Add profiler doc

docs/development/profiler.md

@@ -0,0 +1,55 @@
+## Profiler (Experimental)
+
+Currently, DJL supports experimental profilers for developers that 
+investigate the performance of operator execution as well as memory consumption.
+As we are still working in progress on the feature, different engines have different APIs and produce different output format.
+In the future, we are considering to design a unified APIs and output unified format. 
+
+### MXNet
+
+By setting the following environment variable, it generates `profile.json` after executing the code.
+```
+export MXNET_PROFILER_AUTOSTART=1
+```
+You can view it in a browser using trace consumer like `chrome://tracing `. Here is a snapshot that shows the sample output.
+![img](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/tutorials/python/profiler/profiler_output_chrome.png)
+
+### PyTorch
+
+DJL have integrated PyTorch C++ profiler API and expose `JniUtils.startProfile` and `JniUtils.stopProfile(outputFile)` Java APIs.
+`JniUtils.startProfile` takes `useCuda(boolean)`, `recordShape(boolean)` and `profileMemory(boolean)` arguments respectively.
+`useCuda` indicates if profiler enables timing of CUDA events using the cudaEvent API.
+`recordShape` indicates if information about input dimensions will be collected or not.
+`profileMemory` indicates if profiler report memory usage or not.
+`JniUtils.stopProfile` takes a outputFile of String type.
+
+Wrap the code snippet you want to profile in between `JniUtils.startProfile` and `JniUtils.stopProfile`.
+Here is an example
+```
+try (ZooModel<Image, Classifications> model = ModelZoo.loadModel(criteria)) {
+    try (Predictor<Image, Classifications> predictor = model.newPredictor()) {
+        Image image = ImageFactory.getInstance()
+                        .fromNDArray(manager.zeros(new Shape(3, 224, 224), DataType.UINT8));
+                        
+        JniUtils.startProfile(false, true, true);
+        predictor.predict(image);
+        JniUtils.stopProfile(outputFile);
+    } catch (TranslateException e) {
+        e.printStackTrace();
+}
+```
+The output format is composed of operator execution record. 
+Each record contains `name`(operator name), `dur`(time duration), `shape`(input shapes), `cpu mem`(cpu memory footprint).
+```
+{
+  "name": "aten::empty",
+  "ph": "X",
+  "ts": 24528.313000,
+  "dur": 5.246000,
+  "tid": 1,
+  "pid": "CPU Functions",
+  "shape": [[], [], [], [], [], []],
+  "cpu mem": "0 b",
+  "args": {}
+}
+```

docs/mkdocs.yml

@@ -76,6 +76,7 @@ nav:
       - 'docs/development/memory_management.md'
       - 'docs/development/inference_performance_optimization.md'
       - 'docs/development/benchmark_with_djl.md'
+      - 'docs/development/profiler.md'
   - DJL Community:
       - 'docs/forums.md'
       - 'leaders.md'