Documentation: fix dead links (#2936)

* fixed dead links

* spellcheck correction

Author: agunapal

README.md

@@ -55,14 +55,14 @@ docker pull pytorch/torchserve-nightly
 Refer to [torchserve docker](docker/README.md) for details.
 
 ## ⚡ Why TorchServe
-* Write once, run anywhere, on-prem, on-cloud, supports inference on CPUs, GPUs, AWS Inf1/Inf2/Trn1, Google Cloud TPUs, [Nvidia MPS](master/docs/nvidia_mps.md)
+* Write once, run anywhere, on-prem, on-cloud, supports inference on CPUs, GPUs, AWS Inf1/Inf2/Trn1, Google Cloud TPUs, [Nvidia MPS](docs/nvidia_mps.md)
 * [Model Management API](docs/management_api.md): multi model management with optimized worker to model allocation
 * [Inference API](docs/inference_api.md): REST and gRPC support for batched inference
 * [TorchServe Workflows](examples/Workflows/README.md): deploy complex DAGs with multiple interdependent models
 * Default way to serve PyTorch models in
   * [Sagemaker](https://aws.amazon.com/blogs/machine-learning/serving-pytorch-models-in-production-with-the-amazon-sagemaker-native-torchserve-integration/)
   * [Vertex AI](https://cloud.google.com/blog/topics/developers-practitioners/pytorch-google-cloud-how-deploy-pytorch-models-vertex-ai)
-  * [Kubernetes](master/kubernetes) with support for [autoscaling](kubernetes#session-affinity-with-multiple-torchserve-pods), session-affinity, monitoring using Grafana works on-prem, AWS EKS, Google GKE, Azure AKS
+  * [Kubernetes](kubernetes) with support for [autoscaling](kubernetes#session-affinity-with-multiple-torchserve-pods), session-affinity, monitoring using Grafana works on-prem, AWS EKS, Google GKE, Azure AKS
   * [Kserve](https://kserve.github.io/website/0.8/modelserving/v1beta1/torchserve/): Supports both v1 and v2 API, [autoscaling and canary deployments](kubernetes/kserve/README.md#autoscaling) for A/B testing
   * [Kubeflow](https://v0-5.kubeflow.org/docs/components/pytorchserving/)
   * [MLflow](https://github.com/mlflow/mlflow-torchserve)
@@ -71,11 +71,11 @@ Refer to [torchserve docker](docker/README.md) for details.
 * [Expressive handlers](CONTRIBUTING.md): An expressive handler architecture that makes it trivial to support inferencing for your use case with [many supported out of the box](https://github.com/pytorch/serve/tree/master/ts/torch_handler)
 * [Metrics API](docs/metrics.md): out-of-the-box support for system-level metrics with [Prometheus exports](https://github.com/pytorch/serve/tree/master/examples/custom_metrics), custom metrics,
 * [Large Model Inference Guide](docs/large_model_inference.md): With support for GenAI, LLMs including
-  * [SOTA GenAI performance](https://github.com/pytorch/serve/tree/docs/master/examples/pt2#torchcompile-genai-examples) using `torch.compile`
+  * [SOTA GenAI performance](https://github.com/pytorch/serve/tree/master/examples/pt2#torchcompile-genai-examples) using `torch.compile`
   * Fast Kernels with FlashAttention v2, continuous batching and streaming response
   * PyTorch [Tensor Parallel](examples/large_models/tp_llama) preview, [Pipeline Parallel](examples/large_models/Huggingface_pippy)
   * Microsoft [DeepSpeed](examples/large_models/deepspeed), [DeepSpeed-Mii](examples/large_models/deepspeed_mii)
-  * Hugging Face [Accelerate](large_models/Huggingface_accelerate), [Diffusers](examples/diffusers)
+  * Hugging Face [Accelerate](examples/large_models/Huggingface_accelerate), [Diffusers](examples/diffusers)
   * Running large models on AWS [Sagemaker](https://docs.aws.amazon.com/sagemaker/latest/dg/large-model-inference-tutorials-torchserve.html) and [Inferentia2](https://pytorch.org/blog/high-performance-llama/)
   * Running [Llama 2 Chatbot locally on Mac](examples/LLM/llama2)
 * Monitoring using Grafana and [Datadog](https://www.datadoghq.com/blog/ai-integrations/#model-serving-and-deployment-vertex-ai-amazon-sagemaker-torchserve)
@@ -114,7 +114,7 @@ To learn more about how to contribute, see the contributor guide [here](https://
 ## 📰 News
 * [High performance Llama 2 deployments with AWS Inferentia2 using TorchServe](https://pytorch.org/blog/high-performance-llama/)
 * [Naver Case Study: Transition From High-Cost GPUs to Intel CPUs and oneAPI powered Software with performance](https://pytorch.org/blog/ml-model-server-resource-saving/)
-* [Run multiple generative AI models on GPU using Amazon SageMaker multi-model endpoints with TorchServe and save up to 75% in inference costs](https://aws.amazon.com/blogs/machine-learning/run-multiple-generative-ai-models-on-gpu-using-amazon-sagemaker-multi-model-endpoints-with-torchserve-and-save-up-to-75-in-inference-costs/)
+* [Run multiple generative AI models on GPU using Amazon SageMaker multi-model endpoints with TorchServe and save up to 75% in inference costs](https://pytorch.org/blog/amazon-sagemaker-w-torchserve/)
 * [Deploying your Generative AI model in only four steps with Vertex AI and PyTorch](https://cloud.google.com/blog/products/ai-machine-learning/get-your-genai-model-going-in-four-easy-steps)
 * [PyTorch Model Serving on Google Cloud TPU v5](https://cloud.google.com/tpu/docs/v5e-inference#pytorch-model-inference-and-serving)
 * [Monitoring using Datadog](https://www.datadoghq.com/blog/ai-integrations/#model-serving-and-deployment-vertex-ai-amazon-sagemaker-torchserve)

benchmarks/README.md

@@ -4,7 +4,7 @@ The benchmarks measure the performance of TorchServe on various models and bench
 
 We currently support benchmarking with JMeter, Apache Bench and Locust. One can also profile backend code with snakeviz.
 
-* [Benchmarking with Apache Bench](#benchmarking-with-apache-bench)
+* [Benchmarking with Locust/Apache Bench](#benchmarking-with-locustapache-bench)
 * [Auto Benchmarking with Apache Bench](#auto-benchmarking-with-apache-bench)
 * [Benchmarking and Profiling with JMeter](jmeter.md)
 

docs/Troubleshooting.md

@@ -3,7 +3,7 @@ Refer to this section for common issues faced while deploying your Pytorch model
 
 * [Deployment and config issues](#deployment-and-config-issues)
 * [Snapshot related issues](#snapshot-related-issues)
-* [API related issues](#api-relate-issues)
+* [API related issues](#api-related-issues)
 * [Model-archiver](#model-archiver)
 
 

docs/performance_guide.md

@@ -17,7 +17,7 @@ Models which have been fully optimized with `torch.compile` show performance imp
 
 You can find all the examples of `torch.compile` with TorchServe [here](https://github.com/pytorch/serve/tree/master/examples/pt2)
 
-Details regarding `torch.compile` GenAI examples can be found in this [link](https://github.com/pytorch/serve/tree/docs/master/examples/pt2#torchcompile-genai-examples)
+Details regarding `torch.compile` GenAI examples can be found in this [link](https://github.com/pytorch/serve/tree/master/examples/pt2#torchcompile-genai-examples)
 
 <h4>ONNX and ORT support</h4>
 

examples/pt2/torch_export_aot_compile/README.md

@@ -2,7 +2,7 @@
 
 This example shows how to run TorchServe with Torch exported model with AOTInductor
 
-To understand when to use `torch._export.aot_compile`, please refer to this [section](../README.md/#torchexportaotcompile)
+To understand when to use `torch._export.aot_compile`, please refer to this [section](https://github.com/pytorch/serve/tree/master/examples/pt2#torch_exportaot_compile)
 
 
 ### Pre-requisites

ts_scripts/spellcheck_conf/wordlist.txt

@@ -1181,3 +1181,4 @@ Karpathy's
 Maher's
 warmup
 SOTA
+locustapache