Docs cleanup (#528)

Summary:
Update docs to reflect current verbiage/outputs for builtins, default scheduler and app handles. Also removes some unnecessary arguments in examples and fixes some typos.

Pull Request resolved: #528

Test Plan:
cd docs && make html
(with airflow.md commented out)

Reviewed By: kiukchung, d4l3k

Differential Revision: D37253152

Pulled By: priyaramani

fbshipit-source-id: 7b49cd4f2ab164d051f9784f33e45f0e5aa38eb4

Author: priyaramani

docs/source/basics.rst

@@ -31,7 +31,7 @@ In TorchX an ``AppDef`` is simply a struct with the *definition* of
 the actual application. In scheduler lingo, this is a ``JobDefinition`` and a
 similar concept in Kubernetes is the ``spec.yaml``. To disambiguate between the
 application binary (logic) and the spec, we typically refer to a TorchX
-``AppDef`` as an "app spec" or ``specs.AppDef``. ``specs.AppDef``
+``AppDef`` as an "app spec" or ``specs.AppDef``. It
 is the common interface understood by ``torchx.runner``
 and ``torchx.pipelines`` allowing you to run your app as a standalone job
 or as a stage in an ML pipeline.

docs/source/quickstart.md

@@ -222,7 +222,7 @@ automatically build the image with the newly provided Dockerfile instead of the
 default one.
 
 ```sh
-torchx run --scheduler local_docker utils.python --script timm_app.py "your name"
+torchx run --scheduler local_docker utils.python --script timm_app.py
 ```
 
 ### Slurm

torchx/cli/__init__.py

@@ -26,9 +26,9 @@
 
  $ torchx builtins
  Found <n> builtin configs:
-  1. echo
-  2. simple_example
-  3. touch
+  1. metrics.tensorboard
+  2. serve.torchserve
+  3. utils.binary
   ... <omitted for brevity>
 
 Listing the supported schedulers and arguments
@@ -57,7 +57,7 @@
 
    .. code-block:: shell-session
 
-    $ torchx run --scheduler <sched_name> echo
+    $ torchx run --scheduler <sched_name> utils.echo
 
 2. full python module path of the component function
 
@@ -122,16 +122,17 @@ def my_trainer(foo: int, bar: str) -> specs.AppDef:
 
  $ torchx run --scheduler local_cwd --scheduler_args log_dir=/tmp utils.echo --msg "hello $USER"
  === RUN RESULT ===
- Launched app: local://torchx_kiuk/echo_ecd30f74
+ torchx 2022-06-15 16:08:57 INFO     Log files located in: /tmp/torchx/echo-crls3hcpwjmhc/echo/0
+ local_cwd://torchx/echo-crls3hcpwjmhc
 
 By default the ``run`` subcommand does not block for the job to finish, instead it simply
 schedules the job on the specified scheduler and prints an ``app handle``
-which is a URL of the form: ``$scheduler_name://torchx_$user/$job_id``.
+which is a URL of the form: ``$scheduler_name://torchx/$job_id``.
 Keep note of this handle since this is what you'll need to provide to other
 subcommands to identify your job.
 
 .. note:: If the ``--scheduler`` option is not provided, then it defaults to
-          the scheduler backend ``default`` which points to ``local``. To change
+          the scheduler backend ``default`` which points to ``local_cwd``. To change
           the default scheduler, see: :ref:`advanced:Registering Custom Schedulers`.
 
 

torchx/specs/__init__.py

@@ -77,7 +77,7 @@ def resource(
     Convenience method to create a ``Resource`` object from either the
     raw resource specs (cpu, gpu, memMB) or the registered named resource (``h``).
     Note that the (cpu, gpu, memMB) is mutually exclusive with ``h``
-    with ``h`` taking predecence if specified.
+    taking predecence if specified.
 
     If ``h`` is specified then it is used to look up the
     resource specs from the list of registered named resources.