diff --git a/How_to_guides/Machine_learning_with_ODC.ipynb b/How_to_guides/Machine_learning_with_ODC.ipynb
index cd699548f..4be473721 100644
--- a/How_to_guides/Machine_learning_with_ODC.ipynb
+++ b/How_to_guides/Machine_learning_with_ODC.ipynb
@@ -64,6 +64,7 @@
"source": [
"%matplotlib inline\n",
"\n",
+ "import os\n",
"import subprocess as sp\n",
"import shapely\n",
"import xarray as xr\n",
@@ -160,8 +161,11 @@
}
],
"source": [
- "ncpus=round(get_cpu_quota())\n",
- "print(ncpus)"
+ "if get_cpu_quota() is not None:\n",
+ " ncpus = round(get_cpu_quota())\n",
+ "else:\n",
+ " ncpus = os.cpu_count()\n",
+ "print(f\"ncpus = {ncpus}\")"
]
},
{
@@ -880,101 +884,7 @@
},
"widgets": {
"application/vnd.jupyter.widget-state+json": {
- "state": {
- "1619aa7c0a0d41f892bc55d700874aba": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "HTMLModel",
- "state": {
- "layout": "IPY_MODEL_ef7c7b47129f4bb7be210a31c9c6748c",
- "style": "IPY_MODEL_6c45957fae8a4c9c90d0efa71fb7acd3",
- "value": "100%"
- }
- },
- "1b9dd35d726a487c9e03b50cadec204e": {
- "model_module": "@jupyter-widgets/base",
- "model_module_version": "1.2.0",
- "model_name": "LayoutModel",
- "state": {}
- },
- "27c2edfba16545549cbc2446f65308f9": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "ProgressStyleModel",
- "state": {
- "description_width": ""
- }
- },
- "43e325ac2f4b42659d9ae321464c5dd1": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "FloatProgressModel",
- "state": {
- "bar_style": "success",
- "layout": "IPY_MODEL_9b47cb4d6d96465b8a2446bc022c085d",
- "max": 217,
- "style": "IPY_MODEL_27c2edfba16545549cbc2446f65308f9",
- "value": 217
- }
- },
- "5ce2388f84c64f1eb830509fab75e200": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "HBoxModel",
- "state": {
- "children": [
- "IPY_MODEL_1619aa7c0a0d41f892bc55d700874aba",
- "IPY_MODEL_43e325ac2f4b42659d9ae321464c5dd1",
- "IPY_MODEL_e6391b33874b4787837251849ceb2b1c"
- ],
- "layout": "IPY_MODEL_cc67c362ea5f4f80bfa29df7189ad3e6"
- }
- },
- "6c45957fae8a4c9c90d0efa71fb7acd3": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "DescriptionStyleModel",
- "state": {
- "description_width": ""
- }
- },
- "91df15d5486440d6a2125e74e9befe48": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "DescriptionStyleModel",
- "state": {
- "description_width": ""
- }
- },
- "9b47cb4d6d96465b8a2446bc022c085d": {
- "model_module": "@jupyter-widgets/base",
- "model_module_version": "1.2.0",
- "model_name": "LayoutModel",
- "state": {}
- },
- "cc67c362ea5f4f80bfa29df7189ad3e6": {
- "model_module": "@jupyter-widgets/base",
- "model_module_version": "1.2.0",
- "model_name": "LayoutModel",
- "state": {}
- },
- "e6391b33874b4787837251849ceb2b1c": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "HTMLModel",
- "state": {
- "layout": "IPY_MODEL_1b9dd35d726a487c9e03b50cadec204e",
- "style": "IPY_MODEL_91df15d5486440d6a2125e74e9befe48",
- "value": " 217/217 [01:53<00:00, 2.18it/s]"
- }
- },
- "ef7c7b47129f4bb7be210a31c9c6748c": {
- "model_module": "@jupyter-widgets/base",
- "model_module_version": "1.2.0",
- "model_name": "LayoutModel",
- "state": {}
- }
- },
+ "state": {},
"version_major": 2,
"version_minor": 0
}
diff --git a/Real_world_examples/Scalable_machine_learning/1_Extract_training_data.ipynb b/Real_world_examples/Scalable_machine_learning/1_Extract_training_data.ipynb
index e7c968381..348f3e3ab 100644
--- a/Real_world_examples/Scalable_machine_learning/1_Extract_training_data.ipynb
+++ b/Real_world_examples/Scalable_machine_learning/1_Extract_training_data.ipynb
@@ -62,7 +62,9 @@
{
"cell_type": "code",
"execution_count": 1,
- "metadata": {},
+ "metadata": {
+ "tags": []
+ },
"outputs": [],
"source": [
"%matplotlib inline\n",
@@ -98,7 +100,9 @@
{
"cell_type": "code",
"execution_count": 2,
- "metadata": {},
+ "metadata": {
+ "tags": []
+ },
"outputs": [],
"source": [
"path = 'data/crop_training_WA.geojson' \n",
@@ -115,7 +119,9 @@
{
"cell_type": "code",
"execution_count": 3,
- "metadata": {},
+ "metadata": {
+ "tags": []
+ },
"outputs": [
{
"name": "stdout",
@@ -126,13 +132,18 @@
}
],
"source": [
- "ncpus = round(get_cpu_quota())\n",
- "print('ncpus = ' + str(ncpus))"
+ "if get_cpu_quota() is not None:\n",
+ " ncpus = round(get_cpu_quota())\n",
+ "else:\n",
+ " ncpus = os.cpu_count()\n",
+ "print(f\"ncpus = {ncpus}\")"
]
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "tags": []
+ },
"source": [
"## Preview input data\n",
"\n",
@@ -144,7 +155,9 @@
{
"cell_type": "code",
"execution_count": 4,
- "metadata": {},
+ "metadata": {
+ "tags": []
+ },
"outputs": [
{
"data": {
@@ -226,15 +239,240 @@
{
"cell_type": "code",
"execution_count": 5,
- "metadata": {},
+ "metadata": {
+ "tags": []
+ },
"outputs": [
{
"data": {
"text/html": [
- "Make this Notebook Trusted to load map: File -> Trust Notebook
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