Add form for testing Spark or Dask to documentation (#545)

* add links

* fix warning when building docs

docs/source/guides/performance.rst

@@ -65,10 +65,12 @@ When an entire dataset is not required to calculate the features for a given set
 
 An example of this approach can be seen in the `Predict Next Purchase demo notebook <https://github.com/featuretools/predict_next_purchase>`_. In this example, we partition data by customer and only load a fixed number of customers into memory at any given time. We implement this easily using `Dask <https://dask.pydata.org/>`_, which could also be used to scale the computation to a cluster of computers. A framework like `Spark <https://spark.apache.org/>`_ could be used similarly.
 
-An additional example of partitioning data to distribute on multiple cores or a cluster using Dask can be seen in the `Featuretools on Dask notebook <https://github.com/Featuretools/Automated-Manual-Comparison/blob/master/Loan%20Repayment/notebooks/Featuretools%20on%20Dask.ipynb>`_. This approach is detailed in the `Parallelizing Feature Engineering with Dask article <https://medium.com/feature-labs-engineering/scaling-featuretools-with-dask-ce46f9774c7d>`_ on the Feature Labs engineering blog. Dask allows for simple scaling to multiple cores on a single computer or multiple machines on a cluster. 
+An additional example of partitioning data to distribute on multiple cores or a cluster using Dask can be seen in the `Featuretools on Dask notebook <https://github.com/Featuretools/Automated-Manual-Comparison/blob/master/Loan%20Repayment/notebooks/Featuretools%20on%20Dask.ipynb>`_. This approach is detailed in the `Parallelizing Feature Engineering with Dask article <https://medium.com/feature-labs-engineering/scaling-featuretools-with-dask-ce46f9774c7d>`_ on the Feature Labs engineering blog. Dask allows for simple scaling to multiple cores on a single computer or multiple machines on a cluster.
 
 For a similar partition and distribute implementation using Apache Spark with PySpark, refer to the `Feature Engineering on Spark notebook <https://github.com/Featuretools/predicting-customer-churn/blob/master/churn/4.%20Feature%20Engineering%20on%20Spark.ipynb>`_. This implementation shows how to carry out feature engineering on a cluster of EC2 instances using Spark as the distributed framework. A write-up of this approach is described in the `Featuretools on Spark article <https://blog.featurelabs.com/featuretools-on-spark-2/>`_ on the Feature Labs engineering blog.
 
 Feature Labs
 ------------
-`Feature Labs <https://www.featurelabs.com>`_ provides tools and support to organizations that want to scale their usage of Featuretools. More information is available `here <https://www.featurelabs.com/featuretools>`_.
+`Feature Labs <https://www.featurelabs.com>`_ provides tools and support to organizations that want to scale their usage of Featuretools. More information is available `here <https://www.featurelabs.com/featuretools>`__.
+
+If you would like to test `Feature Labs APIs <https://docs.featurelabs.com/>`_ for running Featuretools natively on Apache Spark or Dask, please let us know `here <https://forms.gle/TtFTH5QKM4gZtu7U7>`__.

docs/source/usage_tips/limitations.rst

@@ -5,6 +5,8 @@ In-memory
 
 Featuretools is intended to be run on datasets that can fit in memory on one machine. For advice on handing large dataset refer to :ref:`performance`.
 
+If you would like to test `Feature Labs APIs <https://docs.featurelabs.com/>`_ for running Featuretools natively on Apache Spark or Dask, please let us know `here <https://forms.gle/TtFTH5QKM4gZtu7U7>`_.
+
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