[Patch] Build PyTorch 2.6 Inference ARM64 [off autopatch]

pytorch/inference/buildspec-arm64.yml

@@ -1 +1 @@
-buildspec_pointer: buildspec-arm64-2-6-ec2.yml
+buildspec_pointer: buildspec-arm64-2-6-ec2.yml

pytorch/inference/docker/2.6/py3/Dockerfile.arm64.cpu

@@ -190,7 +190,6 @@ RUN chmod +x /usr/local/bin/dockerd-entrypoint.py
 # add telemetry
 COPY deep_learning_container.py /usr/local/bin/deep_learning_container.py
 RUN chmod +x /usr/local/bin/deep_learning_container.py
-# COPY sitecustomize.py /usr/local/lib/${PYTHON_SHORT_VERSION}/sitecustomize.py
 
 RUN HOME_DIR=/root \
  && curl -o ${HOME_DIR}/oss_compliance.zip https://aws-dlinfra-utilities.s3.amazonaws.com/oss_compliance.zip \

pytorch/inference/docker/2.6/py3/Dockerfile.ec2.arm64.cpu.py_scan_allowlist.json

@@ -0,0 +1,3 @@
+{
+  "78828": "Affected versions of the PyTorch package are vulnerable to Denial of Service (DoS) due to improper handling in the MKLDNN pooling implementation. The torch.mkldnn_max_pool2d function fails to properly validate input parameters, allowing crafted inputs to trigger resource exhaustion or crashes in the underlying MKLDNN library. An attacker with local access can exploit this vulnerability by passing specially crafted tensor dimensions or parameters to the max pooling function, causing the application to become unresponsive or crash."
+}

pytorch/inference/docker/2.6/py3/cu124/Dockerfile.arm64.gpu

@@ -173,6 +173,7 @@ RUN pip install --no-cache-dir \
     boto3 \
     scipy \
     opencv-python \
+    arrow==1.3.0 \
     nvgpu \
     numpy \
     pyopenssl \
@@ -218,7 +219,6 @@ RUN chmod +x /usr/local/bin/dockerd-entrypoint.py
 
 # add telemetry
 COPY deep_learning_container.py /usr/local/bin/deep_learning_container.py
-COPY sitecustomize.py /usr/local/lib/${PYTHON_SHORT_VERSION}/sitecustomize.py
 RUN chmod +x /usr/local/bin/deep_learning_container.py
 
 RUN HOME_DIR=/root \

pytorch/inference/docker/2.6/py3/cu124/Dockerfile.ec2.arm64.gpu.py_scan_allowlist.json

@@ -0,0 +1,3 @@
+{
+  "78828": "Affected versions of the PyTorch package are vulnerable to Denial of Service (DoS) due to improper handling in the MKLDNN pooling implementation. The torch.mkldnn_max_pool2d function fails to properly validate input parameters, allowing crafted inputs to trigger resource exhaustion or crashes in the underlying MKLDNN library. An attacker with local access can exploit this vulnerability by passing specially crafted tensor dimensions or parameters to the max pooling function, causing the application to become unresponsive or crash."
+}