Add quick start documentation page

src/c++/perf_analyzer/docs/README.md

@@ -0,0 +1,37 @@
+<!--
+Copyright 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions
+are met:
+ * Redistributions of source code must retain the above copyright
+   notice, this list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright
+   notice, this list of conditions and the following disclaimer in the
+   documentation and/or other materials provided with the distribution.
+ * Neither the name of NVIDIA CORPORATION nor the names of its
+   contributors may be used to endorse or promote products derived
+   from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
+EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
+CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
+OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+-->
+
+# **Perf Analyzer Documentation**
+
+| [Getting Started](README.md#getting-started) |
+| -------------------------------------- |
+
+## **Getting Started**
+
+The [Quick Start Guide](quick_start.md) will show you how to use Perf Analyzer
+to profile on a simple PyTorch model.

src/c++/perf_analyzer/docs/quick_start.md

@@ -0,0 +1,83 @@
+<!--
+Copyright 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions
+are met:
+ * Redistributions of source code must retain the above copyright
+   notice, this list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright
+   notice, this list of conditions and the following disclaimer in the
+   documentation and/or other materials provided with the distribution.
+ * Neither the name of NVIDIA CORPORATION nor the names of its
+   contributors may be used to endorse or promote products derived
+   from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
+EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
+CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
+OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+-->
+
+# Profiling a Model Quick Start
+
+The steps below will guide you through using Perf Analyzer to profile a simple
+PyTorch model: `add_sub`.
+
+## Step 1: Download `add_sub` Model
+
+```bash
+git clone --depth=1 https://github.com/triton-inference-server/model_analyzer
+mkdir model_repository
+cp -r model_analyzer/examples/quick-start/add_sub model_repository
+```
+
+## Step 2: Start Triton Server
+
+```bash
+tritonserver --model-repository $(pwd)/model_repository &> /dev/null & 
+```
+
+## Step 3: Run Perf Analyzer
+
+```bash
+perf_analyzer -m add_sub
+```
+
+## Step 4: Observe Output
+
+```
+*** 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: 24024
+    Throughput: 1334.23 infer/sec
+    Avg latency: 747 usec (standard deviation 2253 usec)
+    p50 latency: 737 usec
+    p90 latency: 898 usec
+    p95 latency: 916 usec
+    p99 latency: 1022 usec
+    Avg HTTP time: 737 usec (send/recv 110 usec + response wait 627 usec)
+  Server: 
+    Inference count: 24025
+    Execution count: 24025
+    Successful request count: 24025
+    Avg request latency: 388 usec (overhead 52 usec + queue 50 usec + compute input 68 usec + compute infer 135 usec + compute output 81 usec)
+
+Inferences/Second vs. Client Average Batch Latency
+Concurrency: 1, throughput: 1334.23 infer/sec, latency 747 usec
+```