Add OpenVINO auto-device notebook

AI-Documentation/aidocapp/model.py

@@ -20,7 +20,16 @@ def __init__(
 
         self.template = [{
             "role": "user",
-            "content": "Add a detailed docstring in the style of PEP 257 to the following {} method {}. The docstring should include: - A concise summary of the method's purpose.- A detailed description of each argument (name and type). - A description of the return value (if any).  Add inline comments within the method body to explain complex logic or non-obvious steps. Return the method implementation with the docstring and inline comments as a single markdown code block. Do not modify the code. Do not add any chat-like comments."
+            "content": (
+                "Add a detailed docstring in the style of PEP 257 to the following "
+                "{} method {}. The docstring should include: - A concise summary of "
+                "the method's purpose.- A detailed description of each argument "
+                "(name and type). - A description of the return value (if any). "
+                " Add inline comments within the method body to explain complex "
+                "logic or non-obvious steps. Return the method implementation with "
+                "the docstring and inline comments as a single markdown code block. "
+                "Do not modify the code. Do not add any chat-like comments."
+            ),
         }]
 
     def generate_comments(self, code, language):

openvino_notebooks/auto-device/.gitignore

@@ -0,0 +1,5 @@
+.venv/
+.uv-cache/
+__pycache__/
+.ipynb_checkpoints/
+model/

openvino_notebooks/auto-device/.python-version

@@ -0,0 +1 @@
+3.12

openvino_notebooks/auto-device/README.md

@@ -0,0 +1,91 @@
+# What this notebook does
+
+This notebook shows how to do inference with Automatic Device Selection (AUTO) in OpenVINO and gives a high-level overview of how AUTO chooses the most suitable execution device based on model and hardware availability.
+
+It demonstrates how to compile a model with AUTO, compare first inference latency (model compilation time + first inference time) between GPU and AUTO, and show the difference between THROUGHPUT and LATENCY performance hints.
+
+This notebook provides a practical, engineering-focused introduction to deploying one application across heterogeneous systems (CPU/GPU/NPU) with minimal device-specific branching. It includes explicit device discovery, deterministic fallback behavior, idempotent model preparation/loading, and repeatable runtime measurements for first-inference and steady-state execution.
+
+# Hardware & device support
+
+This notebook supports the following devices:
+
+- CPU — **supported (fallback)**
+- GPU — **supported if available**
+- NPU — **supported if available**
+- AUTO — **primary execution mode**
+
+# Setup
+
+Make sure that **uv** is installed.
+
+## Windows
+irm https://astral.sh/uv/install.ps1 | iex
+
+## macOS / Linux
+curl -LsSf https://astral.sh/uv/install.sh | sh
+
+Verify installation:
+uv --version
+
+From the auto-device notebook folder:
+
+uv sync
+
+uv run jupyter lab auto-device.ipynb
+
+The sample image is included with the notebook. An internet connection is required only on the first run to download the pretrained ResNet-50 weights and create model/resnet50.xml.
+
+# Expected output
+
+When the notebook runs successfully, you should see:
+
+- **Device discovery and selection output**, e.g.
+  `Available devices: ['CPU', 'GPU.0']`
+  `Selected device: GPU`
+
+- **Idempotent model preparation logs**, e.g.
+  `IR model saved to model/resnet50.xml` on first run, then
+  `Read IR model from model/resnet50.xml` on subsequent runs.
+
+- **Compilation and first-inference timing output**, e.g.
+  `Time to load model on GPU device and get first inference: 0.15 seconds.`
+
+- **Performance-hint measurements**, with throughput/latency metrics printed over multiple intervals.
+
+For systems without accelerator devices, CPU fallback output is expected and valid.
+
+# Tested-on
+
+| OS | Python | OpenVINO | Device(s) | Status |
+|----|--------|----------|-----------|--------|
+| Windows 11 | 3.12 | 2026.2 | CPU, GPU | Pass |
+
+# Troubleshooting
+
+### AUTO always selects CPU
+**Cause:** GPU/NPU plugin not available, unsupported hardware, or driver/runtime mismatch.
+**Fix:** Verify `ov.Core().available_devices`, then update Intel GPU/NPU drivers and confirm execution inside the correct uv environment.
+
+### First inference is much slower than later runs
+**Cause:** Model compilation and backend warm-up overhead during first execution.
+**Fix:** Compare first-run timing to repeated runs; use multiple iterations for steady-state performance analysis.
+
+### Notebook import errors (openvino/torchvision/notebook utils)
+**Cause:** Environment not synced or wrong interpreter selected.
+**Fix:** Run `uv sync` in this directory and launch with `uv run jupyter lab auto-device.ipynb`.
+
+### AUTO behavior differs across machines
+**Cause:** Different available hardware backends or plugin versions.
+**Fix:** Log `core.available_devices` at startup and keep OpenVINO/runtime stack consistent across systems.
+
+# References
+
+- Upstream OpenVINO auto-device notebook:
+  https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/auto-device/auto-device.ipynb
+
+- OpenVINO AUTO device documentation:
+  https://docs.openvino.ai/2024/openvino-workflow/running-inference/inference-devices-and-modes/auto-device-selection.html
+
+- OpenVINO performance hints:
+  https://docs.openvino.ai/2024/openvino-workflow/running-inference/performance-hints.html