test ci

sagemaker-python-sdk/mxnet_horovod_maskrcnn/horovod_deployment_notebook.ipynb

@@ -5,6 +5,9 @@
    "metadata": {},
    "source": [
     "# Reduce MaskCNN Training Time with Apache MXNet and Horovod on Amazon SageMaker\n",
+    "\n",
+    "test ci.\n",
+    "\n",
     "Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. SageMaker removes the heavy lifting from each step of the machine learning process to make it easier to develop high quality models. As datasets continue to increase in size, additional compute is required to reduce the amount of time it takes to train. One method to scale horizontally and add these additional resources on SageMaker is through the use of Horovod and Apache MXNet. In this post, we will show how users can reduce training time with MXNet and Horovod on SageMaker. Finally, we will demonstrate how you can improve performance even more with advanced sections on Horovod Timeline, Horovod Autotune, Horovod Fusion, and MXNet Optimization.  \n",
     "\n",
     "## Distributed Training \n",