fix: display sdk notebooks in github pages site

.gitignore

@@ -144,7 +144,6 @@ venv.bak/
 # mkdocs documentation
 **/site/
 **/sdk-docs-site/
-geospatial-studio-sdk/docs/examples/
 
 # mypy
 .mypy_cache/
@@ -176,4 +175,7 @@ resources.qrc
 *.geostudio_apikey*
 tasks/
 *.geostudio_config_file*
+
+# tune-tasks folder
+**/tune-tasks/
 geospatial-studio-sdk/docs/examples/

.pre-commit-config.yaml

@@ -3,6 +3,20 @@
 
 
 repos:
+  - repo: https://github.com/ibm/detect-secrets
+      # If you desire to use a specific version of detect-secrets, you can replace `master` with other git revisions such as branch, tag or commit sha.
+      # You are encouraged to use static refs such as tags, instead of branch name
+      #
+      # Running "pre-commit autoupdate" automatically updates rev to latest tag
+    rev: 0.13.1+ibm.62.dss
+    hooks:
+      - id: detect-secrets # pragma: whitelist secret
+        # Add options for detect-secrets-hook binary. You can run `detect-secrets-hook --help` to list out all possible options.
+        # You may also run `pre-commit run detect-secrets` to preview the scan result.
+        # when "--baseline" without "--use-all-plugins", pre-commit scan with just plugins in baseline file
+        # when "--baseline" with "--use-all-plugins", pre-commit scan with all available plugins
+        # add "--fail-on-unaudited" to fail pre-commit for unaudited potential secrets
+        args: [--baseline, .secrets.baseline, --use-all-plugins]
   - repo: https://github.com/pre-commit/pre-commit-hooks
     rev: v4.4.0
     hooks:

examples/dataset-onboarding/001-Introduction-to-Onboarding-Tuning-Data.ipynb

@@ -15,7 +15,7 @@
    "source": [
     "# 001-Introduction-to-Onboarding-Tuning-Data\n",
     "\n",
-    "Download [001-Introduction-to-Onboarding-Tuning-Data.ipynb](001-Introduction-to-Onboarding-Tuning-Data.ipynb) notebook and try it out\n",
+    "<a href=\"./001-Introduction-to-Onboarding-Tuning-Data.ipynb\" download=\"001-Introduction-to-Onboarding-Tuning-Data.ipynb\">📥 Download 001-Introduction-to-Onboarding-Tuning-Data.ipynb</a> and try it out\n",
     "\n",
     "## Introduction\n",
     "This notebook is intended to be a guide to onboarding a new fine-tuning dataset Geospatial Studio using the python SDK.\n",

examples/e2e-walkthroughs/GeospatialStudio-Walkthrough-BurnScars.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# Mapping wildfire burn scars using HLS data\n",
     "\n",
-    "Download [GeospatialStudio-Walkthrough-BurnScars.ipynb](GeospatialStudio-Walkthrough-BurnScars.ipynb) notebook and try it out\n",
+    "<a href=\"./GeospatialStudio-Walkthrough-BurnScars.ipynb\" download=\"GeospatialStudio-Walkthrough-BurnScars.ipynb\">📥 Download GeospatialStudio-Walkthrough-BurnScars.ipynb</a> and try it out\n",
     "\n",
     "Imagine that you work in disaster response and need a rapid way to map the extent areas burned by wildfires.  You need to do this in an automated, scalable manner.  We can achieve this using an AI model which ingests satellite data (in this instance the NASA Harmonized Landsat Sentinel2 dataset) and outputs a map of burned area.  We could potentially then integrate the burned area extent with details of infrastructure or assets to quantify impact.\n",
     "\n",

examples/e2e-walkthroughs/GeospatialStudio-Walkthrough-Flooding.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "# Mapping flooding using an AI flood model\n",
     "\n",
-    "Download [GeospatialStudio-Walkthrough-Flooding.ipynb](GeospatialStudio-Walkthrough-Flooding.ipynb) notebook and try it out\n",
+    "<a href=\"./GeospatialStudio-Walkthrough-Flooding.ipynb\" download=\"GeospatialStudio-Walkthrough-Flooding.ipynb\">📥 Download GeospatialStudio-Walkthrough-Flooding.ipynb</a> and try it out\n",
     "\n",
     "Assume you are interested in mapping flooding, traditionally you might have either relied on on-the-ground mapping, or possibly for manual analysis of remote-sensing imagery (i.e. satellite or UAV). In order to scale up these efforts and operationalise, we need a way to automate the extraction of flood extent from satellite imagery. This is where we turn to AI models. \n",
     "\n",

examples/fine-tuning/001-Introduction-to-Finetuning.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# 001-Introduction-to-Finetuning\n",
     "\n",
-    "Download [001-Introduction-to-Finetuning.ipynb](001-Introduction-to-Finetuning.ipynb) notebook and try it out\n",
+    "<a href=\"./001-Introduction-to-Finetuning.ipynb\" download=\"001-Introduction-to-Finetuning.ipynb\">📥 Download 001-Introduction-to-Finetuning.ipynb</a> and try it out\n",
     "\n",
     "## Introduction\n",
     "This notebook is intended to be an introduction to using the python SDK to fine-tune a new model from a geospatial foundation model backbone using the Geospatial Studio.\n",

examples/fine-tuning/002-Upload-Complete-Tune-Artifacts.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# 002-Upload-Completed-Tune-Artifacts\n",
     "\n",
-    "Download [002-Upload-Complete-Tune-Artifacts.ipynb](002-Upload-Complete-Tune-Artifacts.ipynb) notebook and try it out\n",
+    "<a href=\"./002-Upload-Complete-Tune-Artifacts.ipynb\" download=\"002-Upload-Complete-Tune-Artifacts.ipynb\">📥 Download 002-Upload-Complete-Tune-Artifacts.ipynb</a> and try it out\n",
     "\n",
     "\n",
     "## Introduction\n",

examples/fine-tuning/003-Terratorch-Iterate.ipynb

@@ -7,6 +7,8 @@
    "source": [
     "# FineTuning: HPO With Terratorch Iterate\n",
     "\n",
+    "<a href=\"./003-Terratorch-Iterate.ipynb\" download=\"003-Terratorch-Iterate.ipynb\">📥 Download 003-Terratorch-Iterate.ipynb</a> and try it out\n",
+    "\n",
     "\n",
     "## Introduction\n",
     "\n",

examples/fine-tuning/004-Create-User-Defined-Tuning-Templates.ipynb

@@ -15,7 +15,7 @@
    "source": [
     "# 004-Create-User-Defined-Tuning-Templates\n",
     "\n",
-    "Download [004-Create-User-Defined-Tuning-Templates.ipynb](004-Create-User-Defined-Tuning-Templates.ipynb) notebook and try it out.\n",
+    "<a href=\"./004-Create-User-Defined-Tuning-Templates.ipynb\" download=\"004-Create-User-Defined-Tuning-Templates.ipynb\">📥 Download 004-Create-User-Defined-Tuning-Templates.ipynb</a> and try it out\n",
     "\n",
     "## Introduction\n",
     "This notebook is intended to be an introduction to creating and managing Tuning templates using the Geospatial Studio SDK.  The tutorial assumes the user wishes to use an existing template and data in the studio. \n",

examples/inference/001-Introduction-to-Inferencing.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# 001-Introduction-to-Inferencing\n",
     "\n",
-    "Download [001-Introduction-to-Inferencing.ipynb](001-Introduction-to-Inferencing.ipynb) notebook and try it out\n",
+    "<a href=\"./001-Introduction-to-Inferencing.ipynb\" download=\"001-Introduction-to-Inferencing.ipynb\">📥 Download 001-Introduction-to-Inferencing.ipynb</a> and try it out\n",
     "\n",
     "## Introduction\n",
     "This notebook is meant for someone with minimal knowledge of Geospatial, to be able to meaningfully use the most important functions of the Geospatial Studio SDK to run a model.\n",

examples/inference/002-Add-Precomputed-Examples.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# 002-Add-Precomputed-Examples\n",
     "\n",
-    "Download [002-Add-Precomputed-Examples.ipynb](002-Add-Precomputed-Examples.ipynb) notebook and try it out\n",
+    "<a href=\"./002-Add-Precomputed-Examples.ipynb\" download=\"002-Add-Precomputed-Examples.ipynb\">📥 Download 002-Add-Precomputed-Examples.ipynb</a> and try it out\n",
     "\n",
     "## Introduction\n",
     "This notebook is meant for someone with minimal knowledge, to be able to meaningfully use the most important functions of the Geospatial SDK.\n",

examples/inference/003-Running-inferences-with-extra-pipeline-steps.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# 003-Running-inferences-with-extra-pipeline-steps\n",
     "\n",
-    "Download [003-Running-inferences-with-extra-pipeline-steps](003-Running-inferences-with-extra-pipeline-steps.ipynb) notebook and try it out\n",
+    "<a href=\"./003-Running-inferences-with-extra-pipeline-steps\" download=\"003-Running-inferences-with-extra-pipeline-steps\">📥 Download 003-Running-inferences-with-extra-pipeline-steps</a> and try it out\n",
     "\n",
     "## Introduction\n",
     "This notebook is meant for someone with minimal knowledge, to be able to meaningfully use the most important functions of the Geospatial SDK.\n",

examples/sample_files/burnscars-iterate-hpo.yaml

@@ -12,13 +12,7 @@ defaults:
   trainer_args:
     accelerator: gpu
     log_every_n_steps: 1
-    logger:
-      class_path: lightning.pytorch.loggers.mlflow.MLFlowLogger
-      init_args:
-        experiment_name: prithvi-eo2
-        run_name: prithvi-eo2-burnscars
-        save_dir: /working/mlflow
-        tracking_uri: https://gfm-mlflow-internal-nasageospatial-dev.cash.sl.cloud9.ibm.com
+    logger: true
     max_epochs: 4
 experiment_name: test_geotune-burnscars-config
 n_trials: 4

examples/sample_files/example-terratorch-config.yaml

@@ -10,15 +10,7 @@ trainer:
   devices: auto
   num_nodes: 1
   precision: 16-mixed
-  logger:
-    class_path: lightning.pytorch.loggers.mlflow.MLFlowLogger
-    init_args:
-      experiment_name: geotune-5vmhksvpy4tg6bm957fw8k # Future version, chnage this to user / email
-      run_name: "Test tune 1"    # Future version, chnage this to tune_id
-      save_dir: /geotunes/tune-tasks/geotune-5vmhksvpy4tg6bm957fw8k/mlflow
-      tags:
-        email: Catherine.Wanjiru@ibm.com
-        name: Catherine.Wanjiru
+  logger: true
 
   callbacks:
     - class_path: RichProgressBar