Merge pull request #30 from ai-cfia/29-Model-SelfServe-Flask

29 Model Self Serve Replicate AzureML

.github/ISSUE_TEMPLATE/model-misclassification.md

@@ -18,5 +18,9 @@ Describe what actually happened.
 
 **Supporting Images**
 To better understand the issue, please add screenshots:
-* **Results**: Include a screenshot of the interface showing the classification error or the issue as it appears in your environment.
-* **Source Image**: Attach the actual picture or image used when you encountered the issue.
+
+* **Results**: Include a screenshot of the interface showing the classification
+ error or the issue as it appears in your environment.
+
+* **Source Image**: Attach the actual picture or image used when you
+ encountered the issue.

.gitignore

@@ -158,3 +158,11 @@ cython_debug/
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
 #.idea/
+bin/
+lib64/
+lib64
+**/*pyvenv.cfg
+share/
+artifacts/
+include/
+logs/

examples/AzureMLSeedCls/Dockerfile

@@ -0,0 +1,20 @@
+FROM mcr.microsoft.com/aifx/acpt/stable-ubuntu2004-cu117-py38-torch1131:biweekly.202311.2
+
+# Install pip dependencies
+COPY requirements.txt .
+RUN pip install -r requirements.txt --no-cache-dir
+
+# Inference requirements
+COPY --from=mcr.microsoft.com/azureml/o16n-base/python-assets:20230419.v1 /artifacts /var/
+RUN /var/requirements/install_system_requirements.sh && \
+   cp /var/configuration/rsyslog.conf /etc/rsyslog.conf && \
+   cp /var/configuration/nginx.conf /etc/nginx/sites-available/app && \
+   ln -sf /etc/nginx/sites-available/app /etc/nginx/sites-enabled/app && \
+   rm -f /etc/nginx/sites-enabled/default
+ENV SVDIR=/var/runit
+ENV WORKER_TIMEOUT=400
+EXPOSE 5001 8883 8888
+
+# support Deepspeed launcher requirement of passwordless ssh login
+RUN apt-get update
+RUN apt-get install -y openssh-server openssh-client

examples/AzureMLSeedCls/docker-compose.yaml

@@ -0,0 +1,19 @@
+---
+services:
+  aml-seed-cls:
+    build:
+      context: .
+      dockerfile: Dockerfile
+    # command: ["/bin/sh", "-c", "hypercorn -b :8080 app:app"]
+    command: bash -c 'azmlinfsrv --entry_script /app/score.py'
+    ports:
+      - "12390:5001"
+      - "12391:8883"
+      - "12392:8888"
+    env_file:
+      - .env
+    environment:
+      - PORT=8080
+      - AZUREML_MODEL_DIR=/app/artifacts
+    volumes:
+      - ../AzureMLSeedCls:/app

examples/AzureMLSeedCls/requirements.txt

@@ -0,0 +1,19 @@
+azureml-core==1.54.0
+azureml-dataset-runtime==1.54.0
+azureml-defaults==1.54.0
+azure-ml==0.0.1
+azure-ml-component==0.9.18.post2
+azureml-mlflow==1.54.0
+azureml-contrib-services==1.54.0
+azureml-contrib-services==1.54.0
+azureml-automl-common-tools==1.54.0
+torch-tb-profiler~=0.4.0
+azureml-inference-server-http~=0.8.0
+inference-schema~=1.5.0
+MarkupSafe==2.1.2
+regex
+pybind11
+urllib3>=1.26.18
+cryptography>=41.0.4
+aiohttp>=3.8.5
+transformers==4.35.0

examples/AzureMLSeedCls/score.py

@@ -0,0 +1,43 @@
+import logging
+from transformers import pipeline
+# import request
+
+
+def init():
+    """
+    This function is called when the container is initialized/started, typically after create/update of the deployment.
+    You can write the logic here to perform init operations like caching the model in memory
+    """
+    global model
+    # AZUREML_MODEL_DIR is an environment variable created during deployment.
+    # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)
+    # Please provide your model's folder name if there is one
+    # print(os.listdir(os.getenv("AZUREML_MODEL_DIR")),'DIR PATHHHHHHHHHH LISTTTTTTTTT','\n\n\n')
+    # model_path = os.path.join(
+    #     os.getenv("AZUREML_MODEL_DIR"),'checkpoint-1500'
+    # )
+    # print(os.listdir("/app/artifacts/"), "\n\n\n")
+    model_path = "/app/artifacts/"
+    # deserialize the model file back into a sklearn model
+    # model = joblib.load(model_path)
+    # model = SwinForImageClassification.from_pretrained(pth)
+    model = pipeline(model=model_path, task="image-classification")
+    logging.info("Init complete")
+
+
+def run(raw_data):
+    """
+    This function is called for every invocation of the endpoint to perform the actual scoring/prediction.
+    In the example we extract the data from the json input and call the scikit-learn model's predict()
+    method and return the result back
+    """
+    # print(raw_data,'RAWWW DATAAAAA','\n\n\n')
+    # model.eval()
+    # image = Image.open(raw_data)
+
+    logging.info("model 1: request received")
+    # data = json.loads(raw_data)["data"]
+    # data = numpy.array(data)
+    results = model(raw_data)
+    logging.info("Request processed")
+    return results

examples/AzureMLSeedDet/Dockerfile

@@ -0,0 +1,24 @@
+FROM mcr.microsoft.com/azureml/curated/azureml-automl-dnn-vision-gpu:131
+
+WORKDIR /app
+
+# ENV QUART_APP=app.py
+# ENV QUART_ENV=development
+# ENV PYTHONUNBUFFERED True
+# ENV PYTHONPATH=/app
+
+COPY ./requirements.txt .
+COPY ./req.txt .
+COPY conda_env_v_1_0_0.yml .
+# COPY scoring_file_v_1_0_0.py .
+# COPY model.pt .
+
+# RUN pip3 install torch==1.12.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
+RUN pip3 install --no-cache-dir -r requirements.txt
+# RUN pip3 install --no-cache-dir -r req.txt
+RUN conda env update -f conda_env_v_1_0_0.yml
+
+COPY . ./
+
+# CMD hypercorn -b :$PORT app:app
+CMD ls && /bin/bash