Add tutorial example (#548)

* Add initial tutorial example for 24.03 release of GenAI-Perf

* Update release example date

* Clarify steps 4 & 5 by combining them.

* Clean up wording and genai-perf command

* Align command line example with args in main instead of frozen 24.03

Author: debermudez

src/c++/perf_analyzer/genai-perf/examples/tutorial.md

@@ -0,0 +1,98 @@
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+
+# Benchmarking LLM
+
+The following guide provides an example on how to use GenAI-Perf
+to measure and characterize the performance behaviors of Large Language Models
+(LLMs).
+
+## Setup: Download and configure Triton Server and Client environment
+
+1. In a clean working directory, start by executing the following code snippet
+to create a triton model repository:
+
+```bash
+MODEL_REPO="${PWD}/models"
+MODEL_NAME="gpt2_vllm"
+
+mkdir -p $MODEL_REPO/$MODEL_NAME/1
+echo '{
+    "model":"gpt2",
+    "disable_log_requests": "true",
+    "gpu_memory_utilization": 0.5
+}' >$MODEL_REPO/$MODEL_NAME/1/model.json
+
+echo 'backend: "vllm"
+instance_group [
+  {
+    count: 1
+    kind: KIND_MODEL
+  }
+]' >$MODEL_REPO/$MODEL_NAME/config.pbtxt
+```
+
+2. Download the pre-built Triton Server Container with the vLLM backend from the
+[NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/tritonserver)
+registry.
+
+```bash
+export RELEASE=<yy.mm> # e.g. to use the release from the end of April of 2024, do `export RELEASE=24.04`
+
+docker pull nvcr.io/nvidia/tritonserver:${RELEASE}-vllm-python-py3
+```
+
+3. Launch the triton container to serve the gpt2_vllm model.
+
+```bash
+docker run -it --gpus all --net=host --rm --shm-size=1G --ulimit memlock=-1 \
+    --ulimit stack=67108864 \
+    -v ${PWD}:/work -w /work \
+    nvcr.io/nvidia/tritonserver:${RELEASE}-vllm-python-py3 \
+    tritonserver --model-repository ./models
+```
+
+4. In a separate terminal window, download the pre-built Triton SDK Container
+which includes GenAI-Perf and Performance Analyzer from the
+[NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/tritonserver)
+registry and launch the container.
+
+```bash
+export RELEASE=<yy.mm>
+docker run --gpus all -it --net host --shm-size=1g --ulimit stack=67108864 \
+  nvcr.io/nvidia/tritonserver:${RELEASE}-py3-sdk \
+  bash
+```
+
+5. Profile GPT2 via GenAI-Perf
+
+```bash
+genai-perf -m gpt2_vllm --backend vllm --streaming
+```
+
+By default, all metrics will saved in the `profile_export_genai_perf.csv` file.