fix outdated RAPIDS versions

README.md

@@ -128,10 +128,10 @@ The RAPIDS versions for things like container images and install instructions ar
 ```python
 versions = {
     "stable": {
-        "rapids_container": "nvcr.io/nvidia/rapidsai/base:24.06-cuda11.8-py3.10",
+        "rapids_container": "nvcr.io/nvidia/rapidsai/base:24.12-cuda12.5-py3.12",
     },
     "nightly": {
-        "rapids_container": "rapidsai/base:24.08a-cuda12.5-py3.11",
+        "rapids_container": "rapidsai/base:25.02a-cuda12.5-py3.12",
     },
 }
 ```

source/examples/rapids-autoscaling-multi-tenant-kubernetes/image-prepuller.yaml

@@ -14,7 +14,7 @@ spec:
     spec:
       initContainers:
         - name: prepull-rapids
-          image: us-central1-docker.pkg.dev/nv-ai-infra/rapidsai/rapidsai/base:23.08-cuda12.0-py3.10
+          image: us-central1-docker.pkg.dev/nv-ai-infra/rapidsai/rapidsai/base:example
           command: ["sh", "-c", "'true'"]
       containers:
         - name: pause

source/examples/rapids-autoscaling-multi-tenant-kubernetes/notebook.ipynb

@@ -222,9 +222,9 @@
     "In order to steam the container image to the GKE nodes our image needs to be stored in [Google Cloud Artifact Registry](https://cloud.google.com/artifact-registry/) in the same region as our cluster.\n",
     "\n",
     "```console\n",
-    "$ docker pull rapidsai/base:24.04-cuda11.8-py3.10\n",
+    "$ docker pull {{ rapids_container }}\n",
     "\n",
-    "$ docker tag rapidsai/base:24.04-cuda11.8-py3.10 REGION-docker.pkg.dev/PROJECT/REPO/IMAGE:TAG\n",
+    "$ docker tag {{ rapids_container }} REGION-docker.pkg.dev/PROJECT/REPO/IMAGE:TAG\n",
     "\n",
     "$ docker push REGION-docker.pkg.dev/PROJECT/REPO/IMAGE:TAG\n",
     "```\n",
@@ -243,7 +243,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -266,7 +266,7 @@
       "    spec:\n",
       "      initContainers:\n",
       "        - name: prepull-rapids\n",
-      "          image: rapidsai/rapidsai-core:23.02-cuda11.8-runtime-ubuntu22.04-py3.10\n",
+      "          image: us-central1-docker.pkg.dev/nv-ai-infra/rapidsai/rapidsai/base:example\n",
       "          command: [\"sh\", \"-c\", \"'true'\"]\n",
       "      containers:\n",
       "        - name: pause\n",
@@ -275,7 +275,7 @@
     }
    ],
    "source": [
-    "! cat image-prepuller.yaml"
+    "! cat ./image-prepuller.yaml"
    ]
   },
   {
@@ -575,7 +575,7 @@
     "\n",
     "cluster = KubeCluster(\n",
     "    name=\"rapids-dask-1\",\n",
-    "    image=\"rapidsai/rapidsai-core:23.02-cuda11.8-runtime-ubuntu22.04-py3.10\",  # Replace me with your cached image\n",
+    "    image=\"{{ rapids_container }}\",  # Replace me with your cached image\n",
     "    n_workers=4,\n",
     "    resources={\"limits\": {\"nvidia.com/gpu\": \"1\"}},\n",
     "    env={\"EXTRA_PIP_PACKAGES\": \"gcsfs\"},\n",
@@ -1117,7 +1117,7 @@
     "\n",
     "    with KubeCluster(\n",
     "        name=\"rapids-dask-\" + uuid.uuid4().hex[:5],\n",
-    "        image=\"rapidsai/rapidsai-core:23.02-cuda11.8-runtime-ubuntu22.04-py3.10\",  # Replace me with your cached image\n",
+    "        image=\"{{ rapids_container }}\",  # Replace me with your cached image\n",
     "        n_workers=2,\n",
     "        resources={\"limits\": {\"nvidia.com/gpu\": \"1\"}},\n",
     "        env={\"EXTRA_PIP_PACKAGES\": \"gcsfs\"},\n",
@@ -1305,7 +1305,7 @@
     "\n",
     "cluster_spec = make_cluster_spec(\n",
     "    name=\"mock-jupyter-cluster\",\n",
-    "    image=\"rapidsai/rapidsai-core:23.02-cuda11.8-runtime-ubuntu22.04-py3.10\",  # Replace me with your cached image\n",
+    "    image=\"{{ rapids_container }}\",  # Replace me with your cached image\n",
     "    n_workers=1,\n",
     "    resources={\"limits\": {\"nvidia.com/gpu\": \"1\"}, \"requests\": {\"cpu\": \"50m\"}},\n",
     "    env={\"EXTRA_PIP_PACKAGES\": \"gcsfs dask-kubernetes\"},\n",
@@ -2234,7 +2234,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.18"
+   "version": "3.12.8"
   },
   "vscode": {
    "interpreter": {

source/examples/rapids-autoscaling-multi-tenant-kubernetes/rapids-notebook.yaml

@@ -69,7 +69,7 @@ spec:
     fsGroup: 0
   containers:
     - name: rapids-notebook
-      image: us-central1-docker.pkg.dev/nv-ai-infra/rapidsai/rapidsai/base:23.08-cuda12.0-py3.10
+      image: us-central1-docker.pkg.dev/nv-ai-infra/rapidsai/rapidsai/base:example
       resources:
         limits:
           nvidia.com/gpu: 1

source/examples/time-series-forecasting-with-hpo/notebook.ipynb

@@ -6481,7 +6481,8 @@
    "outputs": [],
    "source": [
     "# Choose the same RAPIDS image you used for launching the notebook session\n",
-    "rapids_image = \"rapidsai/notebooks:23.10a-cuda12.0-py3.10\"\n",
+    "rapids_image = \"{{ rapids_notebooks_container }}\"\n",
+    "\n",
     "# Use the number of worker nodes in your Kubernetes cluster.\n",
     "n_workers = 2\n",
     "# Bucket that contains the processed data pickles\n",
@@ -8009,7 +8010,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.12.8"
   }
  },
  "nbformat": 4,

source/examples/xgboost-azure-mnmg-daskcloudprovider/configs/cloud_init.yaml.j2

@@ -49,4 +49,4 @@ runcmd:
   - systemctl restart docker
 
   # Attempt to run a RAPIDS container to download the container layers and decompress them
-  - 'docker run --net=host --gpus=all --shm-size=256m rapidsai/rapidsai:cuda11.2-runtime-ubuntu18.04-py3.8 dask-scheduler --version'
+  - 'docker run --net=host --gpus=all --shm-size=256m rapidsai/base:latest --version'

source/examples/xgboost-azure-mnmg-daskcloudprovider/notebook.ipynb

@@ -158,6 +158,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "jp-MarkdownHeadingCollapsed": true,
     "tags": []
    },
    "source": [
@@ -322,7 +323,6 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "jp-MarkdownHeadingCollapsed": true,
     "tags": []
    },
    "source": [
@@ -609,7 +609,6 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "jp-MarkdownHeadingCollapsed": true,
     "tags": []
    },
    "source": [
@@ -638,50 +637,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Creating scheduler instance\n",
-      "Assigned public IP\n",
-      "Network interface ready\n",
-      "Using Marketplace VM image with a Plan\n",
-      "Creating VM\n",
-      "Created VM dask-92c5978e-scheduler\n",
-      "Waiting for scheduler to run at 4.155.2.188:8786\n",
-      "Scheduler is running\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/skirui/anaconda3/envs/rapids-23.08/lib/python3.10/contextlib.py:142: UserWarning: Creating your cluster is taking a surprisingly long time. This is likely due to pending resources. Hang tight! \n",
-      "  next(self.gen)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Creating worker instance\n",
-      "Creating worker instance\n",
-      "Network interface ready\n",
-      "Using Marketplace VM image with a Plan\n",
-      "Creating VM\n",
-      "Network interface ready\n",
-      "Using Marketplace VM image with a Plan\n",
-      "Creating VM\n",
-      "Created VM dask-92c5978e-worker-54f8d057\n",
-      "Created VM dask-92c5978e-worker-9f9a9c9b\n",
-      "CPU times: user 1.22 s, sys: 189 ms, total: 1.41 s\n",
-      "Wall time: 6min 58s\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%time\n",
     "\n",
@@ -1398,6 +1356,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "jp-MarkdownHeadingCollapsed": true,
     "tags": []
    },
    "source": [
@@ -1438,7 +1397,9 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true
+   },
    "source": [
     "### Step 3.b: Data Cleanup, Enhancement and Persisting Scripts"
    ]
@@ -1711,6 +1672,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "jp-MarkdownHeadingCollapsed": true,
     "tags": []
    },
    "source": [
@@ -1726,25 +1688,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/skirui/anaconda3/envs/rapids-23.08/lib/python3.10/site-packages/dask/dataframe/io/parquet/core.py:411: FutureWarning: The `chunksize` argument is deprecated, and will be removed in a future release. Setting the `blocksize` argument instead. Please see documentation on the `blocksize` argument for more information.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Wall clock time taken for ETL and persisting : 83.1002215759363 s\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "tic = timer()\n",
     "X_train, y_train, X_infer, y_infer = taxi_data_loader(\n",
@@ -2101,7 +2047,9 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true
+   },
    "source": [
     "### Step 4.a: Set training Parameters\n",
     "\n",
@@ -2137,6 +2085,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "jp-MarkdownHeadingCollapsed": true,
     "tags": []
    },
    "source": [
@@ -2177,6 +2126,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
+    "jp-MarkdownHeadingCollapsed": true,
     "tags": []
    },
    "source": [
@@ -2427,25 +2377,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/skirui/anaconda3/envs/rapids-23.08/lib/python3.10/site-packages/dask/dataframe/core.py:7047: FutureWarning: Meta is not valid, `map_partitions` and `map_overlap` expects output to be a pandas object. Try passing a pandas object as meta or a dict or tuple representing the (name, dtype) of the columns. In the future the meta you passed will not work.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Wall clock time taken for this cell : 5.638823717948981 s\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "tic = timer()\n",
     "predictions = X_infer.map_partitions(\n",
@@ -2537,9 +2471,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "rapids-23.10",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "rapids-23.10"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -2551,7 +2485,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.12.8"
   }
  },
  "nbformat": 4,