.

Dockerfile

@@ -5,6 +5,7 @@ RUN pip install jupyterlab
 
 # Install additional packages
 RUN pip install plotly
+RUN pip install fastbook
 
 # Set environment variables, etc.
 #ENV MY_ENV_VAR=myvalue

docker-compose.yml

@@ -1 +0,0 @@
-my_build.docker-compose.yml

docker-compose.yml

@@ -0,0 +1,14 @@
+version: '3.8'
+
+services:
+  jupyter:
+    build: /opt/jupyter_gpu
+    image: cvtt/jupyter_gpu:v1.0.2
+    container_name: cvtt_gpu_jupyter
+    runtime: nvidia
+    environment:
+      - JUPYTER_ENABLE_LAB=yes
+    volumes:
+      - ./notebooks:/workspace
+    ports:
+      - "8888:8888"

my_build.docker-compose.yml

@@ -1,13 +0,0 @@
-version: '3.8'
-
-services:
-  jupyter:
-    build: /opt/jupyter_pytorch
-    container_name: my_build_jupyter
-    runtime: nvidia
-    environment:
-      - JUPYTER_ENABLE_LAB=yes
-    volumes:
-      - ./notebooks:/workspace
-    ports:
-      - "8888:8888"

notebooks/oleg/Testing GPU.ipynb

@@ -10,8 +10,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CUDA is available. GPU is ready for use.\n",
+      "CUDA is available. Number of GPUs: 1\n",
-      "Number of GPUs: 1\n",
       "GPU Name: NVIDIA GeForce RTX 3060\n"
      ]
     }
@@ -21,8 +20,7 @@
     "\n",
     "# Check if CUDA is available\n",
     "if torch.cuda.is_available():\n",
-    "    print(\"CUDA is available. GPU is ready for use.\")\n",
+    "    print(f\"CUDA is available. Number of GPUs: {torch.cuda.device_count()}\")\n",
-    "    print(f\"Number of GPUs: {torch.cuda.device_count()}\")\n",
     "    print(f\"GPU Name: {torch.cuda.get_device_name(0)}\")\n",
     "else:\n",
     "    print(\"CUDA is not available. No GPU detected.\")\n"
@@ -41,7 +39,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "53cd2a47-ef77-401a-8497-8fc920919f22",
+   "id": "7313a620-a0eb-4207-a12a-90aeee3cd980",
    "metadata": {},
    "outputs": [],
    "source": []

orig.docker-compose.yml

@@ -1,20 +0,0 @@
-version: '3.8'
-
-services:
-  jupyter:
-    image: pytorch/pytorch:latest
-    container_name: pytorch_jupyter
-    runtime: nvidia
-    deploy:
-      resources:
-        reservations:
-          devices:
-            - capabilities: [gpu]
-    environment:
-      - JUPYTER_ENABLE_LAB=yes
-    volumes:
-      - /opt/jupyter_pytorch/notebooks:/workspace # Map local notebook directory to container
-    ports:
-      - "8888:8888" # Expose port 8888 to the host
-    command: >
-       bash -c "pip install jupyterlab && jupyter-lab --ip=0.0.0.0 --port=8888 --no-browser --allow-root --NotebookApp.token='' --NotebookApp.password=''"