chore(sdk): rename default branch to main (wandb#4374)

.circleci/config.yml

@@ -60,7 +60,7 @@ parameters:
     default: false
   manual_nightly_git_branch:
     type: string
-    default: master
+    default: main
   manual_nightly_slack_notify:
     type: boolean
     default: false
@@ -588,7 +588,7 @@ jobs:
         default: "3.8"
       git_branch:
         type: string
-        default: "master"
+        default: "main"
       execute:
         type: boolean
         default: true

CONTRIBUTING.md

@@ -74,8 +74,8 @@ Please make sure to update the ToC when you update this page!
 3.  Develop you contribution.
     - Make sure your fork is in sync with the main repository:
     ```shell
-    git checkout master
-    git pull upstream master
+    git checkout main
+    git pull upstream main
     ```
     - Create a `git` branch where you will develop your contribution.
       Use a sensible name for the branch, for example:
@@ -510,7 +510,7 @@ We use codecov to ensure we're executing all branches of logic in our tests. Bel
 
 1. If you want to see the lines not covered you click on the “Diff” tab. then look for any “+” lines that have a red block for the line number
 2. If you want more context about the files, go to the “Files” tab, it will highlight diffs, but you have to do even more searching for the lines you might care about
-3. If you don't want to use codecov, you can use local coverage (I tend to do this for speeding things up a bit, run your tests then run tox -e cover ). This will give you the old school text output of missing lines (but not based on a diff from master)
+3. If you don't want to use codecov, you can use local coverage (I tend to do this for speeding things up a bit, run your tests then run tox -e cover ). This will give you the old school text output of missing lines (but not based on a diff from main)
 
 We currently have 8 categories of test coverage:
 

README.md

@@ -3,7 +3,7 @@
   <img src=".github/wb-logo-darkbg.png#gh-dark-mode-only" width="600" alt="Weights & Biases"/>
 </p>
 
-# Weights and Biases [![PyPI](https://img.shields.io/pypi/v/wandb)](https://pypi.python.org/pypi/wandb) [![Conda (channel only)](https://img.shields.io/conda/vn/conda-forge/wandb)](https://anaconda.org/conda-forge/wandb) [![CircleCI](https://img.shields.io/circleci/build/github/wandb/wandb/master)](https://circleci.com/gh/wandb/wandb) [![Codecov](https://img.shields.io/codecov/c/gh/wandb/wandb)](https://codecov.io/gh/wandb/wandb)
+# Weights and Biases [![PyPI](https://img.shields.io/pypi/v/wandb)](https://pypi.python.org/pypi/wandb) [![Conda (channel only)](https://img.shields.io/conda/vn/conda-forge/wandb)](https://anaconda.org/conda-forge/wandb) [![CircleCI](https://img.shields.io/circleci/build/github/wandb/wandb/main)](https://circleci.com/gh/wandb/wandb) [![Codecov](https://img.shields.io/codecov/c/gh/wandb/wandb)](https://codecov.io/gh/wandb/wandb)
 
 Use W&B to build better models faster. Track and visualize all the pieces of your machine learning pipeline, from datasets to production models.
 
@@ -230,7 +230,7 @@ Use Weights & Biases Sweeps to automate hyperparameter optimization and explore
 
 ### Benefits of using W&B Sweeps
 - **Quick to set up:** With just a few lines of code you can run W&B sweeps.
-- **Transparent:** We cite all the algorithms we're using, and our code is [open source](https://github.com/wandb/wandb/tree/master/wandb/sweeps).
+- **Transparent:** We cite all the algorithms we're using, and our code is [open source](https://github.com/wandb/sweeps).
 - **Powerful:** Our sweeps are completely customizable and configurable. You can launch a sweep across dozens of machines, and it's just as easy as starting a sweep on your laptop.
 
 <img src="https://gblobscdn.gitbook.com/assets%2F-Lqya5RvLedGEWPhtkjU%2F-LyfPCyvV8By5YBltxfh%2F-LyfQsxswLC-6WKGgfGj%2Fcentral%20sweep%20server%203.png?alt=media&token=c81e4fe7-7ee4-48ea-a4cd-7b28113c6088" width="400" alt="Weights & Biases" />

package_readme.md

@@ -2,7 +2,7 @@
   <img src="https://i.imgur.com/RUtiVzH.png" width="600" /><br><br>
 </div>
 
-# Weights and Biases [![PyPI](https://img.shields.io/pypi/v/wandb)](https://pypi.python.org/pypi/wandb) [![Conda (channel only)](https://img.shields.io/conda/vn/conda-forge/wandb)](https://anaconda.org/conda-forge/wandb) [![CircleCI](https://img.shields.io/circleci/build/github/wandb/wandb/master)](https://circleci.com/gh/wandb/wandb) [![Codecov](https://img.shields.io/codecov/c/gh/wandb/wandb)](https://codecov.io/gh/wandb/wandb)
+# Weights and Biases [![PyPI](https://img.shields.io/pypi/v/wandb)](https://pypi.python.org/pypi/wandb) [![Conda (channel only)](https://img.shields.io/conda/vn/conda-forge/wandb)](https://anaconda.org/conda-forge/wandb) [![CircleCI](https://img.shields.io/circleci/build/github/wandb/wandb/main)](https://circleci.com/gh/wandb/wandb) [![Codecov](https://img.shields.io/codecov/c/gh/wandb/wandb)](https://codecov.io/gh/wandb/wandb)
 
 Use W&B to build better models faster. Track and visualize all the pieces of your machine learning pipeline, from datasets to production models.
 

wandb/integration/prodigy/prodigy.py

@@ -32,7 +32,7 @@
 
 def named_entity(docs):
     """Creates a named entity visualization.
-    Taken from https://github.com/wandb/wandb/blob/master/wandb/plots/named_entity.py
+    Taken from https://github.com/wandb/wandb/blob/main/wandb/plots/named_entity.py
     """
 
     spacy = util.get_module(