http_server first commit

http_submission/Dockerfile

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+# Specify a base image depending on the project.
+FROM bitnami/python:3.8
+# For more complex examples, might need to use a different base image.
+# FROM pytorch/pytorch:1.9.1-cuda11.1-cudnn8-runtime
+
+WORKDIR /app
+
+ENV HTTP_PORT=4000
+
+RUN apt-get update \
+    && apt-get -y install gcc
+
+COPY ./requirements.txt ./
+RUN python -m pip install -U pip \
+    && python -m pip install -r requirements.txt 
+
+COPY . ./
+
+# This is needed for Singularity builds.
+EXPOSE $HTTP_PORT
+
+# The entrypoint for a container,
+CMD ["gunicorn", "-w", "1", "-b", "0.0.0.0:4000", "--pythonpath", ".", "model_server:app"]

http_submission/README.md

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+# Submission
+TODO: Add a description of the submission process here.
+
+
+
+## Launching the submission container
+TODO: Create a docker-compose file
+```bash
+cd ./http_submission
+docker build -t sample_pysaliency .
+```
+
+```bash
+docker run --name sample_pysaliency -dp 4000:4000 sample_pysaliency
+```
+The above command will launch a container named `sample_pysaliency` and expose the port `4000` to the host machine. The container will be running in the background. 
+
+To test the model server, run the sample_evaluation script (Make sure to have the `pysaliency` package installed):
+```bash
+python ./http_evaluation/sample_evaluation.py
+```
+
+
+To delete the container, run the following command:
+```bash
+docker stop sample_pysaliency && docker rm sample_pysaliency
+```

http_submission/model_server.py

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+from flask import Flask, request, jsonify
+import pickle
+
+# Import your model here
+from sample_submission import SampleScanpathModel
+
+app = Flask("saliency-model-server")
+app.logger.setLevel("DEBUG")
+
+# TODO - replace this with your model
+model = SampleScanpathModel()
+
+
+@app.route("/predict", methods=["POST"])
+def predict():
+    payload = request.get_data()
+    inputs = pickle.loads(payload)
+    app.logger.info(f"Received: {inputs}")
+
+    # TODO - replace this with your model prediction function
+    result = model.conditional_log_density(
+        inputs["stimulus"],
+        inputs["x_hist"],
+        inputs["y_hist"],
+        inputs["t_hist"],
+        inputs["attributes"],
+        inputs["out"],
+    )
+    # resp = pickle.dumps(result)
+    app.logger.info(f"Result: {result}")
+    # The below assumes that the model returns a numpy array.
+    return result.tolist()
+
+
+def main():
+    app.run(host="0.0.0.0", port="4000", debug="True", threaded=True)
+
+
+if __name__ == "__main__":
+    main()

http_submission/requirements.txt

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+cython
+flask
+gunicorn
+numpy
+
+# Add additional dependencies here

http_submission/sample_evaluation.py

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+import numpy as np
+import pickle
+import pysaliency
+import requests
+
+
+class HTTPScanpathModel(pysaliency.ScanpathModel):
+    def __init__(self, url):
+        self.url = url
+
+    def conditional_log_density(
+        self, stimulus, x_hist, y_hist, t_hist, attributes=None, out=None
+    ):
+        inputs = {
+            "stimulus": stimulus,
+            "x_hist": x_hist,
+            "y_hist": y_hist,
+            "t_hist": t_hist,
+            "attributes": attributes,
+            "out": out,
+        }
+        payload = pickle.dumps(inputs)
+        response = requests.post(self.url, data=payload)
+        # print(f"Received: {response.json()}")
+        return np.array(response.json())
+
+
+if __name__ == "__main__":
+    http_model = HTTPScanpathModel("http://localhost:4000/predict")
+
+    print(
+        http_model.conditional_log_density(
+            [1, 1.4, 10, 1],
+            [1, 1, 0.51, 1],
+            [1, 1, 2, 1],
+            [1, 3, 1, 1],
+        )
+    )

http_submission/sample_submission.py

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+import numpy as np
+# import pysaliency
+
+class SampleScanpathModel():
+    def __init__(self):
+        super().__init__()
+
+    def conditional_log_density(self, stimulus, x_hist, y_hist, t_hist, attributes=None, out=None):
+        return np.log(stimulus)