diff --git a/examples/sagemaker/container/Dockerfile b/examples/sagemaker/container/Dockerfile
new file mode 100644
index 0000000..5c07e52
--- /dev/null
+++ b/examples/sagemaker/container/Dockerfile
@@ -0,0 +1,48 @@
+FROM nvidia/cuda:10.2-base-ubuntu18.04 as mambaforge-cuda
+
+ENV CONDA_DIR=/opt/conda
+ENV LANG=C.UTF-8 LC_ALL=C.UTF-8
+ENV PATH=${CONDA_DIR}/bin:${PATH}
+ENV CONDA_OVERRIDE_CUDA=10.2
+
+COPY --from=condaforge/mambaforge /opt/conda /opt/conda
+COPY --from=condaforge/mambaforge /usr/local/bin/tini /usr/local/bin/tini
+COPY --from=condaforge/mambaforge /etc/skel/.bashrc /etc/skel/.bashrc
+COPY --from=condaforge/mambaforge /root/.bashrc /root/.bashrc
+RUN mamba install --yes pynvml
+
+RUN apt-get update && \
+    apt-get install --no-install-recommends --yes wget bzip2 ca-certificates git && \
+    apt-get clean && \
+    rm -rf /var/lib/apt/lists/*
+
+# Cache cudatoolkit
+RUN mamba create -y -n test --download-only cudatoolkit && \
+    conda clean -ty
+
+
+FROM mambaforge-cuda
+
+LABEL com.amazonaws.sagemaker.capabilities.multi-models=true
+LABEL com.amazonaws.sagemaker.capabilities.accept-bind-to-port=true
+
+ENV TZ="UTC"
+ENV DEBIAN_FRONTEND=noninteractive
+
+ENTRYPOINT ["/docker-entrypoint.sh"]
+WORKDIR /home/model-server/
+
+ADD https://raw.githubusercontent.com/seatgeek/bash-aptfile/master/bin/aptfile /usr/local/bin/aptfile
+RUN chmod +x /usr/local/bin/aptfile
+
+# Ref: https://github.com/seatgeek/bash-aptfile
+COPY aptfile /home/model-server
+RUN aptfile
+
+# Darknet Library is *either* compiled for CPU or GPU, so we need two conda environments
+COPY environment-*.yml /home/model-server
+RUN mamba env create -n cpu -f /home/model-server/environment-cpu.yml && \
+    mamba env create -n gpu -f /home/model-server/environment-gpu.yml && \
+    conda clean -tipsy
+
+COPY entrypoint.sh /docker-entrypoint.sh
diff --git a/examples/sagemaker/container/aptfile b/examples/sagemaker/container/aptfile
new file mode 100755
index 0000000..2aa184d
--- /dev/null
+++ b/examples/sagemaker/container/aptfile
@@ -0,0 +1,8 @@
+#!/usr/bin/env aptfile
+
+# trigger and apt-get update
+update
+
+# install some packages
+# use JDK8 because SageMaker passes the -XX:-ContainerSupport option
+package "openjdk-8-jre-headless"
diff --git a/examples/sagemaker/container/entrypoint.sh b/examples/sagemaker/container/entrypoint.sh
new file mode 100755
index 0000000..7e05e88
--- /dev/null
+++ b/examples/sagemaker/container/entrypoint.sh
@@ -0,0 +1,15 @@
+#!/usr/bin/env bash
+source /opt/conda/etc/profile.d/conda.sh
+conda activate base
+conda_environment=$(python -c '
+from pynvml import *
+try:
+  nvmlInit()
+  if nvmlDeviceGetCount() > 0:
+    print("gpu")
+except:
+  print("cpu")
+')
+conda activate $conda_environment
+
+exec python -m darknet.sagemaker
diff --git a/examples/sagemaker/container/environment-cpu.yml b/examples/sagemaker/container/environment-cpu.yml
new file mode 100644
index 0000000..88af6ec
--- /dev/null
+++ b/examples/sagemaker/container/environment-cpu.yml
@@ -0,0 +1,16 @@
+channels:
+  - zeroae
+  - conda-forge
+dependencies:
+  - darknet-cpu
+  - darknet.py 0.3.*
+  - enum-compat
+  - future
+  - psutil
+  - retrying
+  - scipy
+  - six
+  - pip
+  - pip:
+      - multi-model-server ==1.1.*
+      - sagemaker-inference ==1.5.*
diff --git a/examples/sagemaker/container/environment-gpu.yml b/examples/sagemaker/container/environment-gpu.yml
new file mode 100644
index 0000000..340cd7b
--- /dev/null
+++ b/examples/sagemaker/container/environment-gpu.yml
@@ -0,0 +1,16 @@
+channels:
+  - zeroae
+  - conda-forge
+dependencies:
+  - darknet-gpu
+  - darknet.py 0.3.*
+  - enum-compat
+  - future
+  - psutil
+  - retrying
+  - scipy
+  - six
+  - pip
+  - pip:
+      - multi-model-server ==1.1.*
+      - sagemaker-inference ==1.5.*
diff --git a/examples/sagemaker/detect.ipynb b/examples/sagemaker/detect.ipynb
new file mode 100644
index 0000000..2641f8a
--- /dev/null
+++ b/examples/sagemaker/detect.ipynb
@@ -0,0 +1,280 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "import boto3\n",
+    "from sagemaker.local import LocalSession\n",
+    "from sagemaker.model import Model\n",
+    "from sagemaker.predictor import Predictor\n",
+    "from sagemaker.serializers import IdentitySerializer\n",
+    "from sagemaker.deserializers import JSONDeserializer\n",
+    "\n",
+    "DUMMY_IAM_ROLE = 'arn:aws:iam::111111111111:role/service-role/AmazonSageMaker-ExecutionRole-20200101T000001'\n",
+    "\n",
+    "boto3_session = boto3.Session(region_name=\"us-east-1\")\n",
+    "session = LocalSession(boto3_session)\n",
+    "session.config = {\"local\": {\"local_code\": True}}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "outputs": [],
+   "source": [
+    "role = DUMMY_IAM_ROLE\n",
+    "model_dir = \"file://ml/model\"\n",
+    "\n",
+    "model = Model(\n",
+    "    predictor_cls=Predictor,\n",
+    "    image_uri=\"zeroae/sagemaker-darknet-inference\",\n",
+    "    model_data=model_dir,\n",
+    "    role=DUMMY_IAM_ROLE,\n",
+    "    sagemaker_session=session,\n",
+    ")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Attaching to 8y2hfvaxhd-algo-1-9j1rc\n",
+      "\u001B[36m8y2hfvaxhd-algo-1-9j1rc |\u001B[0m Warning: MMS is using non-default JVM parameters: -XX:-UseContainerSupport\r\n",
+      "\u001B[36m8y2hfvaxhd-algo-1-9j1rc |\u001B[0m 2021-11-03 21:19:22,257 [INFO ] main com.amazonaws.ml.mms.ModelServer - \r\n",
+      "\u001B[36m8y2hfvaxhd-algo-1-9j1rc |\u001B[0m MMS Home: /opt/conda/envs/darknet/lib/python3.8/site-packages\r\n",
+      "\u001B[36m8y2hfvaxhd-algo-1-9j1rc |\u001B[0m Current directory: /\r\n",
+      "\u001B[36m8y2hfvaxhd-algo-1-9j1rc |\u001B[0m Temp directory: /tmp\r\n",
+      "\u001B[36m8y2hfvaxhd-algo-1-9j1rc |\u001B[0m Number of GPUs: 0\r\n",
+      "\u001B[36m8y2hfvaxhd-algo-1-9j1rc |\u001B[0m Number of CPUs: 6\r\n",
+      "\u001B[36m8y2hfvaxhd-algo-1-9j1rc |\u001B[0m Max heap size: 1486 M\r\n",
+      "\u001B[36m8y2hfvaxhd-algo-1-9j1rc |\u001B[0m Python executable: /opt/conda/envs/darknet/bin/python3.8\r\n",
+      "\u001B[36m8y2hfvaxhd-algo-1-9j1rc |\u001B[0m Config file: /etc/sagemaker-mms.properties\r\n",
+      "\u001B[36m8y2hfvaxhd-algo-1-9j1rc |\u001B[0m Inference address: http://0.0.0.0:8080\r\n",
+      "\u001B[36m8y2hfvaxhd-algo-1-9j1rc |\u001B[0m Management address: http://0.0.0.0:8080\r\n",
+      "\u001B[36m8y2hfvaxhd-algo-1-9j1rc |\u001B[0m Model Store: /.sagemaker/mms/models\r\n",
+      "\u001B[36m8y2hfvaxhd-algo-1-9j1rc |\u001B[0m Initial Models: ALL\r\n",
+      "\u001B[36m8y2hfvaxhd-algo-1-9j1rc |\u001B[0m Log dir: /logs\r\n",
+      "\u001B[36m8y2hfvaxhd-algo-1-9j1rc |\u001B[0m Metrics dir: /logs\r\n",
+      "\u001B[36m8y2hfvaxhd-algo-1-9j1rc |\u001B[0m Netty threads: 0\r\n",
+      "\u001B[36m8y2hfvaxhd-algo-1-9j1rc |\u001B[0m Netty client threads: 0\r\n",
+      "\u001B[36m8y2hfvaxhd-algo-1-9j1rc |\u001B[0m Default workers per model: 6\r\n",
+      "\u001B[36m8y2hfvaxhd-algo-1-9j1rc |\u001B[0m Blacklist Regex: N/A\r\n",
+      "\u001B[36m8y2hfvaxhd-algo-1-9j1rc |\u001B[0m Maximum Response Size: 6553500\r\n",
+      "\u001B[36m8y2hfvaxhd-algo-1-9j1rc |\u001B[0m Maximum Request Size: 6553500\r\n",
+      "\u001B[36m8y2hfvaxhd-algo-1-9j1rc |\u001B[0m Preload model: false\r\n",
+      "\u001B[36m8y2hfvaxhd-algo-1-9j1rc |\u001B[0m Prefer direct buffer: false\r\n",
+      "\u001B[36m8y2hfvaxhd-algo-1-9j1rc |\u001B[0m 2021-11-03 21:19:22,571 [WARN ] W-9000-model com.amazonaws.ml.mms.wlm.WorkerLifeCycle - attachIOStreams() threadName=W-9000-model\r\n",
+      "\u001B[36m8y2hfvaxhd-algo-1-9j1rc |\u001B[0m 2021-11-03 21:19:22,865 [INFO ] W-9000-model-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - model_service_worker started with args: --sock-type unix --sock-name /tmp/.mms.sock.9000 --handler darknet.sagemaker.detector.handler_service --model-path /.sagemaker/mms/models/model --model-name model --preload-model false --tmp-dir /tmp\r\n",
+      "\u001B[36m8y2hfvaxhd-algo-1-9j1rc |\u001B[0m 2021-11-03 21:19:22,870 [INFO ] W-9000-model-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - Listening on port: /tmp/.mms.sock.9000\r\n",
+      "\u001B[36m8y2hfvaxhd-algo-1-9j1rc |\u001B[0m 2021-11-03 21:19:22,872 [INFO ] W-9000-model-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - [PID] 41\r\n",
+      "\u001B[36m8y2hfvaxhd-algo-1-9j1rc |\u001B[0m 2021-11-03 21:19:22,875 [INFO ] W-9000-model-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - MMS worker started.\r\n",
+      "\u001B[36m8y2hfvaxhd-algo-1-9j1rc |\u001B[0m 2021-11-03 21:19:22,877 [INFO ] W-9000-model-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - Python runtime: 3.8.12\r\n",
+      "\u001B[36m8y2hfvaxhd-algo-1-9j1rc |\u001B[0m 2021-11-03 21:19:22,878 [INFO ] main com.amazonaws.ml.mms.wlm.ModelManager - Model model loaded.\r\n",
+      "\u001B[36m8y2hfvaxhd-algo-1-9j1rc |\u001B[0m 2021-11-03 21:19:22,912 [INFO ] main com.amazonaws.ml.mms.ModelServer - Initialize Inference server with: EpollServerSocketChannel.\r\n",
+      "\u001B[36m8y2hfvaxhd-algo-1-9j1rc |\u001B[0m 2021-11-03 21:19:22,965 [INFO ] W-9000-model com.amazonaws.ml.mms.wlm.WorkerThread - Connecting to: /tmp/.mms.sock.9000\r\n",
+      "\u001B[36m8y2hfvaxhd-algo-1-9j1rc |\u001B[0m 2021-11-03 21:19:22,967 [INFO ] W-9000-model com.amazonaws.ml.mms.wlm.WorkerThread - Connecting to: /tmp/.mms.sock.9000\r\n",
+      "\u001B[36m8y2hfvaxhd-algo-1-9j1rc |\u001B[0m 2021-11-03 21:19:22,967 [INFO ] W-9000-model com.amazonaws.ml.mms.wlm.WorkerThread - Connecting to: /tmp/.mms.sock.9000\r\n",
+      "\u001B[36m8y2hfvaxhd-algo-1-9j1rc |\u001B[0m 2021-11-03 21:19:22,966 [INFO ] W-9000-model com.amazonaws.ml.mms.wlm.WorkerThread - Connecting to: /tmp/.mms.sock.9000\r\n",
+      "\u001B[36m8y2hfvaxhd-algo-1-9j1rc |\u001B[0m 2021-11-03 21:19:22,966 [INFO ] W-9000-model com.amazonaws.ml.mms.wlm.WorkerThread - Connecting to: /tmp/.mms.sock.9000\r\n",
+      "\u001B[36m8y2hfvaxhd-algo-1-9j1rc |\u001B[0m 2021-11-03 21:19:22,965 [INFO ] W-9000-model com.amazonaws.ml.mms.wlm.WorkerThread - Connecting to: /tmp/.mms.sock.9000\r\n",
+      "\u001B[36m8y2hfvaxhd-algo-1-9j1rc |\u001B[0m 2021-11-03 21:19:23,300 [INFO ] main com.amazonaws.ml.mms.ModelServer - Inference API bind to: http://0.0.0.0:8080\r\n",
+      "\u001B[36m8y2hfvaxhd-algo-1-9j1rc |\u001B[0m Model server started.\r\n",
+      "\u001B[36m8y2hfvaxhd-algo-1-9j1rc |\u001B[0m 2021-11-03 21:19:23,322 [WARN ] pool-2-thread-1 com.amazonaws.ml.mms.metrics.MetricCollector - worker pid is not available yet.\r\n",
+      "\u001B[36m8y2hfvaxhd-algo-1-9j1rc |\u001B[0m 2021-11-03 21:19:23,332 [INFO ] W-9000-model-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - Connection accepted: /tmp/.mms.sock.9000.\r\n",
+      "\u001B[36m8y2hfvaxhd-algo-1-9j1rc |\u001B[0m 2021-11-03 21:19:23,350 [INFO ] W-9000-model-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - Connection accepted: /tmp/.mms.sock.9000.\r\n",
+      "\u001B[36m8y2hfvaxhd-algo-1-9j1rc |\u001B[0m 2021-11-03 21:19:23,357 [INFO ] W-9000-model-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - Connection accepted: /tmp/.mms.sock.9000.\r\n",
+      "\u001B[36m8y2hfvaxhd-algo-1-9j1rc |\u001B[0m 2021-11-03 21:19:23,368 [INFO ] W-9000-model-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - Connection accepted: /tmp/.mms.sock.9000.\r\n",
+      "\u001B[36m8y2hfvaxhd-algo-1-9j1rc |\u001B[0m 2021-11-03 21:19:23,382 [INFO ] W-9000-model-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - Connection accepted: /tmp/.mms.sock.9000.\r\n",
+      "\u001B[36m8y2hfvaxhd-algo-1-9j1rc |\u001B[0m 2021-11-03 21:19:23,398 [INFO ] W-9000-model-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - Connection accepted: /tmp/.mms.sock.9000.\r\n",
+      "\u001B[36m8y2hfvaxhd-algo-1-9j1rc |\u001B[0m 2021-11-03 21:19:24,084 [INFO ] pool-1-thread-8 ACCESS_LOG - /172.19.0.1:58948 \"GET /ping HTTP/1.1\" 200 102\r\n",
+      "!"
+     ]
+    }
+   ],
+   "source": [
+    "predictor = model.deploy(\n",
+    "    instance_type=\"local\",\n",
+    "    initial_instance_count=1,\n",
+    "    serializer=IdentitySerializer(\"image/jpeg\"),\n",
+    "    deserializer=JSONDeserializer(),\n",
+    ")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001B[36m8y2hfvaxhd-algo-1-9j1rc |\u001B[0m 2021-11-03 21:20:48,827 [INFO ] W-9000-model com.amazonaws.ml.mms.wlm.WorkerThread - Backend response time: 23483\r\n",
+      "\u001B[36m8y2hfvaxhd-algo-1-9j1rc |\u001B[0m 2021-11-03 21:20:48,831 [INFO ] W-9000-model ACCESS_LOG - /172.19.0.1:58950 \"POST /invocations HTTP/1.1\" 200 23496\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "with open(\"dog.jpg\", \"rb\") as f:\n",
+    "    predictions = predictor.predict(\n",
+    "        f.read()\n",
+    "    )"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\n",
+      "  \"Labels\": [\n",
+      "    {\n",
+      "      \"Name\": \"bicycle\",\n",
+      "      \"Confidence\": 92.4040675163269,\n",
+      "      \"Instances\": [\n",
+      "        {\n",
+      "          \"Confidence\": 92.4040675163269,\n",
+      "          \"BoundingBox\": {\n",
+      "            \"Width\": 458.94085693359375,\n",
+      "            \"Height\": 273.18804931640625,\n",
+      "            \"Left\": 114.1905517578125,\n",
+      "            \"Top\": 410.19049072265625\n",
+      "          }\n",
+      "        }\n",
+      "      ],\n",
+      "      \"Parents\": []\n",
+      "    },\n",
+      "    {\n",
+      "      \"Name\": \"truck\",\n",
+      "      \"Confidence\": 86.79984211921692,\n",
+      "      \"Instances\": [\n",
+      "        {\n",
+      "          \"Confidence\": 86.79984211921692,\n",
+      "          \"BoundingBox\": {\n",
+      "            \"Width\": 227.93612670898438,\n",
+      "            \"Height\": 93.27326202392578,\n",
+      "            \"Left\": 467.2970733642578,\n",
+      "            \"Top\": 169.18019485473633\n",
+      "          }\n",
+      "        }\n",
+      "      ],\n",
+      "      \"Parents\": []\n",
+      "    },\n",
+      "    {\n",
+      "      \"Name\": \"dog\",\n",
+      "      \"Confidence\": 96.15262150764465,\n",
+      "      \"Instances\": [\n",
+      "        {\n",
+      "          \"Confidence\": 96.15262150764465,\n",
+      "          \"BoundingBox\": {\n",
+      "            \"Width\": 181.7491912841797,\n",
+      "            \"Height\": 318.2195739746094,\n",
+      "            \"Left\": 128.4845962524414,\n",
+      "            \"Top\": 541.8526153564453\n",
+      "          }\n",
+      "        }\n",
+      "      ],\n",
+      "      \"Parents\": []\n",
+      "    }\n",
+      "  ]\n",
+      "}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(json.dumps(predictions, indent=2))"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Gracefully stopping... (press Ctrl+C again to force)\n"
+     ]
+    }
+   ],
+   "source": [
+    "predictor.delete_predictor()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/examples/sagemaker/environment.yml b/examples/sagemaker/environment.yml
new file mode 100644
index 0000000..a034d00
--- /dev/null
+++ b/examples/sagemaker/environment.yml
@@ -0,0 +1,8 @@
+channels:
+  - conda-forge
+dependencies:
+  - jupyter
+  - jupyter-lab
+  - pip
+  - pip:
+      - sagemaker[local]
diff --git a/examples/sagemaker/predict.py b/examples/sagemaker/predict.py
new file mode 100755
index 0000000..71c1306
--- /dev/null
+++ b/examples/sagemaker/predict.py
@@ -0,0 +1,25 @@
+#!/usr/bin/env python
+
+import boto3
+import json
+from sagemaker.local import LocalSession
+from sagemaker.predictor import Predictor
+from sagemaker.serializers import IdentitySerializer
+from sagemaker.deserializers import JSONDeserializer
+
+boto3_session = boto3.Session(region_name="us-east-1")
+session = LocalSession(boto3_session)
+session.config = {"local": {"local_code": True}}
+
+predictor = Predictor(
+    sagemaker_session=session,
+    endpoint_name="darknet",
+    serializer=IdentitySerializer("image/jpeg"),
+    deserializer=JSONDeserializer(),
+)
+
+with open("dog.jpg", "rb") as f:
+    predictions = predictor.predict(
+        f.read()
+    )
+print(json.dumps(predictions, indent=2))
diff --git a/examples/sagemaker/serve-local.py b/examples/sagemaker/serve-local.py
new file mode 100755
index 0000000..fa7c345
--- /dev/null
+++ b/examples/sagemaker/serve-local.py
@@ -0,0 +1,38 @@
+#!/usr/bin/env python
+
+import json
+import boto3
+from sagemaker.local import LocalSession
+from sagemaker.model import Model
+from sagemaker.predictor import Predictor
+from sagemaker.serializers import IdentitySerializer
+from sagemaker.deserializers import JSONDeserializer
+
+DUMMY_IAM_ROLE = 'arn:aws:iam::111111111111:role/service-role/AmazonSageMaker-ExecutionRole-20200101T000001'
+
+boto3_session = boto3.Session(region_name="us-east-1")
+session = LocalSession(boto3_session)
+session.config = {"local": {"local_code": True}}
+
+role = DUMMY_IAM_ROLE
+model_dir = "file://ml/model"
+
+model = Model(
+    predictor_cls=Predictor,
+    image_uri="zeroae/sagemaker-darknet-inference",
+    model_data=model_dir,
+    role=DUMMY_IAM_ROLE,
+    env={
+        "SAGEMAKER_MODEL_SERVER_WORKERS": 2
+    },
+    sagemaker_session=session,
+)
+
+predictor = model.deploy(
+    name="darknet",
+    instance_type="local_gpu",
+    initial_instance_count=1,
+    serializer=IdentitySerializer("image/jpeg"),
+    deserializer=JSONDeserializer(),
+)
+