Docker project ready for review

Dockerfile.infer

@@ -1,7 +1,38 @@
-WORKDIR /app
-COPY requirements.txt .
-COPY server.py .
+#WORKDIR /app
+#COPY requirements.txt .
+#COPY server.py .
+#FROM python:3.9-slim
+#CMD ["python", "server.py"]
+#RUN pip install --no-cache-dir -r requirements.txt
+#EXPOSE 8080
+
+
 FROM python:3.9-slim
-CMD ["python", "server.py"]
+
+# TODO: Set a working directory
+WORKDIR /my-workdir
+
+# TODO: Copy the requirements.txt file to the working directory
+COPY requirements.txt /my-workdir/
+
+# TODO: Install the Python dependencies
 RUN pip install --no-cache-dir -r requirements.txt
+
+# TODO: Copy the server script (server.py.py) to the working directory
+COPY server.py /my-workdir/
+
+#TODO: document which port(s) the container is intended to listen on at runtime
 EXPOSE 8080
+
+# TODO: Run the server script that generates the model
+CMD ["python", "server.py"]
+
+
+# command for image build
+#docker build . --tag python-server-sd:v1.0.0 -f Dockerfile.infer
+
+# command to run the docker image
+#docker run --detach --name python_server_docker -p 4441:8080 -v .:/my-workdir python-server-sd:v1.0.0
+
+#access the running docker locally via: http://127.0.0.1:4441/
+

Dockerfile.train

@@ -1,12 +1,25 @@
-FROM <base imagae>
+FROM python:3.9-slim
 
 # TODO: Set a working directory
+WORKDIR /my-workdir
 
 # TODO: Copy the requirements.txt file to the working directory
+COPY requirements.txt /my-workdir/
+
 
 # TODO: Install the Python dependencies
+RUN pip install --no-cache-dir -r requirements.txt
 
 # TODO: Copy the training script (train.py) to the working directory
+COPY train.py /my-workdir/
 
 # TODO: Run the training script that generates the model
-CMD [...]
+CMD ["python", "train.py"]
+
+
+# command for image build
+# docker build . --tag python-train-sd:v1.0.0 -f Dockerfile.train
+
+# command to run the docker image
+# docker run -v .:/my-workdir python-train-sd:v1.0.0
+# output ot the docker run: Model training complete and saved as iris_model.pkl

README.md

@@ -5,40 +5,40 @@ In this project, you will train, run and serve a machine learning model using Do
 
 ## Deliverables
 
-- [ ] Clone this repository to your personal github account
-- [ ] Containerize training the machine learning model
-- [ ] Containerize serving of the machine learning model
-- [ ] Train and run the machine learning model using Docker
-- [ ] Run the Docker container serving the machine learning model
-- [ ] Store the Docker images on your personal account on Docker Hub
-- [ ] Provide the resulting Dockerfiles in GitHub
-- [ ] Build an Apptainer image on a HPC of your choice
-- [ ] Provide the logs of the slurm job in GitHub
-- [ ] Document the steps in a text document in GitHub
+- [X] Clone this repository to your personal github account
+- [X] Containerize training the machine learning model
+- [X] Containerize serving of the machine learning model
+- [X] Train and run the machine learning model using Docker
+- [X] Run the Docker container serving the machine learning model
+- [X] Store the Docker images on your personal account on Docker Hub
+- [X] Provide the resulting Dockerfiles in GitHub
+- [X] Build an Apptainer image on a HPC of your choice
+- [X] Provide the logs of the slurm job in GitHub
+- [X] Document the steps in a text document in GitHub (Step-By-Step.md)
 
 ## Proposed steps - containerize and run training the machine learning model
 
 Complete file named `Dockerfile.train`
 
-- Copy requirements.txt and install dependencies
-- Copy train.py to the working directory
-- Set the command to run train.py
-- Run the training of the model on your computer
-- Document the command as comment in the Dockerfile
-- Store the created Dockerfile in your cloned github repository
+- [X] Copy requirements.txt and install dependencies
+- [X] Copy train.py to the working directory
+- [X] Set the command to run train.py
+- [X] Run the training of the model on your computer
+- [X] Document the command as comment in the Dockerfile
+- [X] Store the created Dockerfile in your cloned github repository
 
 ## Proposed steps - containerize and serve the machine learning model
 
-- Correct the order of the instructions in the Dockerfile.infer
-- Document the steps in the Dockerfile.infer as comments
-- Document the succesful `docker run` command in the Dockerfile.infer as a comment
+- [X] Correct the order of the instructions in the Dockerfile.infer
+- [X] Document the steps in the Dockerfile.infer as comments
+- [X] Document the succesful `docker run` command in the Dockerfile.infer as a comment
 
 ## Proposed steps - store images on Dockerhub and build an Apptainer image on the HPC
 
-- Create an account on Dockerhub
-- Store the built images on your account
-- Create a shell script on the HPC of your preference
-- Store the shell script in your cloned github repository
+- [X] Create an account on Dockerhub
+- [X] Store the built images on your account
+- [X] Create a shell script on the HPC of your preference
+- [X] Store the shell script in your cloned github repository
 
 
 

Step-By-step.md

@@ -0,0 +1,49 @@
+# Clone this repository to your personal github account
+git clone [email protected]:saradufour/project_docker_microcredential.git
+
+# Containerize training the machine learning model
+
+command for image build
+`docker build . --tag python-train-sd:v1.0.0 -f Dockerfile.train`
+
+
+# Containerize serving of the machine learning model
+
+command for image build
+`docker build . --tag python-server-sd:v1.0.0 -f Dockerfile.infer`
+
+
+# Train and run the machine learning model using Docker
+
+
+command to run the docker image (train)
+`docker run -v .:/my-workdir python-train-sd:v1.0.0`
+output ot the docker run: Model training complete and saved as iris_model.pkl
+
+
+# Run the Docker container serving the machine learning model
+
+command to run the docker image (server)
+`docker run --detach --name python_server_docker -p 4441:8080 -v .:/my-workdir python-server-sd:v1.0.0`
+
+access the running docker locally via: http://127.0.0.1:4441/
+
+
+# Store the Docker images on your personal account on Docker Hub
+
+`docker images`
+`docker login`
+`docker tag python-server-sd:v1.0.0 saradufour/python-server-sd:v1.0.0`
+`docker push saradufour/python-server-sd:v1.0.0`
+`docker tag python-train-sd:v1.0.0 saradufour/python-train-sd:v1.0.0`
+`docker push saradufour/python-train-sd:v1.0.0`
+
+
+# Provide the resulting Dockerfiles in GitHub
+`git add *`
+`git commit -m "..."`
+
+# Apptainer
+see VSC folder in github repository
+
+`sbatch docker-project.sh`

VSC/docker-project.sh

@@ -0,0 +1,17 @@
+#!/bin/bash
+
+#SBATCH --job-name=jbuild-apptainer
+#SBATCH --partition=donphan
+#SBATCH --mem=8G
+#SBATCH --time=00:30:00
+
+
+LOG_DIR="$VSC_SCRATCH/logs"
+mkdir -p $LOG_DIR
+
+echo "build containers on VSC"
+
+apptainer build --fakeroot python-train-sd.sif docker://saradufour/python-train-sd:v1.0.0 > $LOG_DIR/python-train-sd-OUT.log 2>&1
+apptainer build --fakeroot python-server-sd.sif docker://saradufour/python-server-sd:v1.0.0 > $LOG_DIR/python-server-sd-OUT.log 2>&1
+
+echo "You can now run your container"

VSC/python-server-sd-OUT.log

@@ -0,0 +1,21 @@
+INFO:    Starting build...
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+Copying config sha256:472c6673fd87607215351b99a6779289b86b2a364d96f2a662cffbd859f146e2
+Writing manifest to image destination
+2025/04/24 14:41:05  info unpack layer: sha256:8a628cdd7ccc83e90e5a95888fcb0ec24b991141176c515ad101f12d6433eb96
+2025/04/24 14:41:07  info unpack layer: sha256:74018f7cfa8f2965fd86b13c38f71417bc846e071a5f5bb5ae569ccb5a6e7248
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+2025/04/24 14:41:11  info unpack layer: sha256:47432841852a4fdb7690e090a9a51676251d42705f1af2e5a1eba975fbeb092b
+INFO:    Creating SIF file...
+INFO:    Build complete: python-server-sd.sif

VSC/python-train-sd-OUT.log

@@ -0,0 +1,21 @@
+INFO:    Starting build...
+Copying blob sha256:24c9ec169934c78008ef30494c5735cce146c038004a2cf1bd5f63b674c75093
+Copying blob sha256:8a628cdd7ccc83e90e5a95888fcb0ec24b991141176c515ad101f12d6433eb96
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+Copying blob sha256:cf612e53a4c896366012dc36f6caea05fdd787b1ef4f52405a61d081f0fc05a0
+Copying config sha256:0ac2dd017a895bf7cb7bda4e539f8b1cd9ad226083ac43541704cad96e34dab9
+Writing manifest to image destination
+2025/04/24 14:40:06  info unpack layer: sha256:8a628cdd7ccc83e90e5a95888fcb0ec24b991141176c515ad101f12d6433eb96
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+2025/04/24 14:40:11  info unpack layer: sha256:cf612e53a4c896366012dc36f6caea05fdd787b1ef4f52405a61d081f0fc05a0
+INFO:    Creating SIF file...
+INFO:    Build complete: python-train-sd.sif

VSC/slurm-20167414.out

@@ -0,0 +1,2 @@
+build containers on VSC
+You can now run your container

server.py

@@ -7,7 +7,7 @@
 app = Flask(__name__)
 
 # Check if the model file exists and wait until it does
-model_path = '/app/models/iris_model.pkl'
+model_path = '/my-workdir/iris_model.pkl'
 
 while not os.path.exists(model_path):
     print(f"Waiting for model file at {model_path}...")

train copy.py

@@ -0,0 +1,17 @@
+from sklearn.svm import SVC
+from sklearn import datasets
+import joblib
+
+#Load the Iris dataset
+iris = datasets.load_iris()
+
+#Create an SVM classifier
+clf = SVC()
+
+#Train the model using the iris dataset
+model = clf.fit(iris.data, iris.target_names[iris.target])
+
+#Save the trained model to the shared volume (make sure to use the correct path)
+joblib.dump(model, '/my-workdir/iris_model.pkl')
+
+print("Model training complete and saved as iris_model.pkl")

train.py

@@ -12,6 +12,6 @@
 model = clf.fit(iris.data, iris.target_names[iris.target])
 
 #Save the trained model to the shared volume (make sure to use the correct path)
-joblib.dump(model, '/app/models/iris_model.pkl')
+joblib.dump(model, '/my-workdir/iris_model.pkl')
 
 print("Model training complete and saved as iris_model.pkl")