AM62A7: Compiling edgeai-tidl-tools: getting error

Jay Kaneria

Part Number: AM62A7

Tool/software:

Hi TI Team,

I am using the edgeai-tidl-tools SDK and compiling the 10.01.04.00 branch to use models on AM62A7 board. While compiling the SDK I am facing the issue.

Below are the steps that I am executing:

# Setup on X86_PC (ubuntu 22.04, python version - Python 3.10.12)
git clone https://github.com/TexasInstruments/edgeai-tidl-tools.git
cd edgeai-tidl-tools
git checkout 10_01_04_00
export SOC=am62a
source ./setup.sh

# Compile and Validate on X86_PC
mkdir build && cd build
source ./scripts/run_python_examples.sh
python3 ./scripts/gen_test_report.py

While running the command "source ./scripts/run_python_examples.sh", ubuntu has giving the below errors:

X64 Architecture
1

Running 4 Models - ['cl-tfl-mobilenet_v1_1.0_224', 'ss-tfl-deeplabv3_mnv2_ade20k_float', 'od-tfl-ssd_mobilenet_v2_300_float', 'od-tfl-ssdlite_mobiledet_dsp_320x320_coco']


Running_Model :  cl-tfl-mobilenet_v1_1.0_224

Running_Model :  ss-tfl-deeplabv3_mnv2_ade20k_float

Running_Model :  od-tfl-ssd_mobilenet_v2_300_float

Running_Model :  od-tfl-ssdlite_mobiledet_dsp_320x320_coco
Downloading   ../../../models/public/deeplabv3_mnv2_ade20k_float.tflite
========================= [Model Compilation Started] =========================

Model compilation will perform the following stages:
1. Parsing
2. Graph Optimization
3. Quantization & Calibration
4. Memory Planning

============================== [Version Summary] ==============================

-------------------------------------------------------------------------------
|          TIDL Tools Version          |              10_01_04_00             |
-------------------------------------------------------------------------------
|         C7x Firmware Version         |              10_01_00_01             |
-------------------------------------------------------------------------------

============================== [Parsing Started] ==============================

[TIDL Import] [PARSER] WARNING: Network not identified as Object Detection network : (1) Ignore if network is not Object Detection network (2) If network is Object Detection network, please specify "model_type":"OD" as part of OSRT compilation options

Total Nodes = 31
-------------------------------------------------------------------------------
|          Core           |      No. of Nodes       |   Number of Subgraphs   |
-------------------------------------------------------------------------------
| C7x                     |                      31 |                       1 |
| CPU                     |                       0 |                       x |
-------------------------------------------------------------------------------
============================= [Parsing Completed] =============================

==================== [Optimization for subgraph_86 started] ====================

Process Process-2:
Traceback (most recent call last):
  File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
    self.run()
  File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
    self._target(*self._args, **self._kwargs)
  File "/home/admin/JayK/tidl-tools/edgeai-tidl-tools/examples/osrt_python/tfl/tflrt_delegate.py", line 240, in run_model
    download_model(models_configs, model)
  File "/home/admin/JayK/tidl-tools/edgeai-tidl-tools/examples/osrt_python/common_utils.py", line 294, in download_model
    tflOpt.tidlTfliteModelOptimize(
NameError: name 'tflOpt' is not defined
[TIDL Import] [PARSER] WARNING: Requested output data convert layer is not added to the network, It is currently not optimal
----------------------------- Optimization Summary -----------------------------
--------------------------------------------------------------------------------
|         Layer         | Nodes before optimization | Nodes after optimization |
--------------------------------------------------------------------------------
| TIDL_SoftMaxLayer     |                         1 |                        1 |
| TIDL_SqueezeLayer     |                         1 |                        0 |
| TIDL_ConvolutionLayer |                        28 |                       28 |
| TIDL_PoolingLayer     |                         1 |                        1 |
--------------------------------------------------------------------------------

=================== [Optimization for subgraph_86 completed] ===================

/home/admin/JayK/tidl-tools/edgeai-tidl-tools/tools/AM62A/tidl_tools/tidl_graphVisualiser.out: error while loading shared libraries: libcgraph.so.6: cannot open shared object file: No such file or directory
[TIDL Import]  WARNING: System command failed with return code : 32512. Skipping Graph Visualization.
The soft limit is 10240
The hard limit is 10240
MEM: Init ... !!!
MEM: Init ... Done !!!
 0.0s:  VX_ZONE_INIT:Enabled
 0.34s:  VX_ZONE_ERROR:Enabled
 0.38s:  VX_ZONE_WARNING:Enabled
 0.48340s:  VX_ZONE_INIT:[tivxInit:190] Initialization Done !!!

 ************ Frame index 1 : Running float inference ****************


 ************ Frame index 2 : Running fixed point mode for calibration ****************


-------- Running Calibration in Float Mode to Collect Tensor Statistics --------
[=============================================================================] 100 %

------------------ Fixed-point Calibration Iteration [1 / 5]: ------------------
[TIDL Import]  ERROR: Failed to run calibration pass, system command returned error: 132 -- [tidl_import_core.cpp, 678]
[TIDL Import]  ERROR: Failed to run Calibration - Failed in function: tidlRunQuantStatsTool -- [tidl_import_core.cpp, 1746]
[TIDL Import] [QUANTIZATION] ERROR:  - Failed in function: TIDL_quantStatsFixedOrFloat -- [tidl_import_quantize.cpp, 3992]
[TIDL Import] [QUANTIZATION] ERROR:  - Failed in function: TIDL_runIterativeCalibration -- [tidl_import_quantize.cpp, 4313]
[TIDL Import] [QUANTIZATION] ERROR:  - Failed in function: TIDL_import_quantize -- [tidl_import_quantize.cpp, 5195]
[TIDL Import]  ERROR:  - Failed in function: TIDL_import_backend -- [tidl_import_core.cpp, 4428]
[TIDL Import]  ERROR:  - Failed in function: TIDL_runtimesPostProcessNet -- [tidl_runtimes_import_common.cpp, 1414]


Completed_Model :     1, Name : cl-tfl-mobilenet_v1_1.0_224                       , Total time :    3555.15, Offload Time :       0.00 , DDR RW MBs : 18446744073709.55, Output Image File : py_out_cl-tfl-mobilenet_v1_1.0_224_ADE_val_00001801.jpg, Output Bin File : py_out_cl-tfl-mobilenet_v1_1.0_224_ADE_val_00001801.bin


MEM: Deinit ... !!!
MEM: Alloc's: 26 alloc's of 68565333 bytes
MEM: Free's : 26 free's  of 68565333 bytes
MEM: Open's : 0 allocs  of 0 bytes
MEM: Deinit ... Done !!!

So, "Fixed-point Calibration Iteration" is failed and is failing every time. I not sure that that is right thing and generated models and mode-artifacts are correct? Please let me know if I have to do any changes here.

Also, getting below error which I am not able to solve.

2025-02-20 14:19:37.717610752 [E:onnxruntime:, sequential_executor.cc:514 ExecuteKernel] Non-zero status code returned while running TIDL_0 node. Name:'TIDLExecutionProvider_TIDL_0_0' Status Message: TIDL Compute Invoke Failed.
Process Process-2:
Traceback (most recent call last):
  File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
    self.run()
  File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
    self._target(*self._args, **self._kwargs)
  File "/home/admin/JayK/tidl-tools/edgeai-tidl-tools/examples/osrt_python/ort/onnxrt_ep.py", line 392, in run_model
    imgs, output, proc_time, sub_graph_time, height, width = infer_image(sess, input_images, config)
  File "/home/admin/JayK/tidl-tools/edgeai-tidl-tools/examples/osrt_python/ort/onnxrt_ep.py", line 208, in infer_image
    output = list(sess.run(None, {input_name: input_data}))
  File "/home/admin/.local/lib/python3.10/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py", line 217, in run
    return self._sess.run(output_names, input_feed, run_options)
onnxruntime.capi.onnxruntime_pybind11_state.Fail: [ONNXRuntimeError] : 1 : FAIL : Non-zero status code returned while running TIDL_0 node. Name:'TIDLExecutionProvider_TIDL_0_0' Status Message: TIDL Compute Invoke Failed.
MEM: Deinit ... !!!
MEM: Alloc's: 27 alloc's of 208282052 bytes
MEM: Free's : 27 free's  of 208282052 bytes
MEM: Open's : 0 allocs  of 0 bytes
MEM: Deinit ... Done !!!

Regards,

Jay

over 1 year ago

0 Reese Grimsley over 1 year ago

TI__Mastermind 19726 points

Hi Jay,

Hmm, I see several errors being thrown here. When I run the same script on my machine, I do not see issues with the calibration iterations failing. Let me explain a few observations.

Error 1)

You have an early error for "NameError: name 'tflOpt' is not defined". if you open a python REPL on command line and run the following, what is the result?

from osrt_model_tools.tflite_tools import tflite_model_opt as tflOpt

This is a local package located at "/edgeai-tidl-tools/osrt-model-tools/osrt_model_tools/tflite_tools/tflite_model_opt.py", and should have been added to your python environment during setup

Error 2)

/home/admin/JayK/tidl-tools/edgeai-tidl-tools/tools/AM62A/tidl_tools/tidl_graphVisualiser.out: error while loading shared libraries: libcgraph.so.6: cannot open shared object file: No such file or directory
[TIDL Import] WARNING: System command failed with return code : 32512. Skipping Graph Visualization.

This is typically not a fatal error, but I'll address it -- your system seems to be missing a dependency that was otherwise not installed by setup.sh. The libcgraph package needs to be installed at through apt.

Perhaps this forum post is helpful: https://stackoverflow.com/a/42315670 -- I recall the same step when first setting up a development machine for TIDL compilation
- this is used to create some SVG files in the model-artifacts/MODEL_NAME/artifacts/tempDir locations. They are helpful for visualizing what TIDL parsed

Error 3)

I do notice that I see the ~7 lines of errors like "[TIDL Import] ERROR: Failed to run calibration pass, system command returned error: 132 " occurring if I ctrl-c during calibration, thereby cancelling the process

This might have been caused by one of the preceding errors, so I suggest resolving those first.

These above where when running the /examples/osrt_python/tfl/tflrt_delegate.py with arguments " -z -c" to use the model zoo and to compile.

BR,
Reese

You have another error related to the onnxruntime. Perhaps this has some relation.. I need to see a bit more logging to understand. Could you edit examples/osrt_python/common_utils.py, and set the "debug_level" variable to 1?

0 Jay Kaneria over 1 year ago in reply to Reese Grimsley

Intellectual 720 points

Hi Reese,

Thank you for your reply.

Reese Grimsley said:
python REPL

While running python3 REPL in command line, it gives below error.

admin@VM2502:~/jay/edgeai-sdk/edgeai-tidl-tools$ python3 REPL
python3: can't open file '/home/admin/jay/edgeai-sdk/edgeai-tidl-tools/REPL': [Errno 2] No such file or directory

RELP is already installed but edgeai package seraches it in the local directory i guess

admin@VM2502:~/jay/edgeai-sdk/edgeai-tidl-tools$ pip3 show repl
Name: repl
Version: 1.0
Summary: Run command as REPL-environment (useful for git!).
Home-page: https://github.com/mbr/repl
Author: Marc Brinkmann
Author-email: git@marcbrinkmann.de
License: MIT
Location: /home/admin/.local/lib/python3.10/site-packages
Requires: click
Required-by:

I am not much familiar with python. so i am not sure about this. Do you have idea about this?

Regards,

Jay

0 Reese Grimsley over 1 year ago in reply to Jay Kaneria

TI__Mastermind 19726 points

Hi Jay,

Forgive my use of jargon -- REPL stands for "Read-Eval-Print Loop" and is a generic term for an interactive coding environment that you can run code in without preparing an actual script or file.

For python, all you need to do is run the `python3` command without any arguments

BR,
Reese

0 Jay Kaneria over 1 year ago in reply to Reese Grimsley

Intellectual 720 points

Hi Reese,

Thank you for your reply.

Reese Grimsley said:
if you open a python REPL on command line and run the following, what is the result?
Fullscreen
1
from osrt_model_tools.tflite_tools import tflite_model_opt as tflOpt
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
from osrt_model_tools.tflite_tools import tflite_model_opt as tflOpt

Output of this on my python3 environment is

admin@VM2502:~$ python3
Python 3.10.12 (main, Feb  4 2025, 14:57:36) [GCC 11.4.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>>
>>> from osrt_model_tools.tflite_tools import tflite_model_opt as tflOpt
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ModuleNotFoundError: No module named 'osrt_model_tools'
>>>
>>>
>>>
>>> from osrt_model_tools.tflite_tools import tflite_model_opt as tflOpt
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ModuleNotFoundError: No module named 'osrt_model_tools'

'osrt_model_tools' module is not found. Doesn't this module getting installed from setup.py script?

Regards,

Jay

0 Reese Grimsley over 1 year ago in reply to Jay Kaneria

TI__Mastermind 19726 points

Hi Jay,

Hmm, so something went wrong during the main setup.sh script. I see within, it tries to run another (identically named) setup.sh in osrt-model-tools. Can you try running this directly?

Script is here in edgeai-tidl-tools: https://github.com/TexasInstruments/edgeai-tidl-tools/blob/master/osrt-model-tools/setup.sh. Please share the output log from this as well. I suspect you ran into a version / dependency conflict.

Also worth asking -- which OS version and python version are you using here? We support Ubuntu 22.04 LTS and Python 3.10. I generally recommend using a virtual environment to avoid dependency conflicts. Tools like conda or the build-in venv (latter is my preference) are good.

Q4/A4 in my FAQ linked below talks some about managing dependencies
- https://e2e.ti.com/support/processors-group/processors/f/processors-forum/1455079/faq-edge-ai-studio-is-sdk-version-important-for-edge-ai-and-ti-deep-learning-tidl-with-c7x-on-am6xa-socs-am62a-am67a-am68a-am68pa-am69a

BR,
Reese

0 Jay Kaneria over 1 year ago in reply to Reese Grimsley

Intellectual 720 points

Hi Reese,

Reese Grimsley said:
Hmm, so something went wrong during the main setup.sh script. I see within, it tries to run another (identically named) setup.sh in osrt-model-tools. Can you try running this directly?

Below is the log file while ruuning the setup.sh from osrt-model-tools and there are errors.

Fullscreen osrt-model-tools-setup-error.txt Download

admin@VM2502:~/jay/edgeai-sdk/edgeai-tidl-tools/osrt-model-tools$ source setup.sh
Installing python packages...
pip3 install --no-input wheel
Defaulting to user installation because normal site-packages is not writeable
Requirement already satisfied: wheel in /usr/lib/python3/dist-packages (0.37.1)
pip3 install --no-input numpy==1.23.0
Defaulting to user installation because normal site-packages is not writeable
Requirement already satisfied: numpy==1.23.0 in /home/admin/.local/lib/python3.10/site-packages (1.23.0)
pip3 install --no-input protobuf==3.20.3
Defaulting to user installation because normal site-packages is not writeable
Requirement already satisfied: protobuf==3.20.3 in /home/admin/.local/lib/python3.10/site-packages (3.20.3)
pip3 install --no-input onnx==1.14.0
Defaulting to user installation because normal site-packages is not writeable
Requirement already satisfied: onnx==1.14.0 in /home/admin/.local/lib/python3.10/site-packages (1.14.0)
Requirement already satisfied: numpy in /home/admin/.local/lib/python3.10/site-packages (from onnx==1.14.0) (1.23.0)
Requirement already satisfied: protobuf>=3.20.2 in /home/admin/.local/lib/python3.10/site-packages (from onnx==1.14.0) (3.20.3)
Requirement already satisfied: typing-extensions>=3.6.2.1 in /home/admin/.local/lib/python3.10/site-packages (from onnx==1.14.0) (4.12.2)
pip3 install --no-input onnxsim==0.4.35
Defaulting to user installation because normal site-packages is not writeable
Requirement already satisfied: onnxsim==0.4.35 in /home/admin/.local/lib/python3.10/site-packages (0.4.35)
Requirement already satisfied: onnx in /home/admin/.local/lib/python3.10/site-packages (from onnxsim==0.4.35) (1.14.0)
Requirement already satisfied: rich in /home/admin/.local/lib/python3.10/site-packages (from onnxsim==0.4.35) (13.9.4)
Requirement already satisfied: numpy in /home/admin/.local/lib/python3.10/site-packages (from onnx->onnxsim==0.4.35) (1.23.0)
Requirement already satisfied: protobuf>=3.20.2 in /home/admin/.local/lib/python3.10/site-packages (from onnx->onnxsim==0.4.35) (3.20.3)
Requirement already satisfied: typing-extensions>=3.6.2.1 in /home/admin/.local/lib/python3.10/site-packages (from onnx->onnxsim==0.4.35) (4.12.2)
Requirement already satisfied: markdown-it-py>=2.2.0 in /home/admin/.local/lib/python3.10/site-packages (from rich->onnxsim==0.4.35) (3.0.0)
Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /home/admin/.local/lib/python3.10/site-packages (from rich->onnxsim==0.4.35) (2.19.1)
Requirement already satisfied: mdurl~=0.1 in /home/admin/.local/lib/python3.10/site-packages (from markdown-it-py>=2.2.0->rich->onnxsim==0.4.35) (0.1.2)
pip3 install --no-input git+https://github.com/NVIDIA/TensorRT@release/8.5#subdirectory=tools/onnx-graphsurgeon
Defaulting to user installation because normal site-packages is not writeable
Collecting git+https://github.com/NVIDIA/TensorRT@release/8.5#subdirectory=tools/onnx-graphsurgeon
  Cloning https://github.com/NVIDIA/TensorRT (to revision release/8.5) to /tmp/pip-req-build-6c65ucjd
  Running command git clone --filter=blob:none --quiet https://github.com/NVIDIA/TensorRT /tmp/pip-req-build-6c65ucjd
  Running command git checkout -b release/8.5 --track origin/release/8.5
  Switched to a new branch 'release/8.5'
  Branch 'release/8.5' set up to track remote branch 'release/8.5' from 'origin'.
  Resolved https://github.com/NVIDIA/TensorRT to commit 68b5072fdb9df6b6edab1392b02a705394b2e906
  Running command git submodule update --init --recursive -q
  Preparing metadata (setup.py) ... error
  error: subprocess-exited-with-error

  × python setup.py egg_info did not run successfully.
  │ exit code: 1
  ╰─> [43 lines of output]
      running egg_info
      creating /tmp/pip-pip-egg-info-m_2u8zd_/onnx_graphsurgeon.egg-info
      writing /tmp/pip-pip-egg-info-m_2u8zd_/onnx_graphsurgeon.egg-info/PKG-INFO
      writing dependency_links to /tmp/pip-pip-egg-info-m_2u8zd_/onnx_graphsurgeon.egg-info/dependency_links.txt
      writing requirements to /tmp/pip-pip-egg-info-m_2u8zd_/onnx_graphsurgeon.egg-info/requires.txt
      writing top-level names to /tmp/pip-pip-egg-info-m_2u8zd_/onnx_graphsurgeon.egg-info/top_level.txt
      writing manifest file '/tmp/pip-pip-egg-info-m_2u8zd_/onnx_graphsurgeon.egg-info/SOURCES.txt'
      reading manifest file '/tmp/pip-pip-egg-info-m_2u8zd_/onnx_graphsurgeon.egg-info/SOURCES.txt'
      adding license file 'LICENSE'
      Traceback (most recent call last):
        File "<string>", line 2, in <module>
        File "<pip-setuptools-caller>", line 34, in <module>
        File "/tmp/pip-req-build-6c65ucjd/tools/onnx-graphsurgeon/setup.py", line 63, in <module>
          main()
        File "/tmp/pip-req-build-6c65ucjd/tools/onnx-graphsurgeon/setup.py", line 38, in main
          setup(
        File "/home/admin/.local/lib/python3.10/site-packages/setuptools/__init__.py", line 117, in setup
          return distutils.core.setup(**attrs)
        File "/home/admin/.local/lib/python3.10/site-packages/setuptools/_distutils/core.py", line 186, in setup
          return run_commands(dist)
        File "/home/admin/.local/lib/python3.10/site-packages/setuptools/_distutils/core.py", line 202, in run_commands
          dist.run_commands()
        File "/home/admin/.local/lib/python3.10/site-packages/setuptools/_distutils/dist.py", line 983, in run_commands
          self.run_command(cmd)
        File "/home/admin/.local/lib/python3.10/site-packages/setuptools/dist.py", line 999, in run_command
          super().run_command(command)
        File "/home/admin/.local/lib/python3.10/site-packages/setuptools/_distutils/dist.py", line 1002, in run_command
          cmd_obj.run()
        File "/home/admin/.local/lib/python3.10/site-packages/setuptools/command/egg_info.py", line 312, in run
          self.find_sources()
        File "/home/admin/.local/lib/python3.10/site-packages/setuptools/command/egg_info.py", line 320, in find_sources
          mm.run()
        File "/home/admin/.local/lib/python3.10/site-packages/setuptools/command/egg_info.py", line 548, in run
          self.prune_file_list()
        File "/home/admin/.local/lib/python3.10/site-packages/setuptools/command/sdist.py", line 162, in prune_file_list
          super().prune_file_list()
        File "/home/admin/.local/lib/python3.10/site-packages/setuptools/_distutils/command/sdist.py", line 380, in prune_file_list
          base_dir = self.distribution.get_fullname()
        File "/home/admin/.local/lib/python3.10/site-packages/setuptools/_core_metadata.py", line 272, in get_fullname
          return _distribution_fullname(self.get_name(), self.get_version())
        File "/home/admin/.local/lib/python3.10/site-packages/setuptools/_core_metadata.py", line 290, in _distribution_fullname
          canonicalize_version(version, strip_trailing_zero=False),
      TypeError: canonicalize_version() got an unexpected keyword argument 'strip_trailing_zero'
      [end of output]

  note: This error originates from a subprocess, and is likely not a problem with pip.
error: metadata-generation-failed

× Encountered error while generating package metadata.
╰─> See above for output.

note: This is an issue with the package mentioned above, not pip.
hint: See above for details.
installing the onnx graph optimization toolkit...
running develop
/home/admin/.local/lib/python3.10/site-packages/setuptools/command/develop.py:41: EasyInstallDeprecationWarning: easy_install command is deprecated.
!!

        ********************************************************************************
        Please avoid running ``setup.py`` and ``easy_install``.
        Instead, use pypa/build, pypa/installer or other
        standards-based tools.

        See https://github.com/pypa/setuptools/issues/917 for details.
        ********************************************************************************

!!
  easy_install.initialize_options(self)
Traceback (most recent call last):
  File "/home/admin/jay/edgeai-sdk/edgeai-tidl-tools/osrt-model-tools/./setup.py", line 3, in <module>
    setup(
  File "/home/admin/.local/lib/python3.10/site-packages/setuptools/__init__.py", line 117, in setup
    return distutils.core.setup(**attrs)
  File "/home/admin/.local/lib/python3.10/site-packages/setuptools/_distutils/core.py", line 186, in setup
    return run_commands(dist)
  File "/home/admin/.local/lib/python3.10/site-packages/setuptools/_distutils/core.py", line 202, in run_commands
    dist.run_commands()
  File "/home/admin/.local/lib/python3.10/site-packages/setuptools/_distutils/dist.py", line 983, in run_commands
    self.run_command(cmd)
  File "/home/admin/.local/lib/python3.10/site-packages/setuptools/dist.py", line 999, in run_command
    super().run_command(command)
  File "/home/admin/.local/lib/python3.10/site-packages/setuptools/_distutils/dist.py", line 1001, in run_command
    cmd_obj.ensure_finalized()
  File "/home/admin/.local/lib/python3.10/site-packages/setuptools/_distutils/cmd.py", line 124, in ensure_finalized
    self.finalize_options()
  File "/home/admin/.local/lib/python3.10/site-packages/setuptools/command/develop.py", line 51, in finalize_options
    easy_install.finalize_options(self)
  File "/home/admin/.local/lib/python3.10/site-packages/setuptools/command/easy_install.py", line 251, in finalize_options
    'dist_fullname': self.distribution.get_fullname(),
  File "/home/admin/.local/lib/python3.10/site-packages/setuptools/_core_metadata.py", line 272, in get_fullname
    return _distribution_fullname(self.get_name(), self.get_version())
  File "/home/admin/.local/lib/python3.10/site-packages/setuptools/_core_metadata.py", line 290, in _distribution_fullname
    canonicalize_version(version, strip_trailing_zero=False),
TypeError: canonicalize_version() got an unexpected keyword argument 'strip_trailing_zero'

Reese Grimsley said:
Also worth asking -- which OS version and python version are you using here? We support Ubuntu 22.04 LTS and Python 3.10

Yes, OS is ubuntu 22.04 and python is 3.10.12.

Reese Grimsley said:
I generally recommend using a virtual environment to avoid dependency conflicts. Tools like conda or the build-in venv (latter is my preference) are good.

Can you provide the reference on how can i setup virtual environment for this?

Regards,

Jay

0 Reese Grimsley over 1 year ago in reply to Jay Kaneria

TI__Mastermind 19726 points

Hi Jay,

I use venv, like so:

python3 -m venv ./venv-tidl-tools-SDKvX.Y #create venv
source ./venv-tidl-tools-SDKvX.Y/bin/activate #activate virtual environment

There are also docker containers available:

https://github.com/TexasInstruments/edgeai-tidl-tools/tree/master/scripts/docker

In your case, TensorRT is failing to install -- this is a dependency for the onnx_graphsurgeon tool. You are using a TFL model, so this may not be so necessary

So here's my suggestion:

Try creating a venv like I suggested above and rerun the main edgeai-tidl-tools/setup.sh script. If this passes, then ignore next suggestion
If above didn't work, let's try docker approach. Please see the build and run scripts within the linked folder above

I cannot tell why TensorRT is failing to install, but looks like more of a setuptools issue. A quick google / stack overflow/exchange search would be worth trying to see if there is a common fix -- I expect virtual env / container can solve this. If this persists, we can probably modify a few python/setup.sh scripts to ignore TensorRT, since you're looking at TFL models anyway.

BR,
Reese

0 Jay Kaneria over 1 year ago in reply to Reese Grimsley

Intellectual 720 points

Hi Reese,

Thank you for suggestion of using the python virtual environment. After setting the virtual environment, There were almost no error in setting up environment by running "setup.sh" script.

But, I am compiling the SDK with GPU option. My understanding is that when edgeai SDK is compiled CPU, models will using cpu when used on EVM and if edgeai SDK is compiled GPU, models will using GPU when used on EVM.

So, when SDK is compiled with GPU option, "./scripts/run_python_examples.sh" script is giving the below error.

(virtual-env-edgeai) admin@VM2502:~/jay/python-virtual-env/edgeai-tidl-tools$ source ./scripts/run_python_examples.sh
X64 Architecture
1





Running 4 Models - ['cl-tfl-mobilenet_v1_1.0_224', 'ss-tfl-deeplabv3_mnv2_ade20k_float', 'od-tfl-ssd_mobilenet_v2_300_float', 'od-tfl-ssdlite_mobiledet_dsp_320x320_coco']


Running_Model :  cl-tfl-mobilenet_v1_1.0_224
Downloading   ../../../models/public/mobilenet_v1_1.0_224.tflite

Running_Model :  ss-tfl-deeplabv3_mnv2_ade20k_float

Running_Model :  od-tfl-ssd_mobilenet_v2_300_float

Running_Model :  od-tfl-ssdlite_mobiledet_dsp_320x320_coco
/home/admin/jay/python-virtual-env/edgeai-tidl-tools/models/public/mobilenet_v1_1.0_224.tflite
Downloading   ../../../models/public/deeplabv3_mnv2_ade20k_float.tflite
Process Process-1:
Traceback (most recent call last):
  File "/home/admin/jay/python-virtual-env/virtual-env-edgeai/lib/python3.10/site-packages/tflite_runtime/interpreter.py", line 166, in load_delegate
    delegate = Delegate(library, options)
  File "/home/admin/jay/python-virtual-env/virtual-env-edgeai/lib/python3.10/site-packages/tflite_runtime/interpreter.py", line 104, in __init__
    raise ValueError(capture.message)
ValueError: could not load library libvx_tidl_rt.so

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
    self.run()
  File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
    self._target(*self._args, **self._kwargs)
  File "/home/admin/jay/python-virtual-env/edgeai-tidl-tools/examples/osrt_python/tfl/tflrt_delegate.py", line 299, in run_model
    tflite.load_delegate(
  File "/home/admin/jay/python-virtual-env/virtual-env-edgeai/lib/python3.10/site-packages/tflite_runtime/interpreter.py", line 168, in load_delegate
    raise ValueError('Failed to load delegate from {}\n{}'.format(
ValueError: Failed to load delegate from /home/admin/jay/python-virtual-env/edgeai-tidl-tools/tools/AM62A/tidl_tools/tidl_model_import_tflite.so
could not load library libvx_tidl_rt.so
/home/admin/jay/python-virtual-env/edgeai-tidl-tools/models/public/deeplabv3_mnv2_ade20k_float.tflite
Downloading   ../../../models/public/ssd_mobilenet_v2_300_float.tflite
Process Process-2:
Traceback (most recent call last):
  File "/home/admin/jay/python-virtual-env/virtual-env-edgeai/lib/python3.10/site-packages/tflite_runtime/interpreter.py", line 166, in load_delegate
    delegate = Delegate(library, options)
  File "/home/admin/jay/python-virtual-env/virtual-env-edgeai/lib/python3.10/site-packages/tflite_runtime/interpreter.py", line 104, in __init__
    raise ValueError(capture.message)
ValueError: could not load library libvx_tidl_rt.so

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
    self.run()
  File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
    self._target(*self._args, **self._kwargs)
  File "/home/admin/jay/python-virtual-env/edgeai-tidl-tools/examples/osrt_python/tfl/tflrt_delegate.py", line 299, in run_model
    tflite.load_delegate(
  File "/home/admin/jay/python-virtual-env/virtual-env-edgeai/lib/python3.10/site-packages/tflite_runtime/interpreter.py", line 168, in load_delegate
    raise ValueError('Failed to load delegate from {}\n{}'.format(
ValueError: Failed to load delegate from /home/admin/jay/python-virtual-env/edgeai-tidl-tools/tools/AM62A/tidl_tools/tidl_model_import_tflite.so
could not load library libvx_tidl_rt.so
/home/admin/jay/python-virtual-env/edgeai-tidl-tools/models/public/ssd_mobilenet_v2_300_float.tflite
Downloading   ../../../models/public/ssdlite_mobiledet_dsp_320x320_coco_20200519.tflite
Process Process-3:
Traceback (most recent call last):
  File "/home/admin/jay/python-virtual-env/virtual-env-edgeai/lib/python3.10/site-packages/tflite_runtime/interpreter.py", line 166, in load_delegate
    delegate = Delegate(library, options)
  File "/home/admin/jay/python-virtual-env/virtual-env-edgeai/lib/python3.10/site-packages/tflite_runtime/interpreter.py", line 104, in __init__
    raise ValueError(capture.message)
ValueError: could not load library libvx_tidl_rt.so

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
    self.run()
  File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
    self._target(*self._args, **self._kwargs)
  File "/home/admin/jay/python-virtual-env/edgeai-tidl-tools/examples/osrt_python/tfl/tflrt_delegate.py", line 299, in run_model
    tflite.load_delegate(
  File "/home/admin/jay/python-virtual-env/virtual-env-edgeai/lib/python3.10/site-packages/tflite_runtime/interpreter.py", line 168, in load_delegate
    raise ValueError('Failed to load delegate from {}\n{}'.format(
ValueError: Failed to load delegate from /home/admin/jay/python-virtual-env/edgeai-tidl-tools/tools/AM62A/tidl_tools/tidl_model_import_tflite.so
could not load library libvx_tidl_rt.so
/home/admin/jay/python-virtual-env/edgeai-tidl-tools/models/public/ssdlite_mobiledet_dsp_320x320_coco_20200519.tflite
Downloading   ../../../models/public/ssdlite_mobiledet_dsp_320x320_coco_20200519.prototxt
Process Process-4:
Traceback (most recent call last):
  File "/home/admin/jay/python-virtual-env/virtual-env-edgeai/lib/python3.10/site-packages/tflite_runtime/interpreter.py", line 166, in load_delegate
    delegate = Delegate(library, options)
  File "/home/admin/jay/python-virtual-env/virtual-env-edgeai/lib/python3.10/site-packages/tflite_runtime/interpreter.py", line 104, in __init__
    raise ValueError(capture.message)
ValueError: could not load library libvx_tidl_rt.so

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
    self.run()
  File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
    self._target(*self._args, **self._kwargs)
  File "/home/admin/jay/python-virtual-env/edgeai-tidl-tools/examples/osrt_python/tfl/tflrt_delegate.py", line 299, in run_model
    tflite.load_delegate(
  File "/home/admin/jay/python-virtual-env/virtual-env-edgeai/lib/python3.10/site-packages/tflite_runtime/interpreter.py", line 168, in load_delegate
    raise ValueError('Failed to load delegate from {}\n{}'.format(
ValueError: Failed to load delegate from /home/admin/jay/python-virtual-env/edgeai-tidl-tools/tools/AM62A/tidl_tools/tidl_model_import_tflite.so
could not load library libvx_tidl_rt.so

I am using the virtual environment. If I don't use virtual environment, then also getting the same error with GPU option. Before this, whatever error I have shared with you, those error were from compilation with CPU option. Above mention error is encountered when SDK is compiled with GPU option.

What could be issue in compiling the SDK with GPU option?

Regards,

Jay

0 Reese Grimsley over 1 year ago in reply to Jay Kaneria

TI__Mastermind 19726 points

Hi Jay,

Jay Kaneria said:
But, I am compiling the SDK with GPU option. My understanding is that when edgeai SDK is compiled CPU, models will using cpu when used on EVM and if edgeai SDK is compiled GPU, models will using GPU when used on EVM.

Ah, this makes sense. Let me explain the GPU vs. CPU tooling -- in short, you should use CPU tooling for now.

The outcome for either toolset is the same -- you receive a set of files called 'artifacts' that let you target the C7xMMA neural-network accelerator on the AM6xA / TDA4x processors. Whether you use GPU or CPU does not impact what TI processors nor the accelerators therein that may be targeted.

So what is the GPU tooling used for? Accelerating the calibration and quantization of the model as it's being converted from floating point. This can be a slow process if you have many calibration_iterations and calibration_frames, but for low values (1-5), CPU is okay

Therefore, I suggest deleting the tools/ subdirectory and re-running the setup.sh script to target CPU-based tools. This is simpler installation

BR,
Reese

0 Jay Kaneria over 1 year ago in reply to Reese Grimsley

Intellectual 720 points

Hi Reese,

Thank you for your reply.

As you suggested, I am compiling the SDK with CPU option now using python virtual environment. I have below two query in that.

Query-1:

While running the "source ./scripts/run_python_examples.sh" in the compilation and validation section, I am getting below errors. I am attaching the full compilation log generated from "source ./scripts/run_python_examples.sh" command.

Fullscreen Error-while-running-run_python_examples.txt Download

(virtual-env-edgeai) admin@VM2502:~/jay/python-virtual-env/edgeai-tidl-tools$ source ./scripts/run_python_examples.sh
X64 Architecture
1

Running 4 Models - ['cl-tfl-mobilenet_v1_1.0_224', 'ss-tfl-deeplabv3_mnv2_ade20k_float', 'od-tfl-ssd_mobilenet_v2_300_float', 'od-tfl-ssdlite_mobiledet_dsp_320x320_coco']


Running_Model :  cl-tfl-mobilenet_v1_1.0_224
Downloading   ../../../models/public/mobilenet_v1_1.0_224.tflite

Running_Model :  ss-tfl-deeplabv3_mnv2_ade20k_float

Running_Model :  od-tfl-ssd_mobilenet_v2_300_float

Running_Model :  od-tfl-ssdlite_mobiledet_dsp_320x320_coco
/home/admin/jay/python-virtual-env/edgeai-tidl-tools/models/public/mobilenet_v1_1.0_224.tflite
Downloading   ../../../models/public/deeplabv3_mnv2_ade20k_float.tflite
========================= [Model Compilation Started] =========================

Model compilation will perform the following stages:
1. Parsing
2. Graph Optimization
3. Quantization & Calibration
4. Memory Planning

============================== [Version Summary] ==============================

-------------------------------------------------------------------------------
|          TIDL Tools Version          |              10_01_04_00             |
-------------------------------------------------------------------------------
|         C7x Firmware Version         |              10_01_00_01             |
-------------------------------------------------------------------------------

============================== [Parsing Started] ==============================

[TIDL Import] [PARSER] WARNING: Network not identified as Object Detection network : (1) Ignore if network is not Object Detection network (2) If network is Object Detection network, please specify "model_type":"OD" as part of OSRT compilation options

Total Nodes = 34
-------------------------------------------------------------------------------
|          Core           |      No. of Nodes       |   Number of Subgraphs   |
-------------------------------------------------------------------------------
| C7x                     |                      34 |                       1 |
| CPU                     |                       0 |                       x |
-------------------------------------------------------------------------------
============================= [Parsing Completed] =============================

==================== [Optimization for subgraph_86 started] ====================

/home/admin/jay/python-virtual-env/edgeai-tidl-tools/models/public/deeplabv3_mnv2_ade20k_float.tflite
Downloading   ../../../models/public/ssd_mobilenet_v2_300_float.tflite
========================= [Model Compilation Started] =========================

Model compilation will perform the following stages:
1. Parsing
2. Graph Optimization
3. Quantization & Calibration
4. Memory Planning

============================== [Version Summary] ==============================

-------------------------------------------------------------------------------
|          TIDL Tools Version          |              10_01_04_00             |
-------------------------------------------------------------------------------
|         C7x Firmware Version         |              10_01_00_01             |
-------------------------------------------------------------------------------

============================== [Parsing Started] ==============================

[TIDL Import] [PARSER] WARNING: Network not identified as Object Detection network : (1) Ignore if network is not Object Detection network (2) If network is Object Detection network, please specify "model_type":"OD" as part of OSRT compilation options
[TIDL Import]  WARNING: Resize layer -  with scales > 4 is not optimal

Total Nodes = 81
-------------------------------------------------------------------------------
|          Core           |      No. of Nodes       |   Number of Subgraphs   |
-------------------------------------------------------------------------------
| C7x                     |                      81 |                       1 |
| CPU                     |                       0 |                       x |
-------------------------------------------------------------------------------
============================= [Parsing Completed] =============================

=================== [Optimization for subgraph_201 started] ===================

[TIDL Import] [PARSER] WARNING: Requested output data convert layer is not added to the network, It is currently not optimal
----------------------------- Optimization Summary -----------------------------
--------------------------------------------------------------------------------
|         Layer         | Nodes before optimization | Nodes after optimization |
--------------------------------------------------------------------------------
| TIDL_SoftMaxLayer     |                         1 |                        1 |
| TIDL_SqueezeLayer     |                         1 |                        0 |
| TIDL_ConvolutionLayer |                        28 |                       28 |
| TIDL_EltWiseLayer     |                         2 |                        0 |
| TIDL_CastLayer        |                         1 |                        0 |
| TIDL_PoolingLayer     |                         1 |                        1 |
--------------------------------------------------------------------------------

=================== [Optimization for subgraph_86 completed] ===================

The soft limit is 10240
The hard limit is 10240
MEM: Init ... !!!
MEM: Init ... Done !!!
 0.0s:  VX_ZONE_INIT:Enabled
 0.18s:  VX_ZONE_ERROR:Enabled
 0.28s:  VX_ZONE_WARNING:Enabled
 0.3446s:  VX_ZONE_INIT:[tivxInit:190] Initialization Done !!!
[TIDL Import] [PARSER] WARNING: Requested output data convert layer is not added to the network, It is currently not optimal

 ************ Frame index 1 : Running float inference ****************

----------------------------- Optimization Summary -----------------------------
--------------------------------------------------------------------------------
|         Layer         | Nodes before optimization | Nodes after optimization |
--------------------------------------------------------------------------------
| TIDL_ArgMaxLayer      |                         1 |                        1 |
| TIDL_ConcatLayer      |                         2 |                        2 |
| TIDL_ResizeLayer      |                         3 |                        5 |
| TIDL_ConvolutionLayer |                        60 |                       60 |
| TIDL_EltWiseLayer     |                        12 |                       10 |
| TIDL_CastLayer        |                         2 |                        0 |
| TIDL_PoolingLayer     |                         1 |                        1 |
--------------------------------------------------------------------------------

================== [Optimization for subgraph_201 completed] ==================

The soft limit is 10240
The hard limit is 10240
MEM: Init ... !!!
MEM: Init ... Done !!!
 0.0s:  VX_ZONE_INIT:Enabled
 0.35s:  VX_ZONE_ERROR:Enabled
 0.54s:  VX_ZONE_WARNING:Enabled
 0.5109s:  VX_ZONE_INIT:[tivxInit:190] Initialization Done !!!

 ************ Frame index 2 : Running fixed point mode for calibration ****************


-------- Running Calibration in Float Mode to Collect Tensor Statistics --------
/home/admin/jay/python-virtual-env/edgeai-tidl-tools/models/public/ssd_mobilenet_v2_300_float.tflite
Downloading   ../../../models/public/ssdlite_mobiledet_dsp_320x320_coco_20200519.tflite
========================= [Model Compilation Started] =========================

Model compilation will perform the following stages:
1. Parsing
2. Graph Optimization
3. Quantization & Calibration
4. Memory Planning

============================== [Version Summary] ==============================

-------------------------------------------------------------------------------
|          TIDL Tools Version          |              10_01_04_00             |
-------------------------------------------------------------------------------
|         C7x Firmware Version         |              10_01_00_01             |
-------------------------------------------------------------------------------

============================== [Parsing Started] ==============================

Number of OD backbone nodes = 89
Size of odBackboneNodeIds = 89

Total Nodes = 107
-------------------------------------------------------------------------------
|          Core           |      No. of Nodes       |   Number of Subgraphs   |
-------------------------------------------------------------------------------
| C7x                     |                     107 |                       1 |
| CPU                     |                       0 |                       x |
-------------------------------------------------------------------------------
============================= [Parsing Completed] =============================

=================== [Optimization for subgraph_264 started] ===================



------------------ Fixed-point Calibration Iteration [1 / 5]: ------------------
[TIDL Import]  ERROR: Failed to run calibration pass, system command returned error: 132 -- [tidl_import_core.cpp, 678]
[TIDL Import]  ERROR: Failed to run Calibration - Failed in function: tidlRunQuantStatsTool -- [tidl_import_core.cpp, 1746]
[TIDL Import] [QUANTIZATION] ERROR:  - Failed in function: TIDL_quantStatsFixedOrFloat -- [tidl_import_quantize.cpp, 3992]
[TIDL Import] [QUANTIZATION] ERROR:  - Failed in function: TIDL_runIterativeCalibration -- [tidl_import_quantize.cpp, 4313]
[TIDL Import] [QUANTIZATION] ERROR:  - Failed in function: TIDL_import_quantize -- [tidl_import_quantize.cpp, 5195]
[TIDL Import]  ERROR:  - Failed in function: TIDL_import_backend -- [tidl_import_core.cpp, 4428]
[TIDL Import]  ERROR:  - Failed in function: TIDL_runtimesPostProcessNet -- [tidl_runtimes_import_common.cpp, 1414]


Completed_Model :     1, Name : cl-tfl-mobilenet_v1_1.0_224                       , Total time :    3066.16, Offload Time :       0.00 , DDR RW MBs : 18446744073709.55, Output Image File : py_out_cl-tfl-mobilenet_v1_1.0_224_ADE_val_00001801.jpg, Output Bin File : py_out_cl-tfl-mobilenet_v1_1.0_224_ADE_val_00001801.bin


MEM: Deinit ... !!!
MEM: Alloc's: 26 alloc's of 68565333 bytes
MEM: Free's : 26 free's  of 68565333 bytes
MEM: Open's : 0 allocs  of 0 bytes
MEM: Deinit ... Done !!!
[TIDL Import] [PARSER] WARNING: Requested output data convert layer is not added to the network, It is currently not optimal
[TIDL Import] [PARSER] WARNING: Requested output data convert layer is not added to the network, It is currently not optimal
[TIDL Import] [PARSER] WARNING: Requested output data convert layer is not added to the network, It is currently not optimal
[TIDL Import] [PARSER] WARNING: Requested output data convert layer is not added to the network, It is currently not optimal
/home/admin/jay/python-virtual-env/edgeai-tidl-tools/models/public/ssdlite_mobiledet_dsp_320x320_coco_20200519.tflite
Downloading   ../../../models/public/ssdlite_mobiledet_dsp_320x320_coco_20200519.prototxt
----------------------------- Optimization Summary -----------------------------
-------------------------------------------------------------------------------------
|            Layer           | Nodes before optimization | Nodes after optimization |
-------------------------------------------------------------------------------------
| TIDL_OdOutputReformatLayer |                         0 |                        4 |
| TIDL_ConvolutionLayer      |                        76 |                       76 |
| TIDL_EltWiseLayer          |                        12 |                       10 |
| TIDL_DetectionOutputLayer  |                         0 |                        1 |
| TIDL_CastLayer             |                         1 |                        0 |
-------------------------------------------------------------------------------------

================== [Optimization for subgraph_264 completed] ==================

The soft limit is 10240
The hard limit is 10240
MEM: Init ... !!!
MEM: Init ... Done !!!
 0.0s:  VX_ZONE_INIT:Enabled
 0.16s:  VX_ZONE_ERROR:Enabled
 0.26s:  VX_ZONE_WARNING:Enabled
 0.3171s:  VX_ZONE_INIT:[tivxInit:190] Initialization Done !!!
========================= [Model Compilation Started] =========================

Model compilation will perform the following stages:
1. Parsing
2. Graph Optimization
3. Quantization & Calibration
4. Memory Planning

============================== [Version Summary] ==============================

-------------------------------------------------------------------------------
|          TIDL Tools Version          |              10_01_04_00             |
-------------------------------------------------------------------------------
|         C7x Firmware Version         |              10_01_00_01             |
-------------------------------------------------------------------------------

============================== [Parsing Started] ==============================

TIDL Meta pipeLine (proto) file  : ../../../models/public/ssdlite_mobiledet_dsp_320x320_coco_20200519.prototxt
Number of OD backbone nodes = 112
Size of odBackboneNodeIds = 112

Total Nodes = 129
-------------------------------------------------------------------------------
|          Core           |      No. of Nodes       |   Number of Subgraphs   |
-------------------------------------------------------------------------------
| C7x                     |                     129 |                       1 |
| CPU                     |                       0 |                       x |
-------------------------------------------------------------------------------
============================= [Parsing Completed] =============================

TF Meta pipeLine (proto) file : ../../../models/public/ssdlite_mobiledet_dsp_320x320_coco_20200519.prototxt
num_classes :              91
y_scale :                  10.000000
x_scale :                  10.000000
w_scale :                  5.000000
h_scale :                  5.000000
num_keypoints :            5.000000
score_threshold :          0.600000
iou_threshold :            0.450000
max_detections_per_class : 200
max_total_detections :     100
      scales, height_stride, width_stride, height_offset, width_offset
   0.2000000,   -1.0000000,   -1.0000000,   -1.0000000,   -1.0000000
   0.3500000,   -1.0000000,   -1.0000000,   -1.0000000,   -1.0000000
   0.5000000,   -1.0000000,   -1.0000000,   -1.0000000,   -1.0000000
   0.6500000,   -1.0000000,   -1.0000000,   -1.0000000,   -1.0000000
   0.8000000,   -1.0000000,   -1.0000000,   -1.0000000,   -1.0000000
   0.9500000,   -1.0000000,   -1.0000000,   -1.0000000,   -1.0000000
aspect_ratios
   1.0000000
   2.0000000
   0.5000000
   3.0000000
   0.3333000
=================== [Optimization for subgraph_321 started] ===================


 ************ Frame index 1 : Running float inference ****************

[TIDL Import] [PARSER] WARNING: Requested output data convert layer is not added to the network, It is currently not optimal
[TIDL Import] [PARSER] WARNING: Requested output data convert layer is not added to the network, It is currently not optimal
[TIDL Import] [PARSER] WARNING: Requested output data convert layer is not added to the network, It is currently not optimal
[TIDL Import] [PARSER] WARNING: Requested output data convert layer is not added to the network, It is currently not optimal
----------------------------- Optimization Summary -----------------------------
-------------------------------------------------------------------------------------
|            Layer           | Nodes before optimization | Nodes after optimization |
-------------------------------------------------------------------------------------
| TIDL_OdOutputReformatLayer |                         0 |                        4 |
| TIDL_ConvolutionLayer      |                        94 |                       94 |
| TIDL_EltWiseLayer          |                        17 |                       15 |
| TIDL_DetectionOutputLayer  |                         0 |                        1 |
| TIDL_CastLayer             |                         1 |                        0 |
-------------------------------------------------------------------------------------

================== [Optimization for subgraph_321 completed] ==================

The soft limit is 10240
The hard limit is 10240
MEM: Init ... !!!
MEM: Init ... Done !!!
 0.0s:  VX_ZONE_INIT:Enabled
 0.25s:  VX_ZONE_ERROR:Enabled
 0.42s:  VX_ZONE_WARNING:Enabled
 0.4709s:  VX_ZONE_INIT:[tivxInit:190] Initialization Done !!!

 ************ Frame index 2 : Running fixed point mode for calibration ****************


-------- Running Calibration in Float Mode to Collect Tensor Statistics --------
[=============================================================================] 100 %
 ************ Frame index 1 : Running float inference ****************



------------------ Fixed-point Calibration Iteration [1 / 5]: ------------------
[TIDL Import]  ERROR: Failed to run calibration pass, system command returned error: 132 -- [tidl_import_core.cpp, 678]
[TIDL Import]  ERROR: Failed to run Calibration - Failed in function: tidlRunQuantStatsTool -- [tidl_import_core.cpp, 1746]
[TIDL Import] [QUANTIZATION] ERROR:  - Failed in function: TIDL_quantStatsFixedOrFloat -- [tidl_import_quantize.cpp, 3992]
[TIDL Import] [QUANTIZATION] ERROR:  - Failed in function: TIDL_runIterativeCalibration -- [tidl_import_quantize.cpp, 4313]
[TIDL Import] [QUANTIZATION] ERROR:  - Failed in function: TIDL_import_quantize -- [tidl_import_quantize.cpp, 5195]
[TIDL Import]  ERROR:  - Failed in function: TIDL_import_backend -- [tidl_import_core.cpp, 4428]
[TIDL Import]  ERROR:  - Failed in function: TIDL_runtimesPostProcessNet -- [tidl_runtimes_import_common.cpp, 1414]


Completed_Model :     3, Name : od-tfl-ssd_mobilenet_v2_300_float                 , Total time :    7701.40, Offload Time :       0.00 , DDR RW MBs : 18446744073709.55, Output Image File : py_out_od-tfl-ssd_mobilenet_v2_300_float_ADE_val_00001801.jpg, Output Bin File : py_out_od-tfl-ssd_mobilenet_v2_300_float_ADE_val_00001801.bin


MEM: Deinit ... !!!
MEM: Alloc's: 29 alloc's of 203910505 bytes
MEM: Free's : 29 free's  of 203910505 bytes
MEM: Open's : 0 allocs  of 0 bytes
MEM: Deinit ... Done !!!

 ************ Frame index 2 : Running fixed point mode for calibration ****************


-------- Running Calibration in Float Mode to Collect Tensor Statistics --------
[=============================================================================] 100 %

------------------ Fixed-point Calibration Iteration [1 / 5]: ------------------
[TIDL Import]  ERROR: Failed to run calibration pass, system command returned error: 132 -- [tidl_import_core.cpp, 678]
[TIDL Import]  ERROR: Failed to run Calibration - Failed in function: tidlRunQuantStatsTool -- [tidl_import_core.cpp, 1746]
[TIDL Import] [QUANTIZATION] ERROR:  - Failed in function: TIDL_quantStatsFixedOrFloat -- [tidl_import_quantize.cpp, 3992]
[TIDL Import] [QUANTIZATION] ERROR:  - Failed in function: TIDL_runIterativeCalibration -- [tidl_import_quantize.cpp, 4313]
[TIDL Import] [QUANTIZATION] ERROR:  - Failed in function: TIDL_import_quantize -- [tidl_import_quantize.cpp, 5195]
[TIDL Import]  ERROR:  - Failed in function: TIDL_import_backend -- [tidl_import_core.cpp, 4428]
[TIDL Import]  ERROR:  - Failed in function: TIDL_runtimesPostProcessNet -- [tidl_runtimes_import_common.cpp, 1414]


Completed_Model :     4, Name : od-tfl-ssdlite_mobiledet_dsp_320x320_coco         , Total time :   11118.42, Offload Time :       0.00 , DDR RW MBs : 18446744073709.55, Output Image File : py_out_od-tfl-ssdlite_mobiledet_dsp_320x320_coco_ADE_val_00001801.jpg, Output Bin File : py_out_od-tfl-ssdlite_mobiledet_dsp_320x320_coco_ADE_val_00001801.bin


MEM: Deinit ... !!!
MEM: Alloc's: 29 alloc's of 102363709 bytes
MEM: Free's : 29 free's  of 102363709 bytes
MEM: Open's : 0 allocs  of 0 bytes
MEM: Deinit ... Done !!!

 ************ Frame index 1 : Running float inference ****************


 ************ Frame index 2 : Running fixed point mode for calibration ****************


-------- Running Calibration in Float Mode to Collect Tensor Statistics --------
[====================================================================
=========] 100 %
------------------ Fixed-point Calibration Iteration [1 / 5]: ------------------
[======================================>
[TIDL Import]  ERROR: Failed to run calibration pass, system command returned error: 132 -- [tidl_import_core.cpp, 678]
[TIDL Import]  ERROR: Failed to run Calibration - Failed in function: tidlRunQuantStatsTool -- [tidl_import_core.cpp, 1746]
[TIDL Import] [QUANTIZATION] ERROR:  - Failed in function: TIDL_quantStatsFixedOrFloat -- [tidl_import_quantize.cpp, 3992]
[TIDL Import] [QUANTIZATION] ERROR:  - Failed in function: TIDL_runIterativeCalibration -- [tidl_import_quantize.cpp, 4313]
[TIDL Import] [QUANTIZATION] ERROR:  - Failed in function: TIDL_import_quantize -- [tidl_import_quantize.cpp, 5195]
[TIDL Import]  ERROR:  - Failed in function: TIDL_import_backend -- [tidl_import_core.cpp, 4428]
[TIDL Import]  ERROR:  - Failed in function: TIDL_runtimesPostProcessNet -- [tidl_runtimes_import_common.cpp, 1414]


Completed_Model :     2, Name : ss-tfl-deeplabv3_mnv2_ade20k_float                , Total time :   93119.76, Offload Time :       0.00 , DDR RW MBs : 18446744073709.55, Output Image File : py_out_ss-tfl-deeplabv3_mnv2_ade20k_float_ADE_val_00001801.jpg, Output Bin File : py_out_ss-tfl-deeplabv3_mnv2_ade20k_float_ADE_val_00001801.bin


MEM: Deinit ... !!!
MEM: Alloc's: 26 alloc's of 327289657 bytes
MEM: Free's : 26 free's  of 327289657 bytes
MEM: Open's : 0 allocs  of 0 bytes
MEM: Deinit ... Done !!!
run python3 tflrt_delegate.py
Running 4 Models - ['cl-tfl-mobilenet_v1_1.0_224', 'ss-tfl-deeplabv3_mnv2_ade20k_float', 'od-tfl-ssd_mobilenet_v2_300_float', 'od-tfl-ssdlite_mobiledet_dsp_320x320_coco']


Running_Model :  cl-tfl-mobilenet_v1_1.0_224

Running_Model :  ss-tfl-deeplabv3_mnv2_ade20k_float

Running_Model :  od-tfl-ssd_mobilenet_v2_300_float

Running_Model :  od-tfl-ssdlite_mobiledet_dsp_320x320_coco

******** WARNING ******* : Could not open ../../../model-artifacts//cl-tfl-mobilenet_v1_1.0_224/artifacts//allowedNode.txt for reading... Entire model will run on ARM without any delegation to TIDL !

 Number of subgraphs:1 , 0 nodes delegated out of 34 nodes


******** WARNING ******* : Could not open ../../../model-artifacts//ss-tfl-deeplabv3_mnv2_ade20k_float/artifacts//allowedNode.txt for reading... Entire model will run on ARM without any delegation to TIDL !

 Number of subgraphs:1 , 0 nodes delegated out of 81 nodes


******** WARNING ******* : Could not open ../../../model-artifacts//od-tfl-ssd_mobilenet_v2_300_float/artifacts//allowedNode.txt for reading... Entire model will run on ARM without any delegation to TIDL !

 Number of subgraphs:1 , 0 nodes delegated out of 107 nodes

INFO: Created TensorFlow Lite XNNPACK delegate for CPU.
INFO: Created TensorFlow Lite XNNPACK delegate for CPU.

******** WARNING ******* : Could not open ../../../model-artifacts//od-tfl-ssdlite_mobiledet_dsp_320x320_coco/artifacts//allowedNode.txt for reading... Entire model will run on ARM without any delegation to TIDL !

 Number of subgraphs:1 , 0 nodes delegated out of 129 nodes

INFO: Created TensorFlow Lite XNNPACK delegate for CPU.
INFO: Created TensorFlow Lite XNNPACK delegate for CPU.

 ,  0  0.546822  warplane, military plane ,,  1  0.270634  missile ,,  2  0.165473  projectile, missile ,,  3  0.015031  aircraft carrier, carrier, flattop, attack aircraft carrier ,,  4  0.000821  submarine, pigboat, sub, U-boat ,

Saving image to  ../../../output_images/

Saving output tensor to  ../../../output_binaries/


Completed_Model :     1, Name : cl-tfl-mobilenet_v1_1.0_224
             , Total time :      87.11, Offload Time :       0.00 , DDR RW MBs : 18446744073709.55, Output Image File : py_out_cl-tfl-mobilenet_v1_1.0_224_airshow.jpg, Output Bin File : py_out_cl-tfl-mobilenet_v1_1.0_224_airshow.bin



Saving image to  ../../../output_images/

Saving output tensor to  ../../../output_binaries/


Completed_Model :     3, Name : od-tfl-ssd_mobilenet_v2_300_float                 , Total time :     291.55, Offload Time :       0.00 , DDR RW MBs : 18446744073709.55, Output Image File : py_out_od-tfl-ssd_mobilenet_v2_300_float_ADE_val_00001801.jpg, Output Bin File : py_out_od-tfl-ssd_mobilenet_v2_300_float_ADE_val_00001801.bin



Saving image to  ../../../output_images/

Saving output tensor to  ../../../output_binaries/


Completed_Model :     4, Name : od-tfl-ssdlite_mobiledet_dsp_320x320_coco         , Total time :     339.23, Offload Time :       0.00 , DDR RW MBs : 18446744073709.55, Output Image File : py_out_od-tfl-ssdlite_mobiledet_dsp_320x320_coco_ADE_val_00001801.jpg, Output Bin File : py_out_od-tfl-ssdlite_mobiledet_dsp_320x320_coco_ADE_val_00001801.bin



Saving image to  ../../../output_images/

Saving output tensor to  ../../../output_binaries/


Completed_Model :     2, Name : ss-tfl-deeplabv3_mnv2_ade20k_float                , Total time :     724.36, Offload Time :       0.00 , DDR RW MBs : 18446744073709.55, Output Image File : py_out_ss-tfl-deeplabv3_mnv2_ade20k_float_ADE_val_00001801.jpg, Output Bin File : py_out_ss-tfl-deeplabv3_mnv2_ade20k_float_ADE_val_00001801.bin


Available execution providers :  ['TIDLExecutionProvider', 'TIDLCompilationProvider', 'CPUExecutionProvider']

Running 2 Models - ['cl-ort-resnet18-v1', 'od-ort-ssd-lite_mobilenetv2_fpn']


Running_Model :  cl-ort-resnet18-v1

Downloading   ../../../models/public/resnet18_opset9.onnx

Running_Model :  od-ort-ssd-lite_mobilenetv2_fpn

Converted model is valid!

Running shape inference on model ../../../models/public/resnet18_opset9.onnx

Downloading   ../../../models/public/ssd-lite_mobilenetv2_fpn.onnx
========================= [Model Compilation Started] =========================

Model compilation will perform the following stages:
1. Parsing
2. Graph Optimization
3. Quantization & Calibration
4. Memory Planning

============================== [Version Summary] ==============================

-------------------------------------------------------------------------------
|          TIDL Tools Version          |              10_01_04_00             |
-------------------------------------------------------------------------------
|         C7x Firmware Version         |              10_01_00_01             |
-------------------------------------------------------------------------------
|            Runtime Version           |                1.15.0                |
-------------------------------------------------------------------------------
|          Model Opset Version         |                   9
         |
-------------------------------------------------------------------------------

============================== [Parsing Started] ==============================

[TIDL Import] [PARSER] WARNING: Network not identified as Object Detection network : (1) Ignore if network is not Object Detection network (2) If network is Object Detection network, please specify "model_type":"OD" as part of OSRT compilation options

------------------------- Subgraph Information Summary -------------------------
-------------------------------------------------------------------------------
|          Core           |      No. of Nodes       |   Number of Subgraphs   |
-------------------------------------------------------------------------------
| C7x                     |                      52 |
       1 |
| CPU                     |                       0 |
       x |
-------------------------------------------------------------------------------
============================= [Parsing Completed] =============================

Converted model is valid!
Downloading   ../../../models/public/ssd-lite_mobilenetv2_fpn.prototxt
==================== [Optimization for subgraph_0 Started] ====================


Running shape inference on model ../../../models/public/ssd-lite_mobilenetv2_fpn.onnx

========================= [Model Compilation Started] =========================

Model compilation will perform the following stages:
1. Parsing
2. Graph Optimization
3. Quantization & Calibration
4. Memory Planning

============================== [Version Summary] ==============================

-------------------------------------------------------------------------------
|          TIDL Tools Version          |              10_01_04_00             |
-------------------------------------------------------------------------------
|         C7x Firmware Version         |              10_01_00_01             |
-------------------------------------------------------------------------------
|            Runtime Version           |                1.15.0                |
-------------------------------------------------------------------------------
|          Model Opset Version         |                  11
         |
-------------------------------------------------------------------------------

============================== [Parsing Started] ==============================

ssd is meta arch name

Number of OD backbone nodes = 159
Size of odBackboneNodeIds = 159

------------------------- Subgraph Information Summary -------------------------
-------------------------------------------------------------------------------
|          Core           |      No. of Nodes       |   Number of Subgraphs   |
-------------------------------------------------------------------------------
| C7x                     |                     478 |
       1 |
| CPU                     |                       0 |
       x |
-------------------------------------------------------------------------------
============================= [Parsing Completed] =============================

----------------------------- Optimization Summary -----------------------------
---------------------------------------------------------------------------------
|          Layer         | Nodes before optimization | Nodes after optimization |
---------------------------------------------------------------------------------
| TIDL_ReLULayer         |                        17 |
         0 |
| TIDL_FlattenLayer      |                         1 |
         0 |
| TIDL_ConvolutionLayer  |                        20 |
        20 |
| TIDL_EltWiseLayer      |                        10 |
         8 |
| TIDL_InnerProductLayer |                         1 |
         1 |
| TIDL_CastLayer         |                         1 |
         0 |
| TIDL_PoolingLayer      |                         2 |
         2 |
---------------------------------------------------------------------------------

=================== [Optimization for subgraph_0 Completed] ===================

The soft limit is 10240
The hard limit is 10240
MEM: Init ... !!!
TIDL Meta pipeLine (proto) file  : ../../../models/public/ssd-lite_mobilenetv2_fpn.prototxt
ssd0s:  VX_ZONE_INIT:Enabled
[TIDL Import]  WARNING: Image dimensions is not provided, Please provide it as part of prior_box_param in form of either (img_w & img_h) or img_size. Proceeding with img_w = 512 and img_h = 512 in prior box decoding
[TIDL Import]  WARNING: Image dimensions is not provided, Please provide it as part of prior_box_param in form of either (img_w & img_h) or img_size. Proceeding with img_w = 512 and img_h = 512 in prior box decoding========
[TIDL Import]  WARNING: Image dimensions is not provided, Please provide it as part of prior_box_param in form of either (img_w & img_h) or img_size. Proceeding with img_w = 512 and img_h = 512 in prior box decoding
[TIDL Import]  WARNING: Image dimensions is not provided, Please provide it as part of prior_box_param in form of either (img_w & img_h) or img_size. Proceeding with img_w = 512 and img_h = 512 in prior box decoding
[TIDL Import]  WARNING: Image dimensions is not provided, Please provide it as part of prior_box_param in form of either (img_w & img_h) or img_size. Proceeding with img_w = 512 and img_h = 512 in prior box decoding
[TIDL Import]  WARNING: Image dimensions is not provided, Please provide it as part of prior_box_param in form of either (img_w & img_h) or img_size. Proceeding with img_w = 512 and img_h = 512 in prior box decoding
==================== [Optimization for subgraph_0 Started] ====================

[TIDL Import] [PARSER] WARNING: Requested output data convert layer is not added to the network, It is currently not optimal
[TIDL Import] [PARSER] WARNING: Requested output data convert layer is not added to the network, It is currently not optimal

-------- Running Calibration in Float Mode to Collect Tensor Statistics --------
----------------------------- Optimization Summary -----------------------------50 %
-------------------------------------------------------------------------------------
|            Layer           | Nodes before optimization | Nodes after optimization |
-------------------------------------------------------------------------------------
| TIDL_OdOutputReformatLayer |                         0 |                        2 |
| TIDL_ReLULayer             |                        52 |                        0 |
| TIDL_ResizeLayer           |                         2 |                        2 |
| TIDL_ConvolutionLayer      |                        90 |                       90 |
| TIDL_EltWiseLayer          |                        14 |                       12 |
| TIDL_DetectionOutputLayer  |                         0 |                        1 |
| TIDL_CastLayer             |                         1 |                        0 |
-------------------------------------------------------------------------------------

=================== [Optimization for subgraph_0 Completed] ===================

The soft limit is 10240
The hard limit is 10240
MEM: Init ... !!!
MEM: Init ... Done !!!
 0.0s:  VX_ZONE_INIT:Enabled
 0.23s:  VX_ZONE_ERROR:Enabled
 0.36s:  VX_ZONE_WARNING:Enabled
 0.4102s:  VX_ZONE_INIT:[tivxInit:190] Initialization Done !!!
============= [Quantization & Calibration for subgraph_0 Started] =============

[=============================================================================] 100 %

------------------ Fixed-point Calibration Iteration [1 / 5]: ------------------
[TIDL Import]  ERROR: Failed to run calibration pass, system command returned error: 132 -- [tidl_import_core.cpp, 678]
[TIDL Import]  ERROR: Failed to run Calibration - Failed in function: tidlRunQuantStatsTool -- [tidl_import_core.cpp, 1746]
[TIDL Import] [QUANTIZATION] ERROR:  - Failed in function: TIDL_quantStatsFixedOrFloat -- [tidl_import_quantize.cpp, 3992]
[TIDL Import] [QUANTIZATION] ERROR:  - Failed in function: TIDL_runIterativeCalibration -- [tidl_import_quantize.cpp, 4313]
[TIDL Import] [QUANTIZATION] ERROR:  - Failed in function: TIDL_import_quantize -- [tidl_import_quantize.cpp, 5195]
[TIDL Import]  ERROR:  - Failed in function: TIDL_import_backend -- [tidl_import_core.cpp, 4428]
[TIDL Import]  ERROR:  - Failed in function: TIDL_runtimesPostProcessNet -- [tidl_runtimes_import_common.cpp, 1414]
[TIDL Import] [PARSER] ERROR:  - Failed in function: TIDL_subgraphImport -- [tidl_onnxRtImport_EP.cpp, 1737]
[TIDL Import] [PARSER] ERROR:  - Failed in function: TIDL_computeInvokeFunc -- [tidl_onnxRtImport_EP.cpp, 2511]
2025-03-03 14:36:21.493473323 [E:onnxruntime:, sequential_executor.cc:514 ExecuteKernel] Non-zero status code returned while running TIDL_0 node. Name:'TIDLExecutionProvider_TIDL_0_0' Status Message: TIDL Compute Invoke Failed.
Process Process-1:
Traceback (most recent call last):
  File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
    self.run()
  File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
    self._target(*self._args, **self._kwargs)
  File "/home/admin/jay/python-virtual-env/edgeai-tidl-tools/examples/osrt_python/ort/onnxrt_ep.py", line 392, in run_model
    imgs, output, proc_time, sub_graph_time, height, width = infer_image(sess, input_images, config)
  File "/home/admin/jay/python-virtual-env/edgeai-tidl-tools/examples/osrt_python/ort/onnxrt_ep.py", line 208, in infer_image
    output = list(sess.run(None, {input_name: input_data}))
  File "/home/admin/jay/python-virtual-env/virtual-env-edgeai/lib/python3.10/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py", line 217, in run
    return self._sess.run(output_names, input_feed, run_options)
onnxruntime.capi.onnxruntime_pybind11_state.Fail: [ONNXRuntimeError] : 1 : FAIL : Non-zero status code returned while running TIDL_0 node. Name:'TIDLExecutionProvider_TIDL_0_0' Status Message: TIDL Compute Invoke Failed.
MEM: Deinit ... !!!
MEM: Alloc's: 26 alloc's of 126261905 bytes
MEM: Free's : 26 free's  of 126261905 bytes
MEM: Open's : 0 allocs  of 0 bytes
MEM: Deinit ... Done !!!

-------- Running Calibration in Float Mode to Collect Tensor Statistics --------
[=============================================================================] 100 %

------------------ Fixed-point Calibration Iteration [1 / 5]: ------------------
[TIDL Import]  ERROR: Failed to run calibration pass, system command returned error: 132 -- [tidl_import_core.cpp, 678]
[TIDL Import]  ERROR: Failed to run Calibration - Failed in function: tidlRunQuantStatsTool -- [tidl_import_core.cpp, 1746]
[TIDL Import] [QUANTIZATION] ERROR:  - Failed in function: TIDL_quantStatsFixedOrFloat -- [tidl_import_quantize.cpp, 3992]
[TIDL Import] [QUANTIZATION] ERROR:  - Failed in function: TIDL_runIterativeCalibration -- [tidl_import_quantize.cpp, 4313]
[TIDL Import] [QUANTIZATION] ERROR:  - Failed in function: TIDL_import_quantize -- [tidl_import_quantize.cpp, 5195]
[TIDL Import]  ERROR:  - Failed in function: TIDL_import_backend -- [tidl_import_core.cpp, 4428]
[TIDL Import]  ERROR:  - Failed in function: TIDL_runtimesPostProcessNet -- [tidl_runtimes_import_common.cpp, 1414]
[TIDL Import] [PARSER] ERROR:  - Failed in function: TIDL_subgraphImport -- [tidl_onnxRtImport_EP.cpp, 1737]
[TIDL Import] [PARSER] ERROR:  - Failed in function: TIDL_computeInvokeFunc -- [tidl_onnxRtImport_EP.cpp, 2511]
2025-03-03 14:36:47.111027859 [E:onnxruntime:, sequential_executor.cc:514 ExecuteKernel] Non-zero status code returned while running TIDL_0 node. Name:'TIDLExecutionProvider_TIDL_0_0' Status Message: TIDL Compute Invoke Failed.
Process Process-2:
Traceback (most recent call last):
  File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
    self.run()
  File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
    self._target(*self._args, **self._kwargs)
  File "/home/admin/jay/python-virtual-env/edgeai-tidl-tools/examples/osrt_python/ort/onnxrt_ep.py", line 392, in run_model
    imgs, output, proc_time, sub_graph_time, height, width = infer_image(sess, input_images, config)
  File "/home/admin/jay/python-virtual-env/edgeai-tidl-tools/examples/osrt_python/ort/onnxrt_ep.py", line 208, in infer_image
    output = list(sess.run(None, {input_name: input_data}))
  File "/home/admin/jay/python-virtual-env/virtual-env-edgeai/lib/python3.10/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py", line 217, in run
    return self._sess.run(output_names, input_feed, run_options)
onnxruntime.capi.onnxruntime_pybind11_state.Fail: [ONNXRuntimeError] : 1 : FAIL : Non-zero status code returned while running TIDL_0 node. Name:'TIDLExecutionProvider_TIDL_0_0' Status Message: TIDL Compute Invoke Failed.
MEM: Deinit ... !!!
MEM: Alloc's: 27 alloc's of 205136324 bytes
MEM: Free's : 27 free's  of 205136324 bytes
MEM: Open's : 0 allocs  of 0 bytes
MEM: Deinit ... Done !!!

I am getting below error 2 time in above command execution. Also compilation stopped there and not moving forward after last error you see in the above logs. After that i have to do ctrl + C to stop the process.

2025-03-03 14:52:01.749303398 [E:onnxruntime:, sequential_executor.cc:514 ExecuteKernel] Non-zero status code returned while running TIDL_0 node. Name:'TIDLExecutionProvider_TIDL_0_0' Status Message: TIDL Compute Invoke Failed.
Process Process-2:
Traceback (most recent call last):
  File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
    self.run()
  File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
    self._target(*self._args, **self._kwargs)
  File "/home/admin/jay/python-virtual-env/edgeai-tidl-tools/examples/osrt_python/ort/onnxrt_ep.py", line 392, in run_model
    imgs, output, proc_time, sub_graph_time, height, width = infer_image(sess, input_images, config)
  File "/home/admin/jay/python-virtual-env/edgeai-tidl-tools/examples/osrt_python/ort/onnxrt_ep.py", line 208, in infer_image
    output = list(sess.run(None, {input_name: input_data}))
  File "/home/admin/jay/python-virtual-env/virtual-env-edgeai/lib/python3.10/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py", line 217, in run
    return self._sess.run(output_names, input_feed, run_options)
onnxruntime.capi.onnxruntime_pybind11_state.Fail: [ONNXRuntimeError] : 1 : FAIL : Non-zero status code returned while running TIDL_0 node. Name:'TIDLExecutionProvider_TIDL_0_0' Status Message: TIDL Compute Invoke Failed.

Query-2:

While running the ''python3 ./scripts/gen_test_report.py" command, compilation stops after the last logs show in the below log. After that I have to press CTRL+C to stop the process. Otherwise process stuck there no matter how log it runs.

(virtual-env-edgeai) admin@VM2502:~/jay/python-virtual-env/edgeai-tidl-tools$ python3 ./scripts/gen_test_report.py
Command : python3 tflrt_delegate.py in Dir : examples/osrt_python/tfl Started
Running 4 Models - ['cl-tfl-mobilenet_v1_1.0_224', 'ss-tfl-deeplabv3_mnv2_ade20k_float', 'od-tfl-ssd_mobilenet_v2_300_float', 'od-tfl-ssdlite_mobiledet_dsp_320x320_coco']

INFO: Created TensorFlow Lite XNNPACK delegate for CPU.
INFO: Created TensorFlow Lite XNNPACK delegate for CPU.
INFO: Created TensorFlow Lite XNNPACK delegate for CPU.

Running_Model :  cl-tfl-mobilenet_v1_1.0_224

 ,  0  0.546822  warplane, military plane ,,  1  0.270634  missile ,,  2  0.165473  projectile, missile ,,  3  0.015031  aircraft carrier, carrier, flattop, attack aircraft carrier ,,  4  0.000821  submarine, pigboat, sub, U-boat ,

Saving image to  ../../../output_images/

Saving output tensor to  ../../../output_binaries/


Completed_Model :     1, Name : cl-tfl-mobilenet_v1_1.0_224                       , Total time :      57.99, Offload Time :       0.00 , DDR RW MBs : 18446744073709.55, Output Image File : py_out_cl-tfl-mobilenet_v1_1.0_224_airshow.jpg, Output Bin File : py_out_cl-tfl-mobilenet_v1_1.0_224_airshow.bin


INFO: Created TensorFlow Lite XNNPACK delegate for CPU.

Running_Model :  od-tfl-ssd_mobilenet_v2_300_float

Saving image to  ../../../output_images/

Saving output tensor to  ../../../output_binaries/


Completed_Model :     3, Name : od-tfl-ssd_mobilenet_v2_300_float                 , Total time :     240.64, Offload Time :       0.00 , DDR RW MBs : 18446744073709.55, Output Image File : py_out_od-tfl-ssd_mobilenet_v2_300_float_ADE_val_00001801.jpg, Output Bin File : py_out_od-tfl-ssd_mobilenet_v2_300_float_ADE_val_00001801.bin



Running_Model :  od-tfl-ssdlite_mobiledet_dsp_320x320_coco

Saving image to  ../../../output_images/

Saving output tensor to  ../../../output_binaries/


Completed_Model :     4, Name : od-tfl-ssdlite_mobiledet_dsp_320x320_coco         , Total time :     280.72, Offload Time :       0.00 , DDR RW MBs : 18446744073709.55, Output Image File : py_out_od-tfl-ssdlite_mobiledet_dsp_320x320_coco_ADE_val_00001801.jpg, Output Bin File : py_out_od-tfl-ssdlite_mobiledet_dsp_320x320_coco_ADE_val_00001801.bin



Running_Model :  ss-tfl-deeplabv3_mnv2_ade20k_float

Saving image to  ../../../output_images/

Saving output tensor to  ../../../output_binaries/


Completed_Model :     2, Name : ss-tfl-deeplabv3_mnv2_ade20k_float                , Total time :     582.02, Offload Time :       0.00 , DDR RW MBs : 18446744073709.55, Output Image File : py_out_ss-tfl-deeplabv3_mnv2_ade20k_float_ADE_val_00001801.jpg, Output Bin File : py_out_ss-tfl-deeplabv3_mnv2_ade20k_float_ADE_val_00001801.bin


Command : python3 onnxrt_ep.py in Dir : examples/osrt_python/ort Started
Available execution providers :  ['TIDLExecutionProvider', 'TIDLCompilationProvider', 'CPUExecutionProvider']

Running 2 Models - ['cl-ort-resnet18-v1', 'od-ort-ssd-lite_mobilenetv2_fpn']

Not sure, why execution of command ''python3 ./scripts/gen_test_report.py" stuck here.

Reese, Please let me know what can I do for these two query.

Regards,

Jay

0 Reese Grimsley over 1 year ago in reply to Jay Kaneria

TI__Mastermind 19726 points

Hi Jay,

Okay, let's figure out these two issues.

Q1)

Jay Kaneria said:
While running the "source ./scripts/run_python_examples.sh" in the compilation and validation section, I am getting below errors. I am attaching the full compilation log generated from "source ./scripts/run_python_examples.sh" command.

And then follows is a failure to run the network for float->fixed point calibration -- each network encounters this, so something is likely wrong with the tools. I suspect the tidl_tools themselves as the problem. Can you show me the following:

echo $TIDL_TOOLS_PATH
##printout for a path
ls $TIDL_TOOLS_PATH
## series of files

I have a feeling that when you first ran setup.sh, it downloaded tidl_tools for GPU backend. Most likely, those files are still present. Then when you reran the setup.sh, it saw that TIDL_TOOLS_PATH was defined and the directory existed, so it didn't overwrite with anything new. This means you might have all dependencies installed for CPU tools, but the downloaded tools are still GPU

if this is the case, unset that environment variable TIDL_TOOLS_PATH and delete the directory it pointed to, then rerun setup.sh targeting CPU.

Q2)

Jay Kaneria said:
While running the ''python3 ./scripts/gen_test_report.py" command, compilation stops after the last logs show in the below log. After that I have to press CTRL+C to stop the process. Otherwise process stuck there no matter how log it runs.

These scripts will try to run the models that were assumed to have been compiled in the run_python_examples.sh script. Since that did not complete, the models were not compiled.

I would expect this to fail, but did not expect it to hang. I'm not able to reproduce this on my side. If the model-artifacts for those models are not present, it should have immediately failed to initialize the model and fallen back the CPU

I think this will also resolve if we fix Q1 with the (I assume) GPU vs. CPU tools still being installed. Let's come back to this one if the issue persists after Q1 issue is resolved.

BR,

Reese

0 Jay Kaneria over 1 year ago in reply to Reese Grimsley

Intellectual 720 points

Hi Reese,

Thank you for your reply.

Reese Grimsley said:
And then follows is a failure to run the network for float->fixed point calibration -- each network encounters this, so something is likely wrong with the tools. I suspect the tidl_tools themselves as the problem. Can you show me the following:
Fullscreen
1
2
3
4
echo $TIDL_TOOLS_PATH
##printout for a path
ls $TIDL_TOOLS_PATH
## series of files
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
echo $TIDL_TOOLS_PATH
##printout for a path
ls $TIDL_TOOLS_PATH
## series of files

Output of the above echo and ls command as below:

(python-virtual-environment) admin@VM139:~/JayK/edgeai-sdk/edgeai-tidl-tools$ echo $TIDL_TOOLS_PATH
/home/admin/JayK/edgeai-sdk/edgeai-tidl-tools/tools/AM62A/tidl_tools
(python-virtual-environment) admin@VM139:~/JayK/edgeai-sdk/edgeai-tidl-tools$ ls -lrt $TIDL_TOOLS_PATH
total 117640
-rw-rw-r-- 1 admin admin      486 Feb 10 11:30 device_config.cfg
-rw-rw-r-- 1 admin admin       51 Feb 10 11:47 version.txt
-rw-rw-r-- 1 admin admin     6248 Feb 10 11:47 itvm_rt.h
-rw-rw-r-- 1 admin admin    14073 Feb 10 11:47 itidl_rt.h
-rwxrwxr-x 1 admin admin    62432 Feb 10 11:47 tidl_graphVisualiser_runtimes.out
-rwxrwxr-x 1 admin admin   194032 Feb 10 11:47 tidl_graphVisualiser.out
-rwxrwxr-x 1 admin admin  4907656 Feb 10 11:47 ti_cnnperfsim.out
-rwxrwxr-x 1 admin admin 22657688 Feb 10 11:47 PC_dsp_test_dl_algo.out
-rwxrwxr-x 1 admin admin 14829672 Feb 10 11:47 tidl_model_import.so
-rwxrwxr-x 1 admin admin 15222864 Feb 10 11:47 tidl_model_import_relay.so
-rwxrwxr-x 1 admin admin 15286384 Feb 10 11:48 tidl_model_import_tflite.so
-rwxrwxr-x 1 admin admin 15237440 Feb 10 11:48 tidl_model_import_onnx.so
-rwxrwxr-x 1 admin admin    16840 Feb 10 11:48 tidl_model_secure.out
-rwxrwxr-x 1 admin admin   117304 Feb 10 11:48 tidl_model_import.out
-rwxrwxr-x 2 admin admin 15859472 Feb 10 11:48 libvx_tidl_rt.so.1.0
-rwxrwxr-x 2 admin admin 15859472 Feb 10 11:48 libvx_tidl_rt.so
-rwxrwxr-x 1 admin admin    46856 Feb 10 11:48 libtidl_tfl_delegate.so
-rwxrwxr-x 1 admin admin   101944 Feb 10 11:48 libtidl_onnxrt_EP.so
lrwxrwxrwx 1 admin admin       15 Mar  6 17:11 osrt_deps -> ../../osrt_deps
(python-virtual-environment) admin@VM139:~/JayK/edgeai-sdk/edgeai-tidl-tools$

Reese Grimsley said:
I have a feeling that when you first ran setup.sh, it downloaded tidl_tools for GPU backend. Most likely, those files are still present. Then when you reran the setup.sh, it saw that TIDL_TOOLS_PATH was defined and the directory existed, so it didn't overwrite with anything new. This means you might have all dependencies installed for CPU tools, but the downloaded tools are still GPU

I have completely deleted the edge-tidl-sdk folder and python virtual environment as well. After that I have done the setup again. But I am having the same issue mentioned in Q1.

So, Q2 is related to Q1. So, I haven't tried the Q2 as Q1 problem is still there. Is there anything I can do for this issue?

Reagrds,

Jay

0 Reese Grimsley over 1 year ago in reply to Jay Kaneria

TI__Mastermind 19726 points

Hi Jay,

Okay, then my assumption about GPU tools being installed is likely wrong -- you have restarted with a clean slate.

So you are still seeing this issue lines like "[TIDL Import] ERROR: Failed to run calibration pass, system command returned error: 132 -- [tidl_import_core.cpp, 678]" that indicate compilation as failed for your machine. I think we will need to go a step deeper to discover why this is failing -- I haven't encountered this error before.

Instead of running from edgeai-tidl-tools/scripts, we're going to go deeper and directly run the applications called by those scripts. Try the following:

cat $TIDL_TOOLS_PATH/version.txt #share this, please
cd edgeai-tidl-tools/examples/osrt_python
vim common_utils.py # or your favorite editor

Modify the debug_level parameter in common_utils.py to be '2' so we can increase verbosity

cd tfl
python3 ./tflrt_delegate.py -m cl-0000_tflitert_imagenet1k_mlperf_mobilenet_v1_1.0_224_tflite -c

This will compile one of those models that we were trying earlier (mobilenetv1) only. This should make the logs easier to follow. Please share the printout from this. It should be much more verbose than before.

BR,
Reese

0 Jay Kaneria over 1 year ago in reply to Reese Grimsley

Intellectual 720 points

Hi Reese,

Sorry for my late reply on this thread. I got stuck in other task

Reese Grimsley said:
Okay, then my assumption about GPU tools being installed is likely wrong -- you have restarted with a clean slate.

As you mentioned in the last threads, I need not to run compilation for GPU. So, I am compiling this with CPU option and in python virtual environment.

Reese Grimsley said:
you have restarted with a clean slate.

This is correct. I have deleted the virtual environment for GPU option and edgeai-tidl-tool directory as well.

Output of "cat $TIDL_TOOLS_PATH/version.txt" command

(virtual-env-edgeai) admin@VM2502:~/jay/python-virtual-env/edgeai-tidl-tools$ cat $TIDL_TOOLS_PATH/version.txt
SOC=am62a
c093213417f7d4899f600d03a2ca22760e637220

Reese Grimsley said:
Modify the debug_level parameter in common_utils.py to be '2' so we can increase verbosity

I have increase the log level to '2' for debugging. After that I have execute the command "python3 ./tflrt_delegate.py -m cl-0000_tflitert_imagenet1k_mlperf_mobilenet_v1_1.0_224_tflite -c". Please check the output logs in below text file.

Fullscreen debug-logs-enabled.txt Download

(virtual-env-edgeai) admin@VM2502:~/jay/python-virtual-env/edgeai-tidl-tools/examples/osrt_python/tfl$ python3 ./tflrt_delegate.py -m cl-0000_tflitert_imagenet1k_mlperf_mobilenet_v1_1.0_224_tflite -c
Running 1 Models - ['cl-0000_tflitert_imagenet1k_mlperf_mobilenet_v1_1.0_224_tflite']


Running_Model :  cl-0000_tflitert_imagenet1k_mlperf_mobilenet_v1_1.0_224_tflite
tidl_tools_path                                 = /home/admin/jay/python-virtual-env/edgeai-tidl-tools/tools/AM62A/tidl_tools
artifacts_folder                                = ../../../model-artifacts//cl-0000_tflitert_imagenet1k_mlperf_mobilenet_v1_1.0_224_tflite/artifacts
tidl_tensor_bits                                = 8
debug_level                                     = 2
num_tidl_subgraphs                              = 16
num_tidl_subgraph_max_node                      = 0
enable_rt_multi_subgraph_support                = 0
tidl_denylist                                   =
tidl_denylist_layer_name                        =
tidl_denylist_layer_type                        =
tidl_allowlist_layer_name                       =
model_type                                      =
tidl_calibration_accuracy_level                 = 7
tidl_calibration_options:num_frames_calibration = 2
tidl_calibration_options:bias_calibration_iterations = 5
mixed_precision_factor = -1.000000
model_group_id = 0
power_of_2_quantization                         = 2
ONNX QDQ Enabled                                = 0
enable_high_resolution_optimization             = 0
pre_batchnorm_fold                              = 1
add_data_convert_ops                            = 3
output_feature_16bit_names_list                 =
m_params_16bit_names_list                       =
m_single_core_layers_names_list                 =
Inference mode                                  = 0
Number of cores                                 = 1
reserved_compile_constraints_flag               = 1601
partial_init_during_compile                     = 0
packetize_mode                                  = 0
ti_internal_reserved_1                          =

========================= [Model Compilation Started] =========================

Model compilation will perform the following stages:
1. Parsing
2. Graph Optimization
3. Quantization & Calibration
4. Memory Planning

============================== [Version Summary] ==============================

-------------------------------------------------------------------------------
|          TIDL Tools Version          |              10_01_04_00             |
-------------------------------------------------------------------------------
|         C7x Firmware Version         |              10_01_00_01             |
-------------------------------------------------------------------------------

============================== [Parsing Started] ==============================

[TIDL Import] [PARSER] WARNING: Network not identified as Object Detection network : (1) Ignore if network is not Object Detection network (2) If network is Object Detection network, please specify "model_type":"OD" as part of OSRT compilation options
[TIDL Import] [PARSER] SUPPORTED: Supported TIDL layer type --- 65 Tflite layer type --- 53 layer output name--- MobilenetV1/MobilenetV1/Conv2d_0/Relu6/Mul/Bias/InCast  -- [tidl_tfLiteRtImport_core.cpp, 3096]
[TIDL Import] [PARSER] SUPPORTED: Supported TIDL layer type --- 5 Tflite layer type --- 0 layer output name--- MobilenetV1/MobilenetV1/Conv2d_0/Relu6/Mul/Bias  -- [tidl_tfLiteRtImport_core.cpp, 3096]
[TIDL Import] [PARSER] SUPPORTED: Supported TIDL layer type --- 5 Tflite layer type --- 18 layer output name--- MobilenetV1/MobilenetV1/Conv2d_0/Relu6/Mul  -- [tidl_tfLiteRtImport_core.cpp, 3096]
[TIDL Import] [PARSER] SUPPORTED: Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- MobilenetV1/MobilenetV1/Conv2d_0/Relu6  -- [tidl_tfLiteRtImport_core.cpp, 3096]
[TIDL Import] [PARSER] SUPPORTED: Supported TIDL layer type --- 1 Tflite layer type --- 4 layer output name--- MobilenetV1/MobilenetV1/Conv2d_1_depthwise/Relu6  -- [tidl_tfLiteRtImport_core.cpp, 3096]
[TIDL Import] [PARSER] SUPPORTED: Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- MobilenetV1/MobilenetV1/Conv2d_1_pointwise/Relu6  -- [tidl_tfLiteRtImport_core.cpp, 3096]
[TIDL Import] [PARSER] SUPPORTED: Supported TIDL layer type --- 1 Tflite layer type --- 4 layer output name--- MobilenetV1/MobilenetV1/Conv2d_2_depthwise/Relu6  -- [tidl_tfLiteRtImport_core.cpp, 3096]
[TIDL Import] [PARSER] SUPPORTED: Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- MobilenetV1/MobilenetV1/Conv2d_2_pointwise/Relu6  -- [tidl_tfLiteRtImport_core.cpp, 3096]
[TIDL Import] [PARSER] SUPPORTED: Supported TIDL layer type --- 1 Tflite layer type --- 4 layer output name--- MobilenetV1/MobilenetV1/Conv2d_3_depthwise/Relu6  -- [tidl_tfLiteRtImport_core.cpp, 3096]
[TIDL Import] [PARSER] SUPPORTED: Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- MobilenetV1/MobilenetV1/Conv2d_3_pointwise/Relu6  -- [tidl_tfLiteRtImport_core.cpp, 3096]
[TIDL Import] [PARSER] SUPPORTED: Supported TIDL layer type --- 1 Tflite layer type --- 4 layer output name--- MobilenetV1/MobilenetV1/Conv2d_4_depthwise/Relu6  -- [tidl_tfLiteRtImport_core.cpp, 3096]
[TIDL Import] [PARSER] SUPPORTED: Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- MobilenetV1/MobilenetV1/Conv2d_4_pointwise/Relu6  -- [tidl_tfLiteRtImport_core.cpp, 3096]
[TIDL Import] [PARSER] SUPPORTED: Supported TIDL layer type --- 1 Tflite layer type --- 4 layer output name--- MobilenetV1/MobilenetV1/Conv2d_5_depthwise/Relu6  -- [tidl_tfLiteRtImport_core.cpp, 3096]
[TIDL Import] [PARSER] SUPPORTED: Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- MobilenetV1/MobilenetV1/Conv2d_5_pointwise/Relu6  -- [tidl_tfLiteRtImport_core.cpp, 3096]
[TIDL Import] [PARSER] SUPPORTED: Supported TIDL layer type --- 1 Tflite layer type --- 4 layer output name--- MobilenetV1/MobilenetV1/Conv2d_6_depthwise/Relu6  -- [tidl_tfLiteRtImport_core.cpp, 3096]
[TIDL Import] [PARSER] SUPPORTED: Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- MobilenetV1/MobilenetV1/Conv2d_6_pointwise/Relu6  -- [tidl_tfLiteRtImport_core.cpp, 3096]
[TIDL Import] [PARSER] SUPPORTED: Supported TIDL layer type --- 1 Tflite layer type --- 4 layer output name--- MobilenetV1/MobilenetV1/Conv2d_7_depthwise/Relu6  -- [tidl_tfLiteRtImport_core.cpp, 3096]
[TIDL Import] [PARSER] SUPPORTED: Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- MobilenetV1/MobilenetV1/Conv2d_7_pointwise/Relu6  -- [tidl_tfLiteRtImport_core.cpp, 3096]
[TIDL Import] [PARSER] SUPPORTED: Supported TIDL layer type --- 1 Tflite layer type --- 4 layer output name--- MobilenetV1/MobilenetV1/Conv2d_8_depthwise/Relu6  -- [tidl_tfLiteRtImport_core.cpp, 3096]
[TIDL Import] [PARSER] SUPPORTED: Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- MobilenetV1/MobilenetV1/Conv2d_8_pointwise/Relu6  -- [tidl_tfLiteRtImport_core.cpp, 3096]
[TIDL Import] [PARSER] SUPPORTED: Supported TIDL layer type --- 1 Tflite layer type --- 4 layer output name--- MobilenetV1/MobilenetV1/Conv2d_9_depthwise/Relu6  -- [tidl_tfLiteRtImport_core.cpp, 3096]
[TIDL Import] [PARSER] SUPPORTED: Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- MobilenetV1/MobilenetV1/Conv2d_9_pointwise/Relu6  -- [tidl_tfLiteRtImport_core.cpp, 3096]
[TIDL Import] [PARSER] SUPPORTED: Supported TIDL layer type --- 1 Tflite layer type --- 4 layer output name--- MobilenetV1/MobilenetV1/Conv2d_10_depthwise/Relu6  -- [tidl_tfLiteRtImport_core.cpp, 3096]
[TIDL Import] [PARSER] SUPPORTED: Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- MobilenetV1/MobilenetV1/Conv2d_10_pointwise/Relu6  -- [tidl_tfLiteRtImport_core.cpp, 3096]
[TIDL Import] [PARSER] SUPPORTED: Supported TIDL layer type --- 1 Tflite layer type --- 4 layer output name--- MobilenetV1/MobilenetV1/Conv2d_11_depthwise/Relu6  -- [tidl_tfLiteRtImport_core.cpp, 3096]
[TIDL Import] [PARSER] SUPPORTED: Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- MobilenetV1/MobilenetV1/Conv2d_11_pointwise/Relu6  -- [tidl_tfLiteRtImport_core.cpp, 3096]
[TIDL Import] [PARSER] SUPPORTED: Supported TIDL layer type --- 1 Tflite layer type --- 4 layer output name--- MobilenetV1/MobilenetV1/Conv2d_12_depthwise/Relu6  -- [tidl_tfLiteRtImport_core.cpp, 3096]
[TIDL Import] [PARSER] SUPPORTED: Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- MobilenetV1/MobilenetV1/Conv2d_12_pointwise/Relu6  -- [tidl_tfLiteRtImport_core.cpp, 3096]
[TIDL Import] [PARSER] SUPPORTED: Supported TIDL layer type --- 1 Tflite layer type --- 4 layer output name--- MobilenetV1/MobilenetV1/Conv2d_13_depthwise/Relu6  -- [tidl_tfLiteRtImport_core.cpp, 3096]
[TIDL Import] [PARSER] SUPPORTED: Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- MobilenetV1/MobilenetV1/Conv2d_13_pointwise/Relu6  -- [tidl_tfLiteRtImport_core.cpp, 3096]
[TIDL Import] [PARSER] SUPPORTED: Supported TIDL layer type --- 2 Tflite layer type --- 1 layer output name--- MobilenetV1/Logits/AvgPool_1a/AvgPool  -- [tidl_tfLiteRtImport_core.cpp, 3096]
[TIDL Import] [PARSER] SUPPORTED: Supported TIDL layer type --- 1 Tflite layer type --- 3 layer output name--- MobilenetV1/Logits/Conv2d_1c_1x1/BiasAdd  -- [tidl_tfLiteRtImport_core.cpp, 3096]
[TIDL Import] [PARSER] SUPPORTED: Supported TIDL layer type --- 34 Tflite layer type --- 43 layer output name--- MobilenetV1/Logits/SpatialSqueeze  -- [tidl_tfLiteRtImport_core.cpp, 3096]
[TIDL Import] [PARSER] SUPPORTED: Supported TIDL layer type --- 7 Tflite layer type --- 25 layer output name--- MobilenetV1/Predictions/Reshape_1  -- [tidl_tfLiteRtImport_core.cpp, 3096]

Total Nodes = 34
-------------------------------------------------------------------------------
|          Core           |      No. of Nodes       |   Number of Subgraphs   |
-------------------------------------------------------------------------------
| C7x                     |                      34 |                       1 |
| CPU                     |                       0 |                       x |
-------------------------------------------------------------------------------
============================= [Parsing Completed] =============================

In TIDL_tfliteRtImportInit subgraph_id=86
Layer 0, subgraph id 86, name=MobilenetV1/Predictions/Reshape_1
Layer 1, subgraph id 86, name=input
In TIDL_tfliteRtImportNode, TIDL Layer type - 65, Tflite builtin code type - 53
In TIDL_tfliteRtImportNode, TIDL Layer type - 5, Tflite builtin code type - 0
In TIDL_tfliteRtImportNode, TIDL Layer type - 5, Tflite builtin code type - 18
In TIDL_tfliteRtImportNode, TIDL Layer type - 1, Tflite builtin code type - 3
In TIDL_tfliteRtImportNode, TIDL Layer type - 1, Tflite builtin code type - 4
In TIDL_tfliteRtImportNode, TIDL Layer type - 1, Tflite builtin code type - 3
In TIDL_tfliteRtImportNode, TIDL Layer type - 1, Tflite builtin code type - 4
In TIDL_tfliteRtImportNode, TIDL Layer type - 1, Tflite builtin code type - 3
In TIDL_tfliteRtImportNode, TIDL Layer type - 1, Tflite builtin code type - 4
In TIDL_tfliteRtImportNode, TIDL Layer type - 1, Tflite builtin code type - 3
In TIDL_tfliteRtImportNode, TIDL Layer type - 1, Tflite builtin code type - 4
In TIDL_tfliteRtImportNode, TIDL Layer type - 1, Tflite builtin code type - 3
In TIDL_tfliteRtImportNode, TIDL Layer type - 1, Tflite builtin code type - 4
In TIDL_tfliteRtImportNode, TIDL Layer type - 1, Tflite builtin code type - 3
In TIDL_tfliteRtImportNode, TIDL Layer type - 1, Tflite builtin code type - 4
In TIDL_tfliteRtImportNode, TIDL Layer type - 1, Tflite builtin code type - 3
In TIDL_tfliteRtImportNode, TIDL Layer type - 1, Tflite builtin code type - 4
In TIDL_tfliteRtImportNode, TIDL Layer type - 1, Tflite builtin code type - 3
In TIDL_tfliteRtImportNode, TIDL Layer type - 1, Tflite builtin code type - 4
In TIDL_tfliteRtImportNode, TIDL Layer type - 1, Tflite builtin code type - 3
In TIDL_tfliteRtImportNode, TIDL Layer type - 1, Tflite builtin code type - 4
In TIDL_tfliteRtImportNode, TIDL Layer type - 1, Tflite builtin code type - 3
In TIDL_tfliteRtImportNode, TIDL Layer type - 1, Tflite builtin code type - 4
In TIDL_tfliteRtImportNode, TIDL Layer type - 1, Tflite builtin code type - 3
In TIDL_tfliteRtImportNode, TIDL Layer type - 1, Tflite builtin code type - 4
In TIDL_tfliteRtImportNode, TIDL Layer type - 1, Tflite builtin code type - 3
In TIDL_tfliteRtImportNode, TIDL Layer type - 1, Tflite builtin code type - 4
In TIDL_tfliteRtImportNode, TIDL Layer type - 1, Tflite builtin code type - 3
In TIDL_tfliteRtImportNode, TIDL Layer type - 1, Tflite builtin code type - 4
In TIDL_tfliteRtImportNode, TIDL Layer type - 1, Tflite builtin code type - 3
In TIDL_tfliteRtImportNode, TIDL Layer type - 2, Tflite builtin code type - 1
In TIDL_tfliteRtImportNode, TIDL Layer type - 1, Tflite builtin code type - 3
In TIDL_tfliteRtImportNode, TIDL Layer type - 34, Tflite builtin code type - 43
In TIDL_tfliteRtImportNode, TIDL Layer type - 7, Tflite builtin code type - 25
==================== [Optimization for subgraph_86 started] ====================

In TIDL_runtimesOptimizeNet: LayerIndex = 36, dataIndex = 35
[TIDL Import] [PARSER] WARNING: Requested output data convert layer is not added to the network, It is currently not optimal
----------------------------- Optimization Summary -----------------------------
--------------------------------------------------------------------------------
|         Layer         | Nodes before optimization | Nodes after optimization |
--------------------------------------------------------------------------------
| TIDL_SoftMaxLayer     |                         1 |                        1 |
| TIDL_SqueezeLayer     |                         1 |                        0 |
| TIDL_ConvolutionLayer |                        28 |                       28 |
| TIDL_EltWiseLayer     |                         2 |                        0 |
| TIDL_CastLayer        |                         1 |                        0 |
| TIDL_PoolingLayer     |                         1 |                        1 |
--------------------------------------------------------------------------------

=================== [Optimization for subgraph_86 completed] ===================

In TIDL_runtimesPostProcessNet
************ in TIDL_subgraphRtCreate ************
 The soft limit is 10240
The hard limit is 10240
MEM: Init ... !!!
MEM: Init ... Done !!!
 0.0s:  VX_ZONE_INIT:Enabled
 0.33s:  VX_ZONE_ERROR:Enabled
 0.42s:  VX_ZONE_WARNING:Enabled
 0.6774s:  VX_ZONE_INIT:[tivxInit:190] Initialization Done !!!

--------------------------------------------
TIDL Memory size requiement (record wise):
MemRecNum   , Space               , Attribute   , Alignment   , Size(KBytes), BasePtr
0           , DDR Cacheable       , Persistent  ,  128, 19.27   , 0x00000000
1           , DDR Cacheable       , Persistent  ,  128, 0.65    , 0x00000000
2           , DDR Cacheable       , Scratch     ,  128, 16.00   , 0x00000000
3           , DDR Cacheable       , Scratch     ,  128, 4.00    , 0x00000000
4           , DDR Cacheable       , Scratch     ,  128, 56.00   , 0x00000000
5           , DDR Cacheable       , Persistent  ,  128, 361.32  , 0x00000000
6           , DDR Cacheable       , Scratch     ,  128, 7244.41 , 0x00000000
7           , DDR Cacheable       , Scratch     ,  128, 0.12    , 0x00000000
8           , DDR Cacheable       , Scratch     ,  128, 4873.25 , 0x00000000
9           , DDR Cacheable       , Scratch     ,  128, 6500.50 , 0x00000000
10          , DDR Cacheable       , Persistent  ,  128, 567.80  , 0x00000000
11          , DDR Cacheable       , Scratch     ,  128, 512.25  , 0x00000000
12          , DDR Cacheable       , Persistent  ,  128, 0.12    , 0x00000000
13          , DDR Cacheable       , Persistent  ,  128, 22768.88, 0x00000000
14          , DDR Cacheable       , Persistent  ,  128, 0.00    , 0x00000000
15          , DDR Cacheable       , Persistent  ,  128, 0.12    , 0x00000000
--------------------------------------------
Total memory size requirement (space wise):
Mem Space , Size(KBytes)
DDR Cacheable, 42924.71
--------------------------------------------
NOTE: Memory requirement in host emulation can be different from the same on EVM
      To get the actual TIDL memory requirement make sure to run on EVM with
      debugTraceLevel = 2

--------------------------------------------
TIDL init call from ivision API

--------------------------------------------
TIDL Memory size requiement (record wise):
MemRecNum   , Space               , Attribute   , Alignment   , Size(KBytes), BasePtr
0           , DDR Cacheable       , Persistent  ,  128, 19.27   , 0x89c13000
1           , DDR Cacheable       , Persistent  ,  128, 0.65    , 0x8c76a000
2           , DDR Cacheable       , Scratch     ,  128, 16.00   , 0x89c0f000
3           , DDR Cacheable       , Scratch     ,  128, 4.00    , 0x8be84000
4           , DDR Cacheable       , Scratch     ,  128, 56.00   , 0x889cb000
5           , DDR Cacheable       , Persistent  ,  128, 361.32  , 0x88970000
6           , DDR Cacheable       , Scratch     ,  128, 7244.41 , 0x872f5000
7           , DDR Cacheable       , Scratch     ,  128, 0.12    , 0x8be83000
8           , DDR Cacheable       , Scratch     ,  128, 4873.25 , 0x86b99000
9           , DDR Cacheable       , Scratch     ,  128, 6500.50 , 0x820a6000
10          , DDR Cacheable       , Persistent  ,  128, 567.80  , 0x888e2000
11          , DDR Cacheable       , Scratch     ,  128, 512.25  , 0x88861000
12          , DDR Cacheable       , Persistent  ,  128, 0.12    , 0x89c0e000
13          , DDR Cacheable       , Persistent  ,  128, 22768.88, 0x2a186000
14          , DDR Cacheable       , Persistent  ,  128, 0.00    , 0x89c0d000
15          , DDR Cacheable       , Persistent  ,  128, 0.12    , 0x89c0c000
--------------------------------------------
Total memory size requirement (space wise):
Mem Space , Size(KBytes)
DDR Cacheable, 42924.71
--------------------------------------------
NOTE: Memory requirement in host emulation can be different from the same on EVM
      To get the actual TIDL memory requirement make sure to run on EVM with
      debugTraceLevel = 2

--------------------------------------------
Alg Init for Layer # -    1
Alg Init for Layer # -    2
Alg Init for Layer # -    3
Alg Init for Layer # -    4
Alg Init for Layer # -    5
Alg Init for Layer # -    6
Alg Init for Layer # -    7
Alg Init for Layer # -    8
Alg Init for Layer # -    9
Alg Init for Layer # -   10
Alg Init for Layer # -   11
Alg Init for Layer # -   12
Alg Init for Layer # -   13
Alg Init for Layer # -   14
Alg Init for Layer # -   15
Alg Init for Layer # -   16
Alg Init for Layer # -   17
Alg Init for Layer # -   18
Alg Init for Layer # -   19
Alg Init for Layer # -   20
Alg Init for Layer # -   21
Alg Init for Layer # -   22
Alg Init for Layer # -   23
Alg Init for Layer # -   24
Alg Init for Layer # -   25
Alg Init for Layer # -   26
Alg Init for Layer # -   27
Alg Init for Layer # -   28
Alg Init for Layer # -   29
Alg Init for Layer # -   30
Alg Init for Layer # -   31
Alg Init for Layer # -   32
Alg Init for Layer # -   33
Alg Init for Layer # -   34
PREEMPTION: Adding a new priority object for targetPriority = 0, handle = 0x7f0889c13000
PREEMPTION: Now total number of priority objects = 1 at priorityId = 0,    with new memRec of base = 0x7f0889c0e000 and size = 128
PREEMPTION: Requesting context memory addr for handle 0x7f0889c13000, return Addr = 0x7f084270a7b8
************ TIDL_subgraphRtCreate done ************
 tidl_tfLiteRtImport_delegate.cpp Invoke 526
*******   In TIDL_subgraphRtInvoke  ********
TIDL_process is started with handle : 0x7f0889c13000
TIDL_activate is called with handle : 0x7f0889c13000 - Copying handle of size 19736 from 0x7f0889c13000 to 0x7f08889cb080
Core 0 Alg Process for Layer # -    0, layer type 0
Core 0 Alg Process for Layer # -    1, layer type 29
Processing Layer # -    1
Core 0 End of Layer # -    1 with outPtrs[0] = 0x7f08872f5000
Core 0 Alg Process for Layer # -    2, layer type 1
Processing Layer # -    2
Core 0 End of Layer # -    2 with outPtrs[0] = 0x7f088738ab00
Core 0 Alg Process for Layer # -    3, layer type 1
Processing Layer # -    3
Core 0 End of Layer # -    3 with outPtrs[0] = 0x7f0887520d00
Core 0 Alg Process for Layer # -    4, layer type 1
Processing Layer # -    4
Core 0 End of Layer # -    4 with outPtrs[0] = 0x7f08876a8d80
Core 0 Alg Process for Layer # -    5, layer type 1
Processing Layer # -    5
Core 0 End of Layer # -    5 with outPtrs[0] = 0x7f0887520d00
Core 0 Alg Process for Layer # -    6, layer type 1
Processing Layer # -    6
Core 0 End of Layer # -    6 with outPtrs[0] = 0x7f08876a8d80
Core 0 Alg Process for Layer # -    7, layer type 1
Processing Layer # -    7
Core 0 End of Layer # -    7 with outPtrs[0] = 0x7f0887520d00
Core 0 Alg Process for Layer # -    8, layer type 1
Processing Layer # -    8
Core 0 End of Layer # -    8 with outPtrs[0] = 0x7f08876a8d80
Core 0 Alg Process for Layer # -    9, layer type 1
Processing Layer # -    9
Core 0 End of Layer # -    9 with outPtrs[0] = 0x7f0887520d00
Core 0 Alg Process for Layer # -   10, layer type 1
Processing Layer # -   10
Core 0 End of Layer # -   10 with outPtrs[0] = 0x7f08876a8d80
Core 0 Alg Process for Layer # -   11, layer type 1
Processing Layer # -   11
Core 0 End of Layer # -   11 with outPtrs[0] = 0x7f0887520d00
Core 0 Alg Process for Layer # -   12, layer type 1
Processing Layer # -   12
Core 0 End of Layer # -   12 with outPtrs[0] = 0x7f08876a8d80
Core 0 Alg Process for Layer # -   13, layer type 1
Processing Layer # -   13
Core 0 End of Layer # -   13 with outPtrs[0] = 0x7f0887520d00
Core 0 Alg Process for Layer # -   14, layer type 1
Processing Layer # -   14
Core 0 End of Layer # -   14 with outPtrs[0] = 0x7f08876a8d80
Core 0 Alg Process for Layer # -   15, layer type 1
Processing Layer # -   15
Core 0 End of Layer # -   15 with outPtrs[0] = 0x7f0887520d00
Core 0 Alg Process for Layer # -   16, layer type 1
Processing Layer # -   16
Core 0 End of Layer # -   16 with outPtrs[0] = 0x7f08876a8d80
Core 0 Alg Process for Layer # -   17, layer type 1
Processing Layer # -   17
Core 0 End of Layer # -   17 with outPtrs[0] = 0x7f0887520d00
Core 0 Alg Process for Layer # -   18, layer type 1
Processing Layer # -   18
Core 0 End of Layer # -   18 with outPtrs[0] = 0x7f08876a8d80
Core 0 Alg Process for Layer # -   19, layer type 1
Processing Layer # -   19
Core 0 End of Layer # -   19 with outPtrs[0] = 0x7f0887520d00
Core 0 Alg Process for Layer # -   20, layer type 1
Processing Layer # -   20
Core 0 End of Layer # -   20 with outPtrs[0] = 0x7f08876a8d80
Core 0 Alg Process for Layer # -   21, layer type 1
Processing Layer # -   21
Core 0 End of Layer # -   21 with outPtrs[0] = 0x7f0887520d00
Core 0 Alg Process for Layer # -   22, layer type 1
Processing Layer # -   22
Core 0 End of Layer # -   22 with outPtrs[0] = 0x7f08876a8d80
Core 0 Alg Process for Layer # -   23, layer type 1
Processing Layer # -   23
Core 0 End of Layer # -   23 with outPtrs[0] = 0x7f0887520d00
Core 0 Alg Process for Layer # -   24, layer type 1
Processing Layer # -   24
Core 0 End of Layer # -   24 with outPtrs[0] = 0x7f08876a8d80
Core 0 Alg Process for Layer # -   25, layer type 1
Processing Layer # -   25
Core 0 End of Layer # -   25 with outPtrs[0] = 0x7f0887520d00
Core 0 Alg Process for Layer # -   26, layer type 1
Processing Layer # -   26
Core 0 End of Layer # -   26 with outPtrs[0] = 0x7f08876a8d80
Core 0 Alg Process for Layer # -   27, layer type 1
Processing Layer # -   27
Core 0 End of Layer # -   27 with outPtrs[0] = 0x7f0887520d00
Core 0 Alg Process for Layer # -   28, layer type 1
Processing Layer # -   28
Core 0 End of Layer # -   28 with outPtrs[0] = 0x7f08879d5c80
Core 0 Alg Process for Layer # -   29, layer type 2
Processing Layer # -   29
Core 0 End of Layer # -   29 with outPtrs[0] = 0x7f0887520d00
Core 0 Alg Process for Layer # -   30, layer type 1
Processing Layer # -   30
Core 0 End of Layer # -   30 with outPtrs[0] = 0x7f08879d5c80
Core 0 Alg Process for Layer # -   31, layer type 29
Processing Layer # -   31
Core 0 End of Layer # -   31 with outPtrs[0] = 0x7f0887520d00
Core 0 Alg Process for Layer # -   32, layer type 38
Processing Layer # -   32
Core 0 End of Layer # -   32 with outPtrs[0] = 0x7f08879d5c80
Core 0 Alg Process for Layer # -   33, layer type 29
Processing Layer # -   33
Core 0 End of Layer # -   33 with outPtrs[0] = 0x7f0887520d00
Core 0 Alg Process for Layer # -   34, layer type 7
Processing Layer # -   34
Core 0 End of Layer # -   34 with outPtrs[0] = 0x7f088d174000
Core 0 Alg Process for Layer # -   35, layer type 0
TIDL_process is completed with handle : 0x7f0889c13000
 Layer,   Layer Cycles,kernelOnlyCycles, coreLoopCycles,LayerSetupCycles,dmaPipeupCycles, dmaPipeDownCycles, PrefetchCycles,copyKerCoeffCycles,LayerDeinitCycles,LastBlockCycles, paddingTrigger,    paddingWait,LayerWithoutPad,LayerHandleCopy,   BackupCycles,  RestoreCycles,Multic7xContextCopyCycles,
     1,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
     2,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
     3,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
     4,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
     5,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
     6,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
     7,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
     8,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
     9,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
    10,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
    11,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
    12,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
    13,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
    14,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
    15,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
    16,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
    17,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
    18,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
    19,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
    20,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
    21,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
    22,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
    23,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
    24,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
    25,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
    26,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
    27,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
    28,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
    29,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
    30,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
    31,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
    32,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
    33,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
    34,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
 Sum of Layer Cycles 0
Sub Graph Stats 1064.000000 1009098.000000 2141.000000
*******  TIDL_subgraphRtInvoke done  ********

 ************ Frame index 1 : Running float inference ****************

tidl_tfLiteRtImport_delegate.cpp Invoke 647
tidl_tfLiteRtImport_delegate.cpp Invoke 526
*******   In TIDL_subgraphRtInvoke  ********
TIDL_process is started with handle : 0x7f0889c13000
Core 0 Alg Process for Layer # -    0, layer type 0
Core 0 Alg Process for Layer # -    1, layer type 29
Processing Layer # -    1
Core 0 End of Layer # -    1 with outPtrs[0] = 0x7f08872f5000
Core 0 Alg Process for Layer # -    2, layer type 1
Processing Layer # -    2
Core 0 End of Layer # -    2 with outPtrs[0] = 0x7f088738ab00
Core 0 Alg Process for Layer # -    3, layer type 1
Processing Layer # -    3
Core 0 End of Layer # -    3 with outPtrs[0] = 0x7f0887520d00
Core 0 Alg Process for Layer # -    4, layer type 1
Processing Layer # -    4
Core 0 End of Layer # -    4 with outPtrs[0] = 0x7f08876a8d80
Core 0 Alg Process for Layer # -    5, layer type 1
Processing Layer # -    5
Core 0 End of Layer # -    5 with outPtrs[0] = 0x7f0887520d00
Core 0 Alg Process for Layer # -    6, layer type 1
Processing Layer # -    6
Core 0 End of Layer # -    6 with outPtrs[0] = 0x7f08876a8d80
Core 0 Alg Process for Layer # -    7, layer type 1
Processing Layer # -    7
Core 0 End of Layer # -    7 with outPtrs[0] = 0x7f0887520d00
Core 0 Alg Process for Layer # -    8, layer type 1
Processing Layer # -    8
Core 0 End of Layer # -    8 with outPtrs[0] = 0x7f08876a8d80
Core 0 Alg Process for Layer # -    9, layer type 1
Processing Layer # -    9
Core 0 End of Layer # -    9 with outPtrs[0] = 0x7f0887520d00
Core 0 Alg Process for Layer # -   10, layer type 1
Processing Layer # -   10
Core 0 End of Layer # -   10 with outPtrs[0] = 0x7f08876a8d80
Core 0 Alg Process for Layer # -   11, layer type 1
Processing Layer # -   11
Core 0 End of Layer # -   11 with outPtrs[0] = 0x7f0887520d00
Core 0 Alg Process for Layer # -   12, layer type 1
Processing Layer # -   12
Core 0 End of Layer # -   12 with outPtrs[0] = 0x7f08876a8d80
Core 0 Alg Process for Layer # -   13, layer type 1
Processing Layer # -   13
Core 0 End of Layer # -   13 with outPtrs[0] = 0x7f0887520d00
Core 0 Alg Process for Layer # -   14, layer type 1
Processing Layer # -   14
Core 0 End of Layer # -   14 with outPtrs[0] = 0x7f08876a8d80
Core 0 Alg Process for Layer # -   15, layer type 1
Processing Layer # -   15
Core 0 End of Layer # -   15 with outPtrs[0] = 0x7f0887520d00
Core 0 Alg Process for Layer # -   16, layer type 1
Processing Layer # -   16
Core 0 End of Layer # -   16 with outPtrs[0] = 0x7f08876a8d80
Core 0 Alg Process for Layer # -   17, layer type 1
Processing Layer # -   17
Core 0 End of Layer # -   17 with outPtrs[0] = 0x7f0887520d00
Core 0 Alg Process for Layer # -   18, layer type 1
Processing Layer # -   18
Core 0 End of Layer # -   18 with outPtrs[0] = 0x7f08876a8d80
Core 0 Alg Process for Layer # -   19, layer type 1
Processing Layer # -   19
Core 0 End of Layer # -   19 with outPtrs[0] = 0x7f0887520d00
Core 0 Alg Process for Layer # -   20, layer type 1
Processing Layer # -   20
Core 0 End of Layer # -   20 with outPtrs[0] = 0x7f08876a8d80
Core 0 Alg Process for Layer # -   21, layer type 1
Processing Layer # -   21
Core 0 End of Layer # -   21 with outPtrs[0] = 0x7f0887520d00
Core 0 Alg Process for Layer # -   22, layer type 1
Processing Layer # -   22
Core 0 End of Layer # -   22 with outPtrs[0] = 0x7f08876a8d80
Core 0 Alg Process for Layer # -   23, layer type 1
Processing Layer # -   23
Core 0 End of Layer # -   23 with outPtrs[0] = 0x7f0887520d00
Core 0 Alg Process for Layer # -   24, layer type 1
Processing Layer # -   24
Core 0 End of Layer # -   24 with outPtrs[0] = 0x7f08876a8d80
Core 0 Alg Process for Layer # -   25, layer type 1
Processing Layer # -   25
Core 0 End of Layer # -   25 with outPtrs[0] = 0x7f0887520d00
Core 0 Alg Process for Layer # -   26, layer type 1
Processing Layer # -   26
Core 0 End of Layer # -   26 with outPtrs[0] = 0x7f08876a8d80
Core 0 Alg Process for Layer # -   27, layer type 1
Processing Layer # -   27
Core 0 End of Layer # -   27 with outPtrs[0] = 0x7f0887520d00
Core 0 Alg Process for Layer # -   28, layer type 1
Processing Layer # -   28
Core 0 End of Layer # -   28 with outPtrs[0] = 0x7f08879d5c80
Core 0 Alg Process for Layer # -   29, layer type 2
Processing Layer # -   29
Core 0 End of Layer # -   29 with outPtrs[0] = 0x7f0887520d00
Core 0 Alg Process for Layer # -   30, layer type 1
Processing Layer # -   30
Core 0 End of Layer # -   30 with outPtrs[0] = 0x7f08879d5c80
Core 0 Alg Process for Layer # -   31, layer type 29
Processing Layer # -   31
Core 0 End of Layer # -   31 with outPtrs[0] = 0x7f0887520d00
Core 0 Alg Process for Layer # -   32, layer type 38
Processing Layer # -   32
Core 0 End of Layer # -   32 with outPtrs[0] = 0x7f08879d5c80
Core 0 Alg Process for Layer # -   33, layer type 29
Processing Layer # -   33
Core 0 End of Layer # -   33 with outPtrs[0] = 0x7f0887520d00
Core 0 Alg Process for Layer # -   34, layer type 7
Processing Layer # -   34
Core 0 End of Layer # -   34 with outPtrs[0] = 0x7f088d174000
Core 0 Alg Process for Layer # -   35, layer type 0
TIDL_process is completed with handle : 0x7f0889c13000
 Layer,   Layer Cycles,kernelOnlyCycles, coreLoopCycles,LayerSetupCycles,dmaPipeupCycles, dmaPipeDownCycles, PrefetchCycles,copyKerCoeffCycles,LayerDeinitCycles,LastBlockCycles, paddingTrigger,    paddingWait,LayerWithoutPad,LayerHandleCopy,   BackupCycles,  RestoreCycles,Multic7xContextCopyCycles,
     1,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
     2,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
     3,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
     4,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
     5,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
     6,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
     7,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
     8,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
     9,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
    10,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
    11,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
    12,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
    13,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
    14,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
    15,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
    16,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
    17,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
    18,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
    19,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
    20,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
    21,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
    22,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
    23,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
    24,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
    25,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
    26,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
    27,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
    28,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
    29,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
    30,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
    31,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
    32,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
    33,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
    34,              0,              0,              0,              0,              0,                 0,              0,                 0,              0,              0,              0,              0,              0,              0,              0,              0,              0,
 Sum of Layer Cycles 0
Sub Graph Stats 190.000000 971528.000000 2140.000000
*******  TIDL_subgraphRtInvoke done  ********

 ************ Frame index 2 : Running fixed point mode for calibration ****************

In TIDL_runtimesPostProcessNet

-------- Running Calibration in Float Mode to Collect Tensor Statistics --------
[=============================================================================] 100 %

------------------ Fixed-point Calibration Iteration [1 / 5]: ------------------
[TIDL Import]  ERROR: Failed to run calibration pass, system command returned error: 132 -- [tidl_import_core.cpp, 678]
[TIDL Import]  ERROR: Failed to run Calibration - Failed in function: tidlRunQuantStatsTool -- [tidl_import_core.cpp, 1746]
[TIDL Import] [QUANTIZATION] ERROR:  - Failed in function: TIDL_quantStatsFixedOrFloat -- [tidl_import_quantize.cpp, 3992]
[TIDL Import] [QUANTIZATION] ERROR:  - Failed in function: TIDL_runIterativeCalibration -- [tidl_import_quantize.cpp, 4313]
[TIDL Import] [QUANTIZATION] ERROR:  - Failed in function: TIDL_import_quantize -- [tidl_import_quantize.cpp, 5195]
[TIDL Import]  ERROR:  - Failed in function: TIDL_import_backend -- [tidl_import_core.cpp, 4428]
[TIDL Import]  ERROR:  - Failed in function: TIDL_runtimesPostProcessNet -- [tidl_runtimes_import_common.cpp, 1414]
tidl_tfLiteRtImport_delegate.cpp Invoke 647


Completed_Model :     1, Name : cl-0000_tflitert_imagenet1k_mlperf_mobilenet_v1_1.0_224_tflite, Total time :    3092.02, Offload Time :       0.00 , DDR RW MBs : 18446744073709.55, Output Image File : py_out_cl-0000_tflitert_imagenet1k_mlperf_mobilenet_v1_1.0_224_tflite_ADE_val_00001801.jpg, Output Bin File : py_out_cl-0000_tflitert_imagenet1k_mlperf_mobilenet_v1_1.0_224_tflite_ADE_val_00001801.bin


************ in TIDL_subgraphRtDelete ************
 TIDL_deactivate is called with handle : 0x7f0889c13000 - Copying handle of size 19736 from 0x7f08889cb080 to 0x7f0889c13000
MEM: Deinit ... !!!
MEM: Alloc's: 26 alloc's of 68565333 bytes
MEM: Free's : 26 free's  of 68565333 bytes
MEM: Open's : 0 allocs  of 0 bytes
MEM: Deinit ... Done !!!

Let me know anything else is required from my side.

Regards,

Jay

0 Reese Grimsley over 1 year ago in reply to Jay Kaneria

TI__Mastermind 19726 points

Hi Jay,

Hmm, okay this is strange. I have the same tools setup on my machine and see no such issue.

Let me check internally why you might be seeing a 'system error 132'. i'll get back to you within the next couple of days.

BR,
Reese

0 Reese Grimsley over 1 year ago in reply to Reese Grimsley

TI__Mastermind 19726 points

Hi Jay,

Thanks for your patience.

The system error 132 is not being passed from the TIDL tooling directly, so it is most likely being passed by your host OS.

Error 132 is likely to mean that a fatal signal was passed where 132 = 128 + SIG. SIGNAL 4 is SIGILL aka illegal instruction.

To me, this implies that the TIDL tooling for quantization was compiled for x86 such that it used an instruction not supported in your CPU. Can you provide me the output of `cat /proc/cpuinfo`?

BR,

Reese

0 Jay Kaneria over 1 year ago in reply to Reese Grimsley

Intellectual 720 points

Hi Reese,

Thank you for your reply.

Reese Grimsley said:
Can you provide me the output of `cat /proc/cpuinfo`?

Here the output of the command.

admin@VM2502:~$ cat /proc/cpuinfo
processor       : 0
vendor_id       : GenuineIntel
cpu family      : 6
model           : 15
model name      : Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz
stepping        : 1
microcode       : 0x2007006
cpu MHz         : 2394.374
cache size      : 28160 KB
physical id     : 0
siblings        : 1
core id         : 0
cpu cores       : 1
apicid          : 0
initial apicid  : 0
fpu             : yes
fpu_exception   : yes
cpuid level     : 10
wp              : yes
flags           : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 syscall nx lm constant_tsc arch_perfmon nopl tsc_reliable nonstop_tsc cpuid tsc_known_freq pni ssse3 cx16 tsc_deadline_timer hypervisor lahf_lm pti tsc_adjust arat
bugs            : cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf mds swapgs itlb_multihit mmio_unknown bhi
bogomips        : 4788.74
clflush size    : 64
cache_alignment : 64
address sizes   : 43 bits physical, 48 bits virtual
power management:

processor       : 1
vendor_id       : GenuineIntel
cpu family      : 6
model           : 15
model name      : Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz
stepping        : 1
microcode       : 0x2007006
cpu MHz         : 2394.374
cache size      : 28160 KB
physical id     : 2
siblings        : 1
core id         : 0
cpu cores       : 1
apicid          : 2
initial apicid  : 2
fpu             : yes
fpu_exception   : yes
cpuid level     : 10
wp              : yes
flags           : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 syscall nx lm constant_tsc arch_perfmon nopl tsc_reliable nonstop_tsc cpuid tsc_known_freq pni ssse3 cx16 tsc_deadline_timer hypervisor lahf_lm pti tsc_adjust arat
bugs            : cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf mds swapgs itlb_multihit mmio_unknown bhi
bogomips        : 4788.74
clflush size    : 64
cache_alignment : 64
address sizes   : 43 bits physical, 48 bits virtual
power management:

processor       : 2
vendor_id       : GenuineIntel
cpu family      : 6
model           : 15
model name      : Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz
stepping        : 1
microcode       : 0x2007006
cpu MHz         : 2394.374
cache size      : 28160 KB
physical id     : 4
siblings        : 1
core id         : 0
cpu cores       : 1
apicid          : 4
initial apicid  : 4
fpu             : yes
fpu_exception   : yes
cpuid level     : 10
wp              : yes
flags           : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 syscall nx lm constant_tsc arch_perfmon nopl tsc_reliable nonstop_tsc cpuid tsc_known_freq pni ssse3 cx16 tsc_deadline_timer hypervisor lahf_lm pti tsc_adjust arat
bugs            : cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf mds swapgs itlb_multihit mmio_unknown bhi
bogomips        : 4788.74
clflush size    : 64
cache_alignment : 64
address sizes   : 43 bits physical, 48 bits virtual
power management:

processor       : 3
vendor_id       : GenuineIntel
cpu family      : 6
model           : 15
model name      : Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz
stepping        : 1
microcode       : 0x2007006
cpu MHz         : 2394.374
cache size      : 28160 KB
physical id     : 6
siblings        : 1
core id         : 0
cpu cores       : 1
apicid          : 6
initial apicid  : 6
fpu             : yes
fpu_exception   : yes
cpuid level     : 10
wp              : yes
flags           : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 syscall nx lm constant_tsc arch_perfmon nopl tsc_reliable nonstop_tsc cpuid tsc_known_freq pni ssse3 cx16 tsc_deadline_timer hypervisor lahf_lm pti tsc_adjust arat
bugs            : cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf mds swapgs itlb_multihit mmio_unknown bhi
bogomips        : 4788.74
clflush size    : 64
cache_alignment : 64
address sizes   : 43 bits physical, 48 bits virtual
power management:

processor       : 4
vendor_id       : GenuineIntel
cpu family      : 6
model           : 15
model name      : Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz
stepping        : 1
microcode       : 0x2007006
cpu MHz         : 2394.374
cache size      : 28160 KB
physical id     : 8
siblings        : 1
core id         : 0
cpu cores       : 1
apicid          : 8
initial apicid  : 8
fpu             : yes
fpu_exception   : yes
cpuid level     : 10
wp              : yes
flags           : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 syscall nx lm constant_tsc arch_perfmon nopl tsc_reliable nonstop_tsc cpuid tsc_known_freq pni ssse3 cx16 tsc_deadline_timer hypervisor lahf_lm pti tsc_adjust arat
bugs            : cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf mds swapgs itlb_multihit mmio_unknown bhi
bogomips        : 4788.74
clflush size    : 64
cache_alignment : 64
address sizes   : 43 bits physical, 48 bits virtual
power management:

processor       : 5
vendor_id       : GenuineIntel
cpu family      : 6
model           : 15
model name      : Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz
stepping        : 1
microcode       : 0x2007006
cpu MHz         : 2394.374
cache size      : 28160 KB
physical id     : 10
siblings        : 1
core id         : 0
cpu cores       : 1
apicid          : 10
initial apicid  : 10
fpu             : yes
fpu_exception   : yes
cpuid level     : 10
wp              : yes
flags           : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 syscall nx lm constant_tsc arch_perfmon nopl tsc_reliable nonstop_tsc cpuid tsc_known_freq pni ssse3 cx16 tsc_deadline_timer hypervisor lahf_lm pti tsc_adjust arat
bugs            : cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf mds swapgs itlb_multihit mmio_unknown bhi
bogomips        : 4788.74
clflush size    : 64
cache_alignment : 64
address sizes   : 43 bits physical, 48 bits virtual
power management:

processor       : 6
vendor_id       : GenuineIntel
cpu family      : 6
model           : 15
model name      : Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz
stepping        : 1
microcode       : 0x2007006
cpu MHz         : 2394.374
cache size      : 28160 KB
physical id     : 12
siblings        : 1
core id         : 0
cpu cores       : 1
apicid          : 12
initial apicid  : 12
fpu             : yes
fpu_exception   : yes
cpuid level     : 10
wp              : yes
flags           : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 syscall nx lm constant_tsc arch_perfmon nopl tsc_reliable nonstop_tsc cpuid tsc_known_freq pni ssse3 cx16 tsc_deadline_timer hypervisor lahf_lm pti tsc_adjust arat
bugs            : cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf mds swapgs itlb_multihit mmio_unknown bhi
bogomips        : 4788.74
clflush size    : 64
cache_alignment : 64
address sizes   : 43 bits physical, 48 bits virtual
power management:

processor       : 7
vendor_id       : GenuineIntel
cpu family      : 6
model           : 15
model name      : Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz
stepping        : 1
microcode       : 0x2007006
cpu MHz         : 2394.374
cache size      : 28160 KB
physical id     : 14
siblings        : 1
core id         : 0
cpu cores       : 1
apicid          : 14
initial apicid  : 14
fpu             : yes
fpu_exception   : yes
cpuid level     : 10
wp              : yes
flags           : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 syscall nx lm constant_tsc arch_perfmon nopl tsc_reliable nonstop_tsc cpuid tsc_known_freq pni ssse3 cx16 tsc_deadline_timer hypervisor lahf_lm pti tsc_adjust arat
bugs            : cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf mds swapgs itlb_multihit mmio_unknown bhi
bogomips        : 4788.74
clflush size    : 64
cache_alignment : 64
address sizes   : 43 bits physical, 48 bits virtual
power management:

processor       : 8
vendor_id       : GenuineIntel
cpu family      : 6
model           : 15
model name      : Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz
stepping        : 1
microcode       : 0x2007006
cpu MHz         : 2394.374
cache size      : 28160 KB
physical id     : 16
siblings        : 1
core id         : 0
cpu cores       : 1
apicid          : 16
initial apicid  : 16
fpu             : yes
fpu_exception   : yes
cpuid level     : 10
wp              : yes
flags           : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 syscall nx lm constant_tsc arch_perfmon nopl tsc_reliable nonstop_tsc cpuid tsc_known_freq pni ssse3 cx16 tsc_deadline_timer hypervisor lahf_lm pti tsc_adjust arat
bugs            : cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf mds swapgs itlb_multihit mmio_unknown bhi
bogomips        : 4788.74
clflush size    : 64
cache_alignment : 64
address sizes   : 43 bits physical, 48 bits virtual
power management:

processor       : 9
vendor_id       : GenuineIntel
cpu family      : 6
model           : 15
model name      : Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz
stepping        : 1
microcode       : 0x2007006
cpu MHz         : 2394.374
cache size      : 28160 KB
physical id     : 18
siblings        : 1
core id         : 0
cpu cores       : 1
apicid          : 18
initial apicid  : 18
fpu             : yes
fpu_exception   : yes
cpuid level     : 10
wp              : yes
flags           : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 syscall nx lm constant_tsc arch_perfmon nopl tsc_reliable nonstop_tsc cpuid tsc_known_freq pni ssse3 cx16 tsc_deadline_timer hypervisor lahf_lm pti tsc_adjust arat
bugs            : cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf mds swapgs itlb_multihit mmio_unknown bhi
bogomips        : 4788.74
clflush size    : 64
cache_alignment : 64
address sizes   : 43 bits physical, 48 bits virtual
power management:

processor       : 10
vendor_id       : GenuineIntel
cpu family      : 6
model           : 15
model name      : Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz
stepping        : 1
microcode       : 0x2007006
cpu MHz         : 2394.374
cache size      : 28160 KB
physical id     : 20
siblings        : 1
core id         : 0
cpu cores       : 1
apicid          : 20
initial apicid  : 20
fpu             : yes
fpu_exception   : yes
cpuid level     : 10
wp              : yes
flags           : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 syscall nx lm constant_tsc arch_perfmon nopl tsc_reliable nonstop_tsc cpuid tsc_known_freq pni ssse3 cx16 tsc_deadline_timer hypervisor lahf_lm pti tsc_adjust arat
bugs            : cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf mds swapgs itlb_multihit mmio_unknown bhi
bogomips        : 4788.74
clflush size    : 64
cache_alignment : 64
address sizes   : 43 bits physical, 48 bits virtual
power management:

processor       : 11
vendor_id       : GenuineIntel
cpu family      : 6
model           : 15
model name      : Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz
stepping        : 1
microcode       : 0x2007006
cpu MHz         : 2394.374
cache size      : 28160 KB
physical id     : 22
siblings        : 1
core id         : 0
cpu cores       : 1
apicid          : 22
initial apicid  : 22
fpu             : yes
fpu_exception   : yes
cpuid level     : 10
wp              : yes
flags           : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 syscall nx lm constant_tsc arch_perfmon nopl tsc_reliable nonstop_tsc cpuid tsc_known_freq pni ssse3 cx16 tsc_deadline_timer hypervisor lahf_lm pti tsc_adjust arat
bugs            : cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf mds swapgs itlb_multihit mmio_unknown bhi
bogomips        : 4788.74
clflush size    : 64
cache_alignment : 64
address sizes   : 43 bits physical, 48 bits virtual
power management:

processor       : 12
vendor_id       : GenuineIntel
cpu family      : 6
model           : 15
model name      : Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz
stepping        : 1
microcode       : 0x2007006
cpu MHz         : 2394.374
cache size      : 28160 KB
physical id     : 24
siblings        : 1
core id         : 0
cpu cores       : 1
apicid          : 24
initial apicid  : 24
fpu             : yes
fpu_exception   : yes
cpuid level     : 10
wp              : yes
flags           : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 syscall nx lm constant_tsc arch_perfmon nopl tsc_reliable nonstop_tsc cpuid tsc_known_freq pni ssse3 cx16 tsc_deadline_timer hypervisor lahf_lm pti tsc_adjust arat
bugs            : cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf mds swapgs itlb_multihit mmio_unknown bhi
bogomips        : 4788.74
clflush size    : 64
cache_alignment : 64
address sizes   : 43 bits physical, 48 bits virtual
power management:

processor       : 13
vendor_id       : GenuineIntel
cpu family      : 6
model           : 15
model name      : Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz
stepping        : 1
microcode       : 0x2007006
cpu MHz         : 2394.374
cache size      : 28160 KB
physical id     : 26
siblings        : 1
core id         : 0
cpu cores       : 1
apicid          : 26
initial apicid  : 26
fpu             : yes
fpu_exception   : yes
cpuid level     : 10
wp              : yes
flags           : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 syscall nx lm constant_tsc arch_perfmon nopl tsc_reliable nonstop_tsc cpuid tsc_known_freq pni ssse3 cx16 tsc_deadline_timer hypervisor lahf_lm pti tsc_adjust arat
bugs            : cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf mds swapgs itlb_multihit mmio_unknown bhi
bogomips        : 4788.74
clflush size    : 64
cache_alignment : 64
address sizes   : 43 bits physical, 48 bits virtual
power management:

processor       : 14
vendor_id       : GenuineIntel
cpu family      : 6
model           : 15
model name      : Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz
stepping        : 1
microcode       : 0x2007006
cpu MHz         : 2394.374
cache size      : 28160 KB
physical id     : 28
siblings        : 1
core id         : 0
cpu cores       : 1
apicid          : 28
initial apicid  : 28
fpu             : yes
fpu_exception   : yes
cpuid level     : 10
wp              : yes
flags           : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 syscall nx lm constant_tsc arch_perfmon nopl tsc_reliable nonstop_tsc cpuid tsc_known_freq pni ssse3 cx16 tsc_deadline_timer hypervisor lahf_lm pti tsc_adjust arat
bugs            : cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf mds swapgs itlb_multihit mmio_unknown bhi
bogomips        : 4788.74
clflush size    : 64
cache_alignment : 64
address sizes   : 43 bits physical, 48 bits virtual
power management:

processor       : 15
vendor_id       : GenuineIntel
cpu family      : 6
model           : 15
model name      : Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz
stepping        : 1
microcode       : 0x2007006
cpu MHz         : 2394.374
cache size      : 28160 KB
physical id     : 30
siblings        : 1
core id         : 0
cpu cores       : 1
apicid          : 30
initial apicid  : 30
fpu             : yes
fpu_exception   : yes
cpuid level     : 10
wp              : yes
flags           : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 syscall nx lm constant_tsc arch_perfmon nopl tsc_reliable nonstop_tsc cpuid tsc_known_freq pni ssse3 cx16 tsc_deadline_timer hypervisor lahf_lm pti tsc_adjust arat
bugs            : cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf mds swapgs itlb_multihit mmio_unknown bhi
bogomips        : 4788.74
clflush size    : 64
cache_alignment : 64
address sizes   : 43 bits physical, 48 bits virtual
power management:

processor       : 16
vendor_id       : GenuineIntel
cpu family      : 6
model           : 15
model name      : Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz
stepping        : 1
microcode       : 0x2007006
cpu MHz         : 2394.374
cache size      : 28160 KB
physical id     : 32
siblings        : 1
core id         : 0
cpu cores       : 1
apicid          : 32
initial apicid  : 32
fpu             : yes
fpu_exception   : yes
cpuid level     : 10
wp              : yes
flags           : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 syscall nx lm constant_tsc arch_perfmon nopl tsc_reliable nonstop_tsc cpuid tsc_known_freq pni ssse3 cx16 tsc_deadline_timer hypervisor lahf_lm pti tsc_adjust arat
bugs            : cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf mds swapgs itlb_multihit mmio_unknown bhi
bogomips        : 4788.74
clflush size    : 64
cache_alignment : 64
address sizes   : 43 bits physical, 48 bits virtual
power management:

processor       : 17
vendor_id       : GenuineIntel
cpu family      : 6
model           : 15
model name      : Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz
stepping        : 1
microcode       : 0x2007006
cpu MHz         : 2394.374
cache size      : 28160 KB
physical id     : 34
siblings        : 1
core id         : 0
cpu cores       : 1
apicid          : 34
initial apicid  : 34
fpu             : yes
fpu_exception   : yes
cpuid level     : 10
wp              : yes
flags           : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 syscall nx lm constant_tsc arch_perfmon nopl tsc_reliable nonstop_tsc cpuid tsc_known_freq pni ssse3 cx16 tsc_deadline_timer hypervisor lahf_lm pti tsc_adjust arat
bugs            : cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf mds swapgs itlb_multihit mmio_unknown bhi
bogomips        : 4788.74
clflush size    : 64
cache_alignment : 64
address sizes   : 43 bits physical, 48 bits virtual
power management:

processor       : 18
vendor_id       : GenuineIntel
cpu family      : 6
model           : 15
model name      : Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz
stepping        : 1
microcode       : 0x2007006
cpu MHz         : 2394.374
cache size      : 28160 KB
physical id     : 36
siblings        : 1
core id         : 0
cpu cores       : 1
apicid          : 36
initial apicid  : 36
fpu             : yes
fpu_exception   : yes
cpuid level     : 10
wp              : yes
flags           : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 syscall nx lm constant_tsc arch_perfmon nopl tsc_reliable nonstop_tsc cpuid tsc_known_freq pni ssse3 cx16 tsc_deadline_timer hypervisor lahf_lm pti tsc_adjust arat
bugs            : cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf mds swapgs itlb_multihit mmio_unknown bhi
bogomips        : 4788.74
clflush size    : 64
cache_alignment : 64
address sizes   : 43 bits physical, 48 bits virtual
power management:

processor       : 19
vendor_id       : GenuineIntel
cpu family      : 6
model           : 15
model name      : Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz
stepping        : 1
microcode       : 0x2007006
cpu MHz         : 2394.374
cache size      : 28160 KB
physical id     : 38
siblings        : 1
core id         : 0
cpu cores       : 1
apicid          : 38
initial apicid  : 38
fpu             : yes
fpu_exception   : yes
cpuid level     : 10
wp              : yes
flags           : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 syscall nx lm constant_tsc arch_perfmon nopl tsc_reliable nonstop_tsc cpuid tsc_known_freq pni ssse3 cx16 tsc_deadline_timer hypervisor lahf_lm pti tsc_adjust arat
bugs            : cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf mds swapgs itlb_multihit mmio_unknown bhi
bogomips        : 4788.74
clflush size    : 64
cache_alignment : 64
address sizes   : 43 bits physical, 48 bits virtual
power management:

Let me know if anything else is required.

Regards,

Jay

0 Reese Grimsley over 1 year ago in reply to Jay Kaneria

TI__Mastermind 19726 points

Hi Jay,

Thanks for sending this. I have forwarded it to the development team to provide analysis. Please allow a few days to receive information back.

My only suggestion right now would be to see if there's an alternative machine you could try in the meantime.

I do note that your CPU is missing AVX (vector instructions) support. I know this used to be required, but we relaxed that constraint before the 9.0 SDK release last year.

BR,
Reese

0 Jay Kaneria over 1 year ago in reply to Reese Grimsley

Intellectual 720 points

Hi Reese,

Thank you for your reply.

Reese Grimsley said:
I have forwarded it to the development team to provide analysis. Please allow a few days to receive information back.

Let me know if development team have any other findings.

Regards,

Jay

0 Qutaiba Saleh over 1 year ago in reply to Jay Kaneria

TI__Expert 5380 points

Hi Jay,

The expert handling the thread is out of office. Expect a response next week.

Best regards,

Qutaiba

0 Reese Grimsley over 1 year ago in reply to Qutaiba Saleh

TI__Mastermind 19726 points

Hi Jay,

Sorry, haven't received any workaround suggestions yet.

I'm looking up this CPU, and I'm surprised it's not showing AVX instructions are available. Intel 6148 Gold was a 2017 release, well after they started shipping CPU's with those vector extensions.

The product page for intel says it includes multiple AVX extensions (Intel® SSE4.2, Intel® AVX, Intel® AVX2, Intel® AVX-512). This isn't reflected in the CPU info for Linux. This is curious. I would recommend looking into this -- it could be a BIOS or kernel setting

BR,
Reese

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