custom mira container

api/mira/sagemaker_custom_model/Dockerfile

@@ -0,0 +1,8 @@
+FROM 763104351884.dkr.ecr.us-west-2.amazonaws.com/tensorflow-inference:1.14-cpu
+COPY requirements.txt /home/requirements.txt
+RUN pip install -r /home/requirements.txt
+# ENV PATH="/opt/ml/code:${PATH}"
+# # COPY ../models/mira-large/code /opt/ml/code
+# # WORKDIR /opt/ml/code
+# ENV SAGEMAKER_SUBMIT_DIRECTORY /opt/ml/code
+# ENV SAGEMAKER_PROGRAM inference.py

api/mira/sagemaker_custom_model/README.md

@@ -0,0 +1,24 @@
+### Local Testing (in development, required to deploy custom container of MIRA on Sagemaker Serverless, an upgrade from Realtime endpoints use dby the above instructions)
+
+Get the model and inference code for MIRA-Large
+```
+bash download_untar_model.sh
+```
+
+Build the custom docker container we need to use Sagemaker Serverless and for local testing. this installs pillow and numpy in the sagemaker container for TF 1.14.
+
+```
+docker build -t test/tf-1.14-pillow-nump:latest .
+```
+
+Start the model server with docker.
+
+```
+bash docker_mira.sh
+```
+
+Attempt to send a request following this example that base64 encodes https://www.tensorflow.org/tfx/serving/api_rest 
+
+```
+python local_test.py
+```

api/mira/sagemaker_custom_model/docker_mira.sh

@@ -0,0 +1,3 @@
+# dockerized version of the above, ran into dependency issue for termcolor
+# https://github.com/pytorch/serve/tree/master/docker
+docker run --rm -p 8080:8080 -p 8081:8081 -p 8082:8082 -p 8501:8501 -p 7070:7070 -p 7071:7071 -v "$(pwd)":/app -it test/tf-1.14-pillow-nump:latest bash /app/serve_mira.sh

api/mira/sagemaker_custom_model/download_untar_model.sh

@@ -0,0 +1,2 @@
+aws s3 cp s3://sagemaker-us-west-2-830244800171/model-mira-large/model.tar.gz ./
+tar -xvzf model.tar.gz

api/mira/sagemaker_custom_model/local_test.py

@@ -0,0 +1,16 @@
+import base64
+import requests
+import json
+API_URL = 'http://localhost:8501/v1/models/mira-large:classify'
+IMG_PATH = '/home/rave/animl/animl-ml/input/sample-img-fox.jpg'
+# Open and read image as bitstring
+input_image = open(IMG_PATH, "rb").read()
+print("Raw bitstring: " + str(input_image[:10]) + " ... " + str(input_image[-10:]))
+
+# Encode image in b64
+encoded_input_string = base64.b64encode(input_image)
+input_string = encoded_input_string.decode("utf-8")
+print("Base64 encoded string: " + input_string[:10] + " ... " + input_string[-10:])
+predict_request = '{"instances" : [{"b64": "%s"}]}' % input_string
+response = requests.post(API_URL, data=predict_request)
+print(response.json())

api/mira/sagemaker_custom_model/requirements.txt

@@ -0,0 +1,2 @@
+pillow==8.3.2
+numpy==1.18.2

api/mira/sagemaker_custom_model/serve_mira.sh

@@ -0,0 +1 @@
+tensorflow_model_server --port=8500 --rest_api_port=8501 --model_name=mira-large --model_base_path=/app/export/Servo/