chore: migrate gsutil usage to gcloud storage

docs/vision/instance_segmentation.ipynb

@@ -511,8 +511,8 @@
         "if not os.path.exists(model_ckpt_path):\n",
         "  os.mkdir(model_ckpt_path)\n",
         "\n",
-        "!gsutil cp gs://tf_model_garden/vision/mobilenet/v2_1.0_float/ckpt-180648.data-00000-of-00001 './model_ckpt/'\n",
-        "!gsutil cp gs://tf_model_garden/vision/mobilenet/v2_1.0_float/ckpt-180648.index './model_ckpt/'"
+        "!gcloud storage cp gs://tf_model_garden/vision/mobilenet/v2_1.0_float/ckpt-180648.data-00000-of-00001 './model_ckpt/'\n",
+        "!gcloud storage cp gs://tf_model_garden/vision/mobilenet/v2_1.0_float/ckpt-180648.index './model_ckpt/'"
       ]
     },
     {

docs/vision/semantic_segmentation.ipynb

@@ -378,8 +378,8 @@
         "if not os.path.exists(model_ckpt_path):\n",
         "  os.mkdir(model_ckpt_path)\n",
         "\n",
-        "!gsutil cp gs://tf_model_garden/cloud/vision-2.0/deeplab/deeplabv3_mobilenetv2_coco/best_ckpt-63.data-00000-of-00001 './model_ckpt/'\n",
-        "!gsutil cp gs://tf_model_garden/cloud/vision-2.0/deeplab/deeplabv3_mobilenetv2_coco/best_ckpt-63.index './model_ckpt/'"
+        "!gcloud storage cp gs://tf_model_garden/cloud/vision-2.0/deeplab/deeplabv3_mobilenetv2_coco/best_ckpt-63.data-00000-of-00001 './model_ckpt/'\n",
+        "!gcloud storage cp gs://tf_model_garden/cloud/vision-2.0/deeplab/deeplabv3_mobilenetv2_coco/best_ckpt-63.index './model_ckpt/'"
       ]
     },
     {

official/projects/edgetpu/vision/serving/inference_visualization_tool.ipynb

@@ -57,7 +57,7 @@
         "  sandbox_path = web_path.split('/')[-1]\n",
         "  !rm -f {sandbox_path}\n",
         "  if web_path[:2] == \"gs\":\n",
-        "    !gsutil cp {web_path} {sandbox_path}\n",
+        "    !gcloud storage cp {web_path} {sandbox_path}\n",
         "  else:\n",
         "    !wget -v {web_path} --no-check-certificate\n",
         "  return sandbox_path\n"
@@ -122,7 +122,7 @@
       },
       "source": [
         "MODEL_HOME='gs://tf_model_garden/models/edgetpu/checkpoint_and_tflite/vision/segmentation-edgetpu/tflite/default_argmax'\n",
-        "!gsutil ls {MODEL_HOME}"
+        "!gcloud storage ls {MODEL_HOME}"
       ],
       "execution_count": null,
       "outputs": []

official/projects/waste_identification_ml/Deploy/detr_cloud_deployment/client/utils.py

@@ -97,10 +97,10 @@ def setup_logger_and_directories(input_dir):
 
   input_directory = (input_dir).rstrip('/\\')
   local_dir = os.path.basename(input_directory)
-  # A failed previous run can leave a file where gsutil needs a directory.
+  # A failed previous run can leave a file where gcloud needs a directory.
   if os.path.isfile(local_dir):
     os.remove(local_dir)
-  command = f'gsutil -m cp -r {input_directory} .'
+  command = f'gcloud storage cp --recursive {input_directory} .'
   subprocess.run(command, shell=True, check=True)
   prediction_folder = os.path.basename(input_directory) + '_prediction'
   os.makedirs(prediction_folder, exist_ok=True)

official/projects/waste_identification_ml/Triton_TF_Cloud_Deployment/client/inference_pipeline.py

@@ -126,7 +126,7 @@ def main(_) -> None:
 
   # Copy the images folder from GCP to the present directory.
   input_directory = (INPUT_DIRECTORY.value).rstrip("/\\")
-  command = f"gsutil -m cp -r {input_directory} ."
+  command = f"gcloud storage cp --recursive {input_directory} ."
   subprocess.run(command, shell=True, check=True)
 
   # Create a folder to store the predictions.
@@ -361,7 +361,7 @@ def main(_) -> None:
 
       # Move the tracking visualization to the output directory.
       commands = [
-          f"gsutil -m cp -r {output_folder} {OUTPUT_DIRECTORY.value}",
+          f"gcloud storage cp --recursive {output_folder} {OUTPUT_DIRECTORY.value}",
           f"rm -r {output_folder}",
       ]
       combined_command_1 = " && ".join(commands)
@@ -383,7 +383,7 @@ def main(_) -> None:
 
       # Move the cropped objects to the output directory.
       commands = [
-          f"gsutil -m cp -r {cropped_obj_folder} {OUTPUT_DIRECTORY.value}",
+          f"gcloud storage cp --recursive {cropped_obj_folder} {OUTPUT_DIRECTORY.value}",
           f"rm -r {cropped_obj_folder}",
       ]
 
@@ -423,11 +423,11 @@ def main(_) -> None:
       # Move the folders to the destination bucket.
       commands = [
           (
-              "gsutil -m cp -r"
+              "gcloud storage cp --recursive"
               f" {os.path.basename(input_directory)} {OUTPUT_DIRECTORY.value}"
           ),
           f"rm -r {os.path.basename(input_directory)}",
-          f"gsutil -m cp -r {prediction_folder} {OUTPUT_DIRECTORY.value}",
+          f"gcloud storage cp --recursive {prediction_folder} {OUTPUT_DIRECTORY.value}",
           f"rm -r {prediction_folder}",
       ]
 

official/projects/waste_identification_ml/llm_applications/milk_pouch_detection/deploy.sh

@@ -183,11 +183,11 @@ echo ""
 # ---
 
 echo "✅ Step 4: Create GCS Buckets..."
-gsutil mb \
-  -p "${PROJECT_ID}" \
-  -l "${REGION}" \
-  -c standard \
-  -b on "gs://${SOURCE_BUCKET_NAME}" \
+gcloud storage buckets create \
+  --project "${PROJECT_ID}" \
+  --location "${REGION}" \
+  --default-storage-class standard \
+  --uniform-bucket-level-access "gs://${SOURCE_BUCKET_NAME}" \
   || echo "Source Bucket 'gs://${SOURCE_BUCKET_NAME}' already exists."
 echo "GCS Buckets are ready."
 echo ""
@@ -324,5 +324,5 @@ echo ""
 
 echo "🚀🚀🚀 Deployment complete! 🚀🚀🚀"
 echo "You can upload files to the source bucket to be processed in the next run:"
-echo "gsutil cp your-local-image.jpg gs://${SOURCE_BUCKET_NAME}/"
+echo "gcloud storage cp your-local-image.jpg gs://${SOURCE_BUCKET_NAME}/"
 echo "Check the results in the bucket's subfolders."

official/projects/waste_identification_ml/model_inference_with_tracking/Inference_Detectron2_Pipeline_with_Tracking_experimental.ipynb

@@ -165,7 +165,7 @@
       "source": [
         "# # Connect to GCP bucket if your data is store there and copy them locally.\n",
         "# !gcloud init\n",
-        "# gsutil -m cp -r gs://input ."
+        "# gcloud storage cp --recursive gs://input ."
       ]
     },
     {

research/object_detection/dockerfiles/android/Dockerfile

@@ -22,7 +22,7 @@ RUN git clone --depth 1 https://github.com/tensorflow/models.git && \
     mv models /tensorflow/models
 
 
-# Install gcloud and gsutil commands
+# Install gcloud commands
 # https://cloud.google.com/sdk/docs/quickstart-debian-ubuntu
 RUN apt-get -y update && apt-get install -y gpg-agent && \
     export CLOUD_SDK_REPO="cloud-sdk-$(lsb_release -c -s)" && \

research/object_detection/dockerfiles/android/README.md

@@ -59,7 +59,7 @@ The Oxford-IIIT Pets dataset, the COCO pre-trained SSD + MobileNet (v1)
 checkpoint, and example
 trained model are all available in `/tmp` in their respective folders.
 
-This container also has the `gsutil` and `gcloud` utilities, the `bazel` build
+This container also has the `gcloud` utilities, the `bazel` build
 tool, and all dependencies necessary to use the Object Detection API, and
 compile and install the TensorFlow Lite Android demo app.
 

research/object_detection/dockerfiles/tf1/Dockerfile

@@ -13,7 +13,7 @@ RUN apt-get update && apt-get install -y \
     python3-tk \
     wget
 
-# Install gcloud and gsutil commands
+# Install gcloud commands
 # https://cloud.google.com/sdk/docs/quickstart-debian-ubuntu
 RUN export CLOUD_SDK_REPO="cloud-sdk-$(lsb_release -c -s)" && \
     echo "deb http://packages.cloud.google.com/apt $CLOUD_SDK_REPO main" | tee -a /etc/apt/sources.list.d/google-cloud-sdk.list && \

research/object_detection/dockerfiles/tf2/Dockerfile

@@ -13,7 +13,7 @@ RUN apt-get update && apt-get install -y \
     python3-tk \
     wget
 
-# Install gcloud and gsutil commands
+# Install gcloud commands
 # https://cloud.google.com/sdk/docs/quickstart-debian-ubuntu
 RUN export CLOUD_SDK_REPO="cloud-sdk-$(lsb_release -c -s)" && \
     echo "deb http://packages.cloud.google.com/apt $CLOUD_SDK_REPO main" | tee -a /etc/apt/sources.list.d/google-cloud-sdk.list && \