add Windows ML to AITK Profiling (#9177)

xieofxie · hualxie · ntrogh · web-flow · commit bf3432a77886 · 2025-12-19T19:29:58.000+01:00
* add Windows ML

* Apply suggestions from code review

Co-authored-by: Nick Trogh &lt;ntrogh@hotmail.com&gt;

* rename to use new name for images

---------

Co-authored-by: hualxie &lt;hualxie@microsoft.com&gt;
Co-authored-by: Nick Trogh &lt;ntrogh@hotmail.com&gt;
diff --git a/docs/intelligentapps/images/profiling/by-model-file-config.png b/docs/intelligentapps/images/profiling/by-model-file-config.png
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diff --git a/docs/intelligentapps/images/profiling/by-model-file-succeeded.png b/docs/intelligentapps/images/profiling/by-model-file-succeeded.png
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diff --git a/docs/intelligentapps/images/profiling/by-model-file.png b/docs/intelligentapps/images/profiling/by-model-file.png
diff --git a/docs/intelligentapps/images/profiling/the-next-session-guide.png b/docs/intelligentapps/images/profiling/the-next-session-guide.png
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diff --git a/docs/intelligentapps/images/profiling/the-next-session.png b/docs/intelligentapps/images/profiling/the-next-session.png
diff --git a/docs/intelligentapps/overview.md b/docs/intelligentapps/overview.md
@@ -21,7 +21,7 @@ AI Toolkit offers seamless integration with popular AI models from providers lik
 | [Fine-tuning](/docs/intelligentapps/finetune) | Customize and adapt models for specific domains and requirements. Train models locally with GPU support or leverage Azure Container Apps for cloud-based fine-tuning. | ![Screenshot showing the Fine-tuning interface with model adaptation and training controls](./images/overview/fine-tune.png) |
 | [Model Conversion](/docs/intelligentapps/modelconversion) | Convert, quantize, and optimize machine learning models for local deployment. Transform models from Hugging Face and other sources to run efficiently on Windows with CPU, GPU, or NPU acceleration. | ![Screenshot showing the Model Conversion interface with tools for optimizing and transforming AI models](./images/overview/conversion.png) |
 | [Tracing](/docs/intelligentapps/tracing) | Monitor and analyze the performance of your AI applications. Collect and visualize trace data to gain insights into model behavior and performance. | ![Screenshot showing the Tracing interface with tools for monitoring AI applications](./images/overview/tracing.png) |
-| [Profiling](/docs/intelligentapps/profiling) | Diagnose the CPU, GPU, NPU resource usages of the process, ONNX model on different execution providers, and Windows ML events. | ![Screenshot showing the Profiling tool](./images/overview/profiling.png) |
+| [Profiling (Windows ML)](/docs/intelligentapps/profiling) | Diagnose the CPU, GPU, NPU resource usages of the process, ONNX model on different execution providers, and Windows Machine Learning events. | ![Screenshot showing the Profiling tool](./images/overview/profiling.png) |
 
 ## Who is AI Toolkit for?
 
diff --git a/docs/intelligentapps/profiling.md b/docs/intelligentapps/profiling.md
@@ -19,7 +19,7 @@ In this article, you could learn how to start profiling and how to inspect the r
 In this mode, the profiling tool profiles the next app that is started and that is sending out Windows ML events.
 This option is ideal for testing a run-once app. In this case, you start profiling, then run the app, and the resource usages will begin showing up.
 
-![Screenshot that shows how to start by the next session](./images/profiling/the-next-session.png)
+![Screenshot that shows how to start by the next session](./images/profiling/the-next-session-guide.png)
 
 The tool starts profiling a newly started app. This means that for profiling a Python notebook, if the kernel is already running, you need to restart the kernel to begin profiling for it. Just starting a new notebook does not automatically start profiling.
 
@@ -42,15 +42,19 @@ This option is ideal for profiling an app that is already running and you're una
 
 ## Profile an ONNX model
 
-In this mode, the profiling tool starts profiling an ONNX model file on a target execution provider (EP) for a given duration. You can see the resource usage while it's running.
+In this mode, the profiling tool starts profiling an ONNX model file on a target execution provider (EP) or device policy for a given duration. You can see the resource usage while it's running.
 
-This option is ideal for profiling an ONNX model on different EPs.
+This option is ideal for profiling an ONNX model on different EPs or device policies.
 
-![Screenshot that shows how to start by model file](./images/profiling/by-model-file.png)
+![Screenshot that shows how to start by model file](./images/profiling/by-model-file-config.png)
 
-After profiling, a report folder is created with logs and data.
+After profiling, a notification shows up to guide you to open or save the report.
 
-![Screenshot that show the report data](./images/profiling/by-model-file-result.png)
+![Screenshot that shows the succeeded notification](./images/profiling/by-model-file-succeeded.png)
+
+The report contains detailed profiling statistics and results for the ONNX model.
+
+![Screenshot that shows the report data](./images/profiling/by-model-file-result.png)
 
 ## Resource Usages view
 
diff --git a/docs/toc.json b/docs/toc.json
@@ -397,7 +397,7 @@
       ["Fine-tuning (Project Template)", "/docs/intelligentapps/finetune-legacy"],
       ["Model Conversion", "/docs/intelligentapps/modelconversion"],
       ["Tracing", "/docs/intelligentapps/tracing"],
-      ["Profiling", "/docs/intelligentapps/profiling"],
+      ["Profiling (Windows ML)", "/docs/intelligentapps/profiling"],
       ["FAQ", "/docs/intelligentapps/faq"],
       ["", "", {
         "name": "Reference",
