docs: contributing: gsoc: 2022: Update with ideas

Signed-off-by: John Andersen <[email protected]>

Author: pdxjohnny

docs/contributing/gsoc/2022/automl.md

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+# Implementing AutoML
+
+AutoML or Automated Machine Learning as the name suggests automates the process
+of solving problems with Machine Learning. AutoML is generally helpful for
+people who aren't either familiar with Machine Learning or the involved
+programming. AutoML aims to improve the efficiency of any task involving
+Machine Learning.
+
+The primary objective we are trying to achieve is to create a model that
+takes as a property of its config a set of models to used for hyperparameter
+tuning. Another property of its config is the set of models which we should
+attempt to tune (via the first set). Default values for these results in using
+all installed models to try to tune all installed model plugins.
+
+- To start, we should define a reduced set of models (not all the ones we have).
+  We'll implement AutoML supporting only this reduced set. The first phase of
+  this project will be to make sure that one model can be used to tune
+  hyperparameters of another model.
+
+- The next phase will be to tune two models using the same tuning model. This
+  followed by tuning two models, using two models which amounts to doing the
+  previous task twice, with a different tuning model the second time.
+
+- The following phase will be to go through each model in each model plugin we
+  have and see which ones have issues being tuned using the approach taken in the
+  previous phase. This phase will help us determine which properties or methods
+  we may need to add to models to help them self identify and thereby indicate
+  their requirements for hyperparameter tuning, or maybe their inherent lack of
+  support for it.
+
+- The final phase will be to implement hyperparameter tuning for N by N models,
+  after implementing what we found to be gaps in the previous phase.<br>
+
+Due to the shortened GSoC cycle, we may end up not doing all of these phases.
+Which one we go to will be decided as we approach the selection process.
+
+## Skills
+
+- Python
+- Intermediate Machine Learning
+- Experience with various machine learning frameworks (AutoML frameworks would
+  be a plus)
+
+## Difficulty
+
+Intermediate/Hard
+
+## Estimated Time Required
+
+350 hours
+
+## Related Readings
+
+- https://github.com/intel/dffml/blob/master/docs/contributing/gsoc/2022/
+- https://scikit-learn.org/stable/model_selection.html#model-selection
+- https://www.automl.org/automl/
+
+## Getting Started
+
+- Read the contributing guidelines
+  - https://intel.github.io/dffml/master/contributing/index.html
+- Go through the quickstart
+  - https://intel.github.io/dffml/master/quickstart/model.html
+- Go trough the model tutorials
+  - https://intel.github.io/dffml/master/tutorials/models/
+- Go through the model plugins
+  - https://intel.github.io/dffml/master/plugins/dffml_model.html
+
+## Potential Mentors
+
+- [John Andersen](https://github.com/pdxjohnny)
+- [Yash Lamba](https://github.com/yashlamba)
+- [Saksham Arora](https://github.com/sakshamarora1)
+
+## Tracking and Discussion
+
+This project is related to the following issues. Please discuss and ask
+questions in the issue comments. Please also ping mentors on
+[Gitter](https://gitter.im/dffml/community) when you post on the following
+issues so that they are sure to see that you've commented.
+
+- https://github.com/intel/dffml/issues/968

docs/contributing/gsoc/2022/dataflow_event_types.md

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+# Refactor of DataFlows to Include Event Type
+
+A large part of DFFML is the concept of a DataFlow.
+
+Chance orchestrator context run so that it yields three objects, context, event
+type, results.
+
+Currently we have a lot of code that looks like this:
+
+```python
+for ctx, results in run(dataflow, [... inputs ...]):
+    print("The results of", ctx, "are", results)
+```
+
+When this project is over, those `for` loops will look like this:
+
+```python
+for ctx, event, data in run(dataflow, [... inputs ...]):
+    if event == EventType.OUTPUT:
+        print("The results of", ctx, "are", data)
+    elif event == EventType.INPUT:
+        print("An input entered network for context", ctx, ":", data)
+```
+
+The way things currently work is that the `run` function `yield`s when the
+context is finished running. It `yield`s the context that was running and the
+results.
+
+We need to add another part to a data flow so we can yield `Input`s. The event
+type would be `INPUT`, and in the DataFlow we should add a section for events.
+In the events section for `INPUT` events we could specify when an
+input should be yielded. We use the inputs section to specify which transitions
+between operations should be yielded.
+
+This will enable us to do things like running a DataFlow and not only `yield`ing
+the results, but data that's moving through the network as the DataFlow is
+running. This allows developers to build applications that show the progress of
+a DataFlow as it's running.
+
+## Skills
+
+- Python
+- Refactoring a large codebase
+- Asyncio knowledge would be very helpful here
+
+## Difficulty
+
+Intermediate/Hard
+
+## Estimated Time Required
+
+350 hours
+
+## Related Readings
+
+- https://intel.github.io/dffml/master/contributing/gsoc/2022/index.html
+
+## Getting Started
+
+- Read the contributing guidelines
+  - https://intel.github.io/dffml/master/contributing/index.html
+- Go through the quickstart
+  - https://intel.github.io/dffml/master/quickstart/model.html
+- Go through the data flow related docs and tutorials
+  - https://intel.github.io/dffml/master/tutorials/dataflows/index.html
+  - https://intel.github.io/dffml/master/examples/integration.html
+  - https://intel.github.io/dffml/master/examples/shouldi.html
+  - https://intel.github.io/dffml/master/examples/dataflows.html
+  - https://intel.github.io/dffml/master/examples/mnist.html
+  - https://intel.github.io/dffml/master/examples/flower17/flower17.html
+  - https://intel.github.io/dffml/master/examples/webhook/index.html
+- Read about what data flows are and how they work
+  - https://intel.github.io/dffml/master/concepts/index.html#dataflows
+  - https://intel.github.io/dffml/master/concepts/dataflow.html
+- Come up with a basic example where the user will see inputs moving through the
+  network.
+  - Make it simple and include a few operations.
+  - Get the DataFlow running.
+- Look at the code in `dffml/df/memory.py` and understand how it relates to the
+  docs covering DataFlows conceptually.
+- Plan out what all needs to change within `dffml/df/memory.py` and the other
+  code and examples that would change as a result.
+
+## Potential Mentors
+
+- [John Andersen](https://github.com/pdxjohnny)
+- [Saksham Arora](https://github.com/sakshamarora1)
+
+## Tracking and Discussion
+
+This project is related to the following issues. Please discuss and ask
+questions in the issue comments. Please also ping mentors on
+[Gitter](https://gitter.im/dffml/community) when you post on the following
+issues so that they are sure to see that you've commented.
+
+- https://github.com/intel/dffml/issues/919

docs/contributing/gsoc/2022/forecasting.md

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+# Time-Series Forecasting and Anomaly Detection
+
+Time-Series Forecasting & Anomaly detection has paramount importance in many
+real-world problems like infrastructure monitoring, stock exchange, etc.
+Currently, DFFML has limited integration with time-series data formats and
+implementation of relevant models. This project aims to add basic support for
+commonly used datasets, models, and data pre-processing/cleaning methods for
+time-series data.
+
+This project consists of four phases (Documentation & Testing is required in every phase):
+
+1. Implementation of Common Datasets:
+    - Related Issue: #1319
+    - Add at least 2 datasets.
+2. Implementation/Updating of Operations:
+    - Related Issue: #1321
+    - Add at least 2 operations.
+3. Implementation of  Relevant Models:
+    - Related Issue: #1320
+    - Add at least 3 models.
+4. Add documentation and examples showing all the three previous phases in
+   action together. You can use Jupyter notebooks, or rST with python scripts
+   and CLI examples, for the same.
+
+## Skills
+
+- Python
+- Familiarity with Machine Learning
+- Experience with various machine learning frameworks (Pytorch & TensorFlow
+  would be a plus)
+
+## Difficulty
+
+Beginner
+
+## Estimated Time Required
+
+350 hours
+
+## Related Readings
+
+- Related readings and links have also been added in the related issues mentioned above.
+- https://intel.github.io/dffml/master/examples/data_cleanup/data_cleanup.html
+- https://intel.github.io/dffml/master/examples/icecream_sales.html
+- https://intel.github.io/dffml/master/examples/or_covid_data_by_county.html
+- https://intel.github.io/dffml/master/api/source/dataset/base.html
+- https://intel.github.io/dffml/master/api/source/dataset/iris.html
+
+## Getting Started
+
+- Read the contributing guidelines
+  - https://intel.github.io/dffml/master/contributing/index.html
+- Go through the quickstart
+  - https://intel.github.io/dffml/master/quickstart/model.html
+- Go through the model tutorials
+  - https://intel.github.io/dffml/master/tutorials/models/
+- Go through the model plugins
+  - https://intel.github.io/dffml/master/plugins/dffml_model.html
+- Go through Jupyter Notebook Examples (they also have video walkthroughs available)
+  - https://intel.github.io/dffml/master/examples/notebooks/index.html
+- You don't need to go through all of them. Just get a feel for running a few
+
+## Potential Mentors
+
+- [John Andersen](https://github.com/pdxjohnny)
+- [Saahil Ali](https://github.com/programmer290399)