vedithal-amd
4a5cbbfba5
* Fix kernel/dispatch fitlering in GUI
* Disallow --kernel and --dispatch filtering in analyze --gui mode since
GUI frontend offers dropdown menu for kernel and dispatch filtering
* Update CHANGELOG and documentation
* Gracefully handle N/A values
* Ensure workload path is valid before using it in GUI
* Ignore kernel filters if dispatch filters provided
* Add documentation for dispatch filtering overriding kernel filtering
* Fix typo
* Fix documentation
* remove unnecessary whitespace
* Address review comments
* Allow kernel/dispatch filtering with --gui
* Address review comments
* Address review comments
* Update CHANGELOG
* Fix formatting
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.. meta::
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:description: ROCm Compute Profiler analysis: Standalone GUI
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:keywords: Omniperf, ROCm, profiler, tool, Instinct, accelerator, GUI, standalone, filter
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***********************
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Standalone GUI analysis
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***********************
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ROCm Compute Profiler's standalone analysis GUI is a lightweight web page that you can
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generate straight from the command line. The standalone analysis GUI is an
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alternative to the CLI if you want to explore profiling results visually. This analysis
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option is implemented as a simple `Flask <https://flask.palletsprojects.com>`_
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application that lets you view results from your preferred web browser.
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.. note::
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A point on *port forwarding*: the standalone GUI analyzer publishes its
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web-based interface on port ``8050`` by default. On production HPC systems
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where profiling jobs run under the control of a resource manager, additional
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SSH tunneling between the desired web browser host (such as a login node or
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remote workstation) and compute host may be required. Alternatively, you
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might find it more convenient to download profiled workloads to perform
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analysis on a local system.
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See the :doc:`/reference/faq` for more details on SSH tunneling.
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Launch the standalone GUI analyzer
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----------------------------------
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To launch the ROCm Compute Profiler GUI analyzer, include the ``--gui`` flag with your
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desired analysis command.
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For example:
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.. code-block:: shell-session
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$ rocprof-compute analyze -p workloads/vcopy/MI200/ --gui
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__ _
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_ __ ___ ___ _ __ _ __ ___ / _| ___ ___ _ __ ___ _ __ _ _| |_ ___
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| '__/ _ \ / __| '_ \| '__/ _ \| |_ _____ / __/ _ \| '_ ` _ \| '_ \| | | | __/ _ \
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| | | (_) | (__| |_) | | | (_) | _|_____| (_| (_) | | | | | | |_) | |_| | || __/
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|_| \___/ \___| .__/|_| \___/|_| \___\___/|_| |_| |_| .__/ \__,_|\__\___|
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|_| |_|
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Analysis mode = web_ui
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[analysis] deriving rocprofiler-compute metrics...
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Dash is running on http://0.0.0.0:8050/
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* Serving Flask app 'rocprof_compute_analyze.analysis_webui' (lazy loading)
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* Environment: production
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WARNING: This is a development server. Do not use it in a production deployment.
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Use a production WSGI server instead.
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* Debug mode: off
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* Running on all addresses (0.0.0.0)
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WARNING: This is a development server. Do not use it in a production deployment.
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* Running on http://127.0.0.1:8050
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* Running on http://10.228.33.172:8050 (Press CTRL+C to quit)
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At this point, you can launch your web browser of choice and navigate to
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``http://localhost:8050/`` to view the analysis interface.
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.. image:: ../../data/analyze/standalone_gui.png
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:align: center
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:alt: ROCm Compute Profiler standalone GUI home screen
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:width: 800
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.. tip::
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To launch the standalone GUI analyzer web app on a port other than ``8050``,
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include the optional argument ``--gui <desired port>``.
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When no filters are applied, you'll see five basic sections derived from your
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application's profiling data:
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#. Memory Chart Analysis
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#. Empirical Roofline Analysis
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Use ``--roofline-data-type`` option to specify which data type(s) you would like displayed on the roofline HTMLs in the standalone analysis GUI.
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Data types can be stacked- for example, "--roofline-data-type FP32 FP64 I32" would display one HTML with FP32 and FP64 stacked, and one HTML with INT32.
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Default roofline data type plotted is FP32.
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#. Top Stats (Top Kernel Statistics)
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#. System Info
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#. System Speed-of-Light
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#. Roofline AI Data Metrics
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To dive deeper, use the dropdown menus at the top of the screen to isolate
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particular kernels or dispatches. You should see the web page update with
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metrics specific to your selected filters.
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Once a filter is applied, you'll see several additional sections become
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available with detailed metrics specific to that area of AMD hardware.
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