diff --git a/source/clear-linux/tutorials/dlrs/dlrs.rst b/source/clear-linux/tutorials/dlrs/dlrs.rst
index ff3f2ca4..3098432c 100644
--- a/source/clear-linux/tutorials/dlrs/dlrs.rst
+++ b/source/clear-linux/tutorials/dlrs/dlrs.rst
@@ -32,7 +32,7 @@ The Deep Learning Reference Stack is available in the following versions:
 * `PyTorch with Intel MKL-DNN`_, which includes PyTorch optimized using Intel®
   Math Kernel Library (Intel® MKL) and Intel MKL-DNN.
 
-.. note::
+.. important::
 
    To take advantage of the Intel® AVX-512 and VNNI functionality with the Deep
    Learning Reference Stack, you must use the following hardware:
@@ -185,6 +185,11 @@ Kubeflow multi-node benchmarks
 The benchmark workload runs in a Kubernetes cluster. The tutorial uses
 `Kubeflow`_ for the Machine Learning workload deployment on three nodes.
 
+.. warning::
+
+   If you choose the Intel® MKL-DNN or Intel® MKL-DNN-VNNI image, your platform must support the Intel® AVX-512 instruction set. Otherwise, an *illegal instruction* error may appear, and you won’t be able to complete this tutorial.
+
+
 Kubernetes setup
 ================
 
@@ -236,9 +241,11 @@ or inject a python script or larger shell script into the container.
 Images
 ======
 
-You must add `launcher.py` to the Docker image to include the Deep
+You must add `launcher.py`_ to the Docker image to include the Deep
 Learning Reference Stack and put the benchmarks repo in the correct
-location. From the Docker image, run the following:
+location. Note that this tutorial uses Kubeflow v0.4.0, and cannot guarantee results if you use a different version. 
+
+From the Docker image, run the following:
 
 .. code-block:: bash
 
@@ -486,6 +493,8 @@ Related topics
 
 .. _kubectl overview: https://kubernetes.io/docs/reference/kubectl/overview/
 
+.. _launcher.py: https://github.com/clearlinux/dockerfiles/tree/master/stacks/dlrs/kubeflow
+
 .. _Release notes on Github\*: https://github.com/clearlinux/dockerfiles/blob/master/stacks/dlrs/releasenote.md
 
 .. _Intel® quantization tools:  https://github.com/IntelAI/tools/blob/master/tensorflow_quantization/README.md#quantization-tools