re-put runtime requirements (#213)

ammico/notebooks/DemoNotebook_ammico.ipynb

@@ -6,7 +6,9 @@
    "source": [
     "# AMMICO Demonstration Notebook\n",
     "With ammico, you can analyze text on images and image content at the same time. This is a demonstration notebook to showcase the capabilities of ammico.\n",
-    "You can run this notebook on google colab or locally / on your own HPC resource. The first cell only runs on google colab; on all other machines, you need to create a conda environment first and install ammico from the Python Package Index using  \n",
+    "You can run this notebook on google colab or locally / on your own HPC resource. The analysis can be quite slow on the google colab default runtime. For production data processing, it is recommended to run the analysis locally on a GPU-supported machine. You can also make use of the colab GPU runtime, or purchase additional runtime. However, google colab comes with pre-installed libraries that can lead to dependency conflicts. The setting on google colab changes frequently, so it is only ensured that this demonstration notebook runs on the default runtime. \n",
+    "\n",
+    "This first cell only runs on google colab; on all other machines, you need to create a conda environment first and install ammico from the Python Package Index using  \n",
     "```pip install ammico```  \n",
     "Alternatively you can install the development version from the GitHub repository  \n",
     "```pip install git+https://github.com/ssciwr/AMMICO.git```"
@@ -232,7 +234,7 @@
     "# if you do not want to re-accept the privacy disclosure every time, you can set this environment variable in your shell\n",
     "# to re-set the environment variable, uncomment the below line\n",
     "accept_privacy = \"PRIVACY_AMMICO\"\n",
-    "os.environ.pop(accept_privacy, None)\n",
+    "# os.environ.pop(accept_privacy, None)\n",
     "_ = ammico.privacy_disclosure(accept_privacy=accept_privacy)"
    ]
   },

docs/source/notebooks/DemoNotebook_ammico.ipynb

@@ -6,7 +6,9 @@
    "source": [
     "# AMMICO Demonstration Notebook\n",
     "With ammico, you can analyze text on images and image content at the same time. This is a demonstration notebook to showcase the capabilities of ammico.\n",
-    "You can run this notebook on google colab or locally / on your own HPC resource. The first cell only runs on google colab; on all other machines, you need to create a conda environment first and install ammico from the Python Package Index using  \n",
+    "You can run this notebook on google colab or locally / on your own HPC resource. The analysis can be quite slow on the google colab default runtime. For production data processing, it is recommended to run the analysis locally on a GPU-supported machine. You can also make use of the colab GPU runtime, or purchase additional runtime. However, google colab comes with pre-installed libraries that can lead to dependency conflicts. The setting on google colab changes frequently, so it is only ensured that this demonstration notebook runs on the default runtime. \n",
+    "\n",
+    "This first cell only runs on google colab; on all other machines, you need to create a conda environment first and install ammico from the Python Package Index using      \n",
     "```pip install ammico```  \n",
     "Alternatively you can install the development version from the GitHub repository  \n",
     "```pip install git+https://github.com/ssciwr/AMMICO.git```"
@@ -161,7 +163,7 @@
     "| `limit` | `int` | maximum number of files to read (defaults to `20`, for all images set to `None` or `-1`) |\n",
     "| `random_seed` | `str` | the random seed for shuffling the images; applies when only a few images are read and the selection should be preserved (defaults to `None`) |\n",
     "\n",
-    "The `find_files` function returns a nested dictionary that contains the file ids and the paths to the files and is empty otherwise. This dict is filled step by step with more data as each detector class is run on the data (see below).\n",
+    "The `find_files` function returns a nested dictionary that contains the file ids and the paths to the files and is empty otherwise. This dictionary is filled step by step with more data as each detector class is run on the data (see below).\n",
     "\n",
     "If you downloaded the test dataset above, you can directly provide the path you already set for the test directory, `data_path`. The below cell is already set up for the test dataset.\n",
     "\n",
@@ -747,7 +749,7 @@
     "| output key | output type | output value |\n",
     "| ---------- | ----------- | ------------ |\n",
     "| `const_image_summary` | `str` | when `analysis_type=\"summary\"` or `\"summary_and_questions\"`, constant image caption (does not change upon re-running the analysis for the same model) |\n",
-    "| `3_non-deterministic_summary` | `list[str]` | when `analysis_type=\"summary\"` or s`ummary_and_questions`, three different captions generated with different random seeds |\n",
+    "| `3_non-deterministic_summary` | `list[str]` | when `analysis_type=\"summary\"` or `summary_and_questions`, three different captions generated with different random seeds |\n",
     "| *a user-defined input question* | `str` | when `analysis_type=\"questions\"` or `summary_and_questions`, the answer to the user-defined input question | \n"
    ]
   },