Migrates links to dagworks

Updates appropriate links/wording too.

CODE_OF_CONDUCT.md

@@ -61,7 +61,7 @@ representative at an online or offline event.
 
 Instances of abusive, harassing, or otherwise unacceptable behavior may be
 reported to the community leaders responsible for enforcement at
-algorithms-opensource@stitchfix.com.
+opensource@dagworks.io.
 All complaints will be reviewed and investigated promptly and fairly.
 
 All community leaders are obligated to respect the privacy and security of the

README.md

@@ -159,8 +159,7 @@ E.g.
 We have a small but active community on [slack](https://join.slack.com/t/hamilton-opensource/shared_invite/zt-1bjs72asx-wcUTgH7q7QX1igiQ5bbdcg). Come join us!
 
 # License
-Hamilton is released under the [BSD 3-Clause Clear License](LICENSE). If you need to get in touch about something,
-contact us at algorithms-opensource (at) stitchfix.com.
+Hamilton is released under the [BSD 3-Clause Clear License](LICENSE).
 
 # Used internally by:
 * [Stitch Fix](https://www.stitchfix.com/)

developer_setup.md

@@ -17,7 +17,7 @@ Fork this repo and clone your fork. ([GitHub docs](https://docs.github.com/en/pu
 GITHUB_USERNAME="YOUR-GITHUB-USERNAME" \
 git clone https://github.com/${GITHUB_USERNAME}/hamilton.git
 cd ./hamilton
-git remote add upstream https://github.com/stitchfix/hamilton.git
+git remote add upstream https://github.com/dagworks-inc/hamilton.git
 ```
 
 Install the project's dependencies in your preferred method for managing python dependencies. For example, run the following to use `venv`.
@@ -70,7 +70,7 @@ pre-commit run --all-files
 
 ...and by following the steps in ["How to run unit tests"](#how-to-run-unit-tests).
 
-Navigate to https://github.com/stitchfix/hamilton/pulls and open a pull request.
+Navigate to https://github.com/dagworks-inc/hamilton/pulls and open a pull request.
 
 ## How to run unit tests
 
@@ -152,7 +152,7 @@ These are the steps to push to pypi. This is taken from the [python packaging tu
 Now that you've pushed to pypi, announce your release! We plan to automate this, but for now...
 
 1. Create a github release (select auto-generate release for painless text generation). Create a tag that's called `sf-hamilton-{version_string}`.
-See [1.2.0](https://github.com/stitchfix/hamilton/releases/tag/sf-hamilton-1.2.0) for an example.
+See [1.2.0](https://github.com/dagworks-inc/hamilton/releases/tag/sf-hamilton-1.2.0) for an example.
 2. Announce on the #announcements channel in slack, following the format presented there.
 3. Thanks for contributing!
 

docs/best-practices/loading-data.rst

@@ -111,7 +111,7 @@ Caching Data Load for Rapid Iteration
 -------------------------------------
 
 Hamilton does not yet have caching built into the framework. That said, we are
-`working on <https://github.com/stitchfix/hamilton/issues/17>`_ introducing this as a feature and deciding on the scope
+`working on <https://github.com/dagworks-inc/hamilton/issues/17>`_ introducing this as a feature and deciding on the scope
 of the implementation. For now, however, the following decorator/toolset can allow you to cache data loaders based off
 of the parameters as well as the code itself. Use this at your own risk though, as it's not a fully functioning cache.
 This can be used as follows:

docs/conf.py

@@ -3,7 +3,7 @@
 html_theme = "furo"
 html_title = "Hamilton"
 html_theme_options = {
-    "source_repository": "https://github.com/stitchfix/hamilton",
+    "source_repository": "https://github.com/dagworks-inc/hamilton",
     "source_branch": "main",
     "source_directory": "docs/",
 }

docs/contributing.rst

@@ -4,7 +4,7 @@ Contributing
 
 For contributing guidelines see github.
 
-We open contributions big and small. See our `contributing guidelines <https://github.com/stitchfix/hamilton/blob/main/CONTRIBUTING.md>`_.
+We open contributions big and small. See our `contributing guidelines <https://github.com/dagworks-inc/hamilton/blob/main/CONTRIBUTING.md>`_.
 
-We also operate under a `Code of Conduct <https://github.com/stitchfix/hamilton/blob/main/CODE\_OF\_CONDUCT.md>`_, and
+We also operate under a `Code of Conduct <https://github.com/dagworks-inc/hamilton/blob/main/CODE\_OF\_CONDUCT.md>`_, and
 expect contributors to do the same.

docs/extensions.rst

@@ -19,7 +19,7 @@ if you write Pandas code!
 All that's needed is to:
 
 #. Import system specific code to setup a client/cluster/etc for that distributed/scalable system.
-#. Import a `GraphAdapter <https://github.com/stitchfix/hamilton/blob/main/hamilton/base.py#L91>`_ that implements using that distributed/scalable system. See :doc:`reference/api-reference/available-graph-adapters` for what is available.
+#. Import a `GraphAdapter <https://github.com/dagworks-inc/hamilton/blob/main/hamilton/base.py#L91>`_ that implements using that distributed/scalable system. See :doc:`reference/api-reference/available-graph-adapters` for what is available.
 #. You may need to provide a specific module that knows how to load data into the scalable system.
 #. Pass the modules, and graph adapter to the Hamilton Driver.
 #. Proceed as you would normally.
@@ -58,7 +58,7 @@ Ray - Experimental!
 
 `Ray <https://ray.io>`_ is a system to scale python workloads. Hamilton makes it very easy for you to use Ray.
 
-See `Scaling Hamilton on Ray <https://github.com/stitchfix/hamilton/tree/main/examples/ry>`_
+See `Scaling Hamilton on Ray <https://github.com/dagworks-inc/hamilton/tree/main/examples/ry>`_
 for an example of using Ray.
 
 Single Machine:
@@ -81,7 +81,7 @@ Dask - Experimental!
 
 `Ray <https://ray.io>`_ is a system to scale python workloads. Hamilton makes it very easy for you to use Ray.
 
-See `Scaling Hamilton on Dask <https://github.com/stitchfix/hamilton/tree/main/examples/dask>`_
+See `Scaling Hamilton on Dask <https://github.com/dagworks-inc/hamilton/tree/main/examples/dask>`_
 for an example of using Dask to scale Hamilton computation.
 
 Single Machine:
@@ -104,7 +104,7 @@ Koalas on Spark, a.k.a. Pandas API on Spark - Experimental!
 was the project code name to implement the `Pandas API on top of Spark <https://spark.apache.org/docs/latest/api/python/user\_guide/pandas\_on\_spark/index.html>`_.
 Hamilton makes it very easy for you to use Koalas on Spark.
 
-See `Scaling Hamilton on Koalas <https://github.com/stitchfix/hamilton/tree/main/examples/spark>`_
+See `Scaling Hamilton on Koalas <https://github.com/dagworks-inc/hamilton/tree/main/examples/spark>`_
 for an example of using Koalas on Spark to scale Hamilton computation.
 
 Single Machine:
@@ -132,9 +132,9 @@ This means, that the result of ``execute()`` can be any python object type!
 How do you change the type of the object returned?
 ==================================================
 
-You need to implement a `ResultMixin <https://github.com/stitchfix/hamilton/blob/main/hamilton/base.py#L18>`_ if there
+You need to implement a `ResultMixin <https://github.com/dagworks-inc/hamilton/blob/main/hamilton/base.py#L18>`_ if there
 isn't one already defined for what you want to do. Then you need to provide that to a
-`GraphAdapter <https://github.com/stitchfix/hamilton/blob/main/hamilton/base.py#L91>`_, similar to what was presented
+`GraphAdapter <https://github.com/dagworks-inc/hamilton/blob/main/hamilton/base.py#L91>`_, similar to what was presented
 above.
 
 See :doc:`reference/api-reference/available-result-builders` for what is provided with Hamilton, or :doc:`reference/api-extensions/custom-result-builders`

docs/index.rst

@@ -51,7 +51,7 @@ Hamilton
 
    unorganized-docs/index
 
-The open source framework `Hamilton <https://github.com/stitchfix/hamilton>`_, originally built to manage and run Stitch
+The open source framework `Hamilton <https://github.com/dagworks-inc/hamilton>`_, originally built to manage and run Stitch
 Fix's data pipelines.
 
 .. _getting started:
@@ -142,8 +142,8 @@ For more information please see :doc:`less-than-15-minutes-to-mastery/installing
 License
 ---------------
 
-Hamilton is released under the `BSD 3-Clause Clear License <https://github.com/stitchfix/hamilton/blob/main/LICENSE>`_.
-If you need to get in touch about something, contact us at algorithms-opensource (at) stitchfix.com.
+Hamilton is released under the `BSD 3-Clause Clear License <https://github.com/dagworks-inc/hamilton/blob/main/LICENSE>`_.
+If you need to get in touch about something, contact us at opensource (at) dagworks.io.
 
 .. _contributing:
 

docs/less-than-15-minutes-to-mastery/writing-your-first-dataflow.rst

@@ -10,8 +10,8 @@ We're jumping in head-first. If you want to start with an overview, skip ahead t
 
 .. note::
 
-    You can follow along in the `examples directory <https://github.com/stitchfix/hamilton/tree/main/examples/hello\_world>`_
-    of the `hamilton repo <https://github.com/stitchfix/hamilton/>`_. We highly recommend forking the repo and playing
+    You can follow along in the `examples directory <https://github.com/dagworks-inc/hamilton/tree/main/examples/hello\_world>`_
+    of the `hamilton repo <https://github.com/dagworks-inc/hamilton/>`_. We highly recommend forking the repo and playing
     around with the code to get comfortable.
 
 Writing some Transforms

docs/license.rst

@@ -4,5 +4,4 @@ License
 
 Hamilton's open source license
 
-Hamilton is released under the `BSD 3-Clause Clear License <https://github.com/stitchfix/hamilton/blob/main/LICENSE>`_.
-If you need to get in touch about something, contact us at algorithms-opensource (at) stitchfix.com.
+Hamilton is released under the `BSD 3-Clause Clear License <https://github.com/dagworks-inc/hamilton/blob/main/LICENSE>`_.

docs/overview-of-concepts/the-hamilton-driver.rst

@@ -191,7 +191,7 @@ Scaling Hamilton: Multi-core & Distributed Execution
 This functionality is currently in an "experimental" state. We think the code is solid, but it hasn't been used in a
 production environment for long. Thus the API to these GraphAdapters might change.
 
-See the `experimental <https://github.com/stitchfix/hamilton/tree/main/hamilton/experimental>`_ package for the current
+See the `experimental <https://github.com/dagworks-inc/hamilton/tree/main/hamilton/experimental>`_ package for the current
 implementations. We encourage you to give them a spin and provide us with feedback. See
 :doc:`../reference/api-reference/available-graph-adapters` for more details.
 

docs/reference/api-extensions/custom-result-builders.rst

@@ -14,7 +14,7 @@ through it can help ensure we aren't duplicating effort, and we can help steer y
 What you need to do
 -------------------
 
-You need to implement a class that implements a single function - see `github code <https://github.com/stitchfix/hamilton/blob/main/hamilton/base.py#L18-L28>`_:
+You need to implement a class that implements a single function - see `github code <https://github.com/dagworks-inc/hamilton/blob/main/hamilton/base.py#L18-L28>`_:
 
 .. code-block:: python
 

docs/reference/api-extensions/index.rst

@@ -12,5 +12,5 @@ API Extensions
 
 Currently the API extensions are all experimental. Note this doesn't mean they're not well-tested or thought out --
 rather that we're actively looking for feedback. More docs upcoming, but for now fish around the
-`experimental package <https://github.com/stitchfix/hamilton/tree/main/hamilton/experimental>`_, and give the
+`experimental package <https://github.com/dagworks-inc/hamilton/tree/main/hamilton/experimental>`_, and give the
 extensions a try!

docs/reference/api-reference/available-decorators.rst

@@ -4,7 +4,7 @@ Available Decorators
 
 While the 1:1 mapping of column -> function implementation is powerful, we've implemented a few decorators to promote
 business-logic reuse. The decorators we've defined are as follows (source can be found in
-`function_modifiers <https://github.com/stitchfix/hamilton/blob/main/hamilton/function\_modifiers.py>`_):
+`function_modifiers <https://github.com/dagworks-inc/hamilton/blob/main/hamilton/function\_modifiers.py>`_):
 
 @tag
 ----
@@ -184,7 +184,7 @@ of the series, and one that checks whether the data is in a certain range.
 
 Note that you can also specify custom decorators using the ``@check_output_custom`` decorator.
 
-See `data_quality <https://github.com/stitchfix/hamilton/blob/main/data\_quality.md>`_ for more information on
+See `data_quality <https://github.com/dagworks-inc/hamilton/blob/main/data\_quality.md>`_ for more information on
 available validators and how to build custom ones.
 
 @parameterize

docs/reference/api-reference/available-drivers.rst

@@ -4,7 +4,7 @@ Available Drivers
 
 API docs for using the drivers
 
-Currently, we have a `single driver <https://github.com/stitchfix/hamilton/blob/8a08a5e3dd69bbf7ddd83b8053c1ba9ed96ab675/hamilton/driver.py>`_.
+Currently, we have a `single driver <https://github.com/dagworks-inc/hamilton/blob/8a08a5e3dd69bbf7ddd83b8053c1ba9ed96ab675/hamilton/driver.py>`_.
 It's highly parameterizable, allowing you to customize:
 
 * The way the DAG is executed (how each node is executed), i.e. either locally, in parallel, or on a cluster!

docs/reference/api-reference/available-graph-adapters.rst

@@ -12,10 +12,10 @@ Use ``from hamilton import base`` to use these Graph Adapters:
    * - Name
      - What it does
      - When you'd use it
-   * - `base.SimplePythonDataFrameGraphAdapter <https://github.com/stitchfix/hamilton/blob/main/hamilton/base.py#L134>`_
+   * - `base.SimplePythonDataFrameGraphAdapter <https://github.com/dagworks-inc/hamilton/blob/main/hamilton/base.py#L134>`_
      - This executes the Hamilton dataflow locally on a machine in a single threaded, single process fashion. It assumes a pandas dataframe as a result.
      - This is the default GraphAdapter that Hamilton uses. Use this when you want to execute on a single machine, without parallelization, and you want a pandas dataframe as output.
-   * - `base.SimplePythonGraphAdapter <https://github.com/stitchfix/hamilton/blob/main/hamilton/base.py#L149>`_
+   * - `base.SimplePythonGraphAdapter <https://github.com/dagworks-inc/hamilton/blob/main/hamilton/base.py#L149>`_
      - This executes the Hamilton dataflow locally on a machine in a single threaded, single process fashion. It allows you to specify a ResultBuilder to control the return type of what ``execute()`` returns.
      - This is the default GraphAdapter that Hamilton uses. Use this when you want to execute on a single machine, without parallelization, and you want to control the return type of the object that ``execute()`` returns.
 
@@ -34,16 +34,16 @@ Use ``from hamilton.experimental import h_[NAME]`` to use these Graph Adapters:
    * - Name
      - What it does
      - When you'd use it
-   * - `h_dask.DaskGraphAdapter <https://github.com/stitchfix/hamilton/blob/main/hamilton/experimental/h_dask.py#L21>`_
+   * - `h_dask.DaskGraphAdapter <https://github.com/dagworks-inc/hamilton/blob/main/hamilton/experimental/h_dask.py#L21>`_
      - | This walks the graph and translates it to run onto `Dask <https://dask.org/>`_.
        | You have the ability to pass in a ResultMixin object to the constructor to control the return type that gets produce by running on Dask.
      - Use this if you want to utilize multiple cores on a single machine, or you want to scale to large data set sizes with a Dask cluster that you can connect to.
-   * - `h_ray.RayGraphAdapter <https://github.com/stitchfix/hamilton/blob/main/hamilton/experimental/h_ray.py#L12>`_
+   * - `h_ray.RayGraphAdapter <https://github.com/dagworks-inc/hamilton/blob/main/hamilton/experimental/h_ray.py#L12>`_
      - | This walks the graph and translates it to run onto `Ray <https://ray.io/>`_.
        | You have the ability to pass in a ResultMixin object to the constructor to control the return type that gets produce by running on Ray.
      - Use this if you want to utilize multiple cores on a single machine, or you want to scale to larger data set sizes with a Ray cluster that you can connect to. Note: you are still constrained by machine memory size with Ray; you can't just scale to any dataset size.
-   * - `h_spark.SparkKoalasGraphAdapter <https://github.com/stitchfix/hamilton/blob/main/hamilton/experimental/h_spark.py#L25>`_
+   * - `h_spark.SparkKoalasGraphAdapter <https://github.com/dagworks-inc/hamilton/blob/main/hamilton/experimental/h_spark.py#L25>`_
      - | This walks the graph and translates it to run onto `Apache Spark <https://spark.apache.org/">`_ using the `Pandas API on Spark <https://spark.apache.org/docs/latest/api/python/user_guide/pandas_on_spark/index.html>`_ (aka `Koalas <https://koalas.readthedocs.io/en/latest>`_).
-       | You only have the ability to return either a Koalas Dataframe or a Pandas Dataframe. To do that you either use the stock `base.PandasDataFrameResult <https://github.com/stitchfix/hamilton/blob/main/hamilton/base.py#L39>`_ ResultMixin, or you use the `h_spark.KoalasDataframeResult <https://github.com/stitchfix/hamilton/blob/main/hamilton/experimental/h_spark.py#L16>`_.
+       | You only have the ability to return either a Koalas Dataframe or a Pandas Dataframe. To do that you either use the stock `base.PandasDataFrameResult <https://github.com/dagworks-inc/hamilton/blob/main/hamilton/base.py#L39>`_ ResultMixin, or you use the `h_spark.KoalasDataframeResult <https://github.com/dagworks-inc/hamilton/blob/main/hamilton/experimental/h_spark.py#L16>`_.
      - | You'd generally use this if you have an existing spark cluster running in your workplace, and you want to scale to very large data set sizes.
        | Note this GraphAdapter has only been tested to work on Spark 3.2+ when Koalas became part of the standard Spark library.

docs/reference/api-reference/available-result-builders.rst

@@ -12,15 +12,15 @@ Use ``from hamilton import base`` to use these Result Builders:
    * - Name
      - What it does
      - When you'd use it
-   * - `base.DictResult <https://github.com/stitchfix/hamilton/blob/main/hamilton/base.py#L31>`_
+   * - `base.DictResult <https://github.com/dagworks-inc/hamilton/blob/main/hamilton/base.py#L31>`_
      - It returns the results as a dictionary, where the keys map to outputs requested, and values map to what was computed for those values.
      - | When you want to:
        | 1. debug dataflows.
        | 2. have heterogenous return types.
        | 3. Want to manually create a return type.
-   * - `base.PandasDataFrameResult <https://github.com/stitchfix/hamilton/blob/main/hamilton/base.py#L39>`_
+   * - `base.PandasDataFrameResult <https://github.com/dagworks-inc/hamilton/blob/main/hamilton/base.py#L39>`_
      - It returns the results as a Pandas Dataframe, where the columns map to outputs requested, and values map to what was computed for those values. Note: this only works if the computed values are pandas series, or scalar values.
      - Use this when you want to create a pandas dataframe.
-   * - `base.NumpyMatrixResult <https://github.com/stitchfix/hamilton/blob/main/hamilton/base.py#L49>`_
+   * - `base.NumpyMatrixResult <https://github.com/dagworks-inc/hamilton/blob/main/hamilton/base.py#L49>`_
      - It returns the results as a numpy matrix, where the columns map to outputs requested (in order), and values map to what was computed for those values. Note: this only works if the computed values are all numpy arrays of the **same length and type**, or scalar values. Scalar values will be made to fill the entire column.
      - Use this when you want to create a numpy matrix of results.

docs/tutorials/dask.rst

@@ -4,4 +4,4 @@ Dask
 
 'TODO: complete this tutorial'
 
-See examples directory `here <https://github.com/stitchfix/hamilton/blob/main/examples/dask/>`_.
+See examples directory `here <https://github.com/dagworks-inc/hamilton/blob/main/examples/dask/>`_.