Address pre-commit error

Author: kthui

README.md

@@ -38,15 +38,15 @@ and corresponds to the 23.07 container release on
 
 ----
 Triton Inference Server is an open source inference serving software that
-streamlines AI inferencing. Triton enables teams to deploy any AI model from 
-multiple deep learning and machine learning frameworks, including TensorRT, 
-TensorFlow, PyTorch, ONNX, OpenVINO, Python, RAPIDS FIL, and more. Triton 
-Inference Server supports inference across cloud, data center, edge and embedded 
-devices on NVIDIA GPUs, x86 and ARM CPU, or AWS Inferentia. Triton Inference 
-Server delivers optimized performance for many query types, including real time, 
+streamlines AI inferencing. Triton enables teams to deploy any AI model from
+multiple deep learning and machine learning frameworks, including TensorRT,
+TensorFlow, PyTorch, ONNX, OpenVINO, Python, RAPIDS FIL, and more. Triton
+Inference Server supports inference across cloud, data center, edge and embedded
+devices on NVIDIA GPUs, x86 and ARM CPU, or AWS Inferentia. Triton Inference
+Server delivers optimized performance for many query types, including real time,
 batched, ensembles and audio/video streaming. Triton inference Server is part of
-[NVIDIA AI Enterprise](https://www.nvidia.com/en-us/data-center/products/ai-enterprise/), 
-a software platform that accelerates the data science pipeline and streamlines 
+[NVIDIA AI Enterprise](https://www.nvidia.com/en-us/data-center/products/ai-enterprise/),
+a software platform that accelerates the data science pipeline and streamlines
 the development and deployment of production AI.
 
 Major features include:

docs/index.md

@@ -60,15 +60,15 @@ Triton Inference Server is an open source inference serving software that stream
 
 # Triton Inference Server
 
-Triton Inference Server enables teams to deploy any AI model from multiple deep 
-learning and machine learning frameworks, including TensorRT, TensorFlow, 
+Triton Inference Server enables teams to deploy any AI model from multiple deep
+learning and machine learning frameworks, including TensorRT, TensorFlow,
 PyTorch, ONNX, OpenVINO, Python, RAPIDS FIL, and more. Triton supports inference
 across cloud, data center, edge and embedded devices on NVIDIA GPUs, x86 and ARM
 CPU, or AWS Inferentia. Triton Inference Server delivers optimized performance
-for many query types, including real time, batched, ensembles and audio/video 
+for many query types, including real time, batched, ensembles and audio/video
 streaming. Triton inference Server is part of
-[NVIDIA AI Enterprise](https://www.nvidia.com/en-us/data-center/products/ai-enterprise/), 
-a software platform that accelerates the data science pipeline and streamlines 
+[NVIDIA AI Enterprise](https://www.nvidia.com/en-us/data-center/products/ai-enterprise/),
+a software platform that accelerates the data science pipeline and streamlines
 the development and deployment of production AI.
 
 Major features include:

docs/user_guide/faq.md

@@ -165,41 +165,41 @@ the backtrace to better help us resolve the problem.
 
 ## What are the benefits of using [Triton Inference Server](https://developer.nvidia.com/triton-inference-server) as part of the [NVIDIA AI Enterprise Software Suite](https://www.nvidia.com/en-us/data-center/products/ai-enterprise/)?
 
-NVIDIA AI Enterprise enables enterprises to implement full AI workflows by 
+NVIDIA AI Enterprise enables enterprises to implement full AI workflows by
 delivering an entire end-to-end AI platform. Four key benefits:
 
 ### Enterprise-Grade Support, Security & API Stability:
 
-Business-critical AI projects stay on track with NVIDIA Enterprise Support, 
-available globally to assist both IT teams with deploying and managing the 
-lifecycle of AI applications and the developer teams with building AI 
-applications.  Support includes maintenance updates, dependable SLAs and 
-response times.  Regular security reviews and priority notifications mitigate 
-potential risk of unmanaged opensource and ensure compliance with corporate 
-standards.  Finally, long term support and regression testing ensures API 
+Business-critical AI projects stay on track with NVIDIA Enterprise Support,
+available globally to assist both IT teams with deploying and managing the
+lifecycle of AI applications and the developer teams with building AI
+applications.  Support includes maintenance updates, dependable SLAs and
+response times.  Regular security reviews and priority notifications mitigate
+potential risk of unmanaged opensource and ensure compliance with corporate
+standards.  Finally, long term support and regression testing ensures API
 stability between releases.
 
-### Speed time to production with AI Workflows & Pretrained Models: 
-To reduce the complexity of developing common AI applications, NVIDIA AI 
-Enterprise includes 
-[AI workflows](https://www.nvidia.com/en-us/launchpad/ai/workflows/) which are 
-reference applications for specific business outcomes such as Intelligent 
-Virtual Assistants and Digital Fingerprinting for real-time cybersecurity threat 
-detection.  AI workflow reference applications may include 
-[AI frameworks](https://docs.nvidia.com/deeplearning/frameworks/index.html) and 
-[pretrained models](https://developer.nvidia.com/ai-models), 
-[Helm Charts](https://catalog.ngc.nvidia.com/helm-charts), 
-[Jupyter Notebooks](https://developer.nvidia.com/run-jupyter-notebooks) and 
+### Speed time to production with AI Workflows & Pretrained Models:
+To reduce the complexity of developing common AI applications, NVIDIA AI
+Enterprise includes
+[AI workflows](https://www.nvidia.com/en-us/launchpad/ai/workflows/) which are
+reference applications for specific business outcomes such as Intelligent
+Virtual Assistants and Digital Fingerprinting for real-time cybersecurity threat
+detection.  AI workflow reference applications may include
+[AI frameworks](https://docs.nvidia.com/deeplearning/frameworks/index.html) and
+[pretrained models](https://developer.nvidia.com/ai-models),
+[Helm Charts](https://catalog.ngc.nvidia.com/helm-charts),
+[Jupyter Notebooks](https://developer.nvidia.com/run-jupyter-notebooks) and
 [documentation](https://docs.nvidia.com/ai-enterprise/index.html#overview).
 
-### Performance for Efficiency and Cost Savings: 
-Using accelerated compute for AI workloads such as data process with 
-[NVIDIA RAPIDS Accelerator](https://developer.nvidia.com/rapids) for Apache 
-Spark and inference with Triton Inference Sever delivers better performance 
-which also improves efficiency and reduces operation and infrastructure costs, 
+### Performance for Efficiency and Cost Savings:
+Using accelerated compute for AI workloads such as data process with
+[NVIDIA RAPIDS Accelerator](https://developer.nvidia.com/rapids) for Apache
+Spark and inference with Triton Inference Sever delivers better performance
+which also improves efficiency and reduces operation and infrastructure costs,
 including savings from reduced time and energy consumption.
 
-### Optimized and Certified to Deploy Everywhere: 
-Cloud, Data Center, Edge Optimized and certified to ensure reliable performance 
-whether it’s running your AI in the public cloud, virtualized data centers, or 
-on DGX systems. 
+### Optimized and Certified to Deploy Everywhere:
+Cloud, Data Center, Edge Optimized and certified to ensure reliable performance
+whether it’s running your AI in the public cloud, virtualized data centers, or
+on DGX systems.