diff --git a/.github/workflows/ci_gpu.yml b/.github/workflows/ci_gpu.yml
index 90dca200..6d4f8c6a 100644
--- a/.github/workflows/ci_gpu.yml
+++ b/.github/workflows/ci_gpu.yml
@@ -8,36 +8,34 @@ on:
     branches:
       - main
 
-  unittest_multi_gpu:
-    runs-on: linux.4xlarge.nvidia.gpu
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v2
+jobs:
+  # Temporarily disable this test since there is no server including multiple GPUs.
+  # unittest_multi_gpu:
+  #   runs-on: 4-core-ubuntu-gpu-t4
+  #   steps:
+  #     - name: Checkout
+  #       uses: actions/checkout@v2
 
-      - name: Set up Python
-        uses: actions/setup-python@v2
-        with:
-          python-version: 3.9
+  #     - name: Display Python version
+  #       run: python3 -c "import sys; print(sys.version)"
 
-      - name: Install dependencies
-        run: |
-          ./scripts/install_via_pip.sh -c
+  #     - name: Set up Python
+  #       uses: actions/setup-python@v2
+  #       with:
+  #         python-version: '3.x'
 
-      - name: Run multi-GPU unit tests
-        run: |
-          nvidia-smi
-          nvcc --version
-          python -m unittest opacus.tests.multigpu_gradcheck.GradientComputationTest.test_gradient_correct
+  #     - name: Install dependencies
+  #       run: |
+  #         python -m pip install --upgrade pip
+  #         ./scripts/install_via_pip.sh -c
+
+  #     - name: Run multi-GPU unit tests
+  #       run: |
+  #         python3 -m unittest opacus.tests.multigpu_gradcheck.GradientComputationTest.test_gradient_correct
 
 
   integrationtest_py39_torch_release_cuda:
-    runs-on: ubuntu-latest
-    container:
-      # https://hub.docker.com/r/nvidia/cuda
-      image: nvidia/cuda:12.3.1-base-ubuntu22.04
-      options: --gpus all
-    env:
-      TZ: 'UTC'
+    runs-on: 4-core-ubuntu-gpu-t4
     steps:
       - name: Checkout
         uses: actions/checkout@v2
@@ -45,24 +43,27 @@ on:
       - name: Set up Python
         uses: actions/setup-python@v2
         with:
-          python-version: 3.9
+          python-version: '3.9'
 
       - name: Install dependencies
         run: |
-          python -m pip install --upgrade pip
+          python3 -m pip install --upgrade pip
           pip install pytest coverage coveralls
           ./scripts/install_via_pip.sh -c
 
-      - name: Install CUDA toolkit and cuDNN
-        run: |
-          apt-get update
-          apt-get install -y --no-install-recommends \
-            cuda-toolkit-11-1 \
-            libcudnn8=8.1.1.33-1+cuda11.1 \
-            libcudnn8-dev=8.1.1.33-1+cuda11.1
+      # Cuda config has already been deployed in the server, so no need to re-install it.
+      # Cuda installation guide: https://medium.com/@milistu/how-to-install-cuda-cudnn-7e4a00ae4f44
+      # - name: Install CUDA toolkit and cuDNN
+      #   run: |
+      #     sudo apt-get update
+      #     sudo apt-get install -y --no-install-recommends \
+      #       cuda-toolkit-11-1 \
+      #       libcudnn8=8.1.1.33-1+cuda11.1 \
+      #       libcudnn8-dev=8.1.1.33-1+cuda11.1
 
       - name: Run MNIST integration test (CUDA)
         run: |
+          nvidia-smi
           mkdir -p runs/mnist/data
           mkdir -p runs/mnist/test-reports
           python examples/mnist.py --lr 0.25 --sigma 0.7 -c 1.5 --batch-size 64 --epochs 1 --data-root runs/mnist/data --n-runs 1 --device cuda
@@ -91,83 +92,85 @@ on:
           name: cifar10-gpu-reports
           path: runs/cifar10/test-reports
 
-      - name: Run IMDb integration test (CUDA)
-        run: |
-          mkdir -p runs/imdb/data
-          mkdir -p runs/imdb/test-reports
-          pip install --user datasets transformers
-          python examples/imdb.py --lr 0.02 --sigma 1.0 -c 1.0 --batch-size 64 --max-sequence-length 256 --epochs 2 --data-root runs/imdb/data --device cuda
-          python -c "import torch; accuracy = torch.load('run_results_imdb_classification.pt'); exit(0) if (accuracy>0.54 and accuracy<0.66) else exit(1)"
-
-      - name: Store IMDb test results
-        uses: actions/upload-artifact@v2
-        with:
-          name: imdb-gpu-reports
-          path: runs/imdb/test-reports
-
-      - name: Run charlstm integration test (CUDA)
-        run: |
-          mkdir -p runs/charlstm/data
-          wget https://download.pytorch.org/tutorial/data.zip -O runs/charlstm/data/data.zip
-          unzip runs/charlstm/data/data.zip -d runs/charlstm/data
-          rm runs/charlstm/data/data.zip
-          mkdir -p runs/charlstm/test-reports
-          pip install scikit-learn
-          python examples/char-lstm-classification.py --epochs=20 --learning-rate=2.0 --hidden-size=128 --delta=8e-5 --batch-size 400 --n-layers=1 --sigma=1.0 --max-per-sample-grad-norm=1.5 --data-root="runs/charlstm/data/data/names/" --device cuda --test-every 5
-          python -c "import torch; accuracy = torch.load('run_results_chr_lstm_classification.pt'); exit(0) if (accuracy>0.60 and accuracy<0.80) else exit(1)"
-
-      - name: Store test results
-        uses: actions/upload-artifact@v2
-        with:
-          name: charlstm-gpu-reports
-          path: runs/charlstm/test-reports
-
-  micro_benchmarks_py39_torch_release_cuda:
-    runs-on: ubuntu-latest
-    needs: [integrationtest_py39_torch_release_cuda]
-    container:
-      # https://hub.docker.com/r/nvidia/cuda
-      image: nvidia/cuda:12.3.1-base-ubuntu22.04
-      options: --gpus all
-    env:
-      TZ: 'UTC'
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v2
-
-      - name: Set up Python
-        uses: actions/setup-python@v2
-        with:
-          python-version: 3.9
-
-      - name: Install dependencies
-        run: |
-          python -m pip install --upgrade pip
-          pip install pytest coverage coveralls
-          ./scripts/install_via_pip.sh
-
-      - name: Install CUDA toolkit and cuDNN
-        run: |
-          apt-get update
-          apt-get install -y --no-install-recommends \
-            cuda-toolkit-11-1 \
-            libcudnn8=8.1.1.33-1+cuda11.1 \
-            libcudnn8-dev=8.1.1.33-1+cuda11.1
-
-      - name: Run benchmark integration tests (CUDA)
-        run: |
-          mkdir -p benchmarks/results/raw
-          python benchmarks/run_benchmarks.py --batch_size 16 --layers "groupnorm instancenorm layernorm" --config_file ./benchmarks/config.json --root ./benchmarks/results/raw/ --cont
-          IFS=$' ';layers=("groupnorm" "instancenorm" "layernorm"); rm -rf /tmp/report_layers; mkdir -p /tmp/report_layers; IFS=$'\n'; files=`( echo "${layers[*]}" ) | sed 's/.*/.\/benchmarks\/results\/raw\/&*/'`
-          cp -v ${files[@]} /tmp/report_layers
-          report_id=`IFS=$'-'; echo "${layers[*]}"`
-          python benchmarks/generate_report.py --path-to-results /tmp/report_layers --save-path benchmarks/results/report-${report_id}.csv --format csv
-          python benchmarks/generate_report.py --path-to-results /tmp/report_layers --save-path benchmarks/results/report-${report_id}.pkl --format pkl
-          python benchmarks/check_threshold.py --report-path "./benchmarks/results/report-"$report_id".pkl" --metric runtime --threshold 3.0 --column "hooks/baseline"
-          python benchmarks/check_threshold.py --report-path "./benchmarks/results/report-"$report_id".pkl" --metric memory --threshold 1.6 --column "hooks/baseline"
-
-      - name: Store artifacts
-        uses: actions/upload-artifact@v2
-        with:
-          name: benchmarks-reports
-          path: benchmarks/results/
+      # To save resouces, there is no need to run all the tests.
+      # - name: Run IMDb integration test (CUDA)
+      #   run: |
+      #     mkdir -p runs/imdb/data
+      #     mkdir -p runs/imdb/test-reports
+      #     pip install --user datasets transformers
+      #     python examples/imdb.py --lr 0.02 --sigma 1.0 -c 1.0 --batch-size 64 --max-sequence-length 256 --epochs 2 --data-root runs/imdb/data --device cuda
+      #     python -c "import torch; accuracy = torch.load('run_results_imdb_classification.pt'); exit(0) if (accuracy>0.54 and accuracy<0.66) else exit(1)"
+
+      # - name: Store IMDb test results
+      #   uses: actions/upload-artifact@v2
+      #   with:
+      #     name: imdb-gpu-reports
+      #     path: runs/imdb/test-reports
+
+      # - name: Run charlstm integration test (CUDA)
+      #   run: |
+      #     mkdir -p runs/charlstm/data
+      #     wget https://download.pytorch.org/tutorial/data.zip -O runs/charlstm/data/data.zip
+      #     unzip runs/charlstm/data/data.zip -d runs/charlstm/data
+      #     rm runs/charlstm/data/data.zip
+      #     mkdir -p runs/charlstm/test-reports
+      #     pip install scikit-learn
+      #     python examples/char-lstm-classification.py --epochs=20 --learning-rate=2.0 --hidden-size=128 --delta=8e-5 --batch-size 400 --n-layers=1 --sigma=1.0 --max-per-sample-grad-norm=1.5 --data-root="runs/charlstm/data/data/names/" --device cuda --test-every 5
+      #     python -c "import torch; accuracy = torch.load('run_results_chr_lstm_classification.pt'); exit(0) if (accuracy>0.60 and accuracy<0.80) else exit(1)"
+
+      # - name: Store test results
+      #   uses: actions/upload-artifact@v2
+      #   with:
+      #     name: charlstm-gpu-reports
+      #     path: runs/charlstm/test-reports
+
+  # We will have new benchmarks for Ghost Clipping.
+  # micro_benchmarks_py39_torch_release_cuda:
+  #   runs-on: ubuntu-latest
+  #   needs: [integrationtest_py39_torch_release_cuda]
+  #   container:
+  #     # https://hub.docker.com/r/nvidia/cuda
+  #     image: nvidia/cuda:12.3.1-base-ubuntu22.04
+  #     options: --gpus all
+  #   env:
+  #     TZ: 'UTC'
+  #   steps:
+  #     - name: Checkout
+  #       uses: actions/checkout@v2
+
+  #     - name: Set up Python
+  #       uses: actions/setup-python@v2
+  #       with:
+  #         python-version: 3.9
+
+  #     - name: Install dependencies
+  #       run: |
+  #         python -m pip install --upgrade pip
+  #         pip install pytest coverage coveralls
+  #         ./scripts/install_via_pip.sh
+
+  #     - name: Install CUDA toolkit and cuDNN
+  #       run: |
+  #         apt-get update
+  #         apt-get install -y --no-install-recommends \
+  #           cuda-toolkit-11-1 \
+  #           libcudnn8=8.1.1.33-1+cuda11.1 \
+  #           libcudnn8-dev=8.1.1.33-1+cuda11.1
+
+  #     - name: Run benchmark integration tests (CUDA)
+  #       run: |
+  #         mkdir -p benchmarks/results/raw
+  #         python benchmarks/run_benchmarks.py --batch_size 16 --layers "groupnorm instancenorm layernorm" --config_file ./benchmarks/config.json --root ./benchmarks/results/raw/ --cont
+  #         IFS=$' ';layers=("groupnorm" "instancenorm" "layernorm"); rm -rf /tmp/report_layers; mkdir -p /tmp/report_layers; IFS=$'\n'; files=`( echo "${layers[*]}" ) | sed 's/.*/.\/benchmarks\/results\/raw\/&*/'`
+  #         cp -v ${files[@]} /tmp/report_layers
+  #         report_id=`IFS=$'-'; echo "${layers[*]}"`
+  #         python benchmarks/generate_report.py --path-to-results /tmp/report_layers --save-path benchmarks/results/report-${report_id}.csv --format csv
+  #         python benchmarks/generate_report.py --path-to-results /tmp/report_layers --save-path benchmarks/results/report-${report_id}.pkl --format pkl
+  #         python benchmarks/check_threshold.py --report-path "./benchmarks/results/report-"$report_id".pkl" --metric runtime --threshold 3.0 --column "hooks/baseline"
+  #         python benchmarks/check_threshold.py --report-path "./benchmarks/results/report-"$report_id".pkl" --metric memory --threshold 1.6 --column "hooks/baseline"
+
+  #     - name: Store artifacts
+  #       uses: actions/upload-artifact@v2
+  #       with:
+  #         name: benchmarks-reports
+  #         path: benchmarks/results/
diff --git a/scripts/install_via_pip.sh b/scripts/install_via_pip.sh
index d906094d..dd34297b 100755
--- a/scripts/install_via_pip.sh
+++ b/scripts/install_via_pip.sh
@@ -35,7 +35,7 @@ while getopts 'ncdv:' flag; do
 
 curl -sL https://dl.yarnpkg.com/debian/pubkey.gpg | sudo apt-key add -
 echo "deb https://dl.yarnpkg.com/debian/ stable main" | sudo tee /etc/apt/sources.list.d/yarn.list
-sudo apt-get update && sudo apt-get install yarn
+sudo apt-get -y update && sudo apt-get -y install yarn
 
 # yarn needs terminal info
 export TERM=xterm