diff --git a/r_examples/r_api_serving_examples/API Serving Examples.ipynb b/r_examples/r_api_serving_examples/API Serving Examples.ipynb
deleted file mode 100644
index cb85c5bac6..0000000000
--- a/r_examples/r_api_serving_examples/API Serving Examples.ipynb	
+++ /dev/null
@@ -1,610 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# R API Serving Examples\n",
-    "\n",
-    "In this example, we demonstrate how to quickly compare the runtimes of three methods for serving a model from an R hosted REST API. The following SageMaker examples discuss each method in detail:\n",
-    "\n",
-    "* **Plumber**\n",
-    " * Website: [https://www.rplumber.io/](https://www.rplumber.io)\n",
-    " * SageMaker Example: [r_serving_with_plumber](../r_serving_with_plumber)\n",
-    "* **RestRServe**\n",
-    " * Website: [https://restrserve.org](https://restrserve.org)\n",
-    " * SageMaker Example: [r_serving_with_restrserve](../r_serving_with_restrserve)\n",
-    "* **FastAPI** (reticulated from Python)\n",
-    " * Website: [https://fastapi.tiangolo.com](https://fastapi.tiangolo.com)\n",
-    " * SageMaker Example: [r_serving_with_fastapi](../r_serving_with_fastapi)\n",
-    " \n",
-    "We will reuse the docker images from each of these examples. Each one is configured to serve a small XGBoost model which has already been trained on the classical Iris dataset."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Building Docker Images for Serving\n",
-    "\n",
-    "First, we will build each docker image from the provided SageMaker Examples."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Plumber Serving Image"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "!cd .. && docker build -t r-plumber -f r_serving_with_plumber/Dockerfile r_serving_with_plumber"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### RestRServe Serving Image"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "!cd .. && docker build -t r-restrserve -f r_serving_with_restrserve/Dockerfile r_serving_with_restrserve"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### FastAPI Serving Image"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "!cd .. && docker build -t r-fastapi -f r_serving_with_fastapi/Dockerfile r_serving_with_fastapi"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Launch Serving Containers"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, we will launch each search container. The containers will be launch on the following ports to avoid port collisions on your local machine or SageMaker Notebook instance:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ports = {\n",
-    "    \"plumber\": 5000,\n",
-    "    \"restrserve\": 5001,\n",
-    "    \"fastapi\": 5002,\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!bash launch.sh"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!docker container list"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Define Simple Client"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import requests\n",
-    "from tqdm import tqdm\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_predictions(examples, instance=requests, port=5000):\n",
-    "    payload = {\"features\": examples}\n",
-    "    return instance.post(f\"http://127.0.0.1:{port}/invocations\", json=payload)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_health(instance=requests, port=5000):\n",
-    "    instance.get(f\"http://127.0.0.1:{port}/ping\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Define Example Inputs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, we define a example inputs from the classical [Iris](https://archive.ics.uci.edu/ml/datasets/iris) dataset.\n",
-    "* Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "column_names = [\"Sepal.Length\", \"Sepal.Width\", \"Petal.Length\", \"Petal.Width\", \"Label\"]\n",
-    "iris = pd.read_csv(\n",
-    "    \"s3://sagemaker-sample-files/datasets/tabular/iris/iris.data\", names=column_names\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "iris_features = iris[[\"Sepal.Length\", \"Sepal.Width\", \"Petal.Length\", \"Petal.Width\"]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "example = iris_features.values[:1].tolist()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "many_examples = iris_features.values[:100].tolist()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Testing\n",
-    "\n",
-    "Now it's time to test how each API server performs under stress."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We will test two use cases:\n",
-    "* **New Requests**: In this scenario, we test how quickly the server can respond with predictions when each client request establishes a new connection with the server. This simulates the server's ability to handle real-time requests. We could make this more realistic by creating an asynchronous environment that tests the server's ability to fulfill concurrent rather than sequential requests.\n",
-    "* **Keep Alive / Reuse Session**: In this scenario, we test how quickly the server can respond with predictions when each client request uses a session to keep its connection to the server alive between requests. This simulates the server's ability to handle sequential batch requests from the same client."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For each of the two use cases, we will test the performance on following situations:\n",
-    "\n",
-    "* 1000 requests of a single example\n",
-    "* 1000 requests of 100 examples\n",
-    "* 1000 pings for health status"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## New Requests"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Plumber"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# verify the prediction output\n",
-    "get_predictions(example, port=ports[\"plumber\"]).json()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for i in tqdm(range(1000)):\n",
-    "    _ = get_predictions(example, port=ports[\"plumber\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for i in tqdm(range(1000)):\n",
-    "    _ = get_predictions(many_examples, port=ports[\"plumber\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for i in tqdm(range(1000)):\n",
-    "    get_health(port=ports[\"plumber\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### RestRserve"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# verify the prediction output\n",
-    "get_predictions(example, port=ports[\"restrserve\"]).json()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for i in tqdm(range(1000)):\n",
-    "    _ = get_predictions(example, port=ports[\"restrserve\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for i in tqdm(range(1000)):\n",
-    "    _ = get_predictions(many_examples, port=ports[\"restrserve\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for i in tqdm(range(1000)):\n",
-    "    get_health(port=ports[\"restrserve\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### FastAPI"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# verify the prediction output\n",
-    "get_predictions(example, port=ports[\"fastapi\"]).json()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for i in tqdm(range(1000)):\n",
-    "    _ = get_predictions(example, port=ports[\"fastapi\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for i in tqdm(range(1000)):\n",
-    "    _ = get_predictions(many_examples, port=ports[\"fastapi\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for i in tqdm(range(1000)):\n",
-    "    get_health(port=ports[\"fastapi\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Keep Alive (Reuse Session)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now, let's test how each one performs when each request reuses a session connection. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# reuse the session for each post and get request\n",
-    "instance = requests.Session()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Plumber"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for i in tqdm(range(1000)):\n",
-    "    _ = get_predictions(example, instance=instance, port=ports[\"plumber\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for i in tqdm(range(1000)):\n",
-    "    _ = get_predictions(many_examples, instance=instance, port=ports[\"plumber\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for i in tqdm(range(1000)):\n",
-    "    get_health(instance=instance, port=ports[\"plumber\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### RestRserve"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for i in tqdm(range(1000)):\n",
-    "    _ = get_predictions(example, instance=instance, port=ports[\"restrserve\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for i in tqdm(range(1000)):\n",
-    "    _ = get_predictions(many_examples, instance=instance, port=ports[\"restrserve\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for i in tqdm(range(1000)):\n",
-    "    get_health(instance=instance, port=ports[\"restrserve\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### FastAPI"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for i in tqdm(range(1000)):\n",
-    "    _ = get_predictions(example, instance=instance, port=ports[\"fastapi\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for i in tqdm(range(1000)):\n",
-    "    _ = get_predictions(many_examples, instance=instance, port=ports[\"fastapi\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for i in tqdm(range(1000)):\n",
-    "    get_health(instance=instance, port=ports[\"fastapi\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Stop All Serving Containers"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Finally, we will shut down the serving containers we launched for the tests."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!docker kill $(docker ps -q)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Conclusion"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this example, we demonstrated how to conduct a simple performance benchmark across three R model serving solutions. We leave the choice of serving solution up to the reader since in some cases it might be appropriate to customize the benchmark in the following ways:\n",
-    "\n",
-    "* Update the serving example to serve a specific model\n",
-    "* Perform the tests across multiple instances types\n",
-    "* Modify the serving example and client to test asynchronous requests.\n",
-    "* Deploy the serving examples to SageMaker Endpoints to test within an autoscaling environment.\n",
-    "\n",
-    "For more information on serving your models in custom containers on SageMaker, please see our [support documentation](https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms-inference-main.html) for the latest updates and best practices."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "conda_python3",
-   "language": "python",
-   "name": "conda_python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/r_examples/r_api_serving_examples/iris.csv b/r_examples/r_api_serving_examples/iris.csv
deleted file mode 100644
index 8b6393099a..0000000000
--- a/r_examples/r_api_serving_examples/iris.csv
+++ /dev/null
@@ -1,151 +0,0 @@
-Sepal.Length,Sepal.Width,Petal.Length,Petal.Width,Species
-5.1,3.5,1.4,0.2,setosa
-4.9,3,1.4,0.2,setosa
-4.7,3.2,1.3,0.2,setosa
-4.6,3.1,1.5,0.2,setosa
-5,3.6,1.4,0.2,setosa
-5.4,3.9,1.7,0.4,setosa
-4.6,3.4,1.4,0.3,setosa
-5,3.4,1.5,0.2,setosa
-4.4,2.9,1.4,0.2,setosa
-4.9,3.1,1.5,0.1,setosa
-5.4,3.7,1.5,0.2,setosa
-4.8,3.4,1.6,0.2,setosa
-4.8,3,1.4,0.1,setosa
-4.3,3,1.1,0.1,setosa
-5.8,4,1.2,0.2,setosa
-5.7,4.4,1.5,0.4,setosa
-5.4,3.9,1.3,0.4,setosa
-5.1,3.5,1.4,0.3,setosa
-5.7,3.8,1.7,0.3,setosa
-5.1,3.8,1.5,0.3,setosa
-5.4,3.4,1.7,0.2,setosa
-5.1,3.7,1.5,0.4,setosa
-4.6,3.6,1,0.2,setosa
-5.1,3.3,1.7,0.5,setosa
-4.8,3.4,1.9,0.2,setosa
-5,3,1.6,0.2,setosa
-5,3.4,1.6,0.4,setosa
-5.2,3.5,1.5,0.2,setosa
-5.2,3.4,1.4,0.2,setosa
-4.7,3.2,1.6,0.2,setosa
-4.8,3.1,1.6,0.2,setosa
-5.4,3.4,1.5,0.4,setosa
-5.2,4.1,1.5,0.1,setosa
-5.5,4.2,1.4,0.2,setosa
-4.9,3.1,1.5,0.2,setosa
-5,3.2,1.2,0.2,setosa
-5.5,3.5,1.3,0.2,setosa
-4.9,3.6,1.4,0.1,setosa
-4.4,3,1.3,0.2,setosa
-5.1,3.4,1.5,0.2,setosa
-5,3.5,1.3,0.3,setosa
-4.5,2.3,1.3,0.3,setosa
-4.4,3.2,1.3,0.2,setosa
-5,3.5,1.6,0.6,setosa
-5.1,3.8,1.9,0.4,setosa
-4.8,3,1.4,0.3,setosa
-5.1,3.8,1.6,0.2,setosa
-4.6,3.2,1.4,0.2,setosa
-5.3,3.7,1.5,0.2,setosa
-5,3.3,1.4,0.2,setosa
-7,3.2,4.7,1.4,versicolor
-6.4,3.2,4.5,1.5,versicolor
-6.9,3.1,4.9,1.5,versicolor
-5.5,2.3,4,1.3,versicolor
-6.5,2.8,4.6,1.5,versicolor
-5.7,2.8,4.5,1.3,versicolor
-6.3,3.3,4.7,1.6,versicolor
-4.9,2.4,3.3,1,versicolor
-6.6,2.9,4.6,1.3,versicolor
-5.2,2.7,3.9,1.4,versicolor
-5,2,3.5,1,versicolor
-5.9,3,4.2,1.5,versicolor
-6,2.2,4,1,versicolor
-6.1,2.9,4.7,1.4,versicolor
-5.6,2.9,3.6,1.3,versicolor
-6.7,3.1,4.4,1.4,versicolor
-5.6,3,4.5,1.5,versicolor
-5.8,2.7,4.1,1,versicolor
-6.2,2.2,4.5,1.5,versicolor
-5.6,2.5,3.9,1.1,versicolor
-5.9,3.2,4.8,1.8,versicolor
-6.1,2.8,4,1.3,versicolor
-6.3,2.5,4.9,1.5,versicolor
-6.1,2.8,4.7,1.2,versicolor
-6.4,2.9,4.3,1.3,versicolor
-6.6,3,4.4,1.4,versicolor
-6.8,2.8,4.8,1.4,versicolor
-6.7,3,5,1.7,versicolor
-6,2.9,4.5,1.5,versicolor
-5.7,2.6,3.5,1,versicolor
-5.5,2.4,3.8,1.1,versicolor
-5.5,2.4,3.7,1,versicolor
-5.8,2.7,3.9,1.2,versicolor
-6,2.7,5.1,1.6,versicolor
-5.4,3,4.5,1.5,versicolor
-6,3.4,4.5,1.6,versicolor
-6.7,3.1,4.7,1.5,versicolor
-6.3,2.3,4.4,1.3,versicolor
-5.6,3,4.1,1.3,versicolor
-5.5,2.5,4,1.3,versicolor
-5.5,2.6,4.4,1.2,versicolor
-6.1,3,4.6,1.4,versicolor
-5.8,2.6,4,1.2,versicolor
-5,2.3,3.3,1,versicolor
-5.6,2.7,4.2,1.3,versicolor
-5.7,3,4.2,1.2,versicolor
-5.7,2.9,4.2,1.3,versicolor
-6.2,2.9,4.3,1.3,versicolor
-5.1,2.5,3,1.1,versicolor
-5.7,2.8,4.1,1.3,versicolor
-6.3,3.3,6,2.5,virginica
-5.8,2.7,5.1,1.9,virginica
-7.1,3,5.9,2.1,virginica
-6.3,2.9,5.6,1.8,virginica
-6.5,3,5.8,2.2,virginica
-7.6,3,6.6,2.1,virginica
-4.9,2.5,4.5,1.7,virginica
-7.3,2.9,6.3,1.8,virginica
-6.7,2.5,5.8,1.8,virginica
-7.2,3.6,6.1,2.5,virginica
-6.5,3.2,5.1,2,virginica
-6.4,2.7,5.3,1.9,virginica
-6.8,3,5.5,2.1,virginica
-5.7,2.5,5,2,virginica
-5.8,2.8,5.1,2.4,virginica
-6.4,3.2,5.3,2.3,virginica
-6.5,3,5.5,1.8,virginica
-7.7,3.8,6.7,2.2,virginica
-7.7,2.6,6.9,2.3,virginica
-6,2.2,5,1.5,virginica
-6.9,3.2,5.7,2.3,virginica
-5.6,2.8,4.9,2,virginica
-7.7,2.8,6.7,2,virginica
-6.3,2.7,4.9,1.8,virginica
-6.7,3.3,5.7,2.1,virginica
-7.2,3.2,6,1.8,virginica
-6.2,2.8,4.8,1.8,virginica
-6.1,3,4.9,1.8,virginica
-6.4,2.8,5.6,2.1,virginica
-7.2,3,5.8,1.6,virginica
-7.4,2.8,6.1,1.9,virginica
-7.9,3.8,6.4,2,virginica
-6.4,2.8,5.6,2.2,virginica
-6.3,2.8,5.1,1.5,virginica
-6.1,2.6,5.6,1.4,virginica
-7.7,3,6.1,2.3,virginica
-6.3,3.4,5.6,2.4,virginica
-6.4,3.1,5.5,1.8,virginica
-6,3,4.8,1.8,virginica
-6.9,3.1,5.4,2.1,virginica
-6.7,3.1,5.6,2.4,virginica
-6.9,3.1,5.1,2.3,virginica
-5.8,2.7,5.1,1.9,virginica
-6.8,3.2,5.9,2.3,virginica
-6.7,3.3,5.7,2.5,virginica
-6.7,3,5.2,2.3,virginica
-6.3,2.5,5,1.9,virginica
-6.5,3,5.2,2,virginica
-6.2,3.4,5.4,2.3,virginica
-5.9,3,5.1,1.8,virginica
diff --git a/r_examples/r_api_serving_examples/launch.sh b/r_examples/r_api_serving_examples/launch.sh
deleted file mode 100644
index e456602d35..0000000000
--- a/r_examples/r_api_serving_examples/launch.sh
+++ /dev/null
@@ -1,11 +0,0 @@
-#!/bin/bash
-
-echo "Launching Plumber"
-docker run -d --rm -p 5000:8080 r-plumber 
-
-echo "Launching RestRServer"
-docker run -d  --rm -p 5001:8080 r-restrserve 
-
-echo "Launching FastAPI"
-docker run -d  --rm -p 5002:8080 r-fastapi 
-