G analytics (#2449)

* Adding Google Analytics

Adding Google Analytics to TorchServe pages for metrics.
Removing indexOld.md file

* Update large_model_inference.md

* Spellcheck update

Adding chatGPT to wordslist.txt

* Update conf.py

* Update conf.py

* Update conf.py

Author: sekyondaMeta

docs/conf.py

@@ -11,7 +11,7 @@
 #
 # All configuration values have a default; values that are commented out
 # serve to show the default.
-
+#
 # If extensions (or modules to document with autodoc) are in another directory,
 # add these directories to sys.path here. If the directory is relative to the
 # documentation root, use os.path.abspath to make it absolute, like shown here.
@@ -131,6 +131,7 @@
     "collapse_navigation": True,
     "display_version": True,
     "logo_only": True,
+    "analytics_id": "GTM-T8XT4PS",
 }
 
 html_logo = "_static/img/pytorch-logo-dark.svg"
@@ -239,7 +240,6 @@ def setup(app):
 
 # Register custom directives
 
-
 rst.directives.register_directive("devices", SupportedDevices)
 rst.directives.register_directive("properties", SupportedProperties)
 rst.directives.register_directive("customcardstart", CustomCardStart)

docs/indexOLD.md

@@ -12,7 +12,7 @@ In addition to this default behavior, TorchServe provides the flexibility for us
 
 Using Pippy integration as an example, the image below illustrates the internals of the TorchServe large model inference.
 
-![ts-lmi-internal](images/ts-lmi-internal.png)
+![ts-lmi-internal](https://raw.githubusercontent.com/pytorch/serve/master/docs/images/ts-lmi-internal.png)
 
 ## PiPPy (PyTorch Native solution for large model inference)
 
@@ -186,7 +186,7 @@ torch-model-archiver --model-name bloom --version 1.0 --handler deepspeed_handle
 #### Tune "[responseTimeout](https://github.com/pytorch/serve/blob/5ee02e4f050c9b349025d87405b246e970ee710b/docs/configuration.md?plain=1#L216)" (see [model config YAML file](https://github.com/pytorch/serve/blob/5ee02e4f050c9b349025d87405b246e970ee710b/model-archiver/README.md?plain=1#L164)) if high model loading or inference latency causes response timeout.
 
 #### Tune torchrun parameters
-User is able to tune torchrun parameters in [model config YAML file](https://github.com/pytorch/serve/blob/2f1f52f553e83703b5c380c2570a36708ee5cafa/model-archiver/README.md?plain=1#L179). The supported parameters are defined at [here](https://github.com/pytorch/serve/blob/2f1f52f553e83703b5c380c2570a36708ee5cafa/frontend/archive/src/main/java/org/pytorch/serve/archive/model/ModelConfig.java#L329). For example, by default, `OMP_NUMNER_T?HREADS` is 1. It can be modified in the YAML file.
+User is able to tune torchrun parameters in [model config YAML file](https://github.com/pytorch/serve/blob/2f1f52f553e83703b5c380c2570a36708ee5cafa/model-archiver/README.md?plain=1#L179). The supported parameters are defined at [here](https://github.com/pytorch/serve/blob/2f1f52f553e83703b5c380c2570a36708ee5cafa/frontend/archive/src/main/java/org/pytorch/serve/archive/model/ModelConfig.java#L329). For example, by default, `OMP_NUMBER_THREADS` is 1. It can be modified in the YAML file.
 ```yaml
 #frontend settings
 torchrun:

docs/large_model_inference.md

@@ -1062,3 +1062,4 @@ XLA
 inferentia
 ActionSLAM
 statins
+chatGPT