docs: Temorarily reference images on mirror of old big repo

Related: #1324
Signed-off-by: John Andersen <johnandersenpdx@gmail.com>

Author: pdxjohnny

docs/about.rst

@@ -154,7 +154,7 @@ This is a high level overview of how DFFML works.
     generate_features --> source
     predict --> source
 
-.. image:: /images/arch.svg
+.. image:: https://dffml.github.io/dffml-pre-image-removal/master/_images/arch.svg
     :alt: Architecture Diagram
 
 Machine Learning
@@ -185,7 +185,7 @@ instance). All the data generated by running these operations is query-able,
 allowing us to structure the output in whatever way is most fitting for our
 application.
 
-.. image:: /images/shouldi-dataflow.svg
+.. image:: https://dffml.github.io/dffml-pre-image-removal/master/_images/shouldi-dataflow.svg
     :alt: DataFlow for shouldi tool
 
 Consistent API

docs/concepts/dataflow.rst

@@ -214,7 +214,7 @@ running a DataFlow. The following sequence of events take place.
 
     opimpctx_run_operation --> |Outputs of Operation become inputs to other operations| inputs
 
-.. image:: /images/dataflow_diagram.svg
+.. image:: https://dffml.github.io/dffml-pre-image-removal/master/_images/dataflow_diagram.svg
     :alt: Flow chart showing how DataFlow Orchestrator works
 
 Benefits of DataFlows

docs/conf.py

@@ -91,7 +91,7 @@
     {% set nb_version = "main/" %}
 {% endif %}
 
-.. image:: ../../images/Download-.ipynb-button.svg
+.. image:: https://dffml.github.io/dffml-pre-image-removal/master/_images/Download-.ipynb-button.svg
     :target: https://intel.github.io/dffml/{{ nb_version }}{{ docname[:-6] }}ipynb
     :alt: Notebook download button
 

docs/contributing/git.rst

@@ -158,7 +158,7 @@ request is ready for review or not.
 If you want to run one of the tests locally, you reference
 :ref:`running_ci_tests_locally`.
 
-.. image:: /images/how-to-read-ci-tests.png
+.. image:: https://dffml.github.io/dffml-pre-image-removal/master/_images/how-to-read-ci-tests.png
     :alt: Screenshot of CI with some tests passing and some failing
 
 Look through all of the tests and identify which ones are failing. Click on the

docs/contributing/gsoc/rubric.rst

@@ -9,7 +9,7 @@ so.
 Grading Rubric
 --------------
 
-.. image:: /images/rubric-table.png
+.. image:: https://dffml.github.io/dffml-pre-image-removal/master/_images/rubric-table.png
     :alt: Screenshot of grading rubric
 
 .. NOTE The text of the table wasn't included in this file because Sphynx

docs/contributing/maintainers.rst

@@ -22,7 +22,7 @@ Once it's be uploaded, we go to PyPi and create an API key which will be used to
 upload it via the automatic release at the end of ``run_plugin`` within the CI
 script.
 
-.. image:: /images/pypi-token-to-github-secret.gif
+.. image:: https://dffml.github.io/dffml-pre-image-removal/master/_images/pypi-token-to-github-secret.gif
 
 Finally, update the ``PYPI_TOKENS`` file in ``.github/workflows/testing.yml``.
 Add a line for the plugin along with it's sceret.

docs/examples/dataflows.rst

@@ -245,7 +245,7 @@ can use ``lines_of_code_to_comments``.
 The ``lines_of_code_to_comments`` operation needs the output given by
 ``lines_of_code_by_language``, which needs a Git repos source code.
 
-.. image:: /images/comments_to_code.svg
+.. image:: https://dffml.github.io/dffml-pre-image-removal/master/_images/comments_to_code.svg
     :alt: Diagram showing DataFlow of comment to code ratio operations
 
 A ``git_repository_checked_out`` is defined as:
@@ -323,7 +323,7 @@ It contains the following files.
 
 We've modified the flow to create the following dataflow
 
-.. image:: /images/shouldi-dataflow-extended.svg
+.. image:: https://dffml.github.io/dffml-pre-image-removal/master/_images/shouldi-dataflow-extended.svg
     :alt: Diagram showing DataFlow with use of comment to code ratio
 
 The diagram above can be re-generated with the following commands

docs/examples/innersource/swportal.rst

@@ -136,7 +136,7 @@ satisfies our constraints.
         repos_yml_source -->|for each repo| dataflow
         dataflow -->|write| repos_json_source
 
-.. image:: /images/swportal-high-level-architecture.svg
+.. image:: https://dffml.github.io/dffml-pre-image-removal/master/_images/swportal-high-level-architecture.svg
     :alt: Flow chart showing high level architechture where we read the repos.yml files run the dataflow on each repo, then save to the repos.json source.
 
 Implementation Plan
@@ -318,7 +318,7 @@ Try listing all the records in the new source to verify it works.
             "extra": {},
             "features": {
                 "_InnerSourceMetadata": {
-                    "logo": "./images/demo/Earth.png",
+                    "logo": "https://dffml.github.io/dffml-pre-image-removal/master/_images/demo/Earth.png",
                     "participation": [
                         15,
                         3,
@@ -392,7 +392,7 @@ Try listing all the records in the new source to verify it works.
                 "name": "earth",
                 "open_issues_count": 98,
                 "owner": {
-                    "avatar_url": "./images/demo/Sol.png",
+                    "avatar_url": "https://dffml.github.io/dffml-pre-image-removal/master/_images/demo/Sol.png",
                     "login": "Sol"
                 },
                 "pushed_at": "2020-10-08T12:18:22Z",

docs/examples/integration.rst

@@ -13,7 +13,7 @@ unmaintained).
 
 To help you track your assigned statues you built a Python 3 CGI web app.
 
-.. image:: /images/website-before.png
+.. image:: https://dffml.github.io/dffml-pre-image-removal/master/_images/website-before.png
     :alt: Screenshot of the very basic web app with a table showing repo URLs and their mantainance status
 
 When you assign a maintenance status to a repo you are *classifying* it.
@@ -371,7 +371,7 @@ operations. Arrows show how data moves between operations.
 
 .. TODO Autogenerate this from the dataflow
 
-.. image:: /images/integration_dataflow.svg
+.. image:: https://dffml.github.io/dffml-pre-image-removal/master/_images/integration_dataflow.svg
     :alt: Diagram showing Dataflow
 
 We can also visualize how the individual inputs and outputs are linked together.
@@ -380,7 +380,7 @@ automatically.
 
 .. TODO Autogenerate this from the dataflow
 
-.. image:: /images/integration_dataflow_complex.svg
+.. image:: https://dffml.github.io/dffml-pre-image-removal/master/_images/integration_dataflow_complex.svg
     :alt: Diagram showing detailed version of Dataflow
 
 The inputs and outputs of operations within a running DataFlow are organized by
@@ -569,4 +569,4 @@ But you can look through the table and you'll find that a prediction has been
 run on the repo. Congratulations! You've automated a manual classification
 process, and integrated machine learning into a legacy application.
 
-.. image:: /images/integration_demo.gif
+.. image:: https://dffml.github.io/dffml-pre-image-removal/master/_images/integration_demo.gif

docs/examples/mnist.rst

@@ -48,7 +48,7 @@ and the seed to have the input of the associate_definition operation come from t
     style 113e51b1af7d424eee96c632d74847f7 fill:#f6dbf9,stroke:#a178ca
     113e51b1af7d424eee96c632d74847f7 --> 0fbe41b549bb236aabadebd7924379fd
 
-.. image:: /images/mnist-normalize.svg
+.. image:: https://dffml.github.io/dffml-pre-image-removal/master/_images/mnist-normalize.svg
     :alt: Diagarm of MNIST normalization dataflow
 
 Train the model.
@@ -95,7 +95,7 @@ and to have the input of the associate_definition operation come from the output
     style 24e79f7035a289834b34967054b338f5 fill:#f6dbf9,stroke:#a178ca
     24e79f7035a289834b34967054b338f5 --> dc3d3b1a336fbbd51585e31c95f45c83
 
-.. image:: /images/mnist-resize.svg
+.. image:: https://dffml.github.io/dffml-pre-image-removal/master/_images/mnist-resize.svg
     :alt: Diagarm of MNIST resizing and normalizing dataflow
 
 In this example, the ``image.csv`` file contains the names of the following images