diff --git a/Makefile b/Makefile
index 710381739..826e5c1e9 100644
--- a/Makefile
+++ b/Makefile
@@ -12,7 +12,7 @@ SA?=source activate
 ENV:=enterprise-gateway-dev
 SHELL:=/bin/bash
 
-VERSION?=2.5.2.dev0
+VERSION?=2.5.2
 SPARK_VERSION?=2.4.6
 
 ifeq (dev, $(findstring dev, $(VERSION)))
diff --git a/docs/source/docker.md b/docs/source/docker.md
index b2940261b..0c64684d7 100644
--- a/docs/source/docker.md
+++ b/docs/source/docker.md
@@ -65,7 +65,7 @@ Users that do not wish to extend an existing kernel image must be cognizant of a
 2. Is the base image one from [Jupyter Docker-stacks](https://github.com/jupyter/docker-stacks)?
 
 #### Requirements for Custom Kernel Images
-Custom kernel images require some support files from the Enterprise Gateway repository.  These are packaged into a tar file for each release starting in `2.5.1`.  This tar file (named `jupyter_enterprise_gateway_kernel_image_files-VERSION.tar.gz`) is composed of a few files - one bootstrap script and a kernel launcher (one per kernel type).
+Custom kernel images require some support files from the Enterprise Gateway repository.  These are packaged into a tar file for each release starting in `2.5.2`.  This tar file (named `jupyter_enterprise_gateway_kernel_image_files-VERSION.tar.gz`) is composed of a few files - one bootstrap script and a kernel launcher (one per kernel type).
 
 ##### Bootstrap-kernel.sh
 Enterprise Gateway provides a single [bootstrap-kernel.sh](https://github.com/jupyter/enterprise_gateway/blob/master/etc/kernel-launchers/bootstrap/bootstrap-kernel.sh) script that handles the three kernel languages supported out of the box - Python, R, and Scala.  When a kernel image is started by Enterprise Gateway, parameters used within the bootstrap-kernel.sh script are conveyed via environment variables.  The bootstrap script is then responsible for validating and converting those parameters to meaningful arguments to the appropriate launcher.
@@ -119,7 +119,7 @@ If your base image is not from docker-stacks, it is recommended that you NOT run
 Aside from configuring the image user, all other aspects of customization are the same.  In this case, we'll use the tensorflow-gpu image and convert it to be usable via Enterprise Gateway as a custom kernel image.  Note that because this image didn't have `wget` we used `curl` to download the supporting kernel-image files.
 
 ```dockerfile
-FROM tensorflow/tensorflow:2.5.1-gpu-jupyter
+FROM tensorflow/tensorflow:2.5.2-gpu-jupyter
 
 USER root
 
diff --git a/docs/source/getting-started.md b/docs/source/getting-started.md
index 17ecb83f6..e1fb16b8f 100644
--- a/docs/source/getting-started.md
+++ b/docs/source/getting-started.md
@@ -148,7 +148,7 @@ Note that because the Apache Toree kernel, and its supporting libraries, will be
 
 #### Sample kernelspecs
 
-We provide sample kernel configuration and launcher tar files as part of [each release](https://github.com/jupyter/enterprise_gateway/releases) (e.g. [jupyter_enterprise_gateway_kernelspecs-2.5.1.tar.gz](https://github.com/jupyter/enterprise_gateway/releases/download/v2.5.1/jupyter_enterprise_gateway_kernelspecs-2.5.1.tar.gz)) that can be extracted and modified to fit your configuration.
+We provide sample kernel configuration and launcher tar files as part of [each release](https://github.com/jupyter/enterprise_gateway/releases) (e.g. [jupyter_enterprise_gateway_kernelspecs-2.5.2.tar.gz](https://github.com/jupyter/enterprise_gateway/releases/download/v2.5.2/jupyter_enterprise_gateway_kernelspecs-2.5.2.tar.gz)) that can be extracted and modified to fit your configuration.
 
 For information about how to build your own kernel-based docker image for use by Enterprise Gateway see [Custom kernel images](docker.html#custom-kernel-images).
 
diff --git a/docs/source/kernel-distributed.md b/docs/source/kernel-distributed.md
index b65021ec1..b86a7972a 100644
--- a/docs/source/kernel-distributed.md
+++ b/docs/source/kernel-distributed.md
@@ -9,9 +9,9 @@ The following sample kernelspecs are currently available on Distributed mode:
 Install the `python_distributed` kernelspec on all nodes.
 
 ``` Bash
-wget https://github.com/jupyter/enterprise_gateway/releases/download/v2.5.1/jupyter_enterprise_gateway_kernelspecs-2.5.1.tar.gz
+wget https://github.com/jupyter/enterprise_gateway/releases/download/v2.5.2/jupyter_enterprise_gateway_kernelspecs-2.5.2.tar.gz
 KERNELS_FOLDER=/usr/local/share/jupyter/kernels
-tar -zxvf jupyter_enterprise_gateway_kernelspecs-2.5.1.tar.gz --strip 1 --directory $KERNELS_FOLDER/python_distributed/ python_distributed/
+tar -zxvf jupyter_enterprise_gateway_kernelspecs-2.5.2.tar.gz --strip 1 --directory $KERNELS_FOLDER/python_distributed/ python_distributed/
 ```
 
 The `python_distributed` kernelspec uses `DistributedProcessProxy` which is responsible for the launch and management of kernels distributed across and explicitly defined set of hosts using ssh. Hosts are determined via a round-robin algorithm (that we should make pluggable someday).
diff --git a/docs/source/kernel-kubernetes.md b/docs/source/kernel-kubernetes.md
index 6bf4402b1..bd1ba6d4c 100644
--- a/docs/source/kernel-kubernetes.md
+++ b/docs/source/kernel-kubernetes.md
@@ -523,7 +523,7 @@ By default, _vanilla_ kernels use a value of `none` for the spark context initia
 
 When the kernel is intended to target _Spark-on-kubernetes_, its launch is very much like kernels launched in YARN _cluster mode_, albeit with a completely different set of parameters.  Here's an example `SPARK_OPTS` string value which best conveys the idea:
 ```
-  "SPARK_OPTS": "--master k8s://https://${KUBERNETES_SERVICE_HOST}:${KUBERNETES_SERVICE_PORT} --deploy-mode cluster --name ${KERNEL_USERNAME}-${KERNEL_ID} --conf spark.kubernetes.driver.label.app=enterprise-gateway --conf spark.kubernetes.driver.label.kernel_id=${KERNEL_ID} --conf spark.kubernetes.executor.label.app=enterprise-gateway --conf spark.kubernetes.executor.label.kernel_id=${KERNEL_ID} --conf spark.kubernetes.driver.docker.image=${KERNEL_IMAGE} --conf spark.kubernetes.executor.docker.image=kubespark/spark-executor-py:v2.5.1-kubernetes-0.5.0 --conf spark.kubernetes.submission.waitAppCompletion=false",
+  "SPARK_OPTS": "--master k8s://https://${KUBERNETES_SERVICE_HOST}:${KUBERNETES_SERVICE_PORT} --deploy-mode cluster --name ${KERNEL_USERNAME}-${KERNEL_ID} --conf spark.kubernetes.driver.label.app=enterprise-gateway --conf spark.kubernetes.driver.label.kernel_id=${KERNEL_ID} --conf spark.kubernetes.executor.label.app=enterprise-gateway --conf spark.kubernetes.executor.label.kernel_id=${KERNEL_ID} --conf spark.kubernetes.driver.docker.image=${KERNEL_IMAGE} --conf spark.kubernetes.executor.docker.image=kubespark/spark-executor-py:v2.5.2-kubernetes-0.5.0 --conf spark.kubernetes.submission.waitAppCompletion=false",
 ```
 Note that each of the labels previously discussed are also applied to the _driver_ and _executor_ pods.
 
@@ -595,9 +595,9 @@ From anywhere with Helm cluster access, create the service and deployment by run
 ```bash
 helm upgrade --install --atomic --namespace enterprise-gateway enterprise-gateway etc/kubernetes/helm/enterprise-gateway
 ```
-the helm chart tarball is also accessible as an asset on our [release](https://github.com/jupyter/enterprise_gateway/releases/tag/v2.5.1) page:
+the helm chart tarball is also accessible as an asset on our [release](https://github.com/jupyter/enterprise_gateway/releases/tag/v2.5.2) page:
 ```bash
-helm install --name enterprise-gateway --atomic --namespace enterprise-gateway https://github.com/jupyter/enterprise_gateway/releases/download/v2.5.1/jupyter_enterprise_gateway_helm-2.5.1.tgz
+helm install --name enterprise-gateway --atomic --namespace enterprise-gateway https://github.com/jupyter/enterprise_gateway/releases/download/v2.5.2/jupyter_enterprise_gateway_helm-2.5.2.tgz
 ```
 ##### Configuration
 
diff --git a/docs/source/kernel-spark-standalone.md b/docs/source/kernel-spark-standalone.md
index ac9def600..864040888 100644
--- a/docs/source/kernel-spark-standalone.md
+++ b/docs/source/kernel-spark-standalone.md
@@ -23,14 +23,14 @@ EG_REMOTE_HOSTS=elyra-node-1.fyre.ibm.com,elyra-node-2.fyre.ibm.com,elyra-node-3
 Although Enterprise Gateway does not currently provide sample kernelspecs for Spark standalone, here are the steps necessary to convert a yarn_client kernelspec to standalone.
 
 For each supported Jupyter Kernel, we have provided sample kernel configurations and launchers as part of the release
-[jupyter_enterprise_gateway_kernelspecs-2.5.1.tar.gz](https://github.com/jupyter/enterprise_gateway/releases/download/v2.5.1/jupyter_enterprise_gateway_kernelspecs-2.5.1.tar.gz).
+[jupyter_enterprise_gateway_kernelspecs-2.5.2.tar.gz](https://github.com/jupyter/enterprise_gateway/releases/download/v2.5.2/jupyter_enterprise_gateway_kernelspecs-2.5.2.tar.gz).
 
 Considering we would like to enable the IPython Kernel that comes pre-installed with Anaconda to run on
 Spark Standalone, we would have to copy the sample configuration folder **spark_python_yarn_client**
 to where the Jupyter kernels are installed (e.g. jupyter kernelspec list) and rename it to **spark_python_spark_standalone***
 
 ``` Bash
-wget https://github.com/jupyter/enterprise_gateway/releases/download/v2.5.1/jupyter_enterprise_gateway_kernelspecs-2.5.1.tar.gz
+wget https://github.com/jupyter/enterprise_gateway/releases/download/v2.5.2/jupyter_enterprise_gateway_kernelspecs-2.5.2.tar.gz
 SCALA_KERNEL_DIR="$(jupyter kernelspec list | grep -w "python3" | awk '{print $2}')"
 KERNELS_FOLDER="$(dirname "${SCALA_KERNEL_DIR}")"
 tar -zxvf enterprise_gateway_kernelspecs.tar.gz --strip 1 --directory $KERNELS_FOLDER/spark_python_yarn_client/ spark_python_yarn_client/
@@ -85,9 +85,9 @@ Follow the steps below to install/configure the Toree kernel:
 Considering we would like to enable the Scala Kernel to run on YARN Cluster and Client mode we would have to copy the sample configuration folder **spark_scala_yarn_client** to where the Jupyter kernels are installed (e.g. jupyter kernelspec list)
 
 ``` Bash
-wget https://github.com/jupyter/enterprise_gateway/releases/download/v2.5.1/jupyter_enterprise_gateway_kernelspecs-2.5.1.tar.gz
+wget https://github.com/jupyter/enterprise_gateway/releases/download/v2.5.2/jupyter_enterprise_gateway_kernelspecs-2.5.2.tar.gz
 KERNELS_FOLDER=/usr/local/share/jupyter/kernels
-tar -zxvf jupyter_enterprise_gateway_kernelspecs-2.5.1.tar.gz --strip 1 --directory $KERNELS_FOLDER/spark_scala_yarn_client/ spark_scala_yarn_client/
+tar -zxvf jupyter_enterprise_gateway_kernelspecs-2.5.2.tar.gz --strip 1 --directory $KERNELS_FOLDER/spark_scala_yarn_client/ spark_scala_yarn_client/
 mv $KERNELS_FOLDER/spark_scala_yarn_client $KERNELS_FOLDER/spark_scala_spark_standalone
 ```
 
@@ -102,9 +102,9 @@ The IPython kernel comes pre-installed with Anaconda and we have tested with its
 Considering we would like to enable the IPython kernel to run on YARN Cluster and Client mode we would have to copy the sample configuration folder **spark_python_yarn_client** to where the Jupyter kernels are installed (e.g. jupyter kernelspec list)
 
 ``` Bash
-wget https://github.com/jupyter/enterprise_gateway/releases/download/v2.5.1/jupyter_enterprise_gateway_kernelspecs-2.5.1.tar.gz
+wget https://github.com/jupyter/enterprise_gateway/releases/download/v2.5.2/jupyter_enterprise_gateway_kernelspecs-2.5.2.tar.gz
 KERNELS_FOLDER=/usr/local/share/jupyter/kernels
-tar -zxvf jupyter_enterprise_gateway_kernelspecs-2.5.1.tar.gz --strip 1 --directory $KERNELS_FOLDER/spark_python_yarn_client/ spark_python_yarn_client/
+tar -zxvf jupyter_enterprise_gateway_kernelspecs-2.5.2.tar.gz --strip 1 --directory $KERNELS_FOLDER/spark_python_yarn_client/ spark_python_yarn_client/
 mv $KERNELS_FOLDER/spark_python_yarn_client $KERNELS_FOLDER/spark_python_spark_standalone
 ```
 
@@ -137,9 +137,9 @@ ls $ANACONDA_HOME/lib/R/library
 Considering we would like to enable the IRkernel to run on YARN Cluster and Client mode we would have to copy the sample configuration folder **spark_R_yarn_client** to where the Jupyter kernels are installed (e.g. jupyter kernelspec list)
 
 ``` Bash
-wget https://github.com/jupyter/enterprise_gateway/releases/download/v2.5.1/jupyter_enterprise_gateway_kernelspecs-2.5.1.tar.gz
+wget https://github.com/jupyter/enterprise_gateway/releases/download/v2.5.2/jupyter_enterprise_gateway_kernelspecs-2.5.2.tar.gz
 KERNELS_FOLDER=/usr/local/share/jupyter/kernels
-tar -zxvf jupyter_enterprise_gateway_kernelspecs-2.5.1.tar.gz --strip 1 --directory $KERNELS_FOLDER/spark_R_yarn_client/ spark_R_yarn_client/
+tar -zxvf jupyter_enterprise_gateway_kernelspecs-2.5.2.tar.gz --strip 1 --directory $KERNELS_FOLDER/spark_R_yarn_client/ spark_R_yarn_client/
 mv $KERNELS_FOLDER/spark_R_yarn_client $KERNELS_FOLDER/spark_R_spark_standalone
 ```
 
diff --git a/docs/source/kernel-yarn-client-mode.md b/docs/source/kernel-yarn-client-mode.md
index f909bb52d..6fc4ce049 100644
--- a/docs/source/kernel-yarn-client-mode.md
+++ b/docs/source/kernel-yarn-client-mode.md
@@ -27,14 +27,14 @@ EG_REMOTE_HOSTS=elyra-node-1.fyre.ibm.com,elyra-node-2.fyre.ibm.com,elyra-node-3
 ```
 
 For each supported Jupyter Kernel, we have provided sample kernel configurations and launchers as part of the release
-[jupyter_enterprise_gateway_kernelspecs-2.5.1.tar.gz](https://github.com/jupyter/enterprise_gateway/releases/download/v2.5.1/jupyter_enterprise_gateway_kernelspecs-2.5.1.tar.gz).
+[jupyter_enterprise_gateway_kernelspecs-2.5.2.tar.gz](https://github.com/jupyter/enterprise_gateway/releases/download/v2.5.2/jupyter_enterprise_gateway_kernelspecs-2.5.2.tar.gz).
 
 Considering we would like to enable the IPython Kernel that comes pre-installed with Anaconda to run on
 Yarn Client mode, we would have to copy the sample configuration folder **spark_python_yarn_client**
 to where the Jupyter kernels are installed (e.g. jupyter kernelspec list)
 
 ``` Bash
-wget https://github.com/jupyter/enterprise_gateway/releases/download/v2.5.1/jupyter_enterprise_gateway_kernelspecs-2.5.1.tar.gz
+wget https://github.com/jupyter/enterprise_gateway/releases/download/v2.5.2/jupyter_enterprise_gateway_kernelspecs-2.5.2.tar.gz
 SCALA_KERNEL_DIR="$(jupyter kernelspec list | grep -w "python3" | awk '{print $2}')"
 KERNELS_FOLDER="$(dirname "${SCALA_KERNEL_DIR}")"
 tar -zxvf enterprise_gateway_kernelspecs.tar.gz --strip 1 --directory $KERNELS_FOLDER/spark_python_yarn_client/ spark_python_yarn_client/
@@ -82,9 +82,9 @@ Follow the steps below to install/configure the Toree kernel:
 Considering we would like to enable the Scala Kernel to run on YARN Cluster and Client mode we would have to copy the sample configuration folder **spark_scala_yarn_client** to where the Jupyter kernels are installed (e.g. jupyter kernelspec list)
 
 ``` Bash
-wget https://github.com/jupyter/enterprise_gateway/releases/download/v2.5.1/jupyter_enterprise_gateway_kernelspecs-2.5.1.tar.gz
+wget https://github.com/jupyter/enterprise_gateway/releases/download/v2.5.2/jupyter_enterprise_gateway_kernelspecs-2.5.2.tar.gz
 KERNELS_FOLDER=/usr/local/share/jupyter/kernels
-tar -zxvf jupyter_enterprise_gateway_kernelspecs-2.5.1.tar.gz --strip 1 --directory $KERNELS_FOLDER/spark_scala_yarn_client/ spark_scala_yarn_client/
+tar -zxvf jupyter_enterprise_gateway_kernelspecs-2.5.2.tar.gz --strip 1 --directory $KERNELS_FOLDER/spark_scala_yarn_client/ spark_scala_yarn_client/
 ```
 
 For more information about the Scala kernel, please visit the [Apache Toree](https://toree.apache.org/) page.
@@ -98,9 +98,9 @@ The IPython kernel comes pre-installed with Anaconda and we have tested with its
 Considering we would like to enable the IPython kernel to run on YARN Cluster and Client mode we would have to copy the sample configuration folder **spark_python_yarn_client** to where the Jupyter kernels are installed (e.g. jupyter kernelspec list)
 
 ``` Bash
-wget https://github.com/jupyter/enterprise_gateway/releases/download/v2.5.1/jupyter_enterprise_gateway_kernelspecs-2.5.1.tar.gz
+wget https://github.com/jupyter/enterprise_gateway/releases/download/v2.5.2/jupyter_enterprise_gateway_kernelspecs-2.5.2.tar.gz
 KERNELS_FOLDER=/usr/local/share/jupyter/kernels
-tar -zxvf jupyter_enterprise_gateway_kernelspecs-2.5.1.tar.gz --strip 1 --directory $KERNELS_FOLDER/spark_python_yarn_client/ spark_python_yarn_client/
+tar -zxvf jupyter_enterprise_gateway_kernelspecs-2.5.2.tar.gz --strip 1 --directory $KERNELS_FOLDER/spark_python_yarn_client/ spark_python_yarn_client/
 ```
 
 For more information about the IPython kernel, please visit the [IPython kernel](https://ipython.readthedocs.io/en/stable/) page.
@@ -132,9 +132,9 @@ ls $ANACONDA_HOME/lib/R/library
 Considering we would like to enable the IRkernel to run on YARN Cluster and Client mode we would have to copy the sample configuration folder **spark_R_yarn_client** to where the Jupyter kernels are installed (e.g. jupyter kernelspec list)
 
 ``` Bash
-wget https://github.com/jupyter/enterprise_gateway/releases/download/v2.5.1/jupyter_enterprise_gateway_kernelspecs-2.5.1.tar.gz
+wget https://github.com/jupyter/enterprise_gateway/releases/download/v2.5.2/jupyter_enterprise_gateway_kernelspecs-2.5.2.tar.gz
 KERNELS_FOLDER=/usr/local/share/jupyter/kernels
-tar -zxvf jupyter_enterprise_gateway_kernelspecs-2.5.1.tar.gz --strip 1 --directory $KERNELS_FOLDER/spark_R_yarn_client/ spark_R_yarn_client/
+tar -zxvf jupyter_enterprise_gateway_kernelspecs-2.5.2.tar.gz --strip 1 --directory $KERNELS_FOLDER/spark_R_yarn_client/ spark_R_yarn_client/
 ```
 
 For more information about the iR kernel, please visit the [IRkernel](https://irkernel.github.io/) page.
diff --git a/docs/source/kernel-yarn-cluster-mode.md b/docs/source/kernel-yarn-cluster-mode.md
index 5130cd605..956154883 100644
--- a/docs/source/kernel-yarn-cluster-mode.md
+++ b/docs/source/kernel-yarn-cluster-mode.md
@@ -28,16 +28,16 @@ EG_ALT_YARN_ENDPOINT=http://${ALT_YARN_RESOURCE_MANAGER_FQDN}:8088/ws/v1/cluster
 
 ### Configuring Kernels for YARN Cluster mode
 
-For each supported Jupyter Kernel, we have provided sample kernel configurations and launchers as part of the release [jupyter_enterprise_gateway_kernelspecs-2.5.1.tar.gz](https://github.com/jupyter/enterprise_gateway/releases/download/v2.5.1/jupyter_enterprise_gateway_kernelspecs-2.5.1.tar.gz).
+For each supported Jupyter Kernel, we have provided sample kernel configurations and launchers as part of the release [jupyter_enterprise_gateway_kernelspecs-2.5.2.tar.gz](https://github.com/jupyter/enterprise_gateway/releases/download/v2.5.2/jupyter_enterprise_gateway_kernelspecs-2.5.2.tar.gz).
 
 Considering we would like to enable the IPython Kernel that comes pre-installed with Anaconda to run on Yarn Cluster mode, we would have to copy the sample configuration folder **spark_python_yarn_cluster** to where the Jupyter kernels are installed (e.g. jupyter kernelspec list)
 
 ``` Bash
-wget https://github.com/jupyter/enterprise_gateway/releases/download/v2.5.1/jupyter_enterprise_gateway_kernelspecs-2.5.1.tar.gz
+wget https://github.com/jupyter/enterprise_gateway/releases/download/v2.5.2/jupyter_enterprise_gateway_kernelspecs-2.5.2.tar.gz
 SCALA_KERNEL_DIR="$(jupyter kernelspec list | grep -w "python3" | awk '{print $2}')"
 KERNELS_FOLDER="$(dirname "${SCALA_KERNEL_DIR}")"
 mkdir $KERNELS_FOLDER/spark_python_yarn_cluster/
-tar -zxvf jupyter_enterprise_gateway_kernelspecs-2.5.1.tar.gz --strip 1 --directory $KERNELS_FOLDER/spark_python_yarn_cluster/ spark_python_yarn_cluster/
+tar -zxvf jupyter_enterprise_gateway_kernelspecs-2.5.2.tar.gz --strip 1 --directory $KERNELS_FOLDER/spark_python_yarn_cluster/ spark_python_yarn_cluster/
 ```
   
 After that, you should have a `kernel.json` that looks similar to the one below:
@@ -80,9 +80,9 @@ Follow the steps below to install/configure the Toree kernel:
 Considering we would like to enable the Scala Kernel to run on YARN Cluster and Client mode we would have to copy the sample configuration folder **spark_scala_yarn_cluster** to where the Jupyter kernels are installed (e.g. jupyter kernelspec list)
 
 ``` Bash
-wget https://github.com/jupyter/enterprise_gateway/releases/download/v2.5.1/jupyter_enterprise_gateway_kernelspecs-2.5.1.tar.gz
+wget https://github.com/jupyter/enterprise_gateway/releases/download/v2.5.2/jupyter_enterprise_gateway_kernelspecs-2.5.2.tar.gz
 KERNELS_FOLDER=/usr/local/share/jupyter/kernels
-tar -zxvf jupyter_enterprise_gateway_kernelspecs-2.5.1.tar.gz --strip 1 --directory $KERNELS_FOLDER/spark_scala_yarn_cluster/ spark_scala_yarn_cluster/
+tar -zxvf jupyter_enterprise_gateway_kernelspecs-2.5.2.tar.gz --strip 1 --directory $KERNELS_FOLDER/spark_scala_yarn_cluster/ spark_scala_yarn_cluster/
 ```
 
 For more information about the Scala kernel, please visit the [Apache Toree](https://toree.apache.org/) page.
@@ -96,9 +96,9 @@ The IPython kernel comes pre-installed with Anaconda and we have tested with its
 Considering we would like to enable the IPython kernel to run on YARN Cluster and Client mode we would have to copy the sample configuration folder **spark_python_yarn_cluster** to where the Jupyter kernels are installed (e.g. jupyter kernelspec list)
 
 ``` Bash
-wget https://github.com/jupyter/enterprise_gateway/releases/download/v2.5.1/jupyter_enterprise_gateway_kernelspecs-2.5.1.tar.gz
+wget https://github.com/jupyter/enterprise_gateway/releases/download/v2.5.2/jupyter_enterprise_gateway_kernelspecs-2.5.2.tar.gz
 KERNELS_FOLDER=/usr/local/share/jupyter/kernels
-tar -zxvf jupyter_enterprise_gateway_kernelspecs-2.5.1.tar.gz --strip 1 --directory $KERNELS_FOLDER/spark_python_yarn_cluster/ spark_python_yarn_cluster/
+tar -zxvf jupyter_enterprise_gateway_kernelspecs-2.5.2.tar.gz --strip 1 --directory $KERNELS_FOLDER/spark_python_yarn_cluster/ spark_python_yarn_cluster/
 ```
 
 For more information about the IPython kernel, please visit the [IPython kernel](https://ipython.readthedocs.io/en/stable/) page.
@@ -130,9 +130,9 @@ ls $ANACONDA_HOME/lib/R/library
 Considering we would like to enable the IRkernel to run on YARN Cluster and Client mode we would have to copy the sample configuration folder **spark_R_yarn_cluster** to where the Jupyter kernels are installed (e.g. jupyter kernelspec list)
 
 ``` Bash
-wget https://github.com/jupyter/enterprise_gateway/releases/download/v2.5.1/jupyter_enterprise_gateway_kernelspecs-2.5.1.tar.gz
+wget https://github.com/jupyter/enterprise_gateway/releases/download/v2.5.2/jupyter_enterprise_gateway_kernelspecs-2.5.2.tar.gz
 KERNELS_FOLDER=/usr/local/share/jupyter/kernels
-tar -zxvf jupyter_enterprise_gateway_kernelspecs-2.5.1.tar.gz --strip 1 --directory $KERNELS_FOLDER/spark_R_yarn_cluster/ spark_R_yarn_cluster/
+tar -zxvf jupyter_enterprise_gateway_kernelspecs-2.5.2.tar.gz --strip 1 --directory $KERNELS_FOLDER/spark_R_yarn_cluster/ spark_R_yarn_cluster/
 ```
 
 For more information about the iR kernel, please visit the [IRkernel](https://irkernel.github.io/) page.
diff --git a/enterprise_gateway/_version.py b/enterprise_gateway/_version.py
index 3dbd075b2..8042bc90e 100644
--- a/enterprise_gateway/_version.py
+++ b/enterprise_gateway/_version.py
@@ -3,4 +3,4 @@
 # Copyright (c) Jupyter Development Team.
 # Distributed under the terms of the Modified BSD License.
 
-__version__ = '2.5.2.dev0'
+__version__ = '2.5.2'
diff --git a/etc/docker/docker-compose.yml b/etc/docker/docker-compose.yml
index 2af3f3589..33b2844d3 100644
--- a/etc/docker/docker-compose.yml
+++ b/etc/docker/docker-compose.yml
@@ -11,7 +11,7 @@ version: "3.5"
 services:
 
   enterprise-gateway:
-    image: elyra/enterprise-gateway:dev
+    image: elyra/enterprise-gateway:2.5.2
     user: root
     volumes:
       - "/var/run/docker.sock:/var/run/docker.sock"
diff --git a/etc/kubernetes/enterprise-gateway.yaml b/etc/kubernetes/enterprise-gateway.yaml
index 553a58abb..698218b2e 100644
--- a/etc/kubernetes/enterprise-gateway.yaml
+++ b/etc/kubernetes/enterprise-gateway.yaml
@@ -144,7 +144,7 @@ spec:
           value: "python_kubernetes"
 
         # Ensure the following VERSION tag is updated to the version of Enterprise Gateway you wish to run
-        image: elyra/enterprise-gateway:dev
+        image: elyra/enterprise-gateway:2.5.2
         # Use IfNotPresent policy so that dev-based systems don't automatically
         # update. This provides more control.  Since formal tags will be release-specific
         # this policy should be sufficient for them as well.
@@ -180,7 +180,7 @@ spec:
     spec:
       containers:
       - name: kernel-image-puller 
-        image: elyra/kernel-image-puller:dev
+        image: elyra/kernel-image-puller:2.5.2
         env:
           - name: KIP_GATEWAY_HOST
             value: "http://enterprise-gateway.enterprise-gateway:8888"
diff --git a/etc/kubernetes/helm/enterprise-gateway/Chart.yaml b/etc/kubernetes/helm/enterprise-gateway/Chart.yaml
index 6f7604e47..a50eeba58 100644
--- a/etc/kubernetes/helm/enterprise-gateway/Chart.yaml
+++ b/etc/kubernetes/helm/enterprise-gateway/Chart.yaml
@@ -1,6 +1,6 @@
 name: enterprise-gateway
 description: A helm chart to deploy Jupyter Enterprise Gateway
-version: 2.5.2.dev0
+version: 2.5.2
 apiVersion: v1
 icon: https://avatars1.githubusercontent.com/u/7388996?s=200&v=4
 home: https://jupyter.org/enterprise_gateway
diff --git a/etc/kubernetes/helm/enterprise-gateway/values.yaml b/etc/kubernetes/helm/enterprise-gateway/values.yaml
index d0f54e1f3..c983c5512 100644
--- a/etc/kubernetes/helm/enterprise-gateway/values.yaml
+++ b/etc/kubernetes/helm/enterprise-gateway/values.yaml
@@ -1,5 +1,5 @@
 # Enterprise Gateway image name and tag to use.
-image: elyra/enterprise-gateway:dev
+image: elyra/enterprise-gateway:2.5.2
 # Enterprise Gateway image pull policy.
 imagePullPolicy: IfNotPresent
 # The primary port on which Enterprise Gateway is servicing requests.
@@ -73,7 +73,7 @@ ingress:
 kip:
   enabled: true
   # Kernel Image Puller image name and tag to use.
-  image: elyra/kernel-image-puller:dev
+  image: elyra/kernel-image-puller:2.5.2
   # Kernel Image Puller image pull policy.
   imagePullPolicy: IfNotPresent
   # Determines whether the Kernel Image Puller will pull kernel images it has previously pulled