diff --git a/doc/source/_templates/breadcrumbs.html b/doc/source/_templates/breadcrumbs.html new file mode 100644 index 000000000..f906c40db --- /dev/null +++ b/doc/source/_templates/breadcrumbs.html @@ -0,0 +1,15 @@ + +
+ +
diff --git a/doc/source/conf.py b/doc/source/conf.py index ee686073f..63a971653 100644 --- a/doc/source/conf.py +++ b/doc/source/conf.py @@ -14,6 +14,7 @@ import sys import os +import urllib import shlex # These lines added to enable Sphinx to work without installing Ray. @@ -330,4 +331,28 @@ texinfo_documents = [ # Python methods should be presented in source code order autodoc_member_order = 'bysource' -# see also http://searchvoidstar.tumblr.com/post/125486358368/making-pdfs-from-markdown-on-readthedocsorg-using +# Taken from https://github.com/edx/edx-documentation +FEEDBACK_FORM_FMT = "https://github.com/ray-project/ray/issues/new?title={title}&labels=docs&body={body}" + + +def feedback_form_url(project, page): + """Create a URL for feedback on a particular page in a project.""" + return FEEDBACK_FORM_FMT.format( + title=urllib.parse.quote( + "[docs] Issue on `{page}.rst`".format(page=page)), + body=urllib.parse.quote( + "# Documentation Problem/Question/Comment\n" + "\n" + "\n" + "\n\n\n\n" + "(Created directly from the docs)\n")) + + +def update_context(app, pagename, templatename, context, doctree): + """Update the page rendering context to include ``feedback_form_url``.""" + context['feedback_form_url'] = feedback_form_url(app.config.project, + pagename) + + +def setup(app): + app.connect('html-page-context', update_context) diff --git a/doc/source/object-store.rst b/doc/source/object-store.rst index 0cf80750b..cc92225e0 100644 --- a/doc/source/object-store.rst +++ b/doc/source/object-store.rst @@ -117,7 +117,7 @@ Serialization: Numpy Arrays --------------------------- Ray optimizes for numpy arrays by using the `Apache Arrow`_ data format. -The numpy array is stored as a read-only object, and all Ray workers on the same node can read the numpy array in the object store without copying (zero-copy reads). Each numpy array object in the worker process holds a pointer to the relevant array held in shared memory. Any writes to the read-only object will result in a copy into the local process memory. +The numpy array is stored as a read-only object, and all Ray workers on the same node can read the numpy array in the object store without copying (zero-copy reads). Each numpy array object in the worker process holds a pointer to the relevant array held in shared memory. Any writes to the read-only object will require the user to first copy it into the local process memory. There are some advantages to this form of serialization: