Merge 7933a83a42 into e79ff9a079

Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>

.github/workflows/ci.yml

@@ -225,3 +225,29 @@ jobs:
           sources: |
             ghcr.io/${{ steps.lowercaseRepo.outputs.lowercase }}:${{ env.SHORT_SHA }}-amd64
             ghcr.io/${{ steps.lowercaseRepo.outputs.lowercase }}:${{ env.SHORT_SHA }}-rpi
+  axera_build:
+    runs-on: ubuntu-22.04
+    name: AXERA Build
+    needs:
+      - amd64_build
+      - arm64_build
+    steps:
+      - name: Check out code
+        uses: actions/checkout@v5
+        with:
+          persist-credentials: false
+      - name: Set up QEMU and Buildx
+        id: setup
+        uses: ./.github/actions/setup
+        with:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+      - name: Build and push Axera build
+        uses: docker/bake-action@v6
+        with:
+          source: .
+          push: true
+          targets: axcl
+          files: docker/axcl/axcl.hcl
+          set: |
+            axcl.tags=${{ steps.setup.outputs.image-name }}-axcl
+            *.cache-from=type=gha

docker/axcl/Dockerfile

@@ -0,0 +1,55 @@
+# syntax=docker/dockerfile:1.6
+
+# https://askubuntu.com/questions/972516/debian-frontend-environment-variable
+ARG DEBIAN_FRONTEND=noninteractive
+
+# Globally set pip break-system-packages option to avoid having to specify it every time
+ARG PIP_BREAK_SYSTEM_PACKAGES=1
+
+
+FROM frigate AS frigate-axcl
+ARG TARGETARCH
+ARG PIP_BREAK_SYSTEM_PACKAGES
+
+# Install axpyengine
+RUN wget https://github.com/AXERA-TECH/pyaxengine/releases/download/0.1.3.rc1/axengine-0.1.3-py3-none-any.whl -O /axengine-0.1.3-py3-none-any.whl
+RUN pip3 install -i https://mirrors.aliyun.com/pypi/simple/ /axengine-0.1.3-py3-none-any.whl \
+    && rm /axengine-0.1.3-py3-none-any.whl
+
+# Install axcl
+RUN if [ "$TARGETARCH" = "amd64" ]; then \
+        echo "Installing x86_64 version of axcl"; \
+        wget https://github.com/ivanshi1108/assets/releases/download/v0.16.2/axcl_host_x86_64_V3.6.5_20250908154509_NO4973.deb -O /axcl.deb; \
+    else \
+        echo "Installing aarch64 version of axcl"; \
+        wget https://github.com/ivanshi1108/assets/releases/download/v0.16.2/axcl_host_aarch64_V3.6.5_20250908154509_NO4973.deb -O /axcl.deb; \
+    fi
+
+RUN mkdir /unpack_axcl && \
+    dpkg-deb -x /axcl.deb /unpack_axcl && \
+    cp -R /unpack_axcl/usr/bin/axcl /usr/bin/ && \
+    cp -R /unpack_axcl/usr/lib/axcl /usr/lib/ && \
+    rm -rf /unpack_axcl /axcl.deb
+
+
+# Install axcl ffmpeg
+RUN mkdir -p /usr/lib/ffmpeg/axcl
+
+RUN if [ "$TARGETARCH" = "amd64" ]; then \
+        wget https://github.com/ivanshi1108/assets/releases/download/v0.16.2/ffmpeg-x64 -O /usr/lib/ffmpeg/axcl/ffmpeg && \
+        wget https://github.com/ivanshi1108/assets/releases/download/v0.16.2/ffprobe-x64 -O /usr/lib/ffmpeg/axcl/ffprobe; \
+    else \
+        wget https://github.com/ivanshi1108/assets/releases/download/v0.16.2/ffmpeg-aarch64 -O /usr/lib/ffmpeg/axcl/ffmpeg && \
+        wget https://github.com/ivanshi1108/assets/releases/download/v0.16.2/ffprobe-aarch64 -O /usr/lib/ffmpeg/axcl/ffprobe; \
+    fi
+
+RUN chmod +x /usr/lib/ffmpeg/axcl/ffmpeg /usr/lib/ffmpeg/axcl/ffprobe
+
+# Set ldconfig path
+RUN echo "/usr/lib/axcl" > /etc/ld.so.conf.d/ax.conf
+
+# Set env
+ENV PATH="$PATH:/usr/bin/axcl"
+ENV LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/lib/axcl"
+
+ENTRYPOINT ["sh", "-c", "ldconfig && exec /init"]

docker/axcl/axcl.hcl

@@ -0,0 +1,13 @@
+target frigate {
+  dockerfile = "docker/main/Dockerfile"
+  platforms = ["linux/amd64", "linux/arm64"]
+  target = "frigate"
+}
+
+target axcl {
+  dockerfile = "docker/axcl/Dockerfile"
+  contexts = {
+    frigate = "target:frigate",
+  }
+  platforms = ["linux/amd64", "linux/arm64"]
+}

docker/axcl/axcl.mk

@@ -0,0 +1,15 @@
+BOARDS += axcl
+
+local-axcl: version
+	docker buildx bake --file=docker/axcl/axcl.hcl axcl \
+		--set axcl.tags=frigate:latest-axcl \
+		--load
+
+build-axcl: version
+	docker buildx bake --file=docker/axcl/axcl.hcl axcl \
+		--set axcl.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-axcl
+
+push-axcl: build-axcl
+	docker buildx bake --file=docker/axcl/axcl.hcl axcl \
+		--set axcl.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-axcl \
+		--push

docker/axcl/user_installation.sh

@@ -0,0 +1,83 @@
+#!/bin/bash
+
+# Update package list and install dependencies
+sudo apt-get update
+sudo apt-get install -y build-essential cmake git wget pciutils kmod udev
+
+# Check if gcc-12 is needed
+current_gcc_version=$(gcc --version | head -n1 | awk '{print $NF}')
+gcc_major_version=$(echo $current_gcc_version | cut -d'.' -f1)
+
+if [[ $gcc_major_version -lt 12 ]]; then
+    echo "Current GCC version ($current_gcc_version) is lower than 12, installing gcc-12..."
+    sudo apt-get install -y gcc-12
+    sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-12 12
+    echo "GCC-12 installed and set as default"
+else
+    echo "Current GCC version ($current_gcc_version) is sufficient, skipping GCC installation"
+fi
+
+# Determine architecture
+arch=$(uname -m)
+download_url=""
+
+if [[ $arch == "x86_64" ]]; then
+    download_url="https://github.com/ivanshi1108/assets/releases/download/v0.16.2/axcl_host_x86_64_V3.6.5_20250908154509_NO4973.deb"
+    deb_file="axcl_host_x86_64_V3.6.5_20250908154509_NO4973.deb"
+elif [[ $arch == "aarch64" ]]; then
+    download_url="https://github.com/ivanshi1108/assets/releases/download/v0.16.2/axcl_host_aarch64_V3.6.5_20250908154509_NO4973.deb"
+    deb_file="axcl_host_aarch64_V3.6.5_20250908154509_NO4973.deb"
+else
+    echo "Unsupported architecture: $arch"
+    exit 1
+fi
+
+# Download AXCL driver
+echo "Downloading AXCL driver for $arch..."
+wget "$download_url" -O "$deb_file"
+
+if [ $? -ne 0 ]; then
+    echo "Failed to download AXCL driver"
+    exit 1
+fi
+
+# Install AXCL driver
+echo "Installing AXCL driver..."
+sudo dpkg -i "$deb_file"
+
+if [ $? -ne 0 ]; then
+    echo "Failed to install AXCL driver, attempting to fix dependencies..."
+    sudo apt-get install -f -y
+    sudo dpkg -i "$deb_file"
+
+    if [ $? -ne 0 ]; then
+        echo "AXCL driver installation failed"
+        exit 1
+    fi
+fi
+
+# Update environment
+echo "Updating environment..."
+source /etc/profile
+
+# Verify installation
+echo "Verifying AXCL installation..."
+if command -v axcl-smi &> /dev/null; then
+    echo "AXCL driver detected, checking AI accelerator status..."
+
+    axcl_output=$(axcl-smi 2>&1)
+    axcl_exit_code=$?
+
+    echo "$axcl_output"
+
+    if [ $axcl_exit_code -eq 0 ]; then
+        echo "AXCL driver installation completed successfully!"
+    else
+        echo "AXCL driver installed but no AI accelerator detected or communication failed."
+        echo "Please check if the AI accelerator is properly connected and powered on."
+        exit 1
+    fi
+else
+    echo "axcl-smi command not found. AXCL driver installation may have failed."
+    exit 1
+fi

docker/main/requirements-wheels.txt

@@ -81,3 +81,5 @@ librosa==0.11.*
 soundfile==0.13.*
 # DeGirum detector
 degirum == 0.16.*
+# Memory profiling
+memray == 1.15.*

docs/docs/configuration/object_detectors.md

@@ -49,6 +49,11 @@ Frigate supports multiple different detectors that work on different types of ha
 
 - [Synaptics](#synaptics): synap models can run on Synaptics devices(e.g astra machina) with included NPUs.
 
+**AXERA** <CommunityBadge />
+
+- [AXEngine](#axera): axmodels can run on AXERA AI acceleration.
+
+
 **For Testing**
 
 - [CPU Detector (not recommended for actual use](#cpu-detector-not-recommended): Use a CPU to run tflite model, this is not recommended and in most cases OpenVINO can be used in CPU mode with better results.
@@ -1438,6 +1443,41 @@ model:
   input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
 ```
 
+## AXERA
+
+Hardware accelerated object detection is supported on the following SoCs:
+
+- AX650N
+- AX8850N
+
+This implementation uses the [AXera Pulsar2 Toolchain](https://huggingface.co/AXERA-TECH/Pulsar2).
+
+See the [installation docs](../frigate/installation.md#axera) for information on configuring the AXEngine hardware.
+
+### Configuration
+
+When configuring the AXEngine detector, you have to specify the model name.
+
+#### yolov9
+
+A yolov9 model is provided in the container at /axmodels and is used by this detector type by default.
+
+Use the model configuration shown below when using the axengine detector with the default axmodel:
+
+```yaml
+detectors:
+  axengine:
+    type: axengine
+
+model:
+  path: frigate-yolov9-tiny
+  model_type: yolo-generic
+  width: 320
+  height: 320
+  tensor_format: bgr
+  labelmap_path: /labelmap/coco-80.txt
+```
+
 # Models
 
 Some model types are not included in Frigate by default.

docs/docs/frigate/hardware.md

@@ -104,6 +104,10 @@ Frigate supports multiple different detectors that work on different types of ha
 
 - [Synaptics](#synaptics): synap models can run on Synaptics devices(e.g astra machina) with included NPUs to provide efficient object detection.
 
+**AXERA** <CommunityBadge />
+
+- [AXEngine](#axera): axera models can run on AXERA NPUs via AXEngine, delivering highly efficient object detection.
+
 :::
 
 ### Hailo-8
@@ -287,6 +291,14 @@ The inference time of a rk3588 with all 3 cores enabled is typically 25-30 ms fo
 | ssd mobilenet | ~ 25 ms                         |
 | yolov5m       | ~ 118 ms                        |
 
+### AXERA
+
+- **AXEngine** Default model is **yolov9**
+
+| Name             | AXERA AX650N/AX8850N Inference Time |
+| ---------------- | ----------------------------------- |
+| yolov9-tiny      | ~ 4 ms                              |
+
 ## What does Frigate use the CPU for and what does it use a detector for? (ELI5 Version)
 
 This is taken from a [user question on reddit](https://www.reddit.com/r/homeassistant/comments/q8mgau/comment/hgqbxh5/?utm_source=share&utm_medium=web2x&context=3). Modified slightly for clarity.

docs/docs/frigate/installation.md

@@ -287,6 +287,42 @@ or add these options to your `docker run` command:
 
 Next, you should configure [hardware object detection](/configuration/object_detectors#synaptics) and [hardware video processing](/configuration/hardware_acceleration_video#synaptics).
 
+### AXERA
+
+<details>
+<summary>AXERA accelerators</summary>
+AXERA accelerators are available in an M.2 form factor, compatible with both Raspberry Pi and Orange Pi. This form factor has also been successfully tested on x86 platforms, making it a versatile choice for various computing environments.
+
+#### Installation
+
+Using AXERA accelerators requires the installation of the AXCL driver. We provide a convenient Linux script to complete this installation.
+
+Follow these steps for installation:
+
+1. Copy or download [this script](https://github.com/ivanshi1108/assets/releases/download/v0.16.2/user_installation.sh).
+2. Ensure it has execution permissions with `sudo chmod +x user_installation.sh`
+3. Run the script with `./user_installation.sh`
+
+#### Setup
+
+To set up Frigate, follow the default installation instructions, for example: `ghcr.io/blakeblackshear/frigate:stable`
+
+Next, grant Docker permissions to access your hardware by adding the following lines to your `docker-compose.yml` file:
+
+```yaml
+devices:
+  - /dev/axcl_host
+  - /dev/ax_mmb_dev
+  - /dev/msg_userdev
+```
+
+If you are using `docker run`, add this option to your command `--device /dev/axcl_host --device /dev/ax_mmb_dev --device /dev/msg_userdev`
+
+#### Configuration
+
+Finally, configure [hardware object detection](/configuration/object_detectors#axera) to complete the setup.
+</details>
+
 ## Docker
 
 Running through Docker with Docker Compose is the recommended install method.

docs/docs/troubleshooting/memory.md

@@ -0,0 +1,129 @@
+---
+id: memory
+title: Memory Troubleshooting
+---
+
+Frigate includes built-in memory profiling using [memray](https://bloomberg.github.io/memray/) to help diagnose memory issues. This feature allows you to profile specific Frigate modules to identify memory leaks, excessive allocations, or other memory-related problems.
+
+## Enabling Memory Profiling
+
+Memory profiling is controlled via the `FRIGATE_MEMRAY_MODULES` environment variable. Set it to a comma-separated list of module names you want to profile:
+
+```bash
+export FRIGATE_MEMRAY_MODULES="frigate.review_segment_manager,frigate.capture"
+```
+
+### Module Names
+
+Frigate processes are named using a module-based naming scheme. Common module names include:
+
+- `frigate.review_segment_manager` - Review segment processing
+- `frigate.recording_manager` - Recording management
+- `frigate.capture` - Camera capture processes (all cameras with this module name)
+- `frigate.process` - Camera processing/tracking (all cameras with this module name)
+- `frigate.output` - Output processing
+- `frigate.audio_manager` - Audio processing
+- `frigate.embeddings` - Embeddings processing
+
+You can also specify the full process name (including camera-specific identifiers) if you want to profile a specific camera:
+
+```bash
+export FRIGATE_MEMRAY_MODULES="frigate.capture:front_door"
+```
+
+When you specify a module name (e.g., `frigate.capture`), all processes with that module prefix will be profiled. For example, `frigate.capture` will profile all camera capture processes.
+
+## How It Works
+
+1. **Binary File Creation**: When profiling is enabled, memray creates a binary file (`.bin`) in `/config/memray_reports/` that is updated continuously in real-time as the process runs.
+
+2. **Automatic HTML Generation**: On normal process exit, Frigate automatically:
+
+   - Stops memray tracking
+   - Generates an HTML flamegraph report
+   - Saves it to `/config/memray_reports/<module_name>.html`
+
+3. **Crash Recovery**: If a process crashes (SIGKILL, segfault, etc.), the binary file is preserved with all data up to the crash point. You can manually generate the HTML report from the binary file.
+
+## Viewing Reports
+
+### Automatic Reports
+
+After a process exits normally, you'll find HTML reports in `/config/memray_reports/`. Open these files in a web browser to view interactive flamegraphs showing memory usage patterns.
+
+### Manual Report Generation
+
+If a process crashes or you want to generate a report from an existing binary file, you can manually create the HTML report:
+
+```bash
+memray flamegraph /config/memray_reports/<module_name>.bin
+```
+
+This will generate an HTML file that you can open in your browser.
+
+## Understanding the Reports
+
+Memray flamegraphs show:
+
+- **Memory allocations over time**: See where memory is being allocated in your code
+- **Call stacks**: Understand the full call chain leading to allocations
+- **Memory hotspots**: Identify functions or code paths that allocate the most memory
+- **Memory leaks**: Spot patterns where memory is allocated but not freed
+
+The interactive HTML reports allow you to:
+
+- Zoom into specific time ranges
+- Filter by function names
+- View detailed allocation information
+- Export data for further analysis
+
+## Best Practices
+
+1. **Profile During Issues**: Enable profiling when you're experiencing memory issues, not all the time, as it adds some overhead.
+
+2. **Profile Specific Modules**: Instead of profiling everything, focus on the modules you suspect are causing issues.
+
+3. **Let Processes Run**: Allow processes to run for a meaningful duration to capture representative memory usage patterns.
+
+4. **Check Binary Files**: If HTML reports aren't generated automatically (e.g., after a crash), check for `.bin` files in `/config/memray_reports/` and generate reports manually.
+
+5. **Compare Reports**: Generate reports at different times to compare memory usage patterns and identify trends.
+
+## Troubleshooting
+
+### No Reports Generated
+
+- Check that the environment variable is set correctly
+- Verify the module name matches exactly (case-sensitive)
+- Check logs for memray-related errors
+- Ensure `/config/memray_reports/` directory exists and is writable
+
+### Process Crashed Before Report Generation
+
+- Look for `.bin` files in `/config/memray_reports/`
+- Manually generate HTML reports using: `memray flamegraph <file>.bin`
+- The binary file contains all data up to the crash point
+
+### Reports Show No Data
+
+- Ensure the process ran long enough to generate meaningful data
+- Check that memray is properly installed (included by default in Frigate)
+- Verify the process actually started and ran (check process logs)
+
+## Example Usage
+
+```bash
+# Enable profiling for review and capture modules
+export FRIGATE_MEMRAY_MODULES="frigate.review_segment_manager,frigate.capture"
+
+# Start Frigate
+# ... let it run for a while ...
+
+# Check for reports
+ls -lh /config/memray_reports/
+
+# If a process crashed, manually generate report
+memray flamegraph /config/memray_reports/frigate_capture_front_door.bin
+```
+
+For more information about memray and interpreting reports, see the [official memray documentation](https://bloomberg.github.io/memray/).

docs/sidebars.ts

@@ -131,6 +131,7 @@ const sidebars: SidebarsConfig = {
       "troubleshooting/recordings",
       "troubleshooting/gpu",
       "troubleshooting/edgetpu",
+      "troubleshooting/memory",
     ],
     Development: [
       "development/contributing",

frigate/detectors/plugins/axengine.py

@@ -0,0 +1,87 @@
+import logging
+import os.path
+import re
+import urllib.request
+from typing import Literal
+
+import axengine as axe
+
+from frigate.const import MODEL_CACHE_DIR
+from frigate.detectors.detection_api import DetectionApi
+from frigate.detectors.detector_config import BaseDetectorConfig, ModelTypeEnum
+from frigate.util.model import post_process_yolo
+
+logger = logging.getLogger(__name__)
+
+DETECTOR_KEY = "axengine"
+
+supported_models = {
+    ModelTypeEnum.yologeneric: "frigate-yolov9-.*$",
+}
+
+model_cache_dir = os.path.join(MODEL_CACHE_DIR, "axengine_cache/")
+
+
+class AxengineDetectorConfig(BaseDetectorConfig):
+    type: Literal[DETECTOR_KEY]
+
+class Axengine(DetectionApi):
+    type_key = DETECTOR_KEY
+    def __init__(self, config: AxengineDetectorConfig):
+        logger.info("__init__ axengine")
+        super().__init__(config)
+        self.height = config.model.height
+        self.width = config.model.width
+        model_path = config.model.path or "frigate-yolov9-tiny"
+        model_props = self.parse_model_input(model_path)
+        self.session = axe.InferenceSession(model_props["path"])
+
+    def __del__(self):
+        pass
+
+    def parse_model_input(self, model_path):
+        model_props = {}
+        model_props["preset"] = True
+
+        model_matched = False
+
+        for model_type, pattern in supported_models.items():
+            if re.match(pattern, model_path):
+                model_matched = True
+                model_props["model_type"] = model_type
+
+        if model_matched:
+            model_props["filename"] = model_path + ".axmodel"
+            model_props["path"] = model_cache_dir + model_props["filename"]
+
+            if not os.path.isfile(model_props["path"]):
+                self.download_model(model_props["filename"])
+        else:
+            supported_models_str = ", ".join(
+                model[1:-1] for model in supported_models
+            )
+            raise Exception(
+                f"Model {model_path} is unsupported. Provide your own model or choose one of the following: {supported_models_str}"
+            )
+        return model_props
+
+    def download_model(self, filename):
+        if not os.path.isdir(model_cache_dir):
+            os.mkdir(model_cache_dir)
+
+        GITHUB_ENDPOINT = os.environ.get("GITHUB_ENDPOINT", "https://github.com")
+        urllib.request.urlretrieve(
+            f"{GITHUB_ENDPOINT}/ivanshi1108/assets/releases/download/v0.16.2/{filename}",
+            model_cache_dir + filename,
+        )
+
+    def detect_raw(self, tensor_input):
+        results = None
+        results = self.session.run(None, {"images": tensor_input})
+        if self.detector_config.model.model_type == ModelTypeEnum.yologeneric:
+            return post_process_yolo(results, self.width, self.height)
+        else:
+            raise ValueError(
+                f'Model type "{self.detector_config.model.model_type}" is currently not supported.'
+            )
+

frigate/util/process.py

@@ -1,7 +1,10 @@
+import atexit
 import faulthandler
 import logging
 import multiprocessing as mp
 import os
+import pathlib
+import subprocess
 import threading
 from logging.handlers import QueueHandler
 from multiprocessing.synchronize import Event as MpEvent
@@ -48,6 +51,7 @@ class FrigateProcess(BaseProcess):
 
     def before_start(self) -> None:
         self.__log_queue = frigate.log.log_listener.queue
+        self.__memray_tracker = None
 
     def pre_run_setup(self, logConfig: LoggerConfig | None = None) -> None:
         os.nice(self.priority)
@@ -64,3 +68,86 @@ class FrigateProcess(BaseProcess):
             frigate.log.apply_log_levels(
                 logConfig.default.value.upper(), logConfig.logs
             )
+
+        self._setup_memray()
+
+    def _setup_memray(self) -> None:
+        """Setup memray profiling if enabled via environment variable."""
+        memray_modules = os.environ.get("FRIGATE_MEMRAY_MODULES", "")
+
+        if not memray_modules:
+            return
+
+        # Extract module name from process name (e.g., "frigate.capture:camera" -> "frigate.capture")
+        process_name = self.name
+        module_name = (
+            process_name.split(":")[0] if ":" in process_name else process_name
+        )
+
+        enabled_modules = [m.strip() for m in memray_modules.split(",")]
+
+        if module_name not in enabled_modules and process_name not in enabled_modules:
+            return
+
+        try:
+            import memray
+
+            reports_dir = pathlib.Path("/config/memray_reports")
+            reports_dir.mkdir(parents=True, exist_ok=True)
+            safe_name = (
+                process_name.replace(":", "_").replace("/", "_").replace("\\", "_")
+            )
+
+            binary_file = reports_dir / f"{safe_name}.bin"
+
+            self.__memray_tracker = memray.Tracker(str(binary_file))
+            self.__memray_tracker.__enter__()
+
+            # Register cleanup handler to stop tracking and generate HTML report
+            # atexit runs on normal exits and most signal-based terminations (SIGTERM, SIGINT)
+            # For hard kills (SIGKILL) or segfaults, the binary file is preserved for manual generation
+            atexit.register(self._cleanup_memray, safe_name, binary_file)
+
+            self.logger.info(
+                f"Memray profiling enabled for module {module_name} (process: {self.name}). "
+                f"Binary file (updated continuously): {binary_file}. "
+                f"HTML report will be generated on exit: {reports_dir}/{safe_name}.html. "
+                f"If process crashes, manually generate with: memray flamegraph {binary_file}"
+            )
+        except Exception as e:
+            self.logger.error(f"Failed to setup memray profiling: {e}", exc_info=True)
+
+    def _cleanup_memray(self, safe_name: str, binary_file: pathlib.Path) -> None:
+        """Stop memray tracking and generate HTML report."""
+        if self.__memray_tracker is None:
+            return
+
+        try:
+            self.__memray_tracker.__exit__(None, None, None)
+            self.__memray_tracker = None
+
+            reports_dir = pathlib.Path("/config/memray_reports")
+            html_file = reports_dir / f"{safe_name}.html"
+
+            result = subprocess.run(
+                ["memray", "flamegraph", "--output", str(html_file), str(binary_file)],
+                capture_output=True,
+                text=True,
+                timeout=10,
+            )
+
+            if result.returncode == 0:
+                self.logger.info(f"Memray report generated: {html_file}")
+            else:
+                self.logger.error(
+                    f"Failed to generate memray report: {result.stderr}. "
+                    f"Binary file preserved at {binary_file} for manual generation."
+                )
+
+            # Keep the binary file for manual report generation if needed
+            # Users can run: memray flamegraph {binary_file}
+
+        except subprocess.TimeoutExpired:
+            self.logger.error("Memray report generation timed out")
+        except Exception as e:
+            self.logger.error(f"Failed to cleanup memray profiling: {e}", exc_info=True)