diff --git a/Dockerfile b/Dockerfile
index 2d9c5d5e..6fbcebb9 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -1,6 +1,6 @@
 # b/157908450 set to latest once numba 0.49.x fixes performance regression for datashader.
 ARG BASE_TAG=m46
-ARG TENSORFLOW_VERSION=2.3.0
+ARG TENSORFLOW_VERSION=2.3.1
 
 FROM gcr.io/kaggle-images/python-tensorflow-whl:${TENSORFLOW_VERSION}-py37 as tensorflow_whl
 FROM gcr.io/deeplearning-platform-release/base-cpu:${BASE_TAG}
diff --git a/gpu.Dockerfile b/gpu.Dockerfile
index 103be21f..c7359e72 100644
--- a/gpu.Dockerfile
+++ b/gpu.Dockerfile
@@ -1,7 +1,7 @@
 ARG BASE_TAG=staging
 
-FROM nvidia/cuda:10.1-cudnn7-devel-ubuntu18.04 AS nvidia
-FROM gcr.io/kaggle-images/python-tensorflow-whl:2.3.0-py37 as tensorflow_whl
+FROM nvidia/cuda:10.2-cudnn7-devel-ubuntu18.04 AS nvidia
+FROM gcr.io/kaggle-images/python-tensorflow-whl:2.3.1-py37 as tensorflow_whl
 FROM gcr.io/kaggle-images/python:${BASE_TAG}
 
 ADD clean-layer.sh  /tmp/clean-layer.sh
@@ -16,8 +16,8 @@ RUN sed -i 's/deb https:\/\/developer.download.nvidia.com/deb http:\/\/developer
 # Ensure the cuda libraries are compatible with the custom Tensorflow wheels.
 # TODO(b/120050292): Use templating to keep in sync or COPY installed binaries from it.
 ENV CUDA_MAJOR_VERSION=10
-ENV CUDA_MINOR_VERSION=1
-ENV CUDA_PATCH_VERSION=243
+ENV CUDA_MINOR_VERSION=2
+ENV CUDA_PATCH_VERSION=89
 ENV CUDA_VERSION=$CUDA_MAJOR_VERSION.$CUDA_MINOR_VERSION.$CUDA_PATCH_VERSION
 ENV CUDA_PKG_VERSION=$CUDA_MAJOR_VERSION-$CUDA_MINOR_VERSION=$CUDA_VERSION-1
 LABEL com.nvidia.volumes.needed="nvidia_driver"