Add quick start documentation page (#261)

* Add quick start documentation page

* Addressed comments

* Addressed comments

* Fixed typo

* Addressed comments

src/c++/perf_analyzer/README.md

@@ -73,6 +73,97 @@ Inferences/Second vs. Client p95 Batch Latency
 Concurrency: 1, throughput: 69.6 infer/sec, latency 19673 usec
 ```
 
+## Quick Start
+
+The steps below will guide you through using Perf Analyzer to profile a simple
+Tensorflow model: `simple`.
+
+### Step 1: Start Triton Container
+
+```bash
+export RELEASE=<yy.mm> # e.g. to use the release from the end of February of 2023, do `export RELEASE=23.02`
+
+docker pull nvcr.io/nvidia/tritonserver:${RELEASE}-py3
+
+docker run --gpus all --rm -it --net host nvcr.io/nvidia/tritonserver:${RELEASE}-py3
+```
+
+### Step 2: Download `simple` Model
+
+```bash
+# inside triton container
+git clone --depth 1 https://github.com/triton-inference-server/server
+
+mkdir model_repository
+
+cp -r server/docs/examples/model_repository/simple model_repository
+```
+
+### Step 3: Start Triton Server
+
+```bash
+# inside triton container
+tritonserver --model-repository $(pwd)/model_repository &> server.log &
+
+# confirm server is ready
+curl -v localhost:8000/v2/health/ready
+# look for 'HTTP/1.1 200 OK'
+
+# detatch (CTRL-p CTRL-q)
+```
+
+### Step 4: Start Triton SDK Container
+
+```bash
+docker pull nvcr.io/nvidia/tritonserver:${RELEASE}-py3-sdk
+
+docker run --gpus all --rm -it --net host nvcr.io/nvidia/tritonserver:${RELEASE}-py3-sdk
+```
+
+### Step 5: Run Perf Analyzer
+
+```bash
+# inside sdk container
+perf_analyzer -m simple
+```
+
+### Step 6: Observe and Analyze Output
+
+```
+$ perf_analyzer -m simple
+*** Measurement Settings ***
+  Batch size: 1
+  Service Kind: Triton
+  Using "time_windows" mode for stabilization
+  Measurement window: 5000 msec
+  Using synchronous calls for inference
+  Stabilizing using average latency
+
+Request concurrency: 1
+  Client: 
+    Request count: 25348
+    Throughput: 1407.84 infer/sec
+    Avg latency: 708 usec (standard deviation 663 usec)
+    p50 latency: 690 usec
+    p90 latency: 881 usec
+    p95 latency: 926 usec
+    p99 latency: 1031 usec
+    Avg HTTP time: 700 usec (send/recv 102 usec + response wait 598 usec)
+  Server: 
+    Inference count: 25348
+    Execution count: 25348
+    Successful request count: 25348
+    Avg request latency: 382 usec (overhead 41 usec + queue 41 usec + compute input 26 usec + compute infer 257 usec + compute output 16 usec)
+
+Inferences/Second vs. Client Average Batch Latency
+Concurrency: 1, throughput: 1407.84 infer/sec, latency 708 usec
+```
+
+We can see from the output that the model was able to complete approximately
+1407.84 inferences per second, with an average latency of 708 microseconds per
+inference request. Concurrency of 1 meant that Perf Analyzer attempted to always
+have 1 outgoing request at all times.
+
 ## Request Concurrency
 
 By default perf_analyzer measures your model's latency and throughput