Rename master to main in embedded links.

Tried to avoid the change on external links to repos that
have not yet renamed master.

.github/workflows/ci-build.yaml

@@ -5,11 +5,9 @@ on:
   # but only for the main branch
   push:
     branches:
-      - master
       - main
   pull_request:
     branches:
-      - master
       - main
 
 jobs:
@@ -21,7 +19,7 @@ jobs:
         uses: styfle/cancel-workflow-action@0.8.0
         with:
           access_token: ${{ github.token }}
-        if: ${{github.ref != 'refs/head/master' && github.ref != 'refs/head/main'}}
+        if: ${{github.ref != 'refs/head/main'}}
       - uses: actions/checkout@v2
       - name: Set up Python 3.8
         uses: actions/setup-python@v2
@@ -67,7 +65,7 @@ jobs:
       uses: styfle/cancel-workflow-action@0.7.0
       with:
         access_token: ${{ github.token }}
-      if: ${{github.ref != 'refs/head/master' && github.ref != 'refs/head/main'}}
+      if: ${{github.ref != 'refs/head/main'}}
     - uses: actions/checkout@v2
     - name: Set up Python ${{ matrix.python-version }}
       uses: actions/setup-python@v2
@@ -114,7 +112,7 @@ jobs:
       uses: styfle/cancel-workflow-action@0.7.0
       with:
         access_token: ${{ github.token }}
-      if: ${{github.ref != 'refs/head/master' && github.ref != 'refs/head/main'}}
+      if: ${{github.ref != 'refs/head/main'}}
     - uses: actions/checkout@v2
     - name: Set up Python ${{ matrix.python-version }}
       uses: actions/setup-python@v2

CHANGELOG.md

@@ -5,11 +5,11 @@ Best viewed [here](https://jax.readthedocs.io/en/latest/changelog.html).
 <!--
 Remember to align the itemized text with the first line of an item within a list.
 
-PLEASE REMEMBER TO CHANGE THE '..master' WITH AN ACTUAL TAG in GITHUB LINK.
+PLEASE REMEMBER TO CHANGE THE '..main' WITH AN ACTUAL TAG in GITHUB LINK.
 -->
 
 ## jax 0.2.15 (unreleased)
-* [GitHub commits](https://github.com/google/jax/compare/jax-v0.2.14...master).
+* [GitHub commits](https://github.com/google/jax/compare/jax-v0.2.14...main).
 * New features:
   * The {func}`jax2tf.convert` supports inequalities and min/max for booleans
     ({jax-issue}`#6956`).
@@ -137,7 +137,7 @@ PLEASE REMEMBER TO CHANGE THE '..master' WITH AN ACTUAL TAG in GITHUB LINK.
     * `TraceContext` --> {func}`~jax.profiler.TraceAnnotation`
     * `StepTraceContext` --> {func}`~jax.profiler.StepTraceAnnotation`
     * `trace_function` --> {func}`~jax.profiler.annotate_function`
-  * Omnistaging can no longer be disabled. See [omnistaging](https://github.com/google/jax/blob/master/design_notes/omnistaging.md)
+  * Omnistaging can no longer be disabled. See [omnistaging](https://github.com/google/jax/blob/main/design_notes/omnistaging.md)
     for more information.
   * Python integers larger than the maximum `int64` value will now lead to an overflow
     in all cases, rather than being silently converted to `uint64` in some cases ({jax-issue}`#6047`).
@@ -185,7 +185,7 @@ PLEASE REMEMBER TO CHANGE THE '..master' WITH AN ACTUAL TAG in GITHUB LINK.
   * {func}`jax.scipy.stats.betabinom` is now available as a distribution with logpmf and pmf methods.
   * Added {func}`jax.experimental.jax2tf.call_tf` to call TensorFlow functions
     from JAX ({jax-issue}`#5627`)
-    and [README](https://github.com/google/jax/blob/master/jax/experimental/jax2tf/README.md#calling-tensorflow-functions-from-jax)).
+    and [README](https://github.com/google/jax/blob/main/jax/experimental/jax2tf/README.md#calling-tensorflow-functions-from-jax)).
   * Extended the batching rule for `lax.pad` to support batching of the padding values.
 * Bug fixes:
   * {func}`jax.numpy.take` properly handles negative indices ({jax-issue}`#5768`)
@@ -300,7 +300,7 @@ PLEASE REMEMBER TO CHANGE THE '..master' WITH AN ACTUAL TAG in GITHUB LINK.
 * [GitHub commits](https://github.com/google/jax/compare/jax-v0.2.5...jax-v0.2.6).
 * New Features:
   * Add support for shape-polymorphic tracing for the jax.experimental.jax2tf converter.
-    See [README.md](https://github.com/google/jax/blob/master/jax/experimental/jax2tf/README.md).
+    See [README.md](https://github.com/google/jax/blob/main/jax/experimental/jax2tf/README.md).
 * Breaking change cleanup
 
   * Raise an error on non-hashable static arguments for jax.jit and
@@ -360,7 +360,7 @@ PLEASE REMEMBER TO CHANGE THE '..master' WITH AN ACTUAL TAG in GITHUB LINK.
 * Improvements:
   * Ensure that `check_jaxpr` does not perform FLOPS.  See {jax-issue}`#4650`.
   * Expanded the set of JAX primitives converted by jax2tf.
-    See [primitives_with_limited_support.md](https://github.com/google/jax/blob/master/jax/experimental/jax2tf/primitives_with_limited_support.md).
+    See [primitives_with_limited_support.md](https://github.com/google/jax/blob/main/jax/experimental/jax2tf/primitives_with_limited_support.md).
 
 ## jax 0.2.4 (October 19 2020)
 
@@ -397,7 +397,7 @@ PLEASE REMEMBER TO CHANGE THE '..master' WITH AN ACTUAL TAG in GITHUB LINK.
 * [GitHub commits](https://github.com/google/jax/compare/jax-v0.1.77...jax-v0.2.0).
 * Improvements:
   * Omnistaging on by default. See {jax-issue}`#3370` and
-    [omnistaging](https://github.com/google/jax/blob/master/design_notes/omnistaging.md)
+    [omnistaging](https://github.com/google/jax/blob/main/design_notes/omnistaging.md)
 
 ## jax (0.1.77) (September 15 2020)
 

README.md

@@ -1,5 +1,5 @@
 <div align="center">
-<img src="https://raw.githubusercontent.com/google/jax/master/images/jax_logo_250px.png" alt="logo"></img>
+<img src="https://raw.githubusercontent.com/google/jax/main/images/jax_logo_250px.png" alt="logo"></img>
 </div>
 
 # JAX: Autograd and XLA
@@ -87,24 +87,24 @@ perex_grads = jit(vmap(grad_fun, in_axes=(None, 0, 0)))  # fast per-example grad
 Jump right in using a notebook in your browser, connected to a Google Cloud GPU.
 Here are some starter notebooks:
 - [The basics: NumPy on accelerators, `grad` for differentiation, `jit` for compilation, and `vmap` for vectorization](https://jax.readthedocs.io/en/latest/notebooks/quickstart.html)
-- [Training a Simple Neural Network, with TensorFlow Dataset Data Loading](https://colab.research.google.com/github/google/jax/blob/master/docs/notebooks/neural_network_with_tfds_data.ipynb)
+- [Training a Simple Neural Network, with TensorFlow Dataset Data Loading](https://colab.research.google.com/github/google/jax/blob/main/docs/notebooks/neural_network_with_tfds_data.ipynb)
 
 **JAX now runs on Cloud TPUs.** To try out the preview, see the [Cloud TPU
-Colabs](https://github.com/google/jax/tree/master/cloud_tpu_colabs).
+Colabs](https://github.com/google/jax/tree/main/cloud_tpu_colabs).
 
 For a deeper dive into JAX:
 - [The Autodiff Cookbook, Part 1: easy and powerful automatic differentiation in JAX](https://jax.readthedocs.io/en/latest/notebooks/autodiff_cookbook.html)
 - [Common gotchas and sharp edges](https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html)
 - See the [full list of
-notebooks](https://github.com/google/jax/tree/master/docs/notebooks).
+notebooks](https://github.com/google/jax/tree/main/docs/notebooks).
 
 You can also take a look at [the mini-libraries in
-`jax.experimental`](https://github.com/google/jax/tree/master/jax/experimental/README.md),
+`jax.experimental`](https://github.com/google/jax/tree/main/jax/experimental/README.md),
 like [`stax` for building neural
-networks](https://github.com/google/jax/tree/master/jax/experimental/README.md#neural-net-building-with-stax)
+networks](https://github.com/google/jax/tree/main/jax/experimental/README.md#neural-net-building-with-stax)
 and [`optimizers` for first-order stochastic
-optimization](https://github.com/google/jax/tree/master/jax/experimental/README.md#first-order-optimization),
-or the [examples](https://github.com/google/jax/tree/master/examples).
+optimization](https://github.com/google/jax/tree/main/jax/experimental/README.md#first-order-optimization),
+or the [examples](https://github.com/google/jax/tree/main/examples).
 
 ## Transformations
 
@@ -310,7 +310,7 @@ print(normalize(jnp.arange(4.)))
 # prints [0.         0.16666667 0.33333334 0.5       ]
 ```
 
-You can even [nest `pmap` functions](https://colab.research.google.com/github/google/jax/blob/master/cloud_tpu_colabs/Pmap_Cookbook.ipynb#scrollTo=MdRscR5MONuN) for more
+You can even [nest `pmap` functions](https://colab.research.google.com/github/google/jax/blob/main/cloud_tpu_colabs/Pmap_Cookbook.ipynb#scrollTo=MdRscR5MONuN) for more
 sophisticated communication patterns.
 
 It all composes, so you're free to differentiate through parallel computations:
@@ -343,9 +343,9 @@ When reverse-mode differentiating a `pmap` function (e.g. with `grad`), the
 backward pass of the computation is parallelized just like the forward pass.
 
 See the [SPMD
-Cookbook](https://colab.research.google.com/github/google/jax/blob/master/cloud_tpu_colabs/Pmap_Cookbook.ipynb)
+Cookbook](https://colab.research.google.com/github/google/jax/blob/main/cloud_tpu_colabs/Pmap_Cookbook.ipynb)
 and the [SPMD MNIST classifier from scratch
-example](https://github.com/google/jax/blob/master/examples/spmd_mnist_classifier_fromscratch.py)
+example](https://github.com/google/jax/blob/main/examples/spmd_mnist_classifier_fromscratch.py)
 for more.
 
 ## Current gotchas
@@ -359,7 +359,7 @@ Some standouts:
 1. [In-place mutating updates of
    arrays](https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#%F0%9F%94%AA-In-Place-Updates), like `x[i] += y`, aren't supported, but [there are functional alternatives](https://jax.readthedocs.io/en/latest/jax.ops.html). Under a `jit`, those functional alternatives will reuse buffers in-place automatically.
 1. [Random numbers are
-   different](https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#%F0%9F%94%AA-Random-Numbers), but for [good reasons](https://github.com/google/jax/blob/master/design_notes/prng.md).
+   different](https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#%F0%9F%94%AA-Random-Numbers), but for [good reasons](https://github.com/google/jax/blob/main/design_notes/prng.md).
 1. If you're looking for [convolution
    operators](https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#%F0%9F%94%AA-Convolutions),
    they're in the `jax.lax` package.
@@ -490,7 +490,7 @@ To cite this repository:
 ```
 
 In the above bibtex entry, names are in alphabetical order, the version number
-is intended to be that from [jax/version.py](../master/jax/version.py), and
+is intended to be that from [jax/version.py](../main/jax/version.py), and
 the year corresponds to the project's open-source release.
 
 A nascent version of JAX, supporting only automatic differentiation and

build/LICENSE.txt

@@ -369,7 +369,7 @@ ISC license used for completely new code in BoringSSL:
 The code in third_party/fiat carries the MIT license:
 
 Copyright (c) 2015-2016 the fiat-crypto authors (see
-https://github.com/mit-plv/fiat-crypto/blob/master/AUTHORS).
+https://github.com/mit-plv/fiat-crypto/blob/main/AUTHORS).
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

cloud_tpu_colabs/JAX_NeurIPS_2020_demo.ipynb

@@ -451,7 +451,7 @@
     "id": "jC-KIMQ1q-lK"
    },
    "source": [
-    "For more, see the [`pmap` cookbook](https://colab.research.google.com/github/google/jax/blob/master/cloud_tpu_colabs/Pmap_Cookbook.ipynb)."
+    "For more, see the [`pmap` cookbook](https://colab.research.google.com/github/google/jax/blob/main/cloud_tpu_colabs/Pmap_Cookbook.ipynb)."
    ]
   },
   {

cloud_tpu_colabs/JAX_demo.ipynb

@@ -861,7 +861,7 @@
     "id": "f-FBsWeo1AXE"
    },
    "source": [
-    "<img src=\"https://raw.githubusercontent.com/google/jax/master/cloud_tpu_colabs/images/nested_pmap.png\" width=\"70%\"/>"
+    "<img src=\"https://raw.githubusercontent.com/google/jax/main/cloud_tpu_colabs/images/nested_pmap.png\" width=\"70%\"/>"
    ]
   },
   {
@@ -871,7 +871,7 @@
     "id": "jC-KIMQ1q-lK"
    },
    "source": [
-    "For more, see the [`pmap` cookbook](https://colab.research.google.com/github/google/jax/blob/master/cloud_tpu_colabs/Pmap_Cookbook.ipynb)."
+    "For more, see the [`pmap` cookbook](https://colab.research.google.com/github/google/jax/blob/main/cloud_tpu_colabs/Pmap_Cookbook.ipynb)."
    ]
   },
   {

cloud_tpu_colabs/Pmap_Cookbook.ipynb

@@ -59,7 +59,7 @@
         "\n",
         "To run this notebook with any parallelism, you'll need multiple XLA devices available, e.g. with a multi-GPU machine or a Cloud TPU.\n",
         "\n",
-        "The code in this notebook is simple. For an example of how to use these tools to do data-parallel neural network training, check out [the SPMD MNIST example](https://github.com/google/jax/blob/master/examples/spmd_mnist_classifier_fromscratch.py) or the much more capable [Trax library](https://github.com/google/trax/)."
+        "The code in this notebook is simple. For an example of how to use these tools to do data-parallel neural network training, check out [the SPMD MNIST example](https://github.com/google/jax/blob/main/examples/spmd_mnist_classifier_fromscratch.py) or the much more capable [Trax library](https://github.com/google/trax/)."
       ]
     },
     {

cloud_tpu_colabs/README.md

@@ -23,25 +23,25 @@ only available with the new architecture, such as complex number support).
 
 The following notebooks showcase how to use and what you can do with Cloud TPUs on Colab:
 
-### [Pmap Cookbook](https://colab.research.google.com/github/google/jax/blob/master/cloud_tpu_colabs/Pmap_Cookbook.ipynb)
+### [Pmap Cookbook](https://colab.research.google.com/github/google/jax/blob/main/cloud_tpu_colabs/Pmap_Cookbook.ipynb)
 A guide to getting started with `pmap`, a transform for easily distributing SPMD
 computations across devices.
 
-### [Lorentz ODE Solver](https://colab.research.google.com/github/google/jax/blob/master/cloud_tpu_colabs/Lorentz_ODE_Solver.ipynb)
+### [Lorentz ODE Solver](https://colab.research.google.com/github/google/jax/blob/main/cloud_tpu_colabs/Lorentz_ODE_Solver.ipynb)
 Contributed by Alex Alemi (alexalemi@)
 
 Solve and plot parallel ODE solutions with `pmap`.
 
-<img src="https://raw.githubusercontent.com/google/jax/master/cloud_tpu_colabs/images/lorentz.png" width=65%></image>
+<img src="https://raw.githubusercontent.com/google/jax/main/cloud_tpu_colabs/images/lorentz.png" width=65%></image>
 
-### [Wave Equation](https://colab.research.google.com/github/google/jax/blob/master/cloud_tpu_colabs/Wave_Equation.ipynb)
+### [Wave Equation](https://colab.research.google.com/github/google/jax/blob/main/cloud_tpu_colabs/Wave_Equation.ipynb)
 Contributed by Stephan Hoyer (shoyer@)
 
 Solve the wave equation with `pmap`, and make cool movies! The spatial domain is partitioned across the 8 cores of a Cloud TPU.
 
-![](https://raw.githubusercontent.com/google/jax/master/cloud_tpu_colabs/images/wave_movie.gif)
+![](https://raw.githubusercontent.com/google/jax/main/cloud_tpu_colabs/images/wave_movie.gif)
 
-### [JAX Demo](https://colab.research.google.com/github/google/jax/blob/master/cloud_tpu_colabs/JAX_demo.ipynb)
+### [JAX Demo](https://colab.research.google.com/github/google/jax/blob/main/cloud_tpu_colabs/JAX_demo.ipynb)
 An overview of JAX presented at the [Program Transformations for ML workshop at NeurIPS 2019](https://program-transformations.github.io/) and the [Compilers for ML workshop at CGO 2020](https://www.c4ml.org/). Covers basic numpy usage, `grad`, `jit`, `vmap`, and `pmap`.
 
 ## Performance notes

docs/autodidax.ipynb

@@ -29,7 +29,7 @@
    },
    "source": [
     "[![Open in\n",
-    "Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/google/jax/blob/master/docs/autodidax.ipynb)"
+    "Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/google/jax/blob/main/docs/autodidax.ipynb)"
    ]
   },
   {

docs/autodidax.md

@@ -33,7 +33,7 @@ limitations under the License.
 ```
 
 [![Open in
-Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/google/jax/blob/master/docs/autodidax.ipynb)
+Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/google/jax/blob/main/docs/autodidax.ipynb)
 
 +++
 

docs/autodidax.py

@@ -27,7 +27,7 @@
 # ---
 
 # [![Open in
-# Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/google/jax/blob/master/docs/autodidax.ipynb)
+# Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/google/jax/blob/main/docs/autodidax.ipynb)
 
 
 # # Autodidax: JAX core from scratch

docs/conf.py

@@ -93,7 +93,7 @@
 # outputs stored in ipynb but not in md, so we must convert the ipynb.
 source_suffix = ['.rst', '.ipynb', '.md']
 
-# The master toctree document.
+# The main toctree document.
 main_doc = 'index'
 
 # The language for content autogenerated by Sphinx. Refer to documentation

docs/contributing.md

@@ -88,7 +88,7 @@ Follow these steps to contribute code:
 
    ```bash
    git fetch upstream
-   git rebase upstream/master
+   git rebase upstream/main
    ```
 
    Finally, push your commit on your development branch and create a remote 

docs/developer.md

@@ -274,15 +274,15 @@ You have to add this metadata by hand in the `.ipynb` file. It will be preserved
 re-saves the notebook.
 
 We exclude some notebooks from the build, e.g., because they contain long computations.
-See `exclude_patterns` in [conf.py](https://github.com/google/jax/blob/master/docs/conf.py).
+See `exclude_patterns` in [conf.py](https://github.com/google/jax/blob/main/docs/conf.py).
 
 ## Documentation building on readthedocs.io
 
 JAX's auto-generated documentations is at <https://jax.readthedocs.io/>.
 
 The documentation building is controlled for the entire project by the
 [readthedocs JAX settings](https://readthedocs.org/dashboard/jax). The current settings
-trigger a documentation build as soon as code is pushed to the GitHub `master` branch.
+trigger a documentation build as soon as code is pushed to the GitHub `main` branch.
 For each code version, the building process is driven by the
 `.readthedocs.yml` and the `docs/conf.py` configuration files.
 

docs/device_memory_profiling.md

@@ -139,4 +139,4 @@ pprof --web --diff_base memory1.prof memory9.prof
 ![Device memory profile at end of execution](_static/device_memory_profile_leak2.svg)
 
 The visualization shows that the memory growth can be attributed to the call to
-`normal` inside `anotherfunc`.
+`normal` inside `anotherfunc`.

docs/faq.rst

@@ -140,7 +140,7 @@ and its use is not recommended.)
 
 For a worked-out example, we recommend reading through
 ``test_computation_follows_data`` in
-`multi_device_test.py <https://github.com/google/jax/blob/master/tests/multi_device_test.py>`_.
+`multi_device_test.py <https://github.com/google/jax/blob/main/tests/multi_device_test.py>`_.
 
 .. _faq-benchmark: