Error creating .har for yolov8s detection

[alcapowned]

Hi there, I have created a custom Yolo v8s model, converted to har, quantized and when I go to compile it to .har I get the following error. I am running the latest version of the AI SW Suite, following the
tutorial DFC_3_Compilation_Tutorial.ipynb. I’m running this on an amazon EC2.

Any ideas?

Thanks in advance!

{
	"name": "BackendAllocatorException",
	"message": "Compilation failed: No successful assignments: concat17 errors:
\tAgent infeasible
",
	"stack": "---------------------------------------------------------------------------
BackendAllocatorException                 Traceback (most recent call last)
Cell In [5], line 1
----> 1 hef = runner.compile()
      3 file_name = f\"{model_name}.hef\"
      4 with open(file_name, \"wb\") as f:

File ~/hailo/hailo_ai_sw_suite/hailo_venv/lib/python3.10/site-packages/hailo_sdk_client/runner/client_runner.py:886, in ClientRunner.compile(self)
    874 def compile(self):
    875     \"\"\"
    876     DFC API for compiling current model to Hailo hardware.
    877 
   (...)
    884 
    885     \"\"\"
--> 886     return self._compile()

File ~/hailo/hailo_ai_sw_suite/hailo_venv/lib/python3.10/site-packages/hailo_sdk_common/states/states.py:16, in allowed_states.<locals>.wrap.<locals>.wrapped_func(self, *args, **kwargs)
     12 if self._state not in states:
     13     raise InvalidStateException(
     14         f\"The execution of {func.__name__} is not available under the state: {self._state.value}\",
     15     )
---> 16 return func(self, *args, **kwargs)

File ~/hailo/hailo_ai_sw_suite/hailo_venv/lib/python3.10/site-packages/hailo_sdk_client/runner/client_runner.py:1094, in ClientRunner._compile(self, fps, mapping_timeout, allocator_script_filename)
   1088         self._logger.warning(
   1089             f\"Taking model script commands from {allocator_script_filename} and ignoring \"
   1090             f\"previous allocation script commands\",
   1091         )
   1092     self.load_model_script(allocator_script_filename)
-> 1094 serialized_hef = self._sdk_backend.compile(fps, self.model_script, mapping_timeout)
   1096 self._auto_model_script = self._sdk_backend.get_auto_alls()
   1097 self._state = States.COMPILED_SLIM_MODEL if orig_state in SLIM_STATES else States.COMPILED_MODEL

File ~/hailo/hailo_ai_sw_suite/hailo_venv/lib/python3.10/site-packages/hailo_sdk_client/sdk_backend/sdk_backend.py:1669, in SdkBackendCompilation.compile(self, fps, allocator_script, mapping_timeout)
   1667 def compile(self, fps, allocator_script=None, mapping_timeout=None):
   1668     self._model.fill_default_quantization_params(logger=self._logger)
-> 1669     hef, mapped_graph_file = self._compile(fps, allocator_script, mapping_timeout)
   1670     # TODO: https://hailotech.atlassian.net/browse/SDK-31038
   1671     if not SDKPaths().is_internal:

File ~/hailo/hailo_ai_sw_suite/hailo_venv/lib/python3.10/site-packages/hailo_sdk_client/sdk_backend/sdk_backend.py:1663, in SdkBackendCompilation._compile(self, fps, allocator_script, mapping_timeout)
   1657 if not model_params and self.requires_quantized_weights:
   1658     raise BackendRuntimeException(
   1659         \"Model requires quantized weights in order to run on HW, but none were given. \"
   1660         \"Did you forget to quantize?\",
   1661     )
-> 1663 hef, mapped_graph_file, auto_alls = self.hef_full_build(fps, mapping_timeout, model_params, allocator_script)
   1664 self._auto_alls = auto_alls
   1665 return hef, mapped_graph_file

File ~/hailo/hailo_ai_sw_suite/hailo_venv/lib/python3.10/site-packages/hailo_sdk_client/sdk_backend/sdk_backend.py:1639, in SdkBackendCompilation.hef_full_build(self, fps, mapping_timeout, params, allocator_script)
   1637 config_paths = ConfigPaths(self._hw_arch, self._model.name)
   1638 config_paths.set_stage(\"inference\")
-> 1639 auto_alls, self._hef_data, self._integrated_graph = allocator.create_mapping_and_full_build_hef(
   1640     config_paths.get_path(\"network_graph\"),
   1641     config_paths.get_path(\"mapped_graph\"),
   1642     config_paths.get_path(\"compilation_output_proto\"),
   1643     params=params,
   1644     allocator_script=allocator_script,
   1645     compiler_statistics_path=config_paths.get_path(\"compiler_statistics\"),
   1646     nms_metadata=self._nms_metadata,
   1647     har=self.har,
   1648     alls_ignore_invalid_cmds=self._alls_ignore_invalid_cmds,
   1649 )
   1651 return self._hef_data, config_paths.get_path(\"mapped_graph\"), auto_alls

File ~/hailo/hailo_ai_sw_suite/hailo_venv/lib/python3.10/site-packages/hailo_sdk_client/allocator/hailo_tools_runner.py:761, in HailoToolsRunner.create_mapping_and_full_build_hef(self, network_graph_path, output_path, compilation_output_proto, agent, strategy, auto_mapping, params, expected_output_tensor, expected_pre_acts, network_inputs, network_outputs, allocator_script, allocator_script_mode, compiler_statistics_path, nms_metadata, har, alls_ignore_invalid_cmds)
    756 if self.hn.net_params.clusters_placement != [[]]:
    757     assert (
    758         len(self.hn.net_params.clusters_placement) <= self._number_of_clusters
    759     ), \"Number of clusters in layer placements is larger than allowed number of clusters\"
--> 761 self.call_builder(
    762     network_graph_path,
    763     output_path,
    764     compilation_output_proto=compilation_output_proto,
    765     agent=agent,
    766     strategy=strategy,
    767     exit_point=BuilderExitPoint.POST_CAT,
    768     params=params,
    769     expected_output_tensor=expected_output_tensor,
    770     expected_pre_acts=expected_pre_acts,
    771     network_inputs=network_inputs,
    772     network_outputs=network_outputs,
    773     allocator_script=allocator_script,
    774     allocator_script_mode=allocator_script_mode,
    775     compiler_statistics_path=compiler_statistics_path,
    776     nms_metadata=nms_metadata,
    777     har=har,
    778     alls_ignore_invalid_cmds=alls_ignore_invalid_cmds,
    779 )
    781 return self._auto_alls, self._output_hef_data, self._output_integrated_pb_graph

File ~/hailo/hailo_ai_sw_suite/hailo_venv/lib/python3.10/site-packages/hailo_sdk_client/allocator/hailo_tools_runner.py:693, in HailoToolsRunner.call_builder(self, network_graph_path, output_path, blind_deserialize, **kwargs)
    691 sys.excepthook = _hailo_tools_exception_hook
    692 try:
--> 693     self.run_builder(network_graph_path, output_path, **kwargs)
    694 except BackendInternalException:
    695     try:

File ~/hailo/hailo_ai_sw_suite/hailo_venv/lib/python3.10/site-packages/hailo_sdk_client/allocator/hailo_tools_runner.py:567, in HailoToolsRunner.run_builder(self, network_graph_filename, output_filename, compilation_output_proto, agent, strategy, exit_point, params, expected_output_tensor, expected_pre_acts, network_inputs, network_outputs, allocator_script, allocator_script_mode, compiler_statistics_path, is_debug, nms_metadata, har, alls_ignore_invalid_cmds)
    565 compiler_msg = e.hailo_tools_error
    566 if compiler_msg:
--> 567     raise e.internal_exception(\"Compilation failed:\", hailo_tools_error=compiler_msg) from None
    568 else:
    569     raise e.internal_exception(\"Compilation failed with unexpected crash\") from None

BackendAllocatorException: Compilation failed: No successful assignments: concat17 errors:
\tAgent infeasible
"
}```

[omria]

Hey @alcapowned,

Welcome to the Hailo Community!

I see you’re encountering an “Agent infeasible” error with concat17 during compilation. This happens when our dataflow compiler struggles to allocate hardware resources for that layer. Let me help you resolve this.

Try these steps:

1. Verify your YOLOv8 model dimensions align with Hailo’s supported configurations and check that quantization was done correctly during preprocessing
2. Consider optimizing your model by reducing feature map sizes or adjusting the network’s layer structure
3. Use Hailo’s allocator scripts to help manage resource allocation - you can use our default scripts or create a custom one for your concat17 operation

Let me know if you need help with any of these steps!