1,6 @@
 ## Example breast density data
 
-Download example data from https://drive.google.com/file/d/1Fd9GLUIzbZrl4FrzI3Huzul__C8wwzyx/view?usp=sharing.
+Download example data from https://developer.download.nvidia.com/assets/Clara/monai/tutorials/fl/preprocessed.zip.
 Extract here.
 
 ## Data source

diff --git a/federated_learning/openfl/openfl_mednist_2d_registration/run_docker.md b/federated_learning/openfl/openfl_mednist_2d_registration/run_docker.md
@@ -118,7 +118,7 @@ $ fx envoy start --shard-name env_two --disable-tls --envoy-config-path envoy_co
 ```
 [13:48:42] INFO     🧿 Starting the Envoy.                                                                                                            envoy.py:53
 Downloading...
-From: https://drive.google.com/uc?id=1QsnnkvZyJPcbRoV_ArW8SnE1OTuoVbKE
+From: https://developer.download.nvidia.com/assets/Clara/monai/tutorials/MedNIST.tar.gz
 To: /tmp/tmpd60wcnn8/MedNIST.tar.gz
 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 61.8M/61.8M [00:04<00:00, 13.8MB/s]
 2022-07-22 13:48:48,735 - INFO - Downloaded: MedNIST.tar.gz

diff --git a/generation/maisi/data/README.md b/generation/maisi/data/README.md
@@ -62,7 +62,7 @@ The table below provides a summary of the number of volumes for each dataset.
 
 #### 3.1 Example preprocessed dataset
 
-We provide the preprocessed subset of [C4KC-KiTS](https://www.cancerimagingarchive.net/collection/c4kc-kits/) dataset used in the finetuning config `environment_maisi_controlnet_train.json`. The dataset and corresponding JSON data list can be downloaded from [this link](https://drive.google.com/drive/folders/1iMStdYxcl26dEXgJEXOjkWvx-I2fYZ2u?usp=sharing) and should be saved in `maisi/dataset/` folder.
+We provide the preprocessed subset of [C4KC-KiTS](https://www.cancerimagingarchive.net/collection/c4kc-kits/) dataset used in the finetuning config `environment_maisi_controlnet_train.json`. The [dataset](https://developer.download.nvidia.com/assets/Clara/monai/tutorials/model_zoo/model_maisi_C4KC-KiTS_subset.zip) and [corresponding JSON data](https://developer.download.nvidia.com/assets/Clara/monai/tutorials/model_zoo/model_maisi_C4KC-KiTS_subset.json) list can be downloaded and should be saved in `maisi/dataset/` folder.
 
 The structure of example folder in the preprocessed dataset is:
 

diff --git a/generation/maisi/maisi_inference_tutorial.ipynb b/generation/maisi/maisi_inference_tutorial.ipynb
@@ -157,35 +157,41 @@
     "files = [\n",
     "    {\n",
     "        \"path\": \"models/autoencoder_epoch273.pt\",\n",
-    "        \"url\": \"https://drive.google.com/file/d/1Ojw25lFO8QbHkxazdK4CgZTyp3GFNZGz/view?usp=sharing\",\n",
+    "        \"url\": \"https://developer.download.nvidia.com/assets/Clara/monai/tutorials\"\n",
+    "        \"/model_zoo/model_maisi_autoencoder_epoch273_alternative.pt\",\n",
     "    },\n",
     "    {\n",
     "        \"path\": \"models/input_unet3d_data-all_steps1000size512ddpm_random_current_inputx_v1.pt\",\n",
-    "        \"url\": \"https://drive.google.com/file/d/1lklNv4MTdI_9bwFRMd98QQ7JLerR5gC_/view?usp=drive_link\",\n",
+    "        \"url\": \"https://developer.download.nvidia.com/assets/Clara/monai/tutorials/model_zoo\"\n",
+    "        \"/model_maisi_input_unet3d_data-all_steps1000size512ddpm_random_current_inputx_v1_alternative.pt\",\n",
     "    },\n",
     "    {\n",
     "        \"path\": \"models/controlnet-20datasets-e20wl100fold0bc_noi_dia_fsize_current.pt\",\n",
-    "        \"url\": \"https://drive.google.com/file/d/1mLYeqeZ819_WpZPlAInhcWuCIHgn3QNT/view?usp=drive_link\",\n",
+    "        \"url\": \"https://developer.download.nvidia.com/assets/Clara/monai/tutorials/model_zoo\"\n",
+    "        \"/model_maisi_controlnet-20datasets-e20wl100fold0bc_noi_dia_fsize_current_alternative.pt\",\n",
     "    },\n",
     "    {\n",
     "        \"path\": \"models/mask_generation_autoencoder.pt\",\n",
-    "        \"url\": \"https://drive.google.com/file/d/19JnX-C6QAg4RfghTwpPnj4KEWhtawpCy/view?usp=drive_link\",\n",
+    "        \"url\": \"https://developer.download.nvidia.com/assets/Clara/monai\" \"/tutorials/mask_generation_autoencoder.pt\",\n",
     "    },\n",
     "    {\n",
     "        \"path\": \"models/mask_generation_diffusion_unet.pt\",\n",
-    "        \"url\": \"https://drive.google.com/file/d/1yOQvlhXFGY1ZYavADM3N34vgg5AEitda/view?usp=drive_link\",\n",
+    "        \"url\": \"https://developer.download.nvidia.com/assets/Clara/monai\"\n",
+    "        \"/tutorials/model_zoo/model_maisi_mask_generation_diffusion_unet_v2.pt\",\n",
     "    },\n",
     "    {\n",
     "        \"path\": \"configs/candidate_masks_flexible_size_and_spacing_3000.json\",\n",
-    "        \"url\": \"https://drive.google.com/file/d/1yMkH-lrAsn2YUGoTuVKNMpicziUmU-1J/view?usp=sharing\",\n",
+    "        \"url\": \"https://developer.download.nvidia.com/assets/Clara/monai\"\n",
+    "        \"/tutorials/candidate_masks_flexible_size_and_spacing_3000.json\",\n",
     "    },\n",
     "    {\n",
     "        \"path\": \"configs/all_anatomy_size_condtions.json\",\n",
-    "        \"url\": \"https://drive.google.com/file/d/1AJyt1DSoUd2x2AOQOgM7IxeSyo4MXNX0/view?usp=sharing\",\n",
+    "        \"url\": \"https://developer.download.nvidia.com/assets/Clara/monai/tutorials/all_anatomy_size_condtions.json\",\n",
     "    },\n",
     "    {\n",
     "        \"path\": \"datasets/all_masks_flexible_size_and_spacing_3000.zip\",\n",
-    "        \"url\": \"https://drive.google.com/file/d/16MKsDKkHvDyF2lEir4dzlxwex_GHStUf/view?usp=sharing\",\n",
+    "        \"url\": \"https://developer.download.nvidia.com/assets/Clara/monai\"\n",
+    "        \"/tutorials/model_zoo/model_maisi_all_masks_flexible_size_and_spacing_3000.zip\",\n",
     "    },\n",
     "]\n",
     "\n",

diff --git a/generation/maisi/scripts/inference.py b/generation/maisi/scripts/inference.py
@@ -76,35 +76,35 @@ def main():
     files = [
         {
             "path": "models/autoencoder_epoch273.pt",
-            "url": "https://drive.google.com/file/d/1Ojw25lFO8QbHkxazdK4CgZTyp3GFNZGz/view?usp=sharing",
+            "url": "https://developer.download.nvidia.com/assets/Clara/monai/tutorials/model_zoo/model_maisi_autoencoder_epoch273_alternative.pt",
         },
         {
             "path": "models/input_unet3d_data-all_steps1000size512ddpm_random_current_inputx_v1.pt",
-            "url": "https://drive.google.com/file/d/1lklNv4MTdI_9bwFRMd98QQ7JLerR5gC_/view?usp=drive_link",
+            "url": "https://developer.download.nvidia.com/assets/Clara/monai/tutorials/model_zoo/model_maisi_input_unet3d_data-all_steps1000size512ddpm_random_current_inputx_v1_alternative.pt",
         },
         {
             "path": "models/controlnet-20datasets-e20wl100fold0bc_noi_dia_fsize_current.pt",
-            "url": "https://drive.google.com/file/d/1mLYeqeZ819_WpZPlAInhcWuCIHgn3QNT/view?usp=drive_link",
+            "url": "https://developer.download.nvidia.com/assets/Clara/monai/tutorials/model_zoo/model_maisi_controlnet-20datasets-e20wl100fold0bc_noi_dia_fsize_current_alternative.pt",
         },
         {
             "path": "models/mask_generation_autoencoder.pt",
-            "url": "https://drive.google.com/file/d/19JnX-C6QAg4RfghTwpPnj4KEWhtawpCy/view?usp=drive_link",
+            "url": "https://developer.download.nvidia.com/assets/Clara/monai/tutorials/mask_generation_autoencoder.pt",
         },
         {
             "path": "models/mask_generation_diffusion_unet.pt",
-            "url": "https://drive.google.com/file/d/1yOQvlhXFGY1ZYavADM3N34vgg5AEitda/view?usp=drive_link",
+            "url": "https://developer.download.nvidia.com/assets/Clara/monai/tutorials/model_zoo/model_maisi_mask_generation_diffusion_unet_v2.pt",
         },
         {
             "path": "configs/candidate_masks_flexible_size_and_spacing_3000.json",
-            "url": "https://drive.google.com/file/d/1yMkH-lrAsn2YUGoTuVKNMpicziUmU-1J/view?usp=sharing",
+            "url": "https://developer.download.nvidia.com/assets/Clara/monai/tutorials/candidate_masks_flexible_size_and_spacing_3000.json",
         },
         {
             "path": "configs/all_anatomy_size_condtions.json",
-            "url": "https://drive.google.com/file/d/1AJyt1DSoUd2x2AOQOgM7IxeSyo4MXNX0/view?usp=sharing",
+            "url": "https://developer.download.nvidia.com/assets/Clara/monai/tutorials/all_anatomy_size_condtions.json",
         },
         {
             "path": "datasets/all_masks_flexible_size_and_spacing_3000.zip",
-            "url": "https://drive.google.com/file/d/16MKsDKkHvDyF2lEir4dzlxwex_GHStUf/view?usp=sharing",
+            "url": "https://developer.download.nvidia.com/assets/Clara/monai/tutorials/model_zoo/model_maisi_all_masks_flexible_size_and_spacing_3000.zip",
         },
     ]
 

diff --git a/modules/benchmark_global_mutual_information.ipynb b/modules/benchmark_global_mutual_information.ipynb
@@ -149,7 +149,7 @@
     "    os.makedirs(directory, exist_ok=True)\n",
     "root_dir = tempfile.mkdtemp() if directory is None else directory\n",
     "print(f\"root dir is: {root_dir}\")\n",
-    "file_url = \"https://drive.google.com/uc?id=17tsDLvG_GZm7a4fCVMCv-KyDx0hqq1ji\"\n",
+    "file_url = \"https://developer.download.nvidia.com/assets/Clara/monai/tutorials/Prostate_T2W_AX_1.nii\"\n",
     "file_path = f\"{root_dir}/Prostate_T2W_AX_1.nii\"\n",
     "download_url(file_url, file_path)"
    ]

diff --git a/modules/engines/gan_training.py b/modules/engines/gan_training.py
@@ -14,7 +14,7 @@
     Sample script using MONAI to train a GAN to synthesize images from a latent code.
 
 ## Get the dataset
-    MedNIST.tar.gz link: https://drive.google.com/uc?id=1QsnnkvZyJPcbRoV_ArW8SnE1OTuoVbKE
+    MedNIST.tar.gz link: https://developer.download.nvidia.com/assets/Clara/monai/tutorials/MedNIST.tar.gz
     Extract tarball and set input_dir variable. GAN script trains using hand CT scan jpg images.
 
     Dataset information available in MedNIST Tutorial

diff --git a/modules/public_datasets.ipynb b/modules/public_datasets.ipynb
@@ -595,7 +595,7 @@
    "outputs": [],
    "source": [
     "class IXIDataset(Randomizable, CacheDataset):\n",
-    "    resource = \"https://drive.google.com/file/d/1f5odq9smadgeJmDeyEy_UOjEtE_pkKc0/view?usp=sharing\"\n",
+    "    resource = \"https://developer.download.nvidia.com/assets/Clara/monai/tutorials/IXI-T1.tar\"\n",
     "    md5 = \"34901a0593b41dd19c1a1f746eac2d58\"\n",
     "\n",
     "    def __init__(\n",

diff --git a/modules/resample_benchmark.ipynb b/modules/resample_benchmark.ipynb
@@ -174,7 +174,7 @@
      "text": [
       "\n",
       "Downloading...\n",
-      "From: https://drive.google.com/uc?id=17tsDLvG_GZm7a4fCVMCv-KyDx0hqq1ji\n",
+      "From: https://developer.download.nvidia.com/assets/Clara/monai/tutorials/Prostate_T2W_AX_1.nii\n",
       "To: /tmp/tmp2euy74rf/mri.nii\n",
       "100%|██████████| 12.1M/12.1M [00:00<00:00, 210MB/s]"
      ]

diff --git a/monailabel/monailabel_wholebody_totalSegmentator_3dslicer.ipynb b/monailabel/monailabel_wholebody_totalSegmentator_3dslicer.ipynb
@@ -94,7 +94,7 @@
     "\n",
     " - If you are going to use full dataset of TotalSegmentator, please refer to the dataset link, download the data, create and preprocess the images following [this page](https://zenodo.org/record/6802614).\n",
     " \n",
-    " - In this tutorial, we prepared a sample subset, resampled and ready to use. The subset is only for demonstration. Download [here](https://drive.google.com/file/d/1DtDmERVMjks1HooUhggOKAuDm0YIEunG/view?usp=sharing).\n",
+    " - In this tutorial, we prepared a sample subset, resampled and ready to use. The subset is only for demonstration. Download [here](https://developer.download.nvidia.com/assets/Clara/monai/tutorials/totalSegmentator_mergedLabel_samples.zip).\n",
     "  \n",
     " To use the bundle, users need to download the data and merge all annotated labels into one NIFTI file. Each file contains 0-104 values, each value represents one anatomy class.\n",
     " \n",

diff --git a/multimodal/openi_multilabel_classification_transchex/README.md b/multimodal/openi_multilabel_classification_transchex/README.md
@@ -16,6 +16,6 @@ completed, the dataset can be readily used for the tutorial.
 1) Create a new folder named 'monai_data' for downloading the raw data and preprocessing.
 2) Download the chest X-ray images in PNG format from this [link](https://openi.nlm.nih.gov/imgs/collections/NLMCXR_png.tgz). Copy the downloaded file (NLMCXR_png.tgz) to 'monai_data' directory and extract it to 'monai_data/dataset_orig/NLMCXR_png/'.
 3) Download the reports in XML format from this [link](https://openi.nlm.nih.gov/imgs/collections/NLMCXR_reports.tgz). Copy the downloaded file (NLMCXR_reports.tgz) to 'monai_data' directory and extract it to 'monai_data/dataset_orig/NLMCXR_reports/'.
-4) Download the splits of train, validation and test datasets from this [link](https://drive.google.com/u/1/uc?id=1jvT0jVl9mgtWy4cS7LYbF43bQE4mrXAY&export=download). Copy the downloaded file (TransChex_openi.zip)
+4) Download the splits of train, validation and test datasets from this [link](https://developer.download.nvidia.com/assets/Clara/monai/tutorials/TransChex_openi.zip). Copy the downloaded file (TransChex_openi.zip)
 to 'monai_data' directory and extract it here.
 5) Run 'preprocess_openi.py' to process the images and reports.
diff --git a/pathology/multiple_instance_learning/README.md b/pathology/multiple_instance_learning/README.md
@@ -49,7 +49,7 @@ python ./panda_mil_train_evaluate_pytorch_gpu.py -h
 
 Train in multi-gpu mode with AMP using all available gpus,
 assuming the training images are in the `/PandaChallenge2020/train_images` folder,
-it will use the pre-defined 80/20 data split in [datalist_panda_0.json](https://drive.google.com/drive/u/0/folders/1CAHXDZqiIn5QUfg5A7XsK1BncRu6Ftbh)
+it will use the pre-defined 80/20 data split in [datalist_panda_0.json](https://developer.download.nvidia.com/assets/Clara/monai/tutorials/datalist_panda_0.json)
 
 ```bash
 python -u panda_mil_train_evaluate_pytorch_gpu.py \

diff --git a/pathology/multiple_instance_learning/panda_mil_train_evaluate_pytorch_gpu.py b/pathology/multiple_instance_learning/panda_mil_train_evaluate_pytorch_gpu.py
@@ -530,7 +530,7 @@ def parse_args():
 
     if args.dataset_json is None:
         # download default json datalist
-        resource = "https://drive.google.com/uc?id=1L6PtKBlHHyUgTE4rVhRuOLTQKgD4tBRK"
+        resource = "https://developer.download.nvidia.com/assets/Clara/monai/tutorials/datalist_panda_0.json"
         dst = "./datalist_panda_0.json"
         if not os.path.exists(dst):
             gdown.download(resource, dst, quiet=False)