docker backend fixes for local mode

cisco-open · Aug 2, 2024 · d80628c · d80628c
1 parent 43c0314
commit d80628c
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Showing 6 changed files with 49 additions and 35 deletions.
diff --git a/backend/Dockerfile b/backend/Dockerfile
@@ -2,26 +2,15 @@ FROM node:20.15.1
 
 WORKDIR /app/backend
 
-RUN apt-get update && apt-get install -y sshpass rsync && rm -rf /var/lib/apt/lists/*
+RUN apt-get update && apt-get install -y sshpass rsync bash wget && rm -rf /var/lib/apt/lists/*
 
 COPY backend /app/backend
 COPY machine_learning_core /app/machine_learning_core
 
 RUN npm install
 
-RUN echo "PORT=3000" > .env && \
-    echo "MACHINE_LEARNING_CORE_PATH=${MACHINE_LEARNING_CORE_PATH}" >> .env && \
-    echo "CONDA_SH_PATH=${CONDA_SH_PATH}" >> .env && \
-    echo "HUGGING_FACE_ACCESS_TOKEN=${HUGGING_FACE_ACCESS_TOKEN}" >> .env && \
-    echo "CONNECTION_TYPE=${CONNECTION_TYPE}" >> .env && \
-    echo "PRIMARY_SSH_HOST=${PRIMARY_SSH_HOST}" >> .env && \
-    echo "PRIMARY_SSH_PORT=${PRIMARY_SSH_PORT}" >> .env && \
-    echo "PRIMARY_SSH_USERNAME=${PRIMARY_SSH_USERNAME}" >> .env && \
-    echo "PRIMARY_SSH_PASSWORD=${PRIMARY_SSH_PASSWORD}" >> .env && \
-    echo "PRIMARY_SSH_PRIVATE_KEY_PATH=${PRIMARY_SSH_PRIVATE_KEY_PATH}" >> .env
-
 RUN chmod +x setup_python_environment.sh
 
 EXPOSE 3000
 
-CMD ["/bin/bash", "-c", "./setup_python_environment.sh \"$CONNECTION_TYPE\" \"$PRIMARY_SSH_HOST\" \"$PRIMARY_SSH_PORT\" \"$PRIMARY_SSH_USERNAME\" \"$PRIMARY_SSH_PASSWORD\" && node src/index.js"]
+CMD ["/bin/bash", "-c", "./setup_python_environment.sh \"$CONNECTION_TYPE\" \"$PRIMARY_SSH_HOST\" \"$PRIMARY_SSH_PORT\" \"$PRIMARY_SSH_USERNAME\" \"$PRIMARY_SSH_PASSWORD\" \"$MACHINE_LEARNING_CORE_PATH\" \"$CONDA_SH_PATH\" \"$HUGGING_FACE_ACCESS_TOKEN\" \"$PRIMARY_SSH_PRIVATE_KEY_PATH\" && node src/index.js"]
diff --git a/backend/setup_python_environment.sh b/backend/setup_python_environment.sh
@@ -7,8 +7,21 @@ PRIMARY_SSH_HOST=$2
 PRIMARY_SSH_PORT=$3
 PRIMARY_SSH_USERNAME=$4
 PRIMARY_SSH_PASSWORD=$5
+MACHINE_LEARNING_CORE_PATH=$6
+CONDA_SH_PATH=$7
+HUGGING_FACE_ACCESS_TOKEN=$8
+PRIMARY_SSH_PRIVATE_KEY_PATH=$9
 
-echo "CONNECTION_TYPE is set to: $CONNECTION_TYPE"
+echo "PORT=3000" > /app/backend/.env
+echo "MACHINE_LEARNING_CORE_PATH=${MACHINE_LEARNING_CORE_PATH}" >> /app/backend/.env
+echo "CONDA_SH_PATH=${CONDA_SH_PATH}" >> /app/backend/.env
+echo "HUGGING_FACE_ACCESS_TOKEN=${HUGGING_FACE_ACCESS_TOKEN}" >> /app/backend/.env
+echo "CONNECTION_TYPE=${CONNECTION_TYPE}" >> /app/backend/.env
+echo "PRIMARY_SSH_HOST=${PRIMARY_SSH_HOST}" >> /app/backend/.env
+echo "PRIMARY_SSH_PORT=${PRIMARY_SSH_PORT}" >> /app/backend/.env
+echo "PRIMARY_SSH_USERNAME=${PRIMARY_SSH_USERNAME}" >> /app/backend/.env
+echo "PRIMARY_SSH_PASSWORD=${PRIMARY_SSH_PASSWORD}" >> /app/backend/.env
+echo "PRIMARY_SSH_PRIVATE_KEY_PATH=${PRIMARY_SSH_PRIVATE_KEY_PATH}" >> /app/backend/.env
 
 install_miniconda3() {
     echo "Installing Miniconda..."
@@ -21,6 +34,10 @@ install_miniconda3() {
 }
 
 setup_local_environment() {
+    if [ -f /root/miniconda3/etc/profile.d/conda.sh ]; then
+        source /root/miniconda3/etc/profile.d/conda.sh
+    fi
+
     if ! command -v conda &> /dev/null; then
         echo "Conda command not found. Installing Miniconda3..."
         install_miniconda3

diff --git a/backend/src/facades/algorithmExecutionFacade.js b/backend/src/facades/algorithmExecutionFacade.js
@@ -30,9 +30,9 @@ class ScriptExecutor {
 
 	buildCommand(scriptPath, algorithm, args) {
 		if (this.type === ALGORITHM_TYPES.MULTIFLOW) {
-			return `source "${process.env.CONDA_SH_PATH}" && cd "${process.env.MACHINE_LEARNING_CORE_PATH}/multiflow" && conda activate modelsmith && python3 ${algorithm} ${args}`;
+			return `bash -c "source ${process.env.CONDA_SH_PATH} && cd ${process.env.MACHINE_LEARNING_CORE_PATH}/multiflow && conda activate modelsmith && python3 ${algorithm} ${args}"`;
 		}
-		return `source "${process.env.CONDA_SH_PATH}" && conda activate modelsmith && python3 "${scriptPath}${algorithm}" ${args}`;
+		return `bash -c "source ${process.env.CONDA_SH_PATH} && conda activate modelsmith && python3 ${scriptPath}${algorithm} ${args}"`;
 	}
 
 	buildPythonCommand(scriptPath, algorithm, args) {

diff --git a/docker-compose.yml b/docker-compose.yml
@@ -20,6 +20,12 @@ services:
       - PRIMARY_SSH_PRIVATE_KEY_PATH
     ports:
       - "3000:3000"
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - count: all
+              capabilities: [gpu]
   frontend:
     build:
       context: ./frontend

diff --git a/docs/configure-modelsmith-on-local-machine-docker.md b/docs/configure-modelsmith-on-local-machine-docker.md
@@ -44,19 +44,21 @@ This command runs the frontend container in detached mode (\`-d\`), names it \`m
 
 Before running the backend container, you need to set up the environment variables. These variables configure various aspects of the backend, including paths, access tokens, and connection details. Here's an explanation of each variable:
 
-- `MACHINE_LEARNING_CORE_PATH`: Path to the machine learning core within the container.
-- `CONDA_SH_PATH`: Path to the Conda shell script for environment activation.
+- `MACHINE_LEARNING_CORE_PATH`: Path to the machine learning core within the container. The default value is "../machine_learning_core".
+- `CONDA_SH_PATH`: Path to the Conda shell script for environment activation within the container. The default value is "../../root/miniconda3/etc/profile.d/conda.sh".
 - `HUGGING_FACE_ACCESS_TOKEN`: Your Hugging Face access token for model downloads. Please refer to the [AutoAWQ Configuration Guide](configure-autoawq.md)
-- `CONNECTION_TYPE`: Type of connection (e.g., "LOCAL" for virtual machine).
+- `CONNECTION_TYPE`: Type of connection ("LOCAL" for docker container machine with host GPU).
+
+Note: The initial backend configuration will take up to 5-10 minutes.
 
 Now, run the backend container with the following command, replacing the placeholder values with your actual configuration:
 
 ```shell
 docker run -d --name ms-backend-container \
-  -e MACHINE_LEARNING_CORE_PATH=machine_learning_core \
-  -e CONDA_SH_PATH=miniconda3/etc/profile.d/conda.sh \
-  -e HUGGING_FACE_ACCESS_TOKEN=your_hugging_face_token_here \
-  -e CONNECTION_TYPE=LOCAL \
+  -e "MACHINE_LEARNING_CORE_PATH=/app/machine_learning_core" \
+  -e "CONDA_SH_PATH=/root/miniconda3/etc/profile.d/conda.sh" \
+  -e "HUGGING_FACE_ACCESS_TOKEN=your_hugging_face_token_here" \
+  -e "CONNECTION_TYPE=LOCAL" \
   -p 3000:3000 \
   ms-backend
 ```

diff --git a/docs/configure-modelsmith-on-vm-docker.md b/docs/configure-modelsmith-on-vm-docker.md
@@ -44,10 +44,10 @@ This command runs the frontend container in detached mode (`-d`), names it `ms-f
 
 Before running the backend container, you need to set up the environment variables. These variables configure various aspects of the backend, including paths, access tokens, and connection details. Here's an explanation of each variable:
 
-- `MACHINE_LEARNING_CORE_PATH`: Path to the machine learning core within the container.
-- `CONDA_SH_PATH`: Path to the Conda shell script for environment activation.
+- `MACHINE_LEARNING_CORE_PATH`: Path to the machine learning core within the container. The default value is "machine_learning_core".
+- `CONDA_SH_PATH`: Path to the Conda shell script for environment activation. The default value is "miniconda3/etc/profile.d/conda.sh".
 - `HUGGING_FACE_ACCESS_TOKEN`: Your Hugging Face access token for model downloads. Please refer to the [AutoAWQ Configuration Guide](configure-autoawq.md)
-- `CONNECTION_TYPE`: Type of connection (e.g., "VM" for virtual machine).
+- `CONNECTION_TYPE`: Type of connection "VM" for virtual machine.
 - `PRIMARY_SSH_HOST`: Hostname or IP address of the primary SSH server.
 - `PRIMARY_SSH_PORT`: Port number for SSH connection.
 - `PRIMARY_SSH_USERNAME`: Username for SSH authentication.
@@ -58,15 +58,15 @@ Now, run the backend container with the following command, replacing the placeho
 
 ```shell
 docker run -d --name ms-backend-container \
-  -e MACHINE_LEARNING_CORE_PATH=machine_learning_core \
-  -e CONDA_SH_PATH=miniconda3/etc/profile.d/conda.sh \
-  -e HUGGING_FACE_ACCESS_TOKEN=your_hugging_face_token_here \
-  -e CONNECTION_TYPE=VM \
-  -e PRIMARY_SSH_HOST=your_ssh_host_here \
-  -e PRIMARY_SSH_PORT=22 \
-  -e PRIMARY_SSH_USERNAME=your_ssh_username \
-  -e PRIMARY_SSH_PASSWORD=your_ssh_password \
-  -e PRIMARY_SSH_PRIVATE_KEY_PATH=/path/to/your/private/key/or_empty \
+  -e "MACHINE_LEARNING_CORE_PATH=machine_learning_core" \
+  -e "CONDA_SH_PATH=miniconda3/etc/profile.d/conda.sh" \
+  -e "HUGGING_FACE_ACCESS_TOKEN=your_hugging_face_token_here" \
+  -e "CONNECTION_TYPE=VM" \
+  -e "PRIMARY_SSH_HOST=your_ssh_host_here" \
+  -e "PRIMARY_SSH_PORT=22" \
+  -e "PRIMARY_SSH_USERNAME=your_ssh_username" \
+  -e "PRIMARY_SSH_PASSWORD=your_ssh_password" \
+  -e "PRIMARY_SSH_PRIVATE_KEY_PATH=/path/to/your/private/key/or_empty" \
   -p 3000:3000 \
   ms-backend
 ```