v2.4.4 release

.github/workflows/DockerBuild.LambdaBaseImage.yaml

@@ -11,11 +11,11 @@ on:
       version:
         description: "Version of ArcGIS API for Python to install in the image"
         type: string
-        default: "2.4.1"
+        default: "2.4.4"
       python_version:
         description: "Python version to base image on"
         type: string
-        default: "3.11"
+        default: "3.13"
       is_latest_release:
         description: "Version of ArcGIS API for Python is Latest current release"
         type: boolean

.github/workflows/DockerBuild.NotebookImage.yaml

@@ -11,11 +11,11 @@ on:
       version:
         description: "Version of ArcGIS API for Python to install in the image"
         type: string
-        default: "2.4.2"
+        default: "2.4.4"
       arcgis_mapping_version:
         description: "Version of arcgis-mapping to install in the image (only used if version >= 2.4.0)"
         type: string
-        default: "4.33.0"
+        default: "5.1.0"
       python_version:
         description: "Python version to base image on"
         type: string

README.md

@@ -27,7 +27,7 @@ Find a bug or want to request a new feature?  Please let us know by submitting a
 Anyone and everyone is welcome to contribute. Please see our contribution guideline [here](https://github.com/Esri/arcgis-python-api/wiki/How-to-contribute-samples-and-guide-chapters).
 
 ## Licensing
-Copyright 2018-2022 Esri
+Copyright 2018-2026 Esri
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.

docker/LambdaBaseImage.Dockerfile

@@ -1,5 +1,5 @@
-ARG python_version=3.11
-# lambda python image, defaults to python 3.11
+ARG python_version=3.13
+# lambda python image, defaults to python 3.13
 FROM public.ecr.aws/lambda/python:${python_version}
 
 # set metadata
@@ -11,7 +11,7 @@ LABEL org.opencontainers.image.source=https://github.com/esri/arcgis-python-api
 # install dependencies, then clean yum cache
 RUN yum -y install gcc gcc-c++ krb5-devel krb5-server krb5-libs && yum clean all && rm -rf /var/cache/yum
 # install arcgis
-ARG arcgis_version="2.4.1"
+ARG arcgis_version="2.4.4"
 # adding .* ensures the latest patch version is installed
 RUN  pip3 install "arcgis==${arcgis_version}.*" --target "${LAMBDA_TASK_ROOT}" && rm -rf /root/.cache/pip
 # set entrypoint to app.py handler method

docker/NotebookImage.Dockerfile

@@ -2,9 +2,9 @@ ARG python_version="3.13"
 FROM quay.io/jupyter/minimal-notebook:python-${python_version}
 
 ARG python_version
-ARG arcgis_version="2.4.2"
-ARG arcgis_mapping_version="4.33.0"
-ARG gdal_version="3.11.4"
+ARG arcgis_version="2.4.4"
+ARG arcgis_mapping_version="5.1.0"
+ARG gdal_version="3.13.1"
 # If arcgis_wheel_url is provided, the Dockerfile will attempt to install the ArcGIS API for Python from that URL
 ARG arcgis_wheel_url=""
 ARG arcgis_mapping_wheel_url=""

pixi.toml

@@ -7,14 +7,14 @@ channels = ["esri", "main"]
 channel-priority = "disabled"
 name = "arcgis-python-api"
 platforms = ["win-64", "linux-64", "osx-arm64", "osx-64"]
-version = "2.4.1"
+version = "2.4.4"
 
 [dependencies]
-python = "3.11.*"
-arcgis = "2.4.1.*"
+python = "3.13.*"
+arcgis = "2.4.4.*"
 
 [feature.mapping.dependencies]
-arcgis-mapping = "4.31.*"
+arcgis-mapping = "5.1.*"
 
 [environments]
 mapping = { features = ["mapping"] }

samples/04_gis_analysts_data_scientists/automate_building_footprint_extraction_using_instance_segmentation.ipynb

@@ -236,9 +236,9 @@
     "warnings.filterwarnings(\"ignore\")\n",
     "\n",
     "import os\n",
+    "from datetime import datetime\n",
     "from pathlib import Path\n",
-    "from arcgis.learn import MaskRCNN, prepare_data\n",
-    "from arcgis.gis import GIS"
+    "from arcgis.learn import MaskRCNN, prepare_data"
    ]
   },
   {
@@ -617,6 +617,16 @@
     "As we can see, with 10 epochs, we are already seeing reasonable results. Further improvments can be achieved through more sophisticated hyperparameter tuning. Let's save the model, so it can be used for inference, or further training subsequently. By default, it will be saved into your data_path that you specified in the very beginning of this notebook, in the `prepare_data` call."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "timestamp = datetime.now().strftime(\"%Y-%m-%d_%H:%M:%S\")\n",
+    "model_name = \"Building_footprint_10epochs_\" + timestamp"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 16,
@@ -677,7 +687,7 @@
     }
    ],
    "source": [
-    "model.save('Building_footprint_10epochs', publish=True)"
+    "model.save(model_name, publish=True)"
    ]
   },
   {
@@ -752,9 +762,9 @@
    "notebookRuntimeVersion": ""
   },
   "kernelspec": {
-   "display_name": "Python [conda env:conda-dl] *",
+   "display_name": "Python [conda env:dlenv_30_9] *",
    "language": "python",
-   "name": "conda-env-conda-dl-py"
+   "name": "conda-env-dlenv_30_9-py"
   },
   "language_info": {
    "codemirror_mode": {
@@ -766,7 +776,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.11"
+   "version": "3.13.7"
   }
  },
  "nbformat": 4,

samples/04_gis_analysts_data_scientists/building_reconstruction_using_mask_rcnn.ipynb

@@ -826,9 +826,9 @@
    "notebookRuntimeVersion": ""
   },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python [conda env:dlenv_30_9] *",
    "language": "python",
-   "name": "python3"
+   "name": "conda-env-dlenv_30_9-py"
   },
   "language_info": {
    "codemirror_mode": {
@@ -840,7 +840,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.0"
+   "version": "3.13.7"
   },
   "toc": {
    "base_numbering": 1,

samples/04_gis_analysts_data_scientists/change_detection_of_buildings_from_satellite_imagery.ipynb

@@ -1719,9 +1719,9 @@
    "notebookRuntimeVersion": ""
   },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python [conda env:dlenv_30_9] *",
    "language": "python",
-   "name": "python3"
+   "name": "conda-env-dlenv_30_9-py"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1733,7 +1733,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.13.7"
   }
  },
  "nbformat": 4,

samples/04_gis_analysts_data_scientists/classifying_human_activity_using_tabPFN_classifier.ipynb

@@ -67,15 +67,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
     "import pandas as pd\n",
     "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
     "from sklearn.preprocessing import StandardScaler\n",
     "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, classification_report\n",
-    "\n",
-    "from arcgis.gis import GIS\n",
     "from arcgis.learn import MLModel, prepare_tabulardata"
    ]
   },
@@ -106,6 +103,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from arcgis.gis import GIS\n",
     "gis = GIS(\"home\")"
    ]
   },
@@ -2187,9 +2185,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "pro3.6_certify2.4.2_24Sep2025 [conda env:conda-pro3.6_certify2.4.2_24Sep2025] *",
+   "display_name": "Python [conda env:dlenv_30_9] *",
    "language": "python",
-   "name": "conda-env-conda-pro3.6_certify2.4.2_24Sep2025-pro3.6_certify2.4.2_24sep2025"
+   "name": "conda-env-dlenv_30_9-py"
   },
   "language_info": {
    "codemirror_mode": {

samples/04_gis_analysts_data_scientists/covid_case_forecasting_for_alabama_state_using_timeseriesmodel_from_arcgis_learn.ipynb

@@ -62,17 +62,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%matplotlib inline\n",
-    "\n",
     "import numpy as np\n",
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
     "from sklearn.model_selection import train_test_split\n",
     "from sklearn.metrics import r2_score\n",
     "import sklearn.metrics as metrics\n",
-    "\n",
-    "from arcgis.gis import GIS\n",
     "from arcgis.learn import TimeSeriesModel, prepare_tabulardata"
    ]
   },
@@ -91,6 +87,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from arcgis.gis import GIS\n",
     "gis = GIS(\"home\")"
    ]
   },
@@ -1903,9 +1900,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python [conda env:dlenv_30_9] *",
    "language": "python",
-   "name": "python3"
+   "name": "conda-env-dlenv_30_9-py"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1917,7 +1914,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.0"
+   "version": "3.13.7"
   }
  },
  "nbformat": 4,

samples/04_gis_analysts_data_scientists/detecting_mussel_farms_using_deep_learning.ipynb

@@ -111,16 +111,6 @@
     "## Export training data"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "ad42be61",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from arcgis.gis import GIS"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 2,
@@ -129,6 +119,7 @@
    "outputs": [],
    "source": [
     "# Connect to GIS\n",
+    "from arcgis.gis import GIS\n",
     "gis = GIS('home')"
    ]
   },
@@ -1134,9 +1125,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python [conda env:dlenv_30_9] *",
    "language": "python",
-   "name": "python3"
+   "name": "conda-env-dlenv_30_9-py"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1148,7 +1139,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.0"
+   "version": "3.13.7"
   }
  },
  "nbformat": 4,