Merge pull request #163 from adiso75/development

Delete old image classification under GPU and fix read.me

demos/gpu/README.ipynb

@@ -25,16 +25,11 @@
     "- A **horovod** directory with applications that use Uber's [Horovod](https://eng.uber.com/horovod/) distributed deep-learning framework, which can be used to convert a single-GPU TensorFlow, Keras, or PyTorch model-training program to a distributed program that trains the model simultaneously over multiple GPUs.\n",
     "    The objective is to speed up your model training with minimal changes to your existing single-GPU code and without complicating the execution.\n",
     "    Horovod code can also run over CPUs with only minor modifications.\n",
-    "    For more information and examples, see the [Horovod GitHub repository](https://github.com/horovod/horovod).\n",
-    "    \n",
     "    The Horovod tutorials include the following:\n",
-    "\n",
-    "    - An image-recognition demo application for execution over GPUs (**image-classification**).\n",
-    "    - A slightly modified version of the GPU image-classification demo application for execution over CPUs (**cpu/image-classification**).\n",
     "    - Benchmark tests (**benchmark-tf.ipynb**, which executes **tf_cnn_benchmarks.py**).\n",
+    "    - Note that under the demo folder you will find an image classificaiton demo that is also running with Horovod and can be set to        run with GPU <br>\n",
     "\n",
     "- A **rapids** directory with applications that use NVIDIA's [RAPIDS](https://rapids.ai/) open-source libraries suite for executing end-to-end data science and analytics pipelines entirely on GPUs.\n",
-    "\n",
     "  The RAPIDS tutorials include the following:\n",
     "\n",
     "    - Demo applications that use the [cuDF](https://rapidsai.github.io/projects/cudf/en/latest/index.html) RAPIDS GPU DataFrame library to perform batching and aggregation of data that's read from a Kafaka stream, and then write the results to a Parquet file.<br>\n",