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Install CMake in Merlin base image (instead of copying from build) #524

Merged
merged 3 commits into from
Aug 10, 2022
Merged

Install CMake in Merlin base image (instead of copying from build) #524

merged 3 commits into from
Aug 10, 2022

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karlhigley
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Since the CMake install is a bit more involved than just copying the executable, let's just install it in the base container directly.

@karlhigley
Install CMake in Merlin base image (instead of copying from build)
df88d4b 
Since the CMake install is a bit more involved than just copying the executable, let's just install it in the base container directly.
@karlhigley karlhigley added chore Infrastructure update ci labels Aug 9, 2022
@karlhigley karlhigley added this to the Merlin 22.08 milestone Aug 9, 2022
@karlhigley karlhigley requested a review from nv-alaiacano August 9, 2022 14:26
@karlhigley karlhigley self-assigned this Aug 9, 2022
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github-actions bot commented Aug 9, 2022

Documentation preview

https://nvidia-merlin.github.io/Merlin/review/pr-524

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Click to view CI Results
GitHub pull request #524 of commit df88d4b685158dfb8ab622772ebb81c61eb770e2, no merge conflicts.
Running as SYSTEM
Setting status of df88d4b685158dfb8ab622772ebb81c61eb770e2 to PENDING with url https://10.20.13.93:8080/job/merlin_merlin/333/console and message: 'Pending'
Using context: Jenkins
Building on master in workspace /var/jenkins_home/workspace/merlin_merlin
using credential systems-login
 > git rev-parse --is-inside-work-tree # timeout=10
Fetching changes from the remote Git repository
 > git config remote.origin.url https://github.com/NVIDIA-Merlin/Merlin # timeout=10
Fetching upstream changes from https://github.com/NVIDIA-Merlin/Merlin
 > git --version # timeout=10
using GIT_ASKPASS to set credentials login for merlin-systems
 > git fetch --tags --force --progress -- https://github.com/NVIDIA-Merlin/Merlin +refs/pull/524/*:refs/remotes/origin/pr/524/* # timeout=10
 > git rev-parse df88d4b685158dfb8ab622772ebb81c61eb770e2^{commit} # timeout=10
Checking out Revision df88d4b685158dfb8ab622772ebb81c61eb770e2 (detached)
 > git config core.sparsecheckout # timeout=10
 > git checkout -f df88d4b685158dfb8ab622772ebb81c61eb770e2 # timeout=10
Commit message: "Install CMake in Merlin base image (instead of copying from build)"
 > git rev-list --no-walk 8a5d7799725b1b558b0e3b06a2219b2deb2ad56f # timeout=10
[merlin_merlin] $ /bin/bash /tmp/jenkins3321469593476589153.sh
============================= test session starts ==============================
platform linux -- Python 3.8.10, pytest-7.1.2, pluggy-1.0.0
rootdir: /var/jenkins_home/workspace/merlin_merlin/merlin
plugins: anyio-3.6.1, xdist-2.5.0, forked-1.4.0, cov-3.0.0
collected 3 items

tests/unit/test_version.py .                                             [ 33%]

tests/unit/examples/test_building_deploying_multi_stage_RecSys.py F      [ 66%]

tests/unit/examples/test_scaling_criteo_merlin_models.py F               [100%]


=================================== FAILURES ===================================

__________________________________ test_func ___________________________________


self = <testbook.client.TestbookNotebookClient object at 0x7f7d1f68ac40>

cell = [53], kwargs = {}, cell_indexes = [53], executed_cells = [], idx = 53


def execute_cell(self, cell, **kwargs) -> Union[Dict, List[Dict]]:
    """
    Executes a cell or list of cells
    """
    if isinstance(cell, slice):
        start, stop = self._cell_index(cell.start), self._cell_index(cell.stop)
        if cell.step is not None:
            raise TestbookError('testbook does not support step argument')

        cell = range(start, stop + 1)
    elif isinstance(cell, str) or isinstance(cell, int):
        cell = [cell]

    cell_indexes = cell

    if all(isinstance(x, str) for x in cell):
        cell_indexes = [self._cell_index(tag) for tag in cell]

    executed_cells = []
    for idx in cell_indexes:
        try:





          cell = super().execute_cell(self.nb['cells'][idx], idx, **kwargs)





/usr/local/lib/python3.8/dist-packages/testbook/client.py:133:




args = (<testbook.client.TestbookNotebookClient object at 0x7f7d1f68ac40>, {'id': '5e776566', 'cell_type': 'code', 'metadata'...ast.py, line 299 in transform>]"\n\nAt:\n  /tmp/examples/poc_ensemble/3_queryfeast/1/model.py(122): execute\n']}]}, 53)

kwargs = {}


def wrapped(*args, **kwargs):





  return just_run(coro(*args, **kwargs))





/usr/local/lib/python3.8/dist-packages/nbclient/util.py:85:




coro = <coroutine object NotebookClient.async_execute_cell at 0x7f7c46ea9340>


def just_run(coro: Awaitable) -> Any:
    """Make the coroutine run, even if there is an event loop running (using nest_asyncio)"""
    try:
        loop = asyncio.get_running_loop()
    except RuntimeError:
        loop = None
    if loop is None:
        had_running_loop = False
        loop = asyncio.new_event_loop()
        asyncio.set_event_loop(loop)
    else:
        had_running_loop = True
    if had_running_loop:
        # if there is a running loop, we patch using nest_asyncio
        # to have reentrant event loops
        check_ipython()
        import nest_asyncio

        nest_asyncio.apply()
        check_patch_tornado()





  return loop.run_until_complete(coro)





/usr/local/lib/python3.8/dist-packages/nbclient/util.py:60:




self = <_UnixSelectorEventLoop running=False closed=False debug=False>

future = <Task finished name='Task-369' coro=<NotebookClient.async_execute_cell() done, defined at /usr/local/lib/python3.8/dis...ps/feast.py, line 299 in transform>]"\n\nAt:\n  /tmp/examples/poc_ensemble/3_queryfeast/1/model.py(122): execute\n\n')>


def run_until_complete(self, future):
    """Run until the Future is done.

    If the argument is a coroutine, it is wrapped in a Task.

    WARNING: It would be disastrous to call run_until_complete()
    with the same coroutine twice -- it would wrap it in two
    different Tasks and that can't be good.

    Return the Future's result, or raise its exception.
    """
    self._check_closed()
    self._check_running()

    new_task = not futures.isfuture(future)
    future = tasks.ensure_future(future, loop=self)
    if new_task:
        # An exception is raised if the future didn't complete, so there
        # is no need to log the "destroy pending task" message
        future._log_destroy_pending = False

    future.add_done_callback(_run_until_complete_cb)
    try:
        self.run_forever()
    except:
        if new_task and future.done() and not future.cancelled():
            # The coroutine raised a BaseException. Consume the exception
            # to not log a warning, the caller doesn't have access to the
            # local task.
            future.exception()
        raise
    finally:
        future.remove_done_callback(_run_until_complete_cb)
    if not future.done():
        raise RuntimeError('Event loop stopped before Future completed.')





  return future.result()





/usr/lib/python3.8/asyncio/base_events.py:616:




self = <testbook.client.TestbookNotebookClient object at 0x7f7d1f68ac40>

cell = {'id': '5e776566', 'cell_type': 'code', 'metadata': {'execution': {'iopub.status.busy': '2022-08-09T14:34:21.098708Z',...ps/feast.py, line 299 in transform>]"\n\nAt:\n  /tmp/examples/poc_ensemble/3_queryfeast/1/model.py(122): execute\n']}]}

cell_index = 53, execution_count = None, store_history = True


async def async_execute_cell(
    self,
    cell: NotebookNode,
    cell_index: int,
    execution_count: t.Optional[int] = None,
    store_history: bool = True,
) -> NotebookNode:
    """
    Executes a single code cell.

    To execute all cells see :meth:`execute`.

    Parameters
    ----------
    cell : nbformat.NotebookNode
        The cell which is currently being processed.
    cell_index : int
        The position of the cell within the notebook object.
    execution_count : int
        The execution count to be assigned to the cell (default: Use kernel response)
    store_history : bool
        Determines if history should be stored in the kernel (default: False).
        Specific to ipython kernels, which can store command histories.

    Returns
    -------
    output : dict
        The execution output payload (or None for no output).

    Raises
    ------
    CellExecutionError
        If execution failed and should raise an exception, this will be raised
        with defaults about the failure.

    Returns
    -------
    cell : NotebookNode
        The cell which was just processed.
    """
    assert self.kc is not None

    await run_hook(self.on_cell_start, cell=cell, cell_index=cell_index)

    if cell.cell_type != 'code' or not cell.source.strip():
        self.log.debug("Skipping non-executing cell %s", cell_index)
        return cell

    if self.skip_cells_with_tag in cell.metadata.get("tags", []):
        self.log.debug("Skipping tagged cell %s", cell_index)
        return cell

    if self.record_timing:  # clear execution metadata prior to execution
        cell['metadata']['execution'] = {}

    self.log.debug("Executing cell:\n%s", cell.source)

    cell_allows_errors = (not self.force_raise_errors) and (
        self.allow_errors or "raises-exception" in cell.metadata.get("tags", [])
    )

    await run_hook(self.on_cell_execute, cell=cell, cell_index=cell_index)
    parent_msg_id = await ensure_async(
        self.kc.execute(
            cell.source, store_history=store_history, stop_on_error=not cell_allows_errors
        )
    )
    await run_hook(self.on_cell_complete, cell=cell, cell_index=cell_index)
    # We launched a code cell to execute
    self.code_cells_executed += 1
    exec_timeout = self._get_timeout(cell)

    cell.outputs = []
    self.clear_before_next_output = False

    task_poll_kernel_alive = asyncio.ensure_future(self._async_poll_kernel_alive())
    task_poll_output_msg = asyncio.ensure_future(
        self._async_poll_output_msg(parent_msg_id, cell, cell_index)
    )
    self.task_poll_for_reply = asyncio.ensure_future(
        self._async_poll_for_reply(
            parent_msg_id, cell, exec_timeout, task_poll_output_msg, task_poll_kernel_alive
        )
    )
    try:
        exec_reply = await self.task_poll_for_reply
    except asyncio.CancelledError:
        # can only be cancelled by task_poll_kernel_alive when the kernel is dead
        task_poll_output_msg.cancel()
        raise DeadKernelError("Kernel died")
    except Exception as e:
        # Best effort to cancel request if it hasn't been resolved
        try:
            # Check if the task_poll_output is doing the raising for us
            if not isinstance(e, CellControlSignal):
                task_poll_output_msg.cancel()
        finally:
            raise

    if execution_count:
        cell['execution_count'] = execution_count
    await run_hook(
        self.on_cell_executed, cell=cell, cell_index=cell_index, execute_reply=exec_reply
    )





  await self._check_raise_for_error(cell, cell_index, exec_reply)





/usr/local/lib/python3.8/dist-packages/nbclient/client.py:1022:




self = <testbook.client.TestbookNotebookClient object at 0x7f7d1f68ac40>

cell = {'id': '5e776566', 'cell_type': 'code', 'metadata': {'execution': {'iopub.status.busy': '2022-08-09T14:34:21.098708Z',...ps/feast.py, line 299 in transform>]"\n\nAt:\n  /tmp/examples/poc_ensemble/3_queryfeast/1/model.py(122): execute\n']}]}

cell_index = 53

exec_reply = {'buffers': [], 'content': {'ename': 'InferenceServerException', 'engine_info': {'engine_id': -1, 'engine_uuid': '4b36...e, 'engine': '4b362f84-d631-428d-bfc1-b310145dc610', 'started': '2022-08-09T14:34:21.098956Z', 'status': 'error'}, ...}


async def _check_raise_for_error(
    self, cell: NotebookNode, cell_index: int, exec_reply: t.Optional[t.Dict]
) -> None:

    if exec_reply is None:
        return None

    exec_reply_content = exec_reply['content']
    if exec_reply_content['status'] != 'error':
        return None

    cell_allows_errors = (not self.force_raise_errors) and (
        self.allow_errors
        or exec_reply_content.get('ename') in self.allow_error_names
        or "raises-exception" in cell.metadata.get("tags", [])
    )
    await run_hook(
        self.on_cell_error, cell=cell, cell_index=cell_index, execute_reply=exec_reply
    )
    if not cell_allows_errors:





      raise CellExecutionError.from_cell_and_msg(cell, exec_reply_content)





E           nbclient.exceptions.CellExecutionError: An error occurred while executing the following cell:

E           ------------------

E

E           import shutil

E           from merlin.models.loader.tf_utils import configure_tensorflow

E           configure_tensorflow()

E           from merlin.systems.triton.utils import run_ensemble_on_tritonserver

E           response = run_ensemble_on_tritonserver(

E               "/tmp/examples/poc_ensemble", outputs, request, "ensemble_model"

E           )

E           response = [x.tolist()[0] for x in response["ordered_ids"]]

E           shutil.rmtree("/tmp/examples/", ignore_errors=True)

E

E           ------------------

E

E           �[0;31m---------------------------------------------------------------------------�[0m

E           �[0;31mInferenceServerException�[0m                  Traceback (most recent call last)

E           Input �[0;32mIn [32]�[0m, in �[0;36m<cell line: 5>�[0;34m()�[0m

E           �[1;32m      3�[0m configure_tensorflow()

E           �[1;32m      4�[0m �[38;5;28;01mfrom�[39;00m �[38;5;21;01mmerlin�[39;00m�[38;5;21;01m.�[39;00m�[38;5;21;01msystems�[39;00m�[38;5;21;01m.�[39;00m�[38;5;21;01mtriton�[39;00m�[38;5;21;01m.�[39;00m�[38;5;21;01mutils�[39;00m �[38;5;28;01mimport�[39;00m run_ensemble_on_tritonserver

E           �[0;32m----> 5�[0m response �[38;5;241m=�[39m �[43mrun_ensemble_on_tritonserver�[49m�[43m(�[49m

E           �[1;32m      6�[0m �[43m    �[49m�[38;5;124;43m"�[39;49m�[38;5;124;43m/tmp/examples/poc_ensemble�[39;49m�[38;5;124;43m"�[39;49m�[43m,�[49m�[43m �[49m�[43moutputs�[49m�[43m,�[49m�[43m �[49m�[43mrequest�[49m�[43m,�[49m�[43m �[49m�[38;5;124;43m"�[39;49m�[38;5;124;43mensemble_model�[39;49m�[38;5;124;43m"�[39;49m

E           �[1;32m      7�[0m �[43m)�[49m

E           �[1;32m      8�[0m response �[38;5;241m=�[39m [x�[38;5;241m.�[39mtolist()[�[38;5;241m0�[39m] �[38;5;28;01mfor�[39;00m x �[38;5;129;01min�[39;00m response[�[38;5;124m"�[39m�[38;5;124mordered_ids�[39m�[38;5;124m"�[39m]]

E           �[1;32m      9�[0m shutil�[38;5;241m.�[39mrmtree(�[38;5;124m"�[39m�[38;5;124m/tmp/examples/�[39m�[38;5;124m"�[39m, ignore_errors�[38;5;241m=�[39m�[38;5;28;01mTrue�[39;00m)

E

E           File �[0;32m/usr/local/lib/python3.8/dist-packages/merlin/systems/triton/utils.py:93�[0m, in �[0;36mrun_ensemble_on_tritonserver�[0;34m(tmpdir, output_columns, df, model_name)�[0m

E           �[1;32m     91�[0m response �[38;5;241m=�[39m �[38;5;28;01mNone�[39;00m

E           �[1;32m     92�[0m �[38;5;28;01mwith�[39;00m run_triton_server(tmpdir) �[38;5;28;01mas�[39;00m client:

E           �[0;32m---> 93�[0m     response �[38;5;241m=�[39m �[43msend_triton_request�[49m�[43m(�[49m�[43mdf�[49m�[43m,�[49m�[43m �[49m�[43moutput_columns�[49m�[43m,�[49m�[43m �[49m�[43mclient�[49m�[38;5;241;43m=�[39;49m�[43mclient�[49m�[43m,�[49m�[43m �[49m�[43mtriton_model�[49m�[38;5;241;43m=�[39;49m�[43mmodel_name�[49m�[43m)�[49m

E           �[1;32m     95�[0m �[38;5;28;01mreturn�[39;00m response

E

E           File �[0;32m/usr/local/lib/python3.8/dist-packages/merlin/systems/triton/utils.py:141�[0m, in �[0;36msend_triton_request�[0;34m(df, outputs_list, client, endpoint, request_id, triton_model)�[0m

E           �[1;32m    139�[0m outputs �[38;5;241m=�[39m [grpcclient�[38;5;241m.�[39mInferRequestedOutput(col) �[38;5;28;01mfor�[39;00m col �[38;5;129;01min�[39;00m outputs_list]

E           �[1;32m    140�[0m �[38;5;28;01mwith�[39;00m client:

E           �[0;32m--> 141�[0m     response �[38;5;241m=�[39m �[43mclient�[49m�[38;5;241;43m.�[39;49m�[43minfer�[49m�[43m(�[49m�[43mtriton_model�[49m�[43m,�[49m�[43m �[49m�[43minputs�[49m�[43m,�[49m�[43m �[49m�[43mrequest_id�[49m�[38;5;241;43m=�[39;49m�[43mrequest_id�[49m�[43m,�[49m�[43m �[49m�[43moutputs�[49m�[38;5;241;43m=�[39;49m�[43moutputs�[49m�[43m)�[49m

E           �[1;32m    143�[0m results �[38;5;241m=�[39m {}

E           �[1;32m    144�[0m �[38;5;28;01mfor�[39;00m col �[38;5;129;01min�[39;00m outputs_list:

E

E           File �[0;32m/usr/local/lib/python3.8/dist-packages/tritonclient/grpc/init.py:1322�[0m, in �[0;36mInferenceServerClient.infer�[0;34m(self, model_name, inputs, model_version, outputs, request_id, sequence_id, sequence_start, sequence_end, priority, timeout, client_timeout, headers, compression_algorithm)�[0m

E           �[1;32m   1320�[0m     �[38;5;28;01mreturn�[39;00m result

E           �[1;32m   1321�[0m �[38;5;28;01mexcept�[39;00m grpc�[38;5;241m.�[39mRpcError �[38;5;28;01mas�[39;00m rpc_error:

E           �[0;32m-> 1322�[0m     �[43mraise_error_grpc�[49m�[43m(�[49m�[43mrpc_error�[49m�[43m)�[49m

E

E           File �[0;32m/usr/local/lib/python3.8/dist-packages/tritonclient/grpc/init.py:62�[0m, in �[0;36mraise_error_grpc�[0;34m(rpc_error)�[0m

E           �[1;32m     61�[0m �[38;5;28;01mdef�[39;00m �[38;5;21mraise_error_grpc�[39m(rpc_error):

E           �[0;32m---> 62�[0m     �[38;5;28;01mraise�[39;00m get_error_grpc(rpc_error) �[38;5;28;01mfrom�[39;00m �[38;5;28mNone�[39m

E

E           �[0;31mInferenceServerException�[0m: [StatusCode.INTERNAL] in ensemble 'ensemble_model', Failed to process the request(s) for model instance '3_queryfeast', message: TypeError: init(): incompatible constructor arguments. The following argument types are supported:

E               1. c_python_backend_utils.InferenceResponse(output_tensors: List[c_python_backend_utils.Tensor], error: c_python_backend_utils.TritonError = None)

E

E           Invoked with: kwargs: tensors=[], error="<class 'TypeError'>, int() argument must be a string, a bytes-like object or a number, not 'NoneType', [<FrameSummary file /tmp/examples/poc_ensemble/3_queryfeast/1/model.py, line 105 in execute>, <FrameSummary file /usr/local/lib/python3.8/dist-packages/merlin/systems/dag/op_runner.py, line 38 in execute>, <FrameSummary file /usr/local/lib/python3.8/dist-packages/merlin/systems/dag/ops/feast.py, line 299 in transform>]"

E

E           At:

E             /tmp/examples/poc_ensemble/3_queryfeast/1/model.py(122): execute

E

E           InferenceServerException: [StatusCode.INTERNAL] in ensemble 'ensemble_model', Failed to process the request(s) for model instance '3_queryfeast', message: TypeError: init(): incompatible constructor arguments. The following argument types are supported:

E               1. c_python_backend_utils.InferenceResponse(output_tensors: List[c_python_backend_utils.Tensor], error: c_python_backend_utils.TritonError = None)

E

E           Invoked with: kwargs: tensors=[], error="<class 'TypeError'>, int() argument must be a string, a bytes-like object or a number, not 'NoneType', [<FrameSummary file /tmp/examples/poc_ensemble/3_queryfeast/1/model.py, line 105 in execute>, <FrameSummary file /usr/local/lib/python3.8/dist-packages/merlin/systems/dag/op_runner.py, line 38 in execute>, <FrameSummary file /usr/local/lib/python3.8/dist-packages/merlin/systems/dag/ops/feast.py, line 299 in transform>]"

E

E           At:

E             /tmp/examples/poc_ensemble/3_queryfeast/1/model.py(122): execute


/usr/local/lib/python3.8/dist-packages/nbclient/client.py:916: CellExecutionError


During handling of the above exception, another exception occurred:


def test_func():
    with testbook(
        REPO_ROOT
        / "examples"
        / "Building-and-deploying-multi-stage-RecSys"
        / "01-Building-Recommender-Systems-with-Merlin.ipynb",
        execute=False,
    ) as tb1:
        tb1.inject(
            """
            import os
            os.environ["DATA_FOLDER"] = "/tmp/data/"
            os.environ["NUM_ROWS"] = "10000"
            os.system("mkdir -p /tmp/examples")
            os.environ["BASE_DIR"] = "/tmp/examples/"
            """
        )
        tb1.execute()
        assert os.path.isdir("/tmp/examples/dlrm")
        assert os.path.isdir("/tmp/examples/feature_repo")
        assert os.path.isdir("/tmp/examples/query_tower")
        assert os.path.isfile("/tmp/examples/item_embeddings.parquet")
        assert os.path.isfile("/tmp/examples/feature_repo/user_features.py")
        assert os.path.isfile("/tmp/examples/feature_repo/item_features.py")

    with testbook(
        REPO_ROOT
        / "examples"
        / "Building-and-deploying-multi-stage-RecSys"
        / "02-Deploying-multi-stage-RecSys-with-Merlin-Systems.ipynb",
        execute=False,
    ) as tb2:
        tb2.inject(
            """
            import os
            os.environ["DATA_FOLDER"] = "/tmp/data/"
            os.environ["BASE_DIR"] = "/tmp/examples/"
            """
        )
        NUM_OF_CELLS = len(tb2.cells)
        tb2.execute_cell(list(range(0, NUM_OF_CELLS - 3)))
        top_k = tb2.ref("top_k")
        outputs = tb2.ref("outputs")
        assert outputs[0] == "ordered_ids"





      tb2.inject(





            """
            import shutil
            from merlin.models.loader.tf_utils import configure_tensorflow
            configure_tensorflow()
            from merlin.systems.triton.utils import run_ensemble_on_tritonserver
            response = run_ensemble_on_tritonserver(
                "/tmp/examples/poc_ensemble", outputs, request, "ensemble_model"
            )
            response = [x.tolist()[0] for x in response["ordered_ids"]]
            shutil.rmtree("/tmp/examples/", ignore_errors=True)
            """
        )



tests/unit/examples/test_building_deploying_multi_stage_RecSys.py:57:




/usr/local/lib/python3.8/dist-packages/testbook/client.py:237: in inject

cell = TestbookNode(self.execute_cell(inject_idx)) if run else TestbookNode(code_cell)




self = <testbook.client.TestbookNotebookClient object at 0x7f7d1f68ac40>

cell = [53], kwargs = {}, cell_indexes = [53], executed_cells = [], idx = 53


def execute_cell(self, cell, **kwargs) -> Union[Dict, List[Dict]]:
    """
    Executes a cell or list of cells
    """
    if isinstance(cell, slice):
        start, stop = self._cell_index(cell.start), self._cell_index(cell.stop)
        if cell.step is not None:
            raise TestbookError('testbook does not support step argument')

        cell = range(start, stop + 1)
    elif isinstance(cell, str) or isinstance(cell, int):
        cell = [cell]

    cell_indexes = cell

    if all(isinstance(x, str) for x in cell):
        cell_indexes = [self._cell_index(tag) for tag in cell]

    executed_cells = []
    for idx in cell_indexes:
        try:
            cell = super().execute_cell(self.nb['cells'][idx], idx, **kwargs)
        except CellExecutionError as ce:





          raise TestbookRuntimeError(ce.evalue, ce, self._get_error_class(ce.ename))





E               testbook.exceptions.TestbookRuntimeError: An error occurred while executing the following cell:

E               ------------------

E

E               import shutil

E               from merlin.models.loader.tf_utils import configure_tensorflow

E               configure_tensorflow()

E               from merlin.systems.triton.utils import run_ensemble_on_tritonserver

E               response = run_ensemble_on_tritonserver(

E                   "/tmp/examples/poc_ensemble", outputs, request, "ensemble_model"

E               )

E               response = [x.tolist()[0] for x in response["ordered_ids"]]

E               shutil.rmtree("/tmp/examples/", ignore_errors=True)

E

E               ------------------

E

E               �[0;31m---------------------------------------------------------------------------�[0m

E               �[0;31mInferenceServerException�[0m                  Traceback (most recent call last)

E               Input �[0;32mIn [32]�[0m, in �[0;36m<cell line: 5>�[0;34m()�[0m

E               �[1;32m      3�[0m configure_tensorflow()

E               �[1;32m      4�[0m �[38;5;28;01mfrom�[39;00m �[38;5;21;01mmerlin�[39;00m�[38;5;21;01m.�[39;00m�[38;5;21;01msystems�[39;00m�[38;5;21;01m.�[39;00m�[38;5;21;01mtriton�[39;00m�[38;5;21;01m.�[39;00m�[38;5;21;01mutils�[39;00m �[38;5;28;01mimport�[39;00m run_ensemble_on_tritonserver

E               �[0;32m----> 5�[0m response �[38;5;241m=�[39m �[43mrun_ensemble_on_tritonserver�[49m�[43m(�[49m

E               �[1;32m      6�[0m �[43m    �[49m�[38;5;124;43m"�[39;49m�[38;5;124;43m/tmp/examples/poc_ensemble�[39;49m�[38;5;124;43m"�[39;49m�[43m,�[49m�[43m �[49m�[43moutputs�[49m�[43m,�[49m�[43m �[49m�[43mrequest�[49m�[43m,�[49m�[43m �[49m�[38;5;124;43m"�[39;49m�[38;5;124;43mensemble_model�[39;49m�[38;5;124;43m"�[39;49m

E               �[1;32m      7�[0m �[43m)�[49m

E               �[1;32m      8�[0m response �[38;5;241m=�[39m [x�[38;5;241m.�[39mtolist()[�[38;5;241m0�[39m] �[38;5;28;01mfor�[39;00m x �[38;5;129;01min�[39;00m response[�[38;5;124m"�[39m�[38;5;124mordered_ids�[39m�[38;5;124m"�[39m]]

E               �[1;32m      9�[0m shutil�[38;5;241m.�[39mrmtree(�[38;5;124m"�[39m�[38;5;124m/tmp/examples/�[39m�[38;5;124m"�[39m, ignore_errors�[38;5;241m=�[39m�[38;5;28;01mTrue�[39;00m)

E

E               File �[0;32m/usr/local/lib/python3.8/dist-packages/merlin/systems/triton/utils.py:93�[0m, in �[0;36mrun_ensemble_on_tritonserver�[0;34m(tmpdir, output_columns, df, model_name)�[0m

E               �[1;32m     91�[0m response �[38;5;241m=�[39m �[38;5;28;01mNone�[39;00m

E               �[1;32m     92�[0m �[38;5;28;01mwith�[39;00m run_triton_server(tmpdir) �[38;5;28;01mas�[39;00m client:

E               �[0;32m---> 93�[0m     response �[38;5;241m=�[39m �[43msend_triton_request�[49m�[43m(�[49m�[43mdf�[49m�[43m,�[49m�[43m �[49m�[43moutput_columns�[49m�[43m,�[49m�[43m �[49m�[43mclient�[49m�[38;5;241;43m=�[39;49m�[43mclient�[49m�[43m,�[49m�[43m �[49m�[43mtriton_model�[49m�[38;5;241;43m=�[39;49m�[43mmodel_name�[49m�[43m)�[49m

E               �[1;32m     95�[0m �[38;5;28;01mreturn�[39;00m response

E

E               File �[0;32m/usr/local/lib/python3.8/dist-packages/merlin/systems/triton/utils.py:141�[0m, in �[0;36msend_triton_request�[0;34m(df, outputs_list, client, endpoint, request_id, triton_model)�[0m

E               �[1;32m    139�[0m outputs �[38;5;241m=�[39m [grpcclient�[38;5;241m.�[39mInferRequestedOutput(col) �[38;5;28;01mfor�[39;00m col �[38;5;129;01min�[39;00m outputs_list]

E               �[1;32m    140�[0m �[38;5;28;01mwith�[39;00m client:

E               �[0;32m--> 141�[0m     response �[38;5;241m=�[39m �[43mclient�[49m�[38;5;241;43m.�[39;49m�[43minfer�[49m�[43m(�[49m�[43mtriton_model�[49m�[43m,�[49m�[43m �[49m�[43minputs�[49m�[43m,�[49m�[43m �[49m�[43mrequest_id�[49m�[38;5;241;43m=�[39;49m�[43mrequest_id�[49m�[43m,�[49m�[43m �[49m�[43moutputs�[49m�[38;5;241;43m=�[39;49m�[43moutputs�[49m�[43m)�[49m

E               �[1;32m    143�[0m results �[38;5;241m=�[39m {}

E               �[1;32m    144�[0m �[38;5;28;01mfor�[39;00m col �[38;5;129;01min�[39;00m outputs_list:

E

E               File �[0;32m/usr/local/lib/python3.8/dist-packages/tritonclient/grpc/init.py:1322�[0m, in �[0;36mInferenceServerClient.infer�[0;34m(self, model_name, inputs, model_version, outputs, request_id, sequence_id, sequence_start, sequence_end, priority, timeout, client_timeout, headers, compression_algorithm)�[0m

E               �[1;32m   1320�[0m     �[38;5;28;01mreturn�[39;00m result

E               �[1;32m   1321�[0m �[38;5;28;01mexcept�[39;00m grpc�[38;5;241m.�[39mRpcError �[38;5;28;01mas�[39;00m rpc_error:

E               �[0;32m-> 1322�[0m     �[43mraise_error_grpc�[49m�[43m(�[49m�[43mrpc_error�[49m�[43m)�[49m

E

E               File �[0;32m/usr/local/lib/python3.8/dist-packages/tritonclient/grpc/init.py:62�[0m, in �[0;36mraise_error_grpc�[0;34m(rpc_error)�[0m

E               �[1;32m     61�[0m �[38;5;28;01mdef�[39;00m �[38;5;21mraise_error_grpc�[39m(rpc_error):

E               �[0;32m---> 62�[0m     �[38;5;28;01mraise�[39;00m get_error_grpc(rpc_error) �[38;5;28;01mfrom�[39;00m �[38;5;28mNone�[39m

E

E               �[0;31mInferenceServerException�[0m: [StatusCode.INTERNAL] in ensemble 'ensemble_model', Failed to process the request(s) for model instance '3_queryfeast', message: TypeError: init(): incompatible constructor arguments. The following argument types are supported:

E                   1. c_python_backend_utils.InferenceResponse(output_tensors: List[c_python_backend_utils.Tensor], error: c_python_backend_utils.TritonError = None)

E

E               Invoked with: kwargs: tensors=[], error="<class 'TypeError'>, int() argument must be a string, a bytes-like object or a number, not 'NoneType', [<FrameSummary file /tmp/examples/poc_ensemble/3_queryfeast/1/model.py, line 105 in execute>, <FrameSummary file /usr/local/lib/python3.8/dist-packages/merlin/systems/dag/op_runner.py, line 38 in execute>, <FrameSummary file /usr/local/lib/python3.8/dist-packages/merlin/systems/dag/ops/feast.py, line 299 in transform>]"

E

E               At:

E                 /tmp/examples/poc_ensemble/3_queryfeast/1/model.py(122): execute

E

E               InferenceServerException: [StatusCode.INTERNAL] in ensemble 'ensemble_model', Failed to process the request(s) for model instance '3_queryfeast', message: TypeError: init(): incompatible constructor arguments. The following argument types are supported:

E                   1. c_python_backend_utils.InferenceResponse(output_tensors: List[c_python_backend_utils.Tensor], error: c_python_backend_utils.TritonError = None)

E

E               Invoked with: kwargs: tensors=[], error="<class 'TypeError'>, int() argument must be a string, a bytes-like object or a number, not 'NoneType', [<FrameSummary file /tmp/examples/poc_ensemble/3_queryfeast/1/model.py, line 105 in execute>, <FrameSummary file /usr/local/lib/python3.8/dist-packages/merlin/systems/dag/op_runner.py, line 38 in execute>, <FrameSummary file /usr/local/lib/python3.8/dist-packages/merlin/systems/dag/ops/feast.py, line 299 in transform>]"

E

E               At:

E                 /tmp/examples/poc_ensemble/3_queryfeast/1/model.py(122): execute


/usr/local/lib/python3.8/dist-packages/testbook/client.py:135: TestbookRuntimeError

----------------------------- Captured stdout call -----------------------------

Signal (2) received.

----------------------------- Captured stderr call -----------------------------

2022-08-09 14:33:04.342035: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA

To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.

2022-08-09 14:33:06.346119: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 1627 MB memory:  -> device: 0, name: Tesla P100-DGXS-16GB, pci bus id: 0000:07:00.0, compute capability: 6.0

2022-08-09 14:33:06.346847: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 14532 MB memory:  -> device: 1, name: Tesla P100-DGXS-16GB, pci bus id: 0000:08:00.0, compute capability: 6.0

Error in atexit._run_exitfuncs:

Traceback (most recent call last):

File "/usr/lib/python3.8/logging/init.py", line 2127, in shutdown

h.close()

File "/usr/local/lib/python3.8/dist-packages/absl/logging/init.py", line 934, in close

self.stream.close()

File "/usr/local/lib/python3.8/dist-packages/ipykernel/iostream.py", line 438, in close

self.watch_fd_thread.join()

AttributeError: 'OutStream' object has no attribute 'watch_fd_thread'

WARNING clustering 247 points to 32 centroids: please provide at least 1248 training points

2022-08-09 14:34:14.134879: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA

To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.

2022-08-09 14:34:16.125851: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 1627 MB memory:  -> device: 0, name: Tesla P100-DGXS-16GB, pci bus id: 0000:07:00.0, compute capability: 6.0

2022-08-09 14:34:16.126570: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 14532 MB memory:  -> device: 1, name: Tesla P100-DGXS-16GB, pci bus id: 0000:08:00.0, compute capability: 6.0

I0809 14:34:21.361468 8676 pinned_memory_manager.cc:240] Pinned memory pool is created at '0x7f01d6000000' with size 268435456

I0809 14:34:21.362193 8676 cuda_memory_manager.cc:105] CUDA memory pool is created on device 0 with size 67108864

I0809 14:34:21.369231 8676 model_repository_manager.cc:1191] loading: 0_queryfeast:1

I0809 14:34:21.469503 8676 model_repository_manager.cc:1191] loading: 1_predicttensorflow:1

I0809 14:34:21.475426 8676 python.cc:2388] TRITONBACKEND_ModelInstanceInitialize: 0_queryfeast (GPU device 0)

I0809 14:34:21.569874 8676 model_repository_manager.cc:1191] loading: 2_queryfaiss:1

I0809 14:34:21.670117 8676 model_repository_manager.cc:1191] loading: 3_queryfeast:1

I0809 14:34:21.770450 8676 model_repository_manager.cc:1191] loading: 4_unrollfeatures:1

I0809 14:34:21.870769 8676 model_repository_manager.cc:1191] loading: 5_predicttensorflow:1

I0809 14:34:21.971030 8676 model_repository_manager.cc:1191] loading: 6_softmaxsampling:1

I0809 14:34:23.819137 8676 model_repository_manager.cc:1345] successfully loaded '0_queryfeast' version 1

I0809 14:34:24.105006 8676 tensorflow.cc:2181] TRITONBACKEND_Initialize: tensorflow

I0809 14:34:24.105044 8676 tensorflow.cc:2191] Triton TRITONBACKEND API version: 1.9

I0809 14:34:24.105051 8676 tensorflow.cc:2197] 'tensorflow' TRITONBACKEND API version: 1.9

I0809 14:34:24.105057 8676 tensorflow.cc:2221] backend configuration:

{"cmdline":{"auto-complete-config":"false","backend-directory":"/opt/tritonserver/backends","min-compute-capability":"6.000000","version":"2","default-max-batch-size":"4"}}

I0809 14:34:24.105096 8676 tensorflow.cc:2281] TRITONBACKEND_ModelInitialize: 1_predicttensorflow (version 1)

I0809 14:34:24.107748 8676 tensorflow.cc:2281] TRITONBACKEND_ModelInitialize: 5_predicttensorflow (version 1)

I0809 14:34:24.111030 8676 tensorflow.cc:2330] TRITONBACKEND_ModelInstanceInitialize: 1_predicttensorflow (GPU device 0)

2022-08-09 14:34:24.458835: I tensorflow/cc/saved_model/reader.cc:43] Reading SavedModel from: /tmp/examples/poc_ensemble/1_predicttensorflow/1/model.savedmodel

2022-08-09 14:34:24.462472: I tensorflow/cc/saved_model/reader.cc:78] Reading meta graph with tags { serve }

2022-08-09 14:34:24.462501: I tensorflow/cc/saved_model/reader.cc:119] Reading SavedModel debug info (if present) from: /tmp/examples/poc_ensemble/1_predicttensorflow/1/model.savedmodel

2022-08-09 14:34:24.462609: I tensorflow/core/platform/cpu_feature_guard.cc:152] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE3 SSE4.1 SSE4.2 AVX

To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.

2022-08-09 14:34:24.508999: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 11753 MB memory:  -> device: 0, name: Tesla P100-DGXS-16GB, pci bus id: 0000:07:00.0, compute capability: 6.0

2022-08-09 14:34:24.558114: I tensorflow/cc/saved_model/loader.cc:230] Restoring SavedModel bundle.

2022-08-09 14:34:24.646215: I tensorflow/cc/saved_model/loader.cc:214] Running initialization op on SavedModel bundle at path: /tmp/examples/poc_ensemble/1_predicttensorflow/1/model.savedmodel

2022-08-09 14:34:24.670328: I tensorflow/cc/saved_model/loader.cc:321] SavedModel load for tags { serve }; Status: success: OK. Took 211513 microseconds.

I0809 14:34:24.670447 8676 python.cc:2388] TRITONBACKEND_ModelInstanceInitialize: 2_queryfaiss (GPU device 0)

I0809 14:34:24.670543 8676 model_repository_manager.cc:1345] successfully loaded '1_predicttensorflow' version 1

I0809 14:34:27.101471 8676 python.cc:2388] TRITONBACKEND_ModelInstanceInitialize: 3_queryfeast (GPU device 0)

I0809 14:34:27.103213 8676 model_repository_manager.cc:1345] successfully loaded '2_queryfaiss' version 1

I0809 14:34:29.473684 8676 tensorflow.cc:2330] TRITONBACKEND_ModelInstanceInitialize: 5_predicttensorflow (GPU device 0)

I0809 14:34:29.473935 8676 model_repository_manager.cc:1345] successfully loaded '3_queryfeast' version 1

2022-08-09 14:34:29.474172: I tensorflow/cc/saved_model/reader.cc:43] Reading SavedModel from: /tmp/examples/poc_ensemble/5_predicttensorflow/1/model.savedmodel

2022-08-09 14:34:29.483878: I tensorflow/cc/saved_model/reader.cc:78] Reading meta graph with tags { serve }

2022-08-09 14:34:29.483916: I tensorflow/cc/saved_model/reader.cc:119] Reading SavedModel debug info (if present) from: /tmp/examples/poc_ensemble/5_predicttensorflow/1/model.savedmodel

2022-08-09 14:34:29.485875: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 11753 MB memory:  -> device: 0, name: Tesla P100-DGXS-16GB, pci bus id: 0000:07:00.0, compute capability: 6.0

2022-08-09 14:34:29.508088: I tensorflow/cc/saved_model/loader.cc:230] Restoring SavedModel bundle.

2022-08-09 14:34:29.654305: I tensorflow/cc/saved_model/loader.cc:214] Running initialization op on SavedModel bundle at path: /tmp/examples/poc_ensemble/5_predicttensorflow/1/model.savedmodel

2022-08-09 14:34:29.711305: I tensorflow/cc/saved_model/loader.cc:321] SavedModel load for tags { serve }; Status: success: OK. Took 237141 microseconds.

I0809 14:34:29.711461 8676 python.cc:2388] TRITONBACKEND_ModelInstanceInitialize: 4_unrollfeatures (GPU device 0)

I0809 14:34:29.711543 8676 model_repository_manager.cc:1345] successfully loaded '5_predicttensorflow' version 1

I0809 14:34:31.832639 8676 python.cc:2388] TRITONBACKEND_ModelInstanceInitialize: 6_softmaxsampling (GPU device 0)

I0809 14:34:31.832905 8676 model_repository_manager.cc:1345] successfully loaded '4_unrollfeatures' version 1

I0809 14:34:33.945834 8676 model_repository_manager.cc:1345] successfully loaded '6_softmaxsampling' version 1

I0809 14:34:33.948757 8676 model_repository_manager.cc:1191] loading: ensemble_model:1

I0809 14:34:34.049607 8676 model_repository_manager.cc:1345] successfully loaded 'ensemble_model' version 1

I0809 14:34:34.049786 8676 server.cc:556]

+------------------+------+

| Repository Agent | Path |

+------------------+------+

+------------------+------+


I0809 14:34:34.049899 8676 server.cc:583]

+------------+-----------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

| Backend    | Path                                                            | Config                                                                                                                                                                       |

+------------+-----------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

| python     | /opt/tritonserver/backends/python/libtriton_python.so           | {"cmdline":{"auto-complete-config":"false","min-compute-capability":"6.000000","backend-directory":"/opt/tritonserver/backends","default-max-batch-size":"4"}}               |

| tensorflow | /opt/tritonserver/backends/tensorflow2/libtriton_tensorflow2.so | {"cmdline":{"auto-complete-config":"false","backend-directory":"/opt/tritonserver/backends","min-compute-capability":"6.000000","version":"2","default-max-batch-size":"4"}} |

+------------+-----------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+


I0809 14:34:34.050012 8676 server.cc:626]

+---------------------+---------+--------+

| Model               | Version | Status |

+---------------------+---------+--------+

| 0_queryfeast        | 1       | READY  |

| 1_predicttensorflow | 1       | READY  |

| 2_queryfaiss        | 1       | READY  |

| 3_queryfeast        | 1       | READY  |

| 4_unrollfeatures    | 1       | READY  |

| 5_predicttensorflow | 1       | READY  |

| 6_softmaxsampling   | 1       | READY  |

| ensemble_model      | 1       | READY  |

+---------------------+---------+--------+


I0809 14:34:34.116189 8676 metrics.cc:650] Collecting metrics for GPU 0: Tesla P100-DGXS-16GB

I0809 14:34:34.117535 8676 tritonserver.cc:2138]

+----------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

| Option                           | Value                                                                                                                                                                                        |

+----------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

| server_id                        | triton                                                                                                                                                                                       |

| server_version                   | 2.22.0                                                                                                                                                                                       |

| server_extensions                | classification sequence model_repository model_repository(unload_dependents) schedule_policy model_configuration system_shared_memory cuda_shared_memory binary_tensor_data statistics trace |

| model_repository_path[0]         | /tmp/examples/poc_ensemble                                                                                                                                                                   |

| model_control_mode               | MODE_NONE                                                                                                                                                                                    |

| strict_model_config              | 1                                                                                                                                                                                            |

| rate_limit                       | OFF                                                                                                                                                                                          |

| pinned_memory_pool_byte_size     | 268435456                                                                                                                                                                                    |

| cuda_memory_pool_byte_size{0}    | 67108864                                                                                                                                                                                     |

| response_cache_byte_size         | 0                                                                                                                                                                                            |

| min_supported_compute_capability | 6.0                                                                                                                                                                                          |

| strict_readiness                 | 1                                                                                                                                                                                            |

| exit_timeout                     | 30                                                                                                                                                                                           |

+----------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+


I0809 14:34:34.118391 8676 grpc_server.cc:4589] Started GRPCInferenceService at 0.0.0.0:8001

I0809 14:34:34.118917 8676 http_server.cc:3303] Started HTTPService at 0.0.0.0:8000

I0809 14:34:34.160221 8676 http_server.cc:178] Started Metrics Service at 0.0.0.0:8002

W0809 14:34:35.137989 8676 metrics.cc:468] Unable to get energy consumption for GPU 0. Status:Success, value:0

W0809 14:34:35.138032 8676 metrics.cc:507] Unable to get memory usage for GPU 0. Memory usage status:Success, value:0. Memory total status:Success, value:0

W0809 14:34:36.138185 8676 metrics.cc:468] Unable to get energy consumption for GPU 0. Status:Success, value:0

W0809 14:34:36.138241 8676 metrics.cc:507] Unable to get memory usage for GPU 0. Memory usage status:Success, value:0. Memory total status:Success, value:0

0809 14:34:36.629751 9117 pb_stub.cc:749] Failed to process the request(s) for model '3_queryfeast', message: TypeError: init(): incompatible constructor arguments. The following argument types are supported:

1. c_python_backend_utils.InferenceResponse(output_tensors: List[c_python_backend_utils.Tensor], error: c_python_backend_utils.TritonError = None)


Invoked with: kwargs: tensors=[], error="<class 'TypeError'>, int() argument must be a string, a bytes-like object or a number, not 'NoneType', [<FrameSummary file /tmp/examples/poc_ensemble/3_queryfeast/1/model.py, line 105 in execute>, <FrameSummary file /usr/local/lib/python3.8/dist-packages/merlin/systems/dag/op_runner.py, line 38 in execute>, <FrameSummary file /usr/local/lib/python3.8/dist-packages/merlin/systems/dag/ops/feast.py, line 299 in transform>]"


At:

/tmp/examples/poc_ensemble/3_queryfeast/1/model.py(122): execute


I0809 14:34:36.634230 8676 server.cc:257] Waiting for in-flight requests to complete.

I0809 14:34:36.634326 8676 server.cc:273] Timeout 30: Found 0 model versions that have in-flight inferences

I0809 14:34:36.634346 8676 model_repository_manager.cc:1223] unloading: ensemble_model:1

I0809 14:34:36.634439 8676 model_repository_manager.cc:1223] unloading: 6_softmaxsampling:1

I0809 14:34:36.634506 8676 model_repository_manager.cc:1223] unloading: 5_predicttensorflow:1

I0809 14:34:36.634545 8676 model_repository_manager.cc:1328] successfully unloaded 'ensemble_model' version 1

I0809 14:34:36.634595 8676 model_repository_manager.cc:1223] unloading: 4_unrollfeatures:1

I0809 14:34:36.634654 8676 model_repository_manager.cc:1223] unloading: 3_queryfeast:1

I0809 14:34:36.634681 8676 tensorflow.cc:2368] TRITONBACKEND_ModelInstanceFinalize: delete instance state

I0809 14:34:36.634721 8676 model_repository_manager.cc:1223] unloading: 2_queryfaiss:1

I0809 14:34:36.634782 8676 model_repository_manager.cc:1223] unloading: 1_predicttensorflow:1

I0809 14:34:36.634845 8676 model_repository_manager.cc:1223] unloading: 0_queryfeast:1

I0809 14:34:36.634891 8676 server.cc:288] All models are stopped, unloading models

I0809 14:34:36.634913 8676 server.cc:295] Timeout 30: Found 7 live models and 0 in-flight non-inference requests

I0809 14:34:36.634962 8676 tensorflow.cc:2368] TRITONBACKEND_ModelInstanceFinalize: delete instance state

I0809 14:34:36.634972 8676 tensorflow.cc:2307] TRITONBACKEND_ModelFinalize: delete model state

I0809 14:34:36.635052 8676 tensorflow.cc:2307] TRITONBACKEND_ModelFinalize: delete model state

I0809 14:34:36.642530 8676 model_repository_manager.cc:1328] successfully unloaded '1_predicttensorflow' version 1

I0809 14:34:36.663782 8676 model_repository_manager.cc:1328] successfully unloaded '5_predicttensorflow' version 1

W0809 14:34:37.157133 8676 metrics.cc:468] Unable to get energy consumption for GPU 0. Status:Success, value:0

W0809 14:34:37.157170 8676 metrics.cc:507] Unable to get memory usage for GPU 0. Memory usage status:Success, value:0. Memory total status:Success, value:0

I0809 14:34:37.635141 8676 server.cc:295] Timeout 29: Found 5 live models and 0 in-flight non-inference requests

I0809 14:34:37.961392 8676 model_repository_manager.cc:1328] successfully unloaded '6_softmaxsampling' version 1

I0809 14:34:38.114103 8676 model_repository_manager.cc:1328] successfully unloaded '2_queryfaiss' version 1

I0809 14:34:38.252013 8676 model_repository_manager.cc:1328] successfully unloaded '4_unrollfeatures' version 1

I0809 14:34:38.635276 8676 server.cc:295] Timeout 28: Found 2 live models and 0 in-flight non-inference requests

I0809 14:34:39.635406 8676 server.cc:295] Timeout 27: Found 2 live models and 0 in-flight non-inference requests

/usr/local/lib/python3.8/dist-packages/merlin/systems/dag/ops/feast.py:15: DeprecationWarning: np.float is a deprecated alias for the builtin float. To silence this warning, use float by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use np.float64 here.

Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations

ValueType.FLOAT: (np.float, False, False),

I0809 14:34:40.430855 8676 model_repository_manager.cc:1328] successfully unloaded '0_queryfeast' version 1

I0809 14:34:40.635541 8676 server.cc:295] Timeout 26: Found 1 live models and 0 in-flight non-inference requests

I0809 14:34:41.635673 8676 server.cc:295] Timeout 25: Found 1 live models and 0 in-flight non-inference requests

I0809 14:34:42.635809 8676 server.cc:295] Timeout 24: Found 1 live models and 0 in-flight non-inference requests

I0809 14:34:43.635947 8676 server.cc:295] Timeout 23: Found 1 live models and 0 in-flight non-inference requests

I0809 14:34:44.636081 8676 server.cc:295] Timeout 22: Found 1 live models and 0 in-flight non-inference requests

I0809 14:34:45.636207 8676 server.cc:295] Timeout 21: Found 1 live models and 0 in-flight non-inference requests

I0809 14:34:46.636330 8676 server.cc:295] Timeout 20: Found 1 live models and 0 in-flight non-inference requests

/usr/local/lib/python3.8/dist-packages/merlin/systems/dag/ops/feast.py:15: DeprecationWarning: np.float is a deprecated alias for the builtin float. To silence this warning, use float by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use np.float64 here.

Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations

ValueType.FLOAT: (np.float, False, False),

I0809 14:34:47.636503 8676 server.cc:295] Timeout 19: Found 1 live models and 0 in-flight non-inference requests

I0809 14:34:47.819647 8676 model_repository_manager.cc:1328] successfully unloaded '3_queryfeast' version 1

I0809 14:34:48.636634 8676 server.cc:295] Timeout 18: Found 0 live models and 0 in-flight non-inference requests

__________________________________ test_func ___________________________________


def test_func():
    with testbook(
        REPO_ROOT / "examples" / "scaling-criteo" / "02-ETL-with-NVTabular.ipynb",
        execute=False,
        timeout=180,
    ) as tb1:
        tb1.inject(
            """
            import os
            os.environ["BASE_DIR"] = "/tmp/input/criteo/"
            os.environ["INPUT_DATA_DIR"] = "/tmp/input/criteo/"
            os.environ["OUTPUT_DATA_DIR"] = "/tmp/output/criteo/"
            os.system("mkdir -p /tmp/input/criteo")
            os.system("mkdir -p /tmp/output/criteo")

            from merlin.datasets.synthetic import generate_data

            train, valid = generate_data("criteo", int(1000000), set_sizes=(0.7, 0.3))

            train.to_ddf().compute().to_parquet('/tmp/input/criteo/day_0.parquet')
            valid.to_ddf().compute().to_parquet('/tmp/input/criteo/day_1.parquet')
            """
        )





      tb1.execute()





tests/unit/examples/test_scaling_criteo_merlin_models.py:31:




/usr/local/lib/python3.8/dist-packages/testbook/client.py:147: in execute

super().execute_cell(cell, index)

/usr/local/lib/python3.8/dist-packages/nbclient/util.py:85: in wrapped

return just_run(coro(*args, **kwargs))

/usr/local/lib/python3.8/dist-packages/nbclient/util.py:60: in just_run

return loop.run_until_complete(coro)

/usr/lib/python3.8/asyncio/base_events.py:616: in run_until_complete

return future.result()

/usr/local/lib/python3.8/dist-packages/nbclient/client.py:1022: in async_execute_cell

await self._check_raise_for_error(cell, cell_index, exec_reply)




self = <testbook.client.TestbookNotebookClient object at 0x7f7c473e90a0>

cell = {'id': '31b63ee7', 'cell_type': 'code', 'metadata': {'execution': {'iopub.status.busy': '2022-08-09T14:35:08.618610Z',...ry: CUDA error at: /usr/include/rmm/mr/device/cuda_memory_resource.hpp:70: cudaErrorMemoryAllocation out of memory']}]}

cell_index = 27

exec_reply = {'buffers': [], 'content': {'ename': 'MemoryError', 'engine_info': {'engine_id': -1, 'engine_uuid': 'b9e97991-b585-4d4...e, 'engine': 'b9e97991-b585-4d48-b51b-22cd294c6c0a', 'started': '2022-08-09T14:35:08.618894Z', 'status': 'error'}, ...}


async def _check_raise_for_error(
    self, cell: NotebookNode, cell_index: int, exec_reply: t.Optional[t.Dict]
) -> None:

    if exec_reply is None:
        return None

    exec_reply_content = exec_reply['content']
    if exec_reply_content['status'] != 'error':
        return None

    cell_allows_errors = (not self.force_raise_errors) and (
        self.allow_errors
        or exec_reply_content.get('ename') in self.allow_error_names
        or "raises-exception" in cell.metadata.get("tags", [])
    )
    await run_hook(
        self.on_cell_error, cell=cell, cell_index=cell_index, execute_reply=exec_reply
    )
    if not cell_allows_errors:





      raise CellExecutionError.from_cell_and_msg(cell, exec_reply_content)





E           nbclient.exceptions.CellExecutionError: An error occurred while executing the following cell:

E           ------------------

E

E           import os

E           os.environ["BASE_DIR"] = "/tmp/input/criteo/"

E           os.environ["INPUT_DATA_DIR"] = "/tmp/input/criteo/"

E           os.environ["OUTPUT_DATA_DIR"] = "/tmp/output/criteo/"

E           os.system("mkdir -p /tmp/input/criteo")

E           os.system("mkdir -p /tmp/output/criteo")

E

E           from merlin.datasets.synthetic import generate_data

E

E           train, valid = generate_data("criteo", int(1000000), set_sizes=(0.7, 0.3))

E

E           train.to_ddf().compute().to_parquet('/tmp/input/criteo/day_0.parquet')

E           valid.to_ddf().compute().to_parquet('/tmp/input/criteo/day_1.parquet')

E

E           ------------------

E

E           �[0;31m---------------------------------------------------------------------------�[0m

E           �[0;31mMemoryError�[0m                               Traceback (most recent call last)

E           Input �[0;32mIn [16]�[0m, in �[0;36m<cell line: 12>�[0;34m()�[0m

E           �[1;32m      8�[0m �[38;5;28;01mfrom�[39;00m �[38;5;21;01mmerlin�[39;00m�[38;5;21;01m.�[39;00m�[38;5;21;01mdatasets�[39;00m�[38;5;21;01m.�[39;00m�[38;5;21;01msynthetic�[39;00m �[38;5;28;01mimport�[39;00m generate_data

E           �[1;32m     10�[0m train, valid �[38;5;241m=�[39m generate_data(�[38;5;124m"�[39m�[38;5;124mcriteo�[39m�[38;5;124m"�[39m, �[38;5;28mint�[39m(�[38;5;241m1000000�[39m), set_sizes�[38;5;241m=�[39m(�[38;5;241m0.7�[39m, �[38;5;241m0.3�[39m))

E           �[0;32m---> 12�[0m �[43mtrain�[49m�[38;5;241;43m.�[39;49m�[43mto_ddf�[49m�[43m(�[49m�[43m)�[49m�[38;5;241;43m.�[39;49m�[43mcompute�[49m�[43m(�[49m�[43m)�[49m�[38;5;241;43m.�[39;49m�[43mto_parquet�[49m�[43m(�[49m�[38;5;124;43m'�[39;49m�[38;5;124;43m/tmp/input/criteo/day_0.parquet�[39;49m�[38;5;124;43m'�[39;49m�[43m)�[49m

E           �[1;32m     13�[0m valid�[38;5;241m.�[39mto_ddf()�[38;5;241m.�[39mcompute()�[38;5;241m.�[39mto_parquet(�[38;5;124m'�[39m�[38;5;124m/tmp/input/criteo/day_1.parquet�[39m�[38;5;124m'�[39m)

E

E           File �[0;32m/usr/local/lib/python3.8/dist-packages/cudf/core/dataframe.py:5535�[0m, in �[0;36mDataFrame.to_parquet�[0;34m(self, path, args, **kwargs)�[0m

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E           File �[0;32m/usr/local/lib/python3.8/dist-packages/nvtx/nvtx.py:101�[0m, in �[0;36mannotate.call..inner�[0;34m(args, **kwargs)�[0m

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E           File �[0;32mcudf/_lib/parquet.pyx:397�[0m, in �[0;36mcudf._lib.parquet.write_parquet�[0;34m()�[0m

E

E           �[0;31mMemoryError�[0m: std::bad_alloc: out_of_memory: CUDA error at: /usr/include/rmm/mr/device/cuda_memory_resource.hpp:70: cudaErrorMemoryAllocation out of memory

E           MemoryError: std::bad_alloc: out_of_memory: CUDA error at: /usr/include/rmm/mr/device/cuda_memory_resource.hpp:70: cudaErrorMemoryAllocation out of memory


/usr/local/lib/python3.8/dist-packages/nbclient/client.py:916: CellExecutionError

----------------------------- Captured stderr call -----------------------------

2022-08-09 14:35:01,095 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize

2022-08-09 14:35:01,120 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize

/usr/local/lib/python3.8/dist-packages/cudf/core/frame.py:384: UserWarning: The deep parameter is ignored and is only included for pandas compatibility.

warnings.warn(

/usr/local/lib/python3.8/dist-packages/cudf/core/frame.py:384: UserWarning: The deep parameter is ignored and is only included for pandas compatibility.

warnings.warn(

Process Dask Worker process (from Nanny):

Traceback (most recent call last):

File "/usr/lib/python3.8/multiprocessing/process.py", line 315, in _bootstrap

self.run()

File "/usr/lib/python3.8/multiprocessing/process.py", line 108, in run

self._target(*self._args, **self._kwargs)

File "/usr/local/lib/python3.8/dist-packages/distributed/process.py", line 175, in _run

target(*args, **kwargs)

File "/usr/local/lib/python3.8/dist-packages/distributed/nanny.py", line 918, in _run

loop.run_sync(do_stop)

File "/usr/local/lib/python3.8/dist-packages/tornado/ioloop.py", line 524, in run_sync

self.start()

File "/usr/local/lib/python3.8/dist-packages/tornado/platform/asyncio.py", line 199, in start

self.asyncio_loop.run_forever()

File "/usr/lib/python3.8/asyncio/base_events.py", line 570, in run_forever

self._run_once()

File "/usr/lib/python3.8/asyncio/base_events.py", line 1859, in _run_once

handle._run()

File "/usr/lib/python3.8/asyncio/events.py", line 81, in _run

self._context.run(self._callback, *self._args)

File "/usr/local/lib/python3.8/dist-packages/tornado/ioloop.py", line 688, in 

lambda f: self._run_callback(functools.partial(callback, future))

File "/usr/local/lib/python3.8/dist-packages/tornado/ioloop.py", line 741, in _run_callback

ret = callback()

File "/usr/local/lib/python3.8/dist-packages/tornado/ioloop.py", line 765, in _discard_future_result

future.result()

File "/usr/local/lib/python3.8/dist-packages/distributed/nanny.py", line 911, in _run

loop.run_sync(run)

File "/usr/local/lib/python3.8/dist-packages/tornado/ioloop.py", line 524, in run_sync

self.start()

File "/usr/local/lib/python3.8/dist-packages/tornado/platform/asyncio.py", line 199, in start

self.asyncio_loop.run_forever()

File "/usr/lib/python3.8/asyncio/base_events.py", line 570, in run_forever

self._run_once()

File "/usr/lib/python3.8/asyncio/base_events.py", line 1859, in _run_once

handle._run()

File "/usr/lib/python3.8/asyncio/events.py", line 81, in _run

self._context.run(self._callback, *self._args)

File "/usr/local/lib/python3.8/dist-packages/distributed/worker.py", line 1116, in heartbeat

response = await retry_operation(

File "/usr/local/lib/python3.8/dist-packages/distributed/utils_comm.py", line 386, in retry_operation

return await retry(

File "/usr/local/lib/python3.8/dist-packages/distributed/utils_comm.py", line 371, in retry

return await coro()

File "/usr/local/lib/python3.8/dist-packages/distributed/core.py", line 922, in send_recv_from_rpc

return await send_recv(comm=comm, op=key, **kwargs)

File "/usr/local/lib/python3.8/dist-packages/distributed/core.py", line 691, in send_recv

response = await comm.read(deserializers=deserializers)

File "/usr/local/lib/python3.8/dist-packages/distributed/comm/tcp.py", line 234, in read

n = await stream.read_into(chunk)

File "/usr/local/lib/python3.8/dist-packages/tornado/iostream.py", line 456, in read_into

buf[:] = memoryview(self._read_buffer)[self._read_buffer_pos : end]

KeyboardInterrupt

/usr/lib/python3.8/multiprocessing/resource_tracker.py:216: UserWarning: resource_tracker: There appear to be 15 leaked semaphore objects to clean up at shutdown

warnings.warn('resource_tracker: There appear to be %d '

=========================== short test summary info ============================

FAILED tests/unit/examples/test_building_deploying_multi_stage_RecSys.py::test_func

FAILED tests/unit/examples/test_scaling_criteo_merlin_models.py::test_func - ...

=================== 2 failed, 1 passed in 137.54s (0:02:17) ====================

Build step 'Execute shell' marked build as failure

Performing Post build task...

Match found for : : True

Logical operation result is TRUE

Running script  : #!/bin/bash

cd /var/jenkins_home/

CUDA_VISIBLE_DEVICES=1 python test_res_push.py "https://api.GitHub.com/repos/NVIDIA-Merlin/Merlin/issues/$ghprbPullId/comments" "/var/jenkins_home/jobs/$JOB_NAME/builds/$BUILD_NUMBER/log"

[merlin_merlin] $ /bin/bash /tmp/jenkins14814933270477495450.sh

@nvidia-merlin-bot
Copy link
Contributor

Click to view CI Results
GitHub pull request #524 of commit a1c95f262485c8f5d7f6e07d42b1a678d011f7bf, no merge conflicts.
Running as SYSTEM
Setting status of a1c95f262485c8f5d7f6e07d42b1a678d011f7bf to PENDING with url https://10.20.13.93:8080/job/merlin_merlin/339/console and message: 'Pending'
Using context: Jenkins
Building on master in workspace /var/jenkins_home/workspace/merlin_merlin
using credential systems-login
 > git rev-parse --is-inside-work-tree # timeout=10
Fetching changes from the remote Git repository
 > git config remote.origin.url https://github.com/NVIDIA-Merlin/Merlin # timeout=10
Fetching upstream changes from https://github.com/NVIDIA-Merlin/Merlin
 > git --version # timeout=10
using GIT_ASKPASS to set credentials login for merlin-systems
 > git fetch --tags --force --progress -- https://github.com/NVIDIA-Merlin/Merlin +refs/pull/524/*:refs/remotes/origin/pr/524/* # timeout=10
 > git rev-parse a1c95f262485c8f5d7f6e07d42b1a678d011f7bf^{commit} # timeout=10
Checking out Revision a1c95f262485c8f5d7f6e07d42b1a678d011f7bf (detached)
 > git config core.sparsecheckout # timeout=10
 > git checkout -f a1c95f262485c8f5d7f6e07d42b1a678d011f7bf # timeout=10
Commit message: "Merge branch 'main' into fix/cmake-install-v2"
 > git rev-list --no-walk b7c1dc3c34920a5fa4c512d129c0f8c7097ce4d1 # timeout=10
[merlin_merlin] $ /bin/bash /tmp/jenkins3151402713106632480.sh
============================= test session starts ==============================
platform linux -- Python 3.8.10, pytest-7.1.2, pluggy-1.0.0
rootdir: /var/jenkins_home/workspace/merlin_merlin/merlin
plugins: anyio-3.6.1, xdist-2.5.0, forked-1.4.0, cov-3.0.0
collected 3 items

tests/unit/test_version.py .                                             [ 33%]

tests/unit/examples/test_building_deploying_multi_stage_RecSys.py F      [ 66%]

tests/unit/examples/test_scaling_criteo_merlin_models.py .               [100%]


=================================== FAILURES ===================================

__________________________________ test_func ___________________________________


self = <testbook.client.TestbookNotebookClient object at 0x7fc0487e1610>

cell = [53], kwargs = {}, cell_indexes = [53], executed_cells = [], idx = 53


def execute_cell(self, cell, **kwargs) -> Union[Dict, List[Dict]]:
    """
    Executes a cell or list of cells
    """
    if isinstance(cell, slice):
        start, stop = self._cell_index(cell.start), self._cell_index(cell.stop)
        if cell.step is not None:
            raise TestbookError('testbook does not support step argument')

        cell = range(start, stop + 1)
    elif isinstance(cell, str) or isinstance(cell, int):
        cell = [cell]

    cell_indexes = cell

    if all(isinstance(x, str) for x in cell):
        cell_indexes = [self._cell_index(tag) for tag in cell]

    executed_cells = []
    for idx in cell_indexes:
        try:





          cell = super().execute_cell(self.nb['cells'][idx], idx, **kwargs)





/usr/local/lib/python3.8/dist-packages/testbook/client.py:133:




args = (<testbook.client.TestbookNotebookClient object at 0x7fc0487e1610>, {'id': '1a690c0b', 'cell_type': 'code', 'metadata'...ast.py, line 299 in transform>]"\n\nAt:\n  /tmp/examples/poc_ensemble/3_queryfeast/1/model.py(122): execute\n']}]}, 53)

kwargs = {}


def wrapped(*args, **kwargs):





  return just_run(coro(*args, **kwargs))





/usr/local/lib/python3.8/dist-packages/nbclient/util.py:85:




coro = <coroutine object NotebookClient.async_execute_cell at 0x7fc047691dc0>


def just_run(coro: Awaitable) -> Any:
    """Make the coroutine run, even if there is an event loop running (using nest_asyncio)"""
    try:
        loop = asyncio.get_running_loop()
    except RuntimeError:
        loop = None
    if loop is None:
        had_running_loop = False
        loop = asyncio.new_event_loop()
        asyncio.set_event_loop(loop)
    else:
        had_running_loop = True
    if had_running_loop:
        # if there is a running loop, we patch using nest_asyncio
        # to have reentrant event loops
        check_ipython()
        import nest_asyncio

        nest_asyncio.apply()
        check_patch_tornado()





  return loop.run_until_complete(coro)





/usr/local/lib/python3.8/dist-packages/nbclient/util.py:60:




self = <_UnixSelectorEventLoop running=False closed=False debug=False>

future = <Task finished name='Task-369' coro=<NotebookClient.async_execute_cell() done, defined at /usr/local/lib/python3.8/dis...ps/feast.py, line 299 in transform>]"\n\nAt:\n  /tmp/examples/poc_ensemble/3_queryfeast/1/model.py(122): execute\n\n')>


def run_until_complete(self, future):
    """Run until the Future is done.

    If the argument is a coroutine, it is wrapped in a Task.

    WARNING: It would be disastrous to call run_until_complete()
    with the same coroutine twice -- it would wrap it in two
    different Tasks and that can't be good.

    Return the Future's result, or raise its exception.
    """
    self._check_closed()
    self._check_running()

    new_task = not futures.isfuture(future)
    future = tasks.ensure_future(future, loop=self)
    if new_task:
        # An exception is raised if the future didn't complete, so there
        # is no need to log the "destroy pending task" message
        future._log_destroy_pending = False

    future.add_done_callback(_run_until_complete_cb)
    try:
        self.run_forever()
    except:
        if new_task and future.done() and not future.cancelled():
            # The coroutine raised a BaseException. Consume the exception
            # to not log a warning, the caller doesn't have access to the
            # local task.
            future.exception()
        raise
    finally:
        future.remove_done_callback(_run_until_complete_cb)
    if not future.done():
        raise RuntimeError('Event loop stopped before Future completed.')





  return future.result()





/usr/lib/python3.8/asyncio/base_events.py:616:




self = <testbook.client.TestbookNotebookClient object at 0x7fc0487e1610>

cell = {'id': '1a690c0b', 'cell_type': 'code', 'metadata': {'execution': {'iopub.status.busy': '2022-08-10T11:57:48.448734Z',...ps/feast.py, line 299 in transform>]"\n\nAt:\n  /tmp/examples/poc_ensemble/3_queryfeast/1/model.py(122): execute\n']}]}

cell_index = 53, execution_count = None, store_history = True


async def async_execute_cell(
    self,
    cell: NotebookNode,
    cell_index: int,
    execution_count: t.Optional[int] = None,
    store_history: bool = True,
) -> NotebookNode:
    """
    Executes a single code cell.

    To execute all cells see :meth:`execute`.

    Parameters
    ----------
    cell : nbformat.NotebookNode
        The cell which is currently being processed.
    cell_index : int
        The position of the cell within the notebook object.
    execution_count : int
        The execution count to be assigned to the cell (default: Use kernel response)
    store_history : bool
        Determines if history should be stored in the kernel (default: False).
        Specific to ipython kernels, which can store command histories.

    Returns
    -------
    output : dict
        The execution output payload (or None for no output).

    Raises
    ------
    CellExecutionError
        If execution failed and should raise an exception, this will be raised
        with defaults about the failure.

    Returns
    -------
    cell : NotebookNode
        The cell which was just processed.
    """
    assert self.kc is not None

    await run_hook(self.on_cell_start, cell=cell, cell_index=cell_index)

    if cell.cell_type != 'code' or not cell.source.strip():
        self.log.debug("Skipping non-executing cell %s", cell_index)
        return cell

    if self.skip_cells_with_tag in cell.metadata.get("tags", []):
        self.log.debug("Skipping tagged cell %s", cell_index)
        return cell

    if self.record_timing:  # clear execution metadata prior to execution
        cell['metadata']['execution'] = {}

    self.log.debug("Executing cell:\n%s", cell.source)

    cell_allows_errors = (not self.force_raise_errors) and (
        self.allow_errors or "raises-exception" in cell.metadata.get("tags", [])
    )

    await run_hook(self.on_cell_execute, cell=cell, cell_index=cell_index)
    parent_msg_id = await ensure_async(
        self.kc.execute(
            cell.source, store_history=store_history, stop_on_error=not cell_allows_errors
        )
    )
    await run_hook(self.on_cell_complete, cell=cell, cell_index=cell_index)
    # We launched a code cell to execute
    self.code_cells_executed += 1
    exec_timeout = self._get_timeout(cell)

    cell.outputs = []
    self.clear_before_next_output = False

    task_poll_kernel_alive = asyncio.ensure_future(self._async_poll_kernel_alive())
    task_poll_output_msg = asyncio.ensure_future(
        self._async_poll_output_msg(parent_msg_id, cell, cell_index)
    )
    self.task_poll_for_reply = asyncio.ensure_future(
        self._async_poll_for_reply(
            parent_msg_id, cell, exec_timeout, task_poll_output_msg, task_poll_kernel_alive
        )
    )
    try:
        exec_reply = await self.task_poll_for_reply
    except asyncio.CancelledError:
        # can only be cancelled by task_poll_kernel_alive when the kernel is dead
        task_poll_output_msg.cancel()
        raise DeadKernelError("Kernel died")
    except Exception as e:
        # Best effort to cancel request if it hasn't been resolved
        try:
            # Check if the task_poll_output is doing the raising for us
            if not isinstance(e, CellControlSignal):
                task_poll_output_msg.cancel()
        finally:
            raise

    if execution_count:
        cell['execution_count'] = execution_count
    await run_hook(
        self.on_cell_executed, cell=cell, cell_index=cell_index, execute_reply=exec_reply
    )





  await self._check_raise_for_error(cell, cell_index, exec_reply)





/usr/local/lib/python3.8/dist-packages/nbclient/client.py:1022:




self = <testbook.client.TestbookNotebookClient object at 0x7fc0487e1610>

cell = {'id': '1a690c0b', 'cell_type': 'code', 'metadata': {'execution': {'iopub.status.busy': '2022-08-10T11:57:48.448734Z',...ps/feast.py, line 299 in transform>]"\n\nAt:\n  /tmp/examples/poc_ensemble/3_queryfeast/1/model.py(122): execute\n']}]}

cell_index = 53

exec_reply = {'buffers': [], 'content': {'ename': 'InferenceServerException', 'engine_info': {'engine_id': -1, 'engine_uuid': 'cd0a...e, 'engine': 'cd0a5f3e-5bce-4228-ba69-f0e127c3795d', 'started': '2022-08-10T11:57:48.448975Z', 'status': 'error'}, ...}


async def _check_raise_for_error(
    self, cell: NotebookNode, cell_index: int, exec_reply: t.Optional[t.Dict]
) -> None:

    if exec_reply is None:
        return None

    exec_reply_content = exec_reply['content']
    if exec_reply_content['status'] != 'error':
        return None

    cell_allows_errors = (not self.force_raise_errors) and (
        self.allow_errors
        or exec_reply_content.get('ename') in self.allow_error_names
        or "raises-exception" in cell.metadata.get("tags", [])
    )
    await run_hook(
        self.on_cell_error, cell=cell, cell_index=cell_index, execute_reply=exec_reply
    )
    if not cell_allows_errors:





      raise CellExecutionError.from_cell_and_msg(cell, exec_reply_content)





E           nbclient.exceptions.CellExecutionError: An error occurred while executing the following cell:

E           ------------------

E

E           import shutil

E           from merlin.models.loader.tf_utils import configure_tensorflow

E           configure_tensorflow()

E           from merlin.systems.triton.utils import run_ensemble_on_tritonserver

E           response = run_ensemble_on_tritonserver(

E               "/tmp/examples/poc_ensemble", outputs, request, "ensemble_model"

E           )

E           response = [x.tolist()[0] for x in response["ordered_ids"]]

E           shutil.rmtree("/tmp/examples/", ignore_errors=True)

E

E           ------------------

E

E           �[0;31m---------------------------------------------------------------------------�[0m

E           �[0;31mInferenceServerException�[0m                  Traceback (most recent call last)

E           Input �[0;32mIn [32]�[0m, in �[0;36m<cell line: 5>�[0;34m()�[0m

E           �[1;32m      3�[0m configure_tensorflow()

E           �[1;32m      4�[0m �[38;5;28;01mfrom�[39;00m �[38;5;21;01mmerlin�[39;00m�[38;5;21;01m.�[39;00m�[38;5;21;01msystems�[39;00m�[38;5;21;01m.�[39;00m�[38;5;21;01mtriton�[39;00m�[38;5;21;01m.�[39;00m�[38;5;21;01mutils�[39;00m �[38;5;28;01mimport�[39;00m run_ensemble_on_tritonserver

E           �[0;32m----> 5�[0m response �[38;5;241m=�[39m �[43mrun_ensemble_on_tritonserver�[49m�[43m(�[49m

E           �[1;32m      6�[0m �[43m    �[49m�[38;5;124;43m"�[39;49m�[38;5;124;43m/tmp/examples/poc_ensemble�[39;49m�[38;5;124;43m"�[39;49m�[43m,�[49m�[43m �[49m�[43moutputs�[49m�[43m,�[49m�[43m �[49m�[43mrequest�[49m�[43m,�[49m�[43m �[49m�[38;5;124;43m"�[39;49m�[38;5;124;43mensemble_model�[39;49m�[38;5;124;43m"�[39;49m

E           �[1;32m      7�[0m �[43m)�[49m

E           �[1;32m      8�[0m response �[38;5;241m=�[39m [x�[38;5;241m.�[39mtolist()[�[38;5;241m0�[39m] �[38;5;28;01mfor�[39;00m x �[38;5;129;01min�[39;00m response[�[38;5;124m"�[39m�[38;5;124mordered_ids�[39m�[38;5;124m"�[39m]]

E           �[1;32m      9�[0m shutil�[38;5;241m.�[39mrmtree(�[38;5;124m"�[39m�[38;5;124m/tmp/examples/�[39m�[38;5;124m"�[39m, ignore_errors�[38;5;241m=�[39m�[38;5;28;01mTrue�[39;00m)

E

E           File �[0;32m/usr/local/lib/python3.8/dist-packages/merlin/systems/triton/utils.py:93�[0m, in �[0;36mrun_ensemble_on_tritonserver�[0;34m(tmpdir, output_columns, df, model_name)�[0m

E           �[1;32m     91�[0m response �[38;5;241m=�[39m �[38;5;28;01mNone�[39;00m

E           �[1;32m     92�[0m �[38;5;28;01mwith�[39;00m run_triton_server(tmpdir) �[38;5;28;01mas�[39;00m client:

E           �[0;32m---> 93�[0m     response �[38;5;241m=�[39m �[43msend_triton_request�[49m�[43m(�[49m�[43mdf�[49m�[43m,�[49m�[43m �[49m�[43moutput_columns�[49m�[43m,�[49m�[43m �[49m�[43mclient�[49m�[38;5;241;43m=�[39;49m�[43mclient�[49m�[43m,�[49m�[43m �[49m�[43mtriton_model�[49m�[38;5;241;43m=�[39;49m�[43mmodel_name�[49m�[43m)�[49m

E           �[1;32m     95�[0m �[38;5;28;01mreturn�[39;00m response

E

E           File �[0;32m/usr/local/lib/python3.8/dist-packages/merlin/systems/triton/utils.py:141�[0m, in �[0;36msend_triton_request�[0;34m(df, outputs_list, client, endpoint, request_id, triton_model)�[0m

E           �[1;32m    139�[0m outputs �[38;5;241m=�[39m [grpcclient�[38;5;241m.�[39mInferRequestedOutput(col) �[38;5;28;01mfor�[39;00m col �[38;5;129;01min�[39;00m outputs_list]

E           �[1;32m    140�[0m �[38;5;28;01mwith�[39;00m client:

E           �[0;32m--> 141�[0m     response �[38;5;241m=�[39m �[43mclient�[49m�[38;5;241;43m.�[39;49m�[43minfer�[49m�[43m(�[49m�[43mtriton_model�[49m�[43m,�[49m�[43m �[49m�[43minputs�[49m�[43m,�[49m�[43m �[49m�[43mrequest_id�[49m�[38;5;241;43m=�[39;49m�[43mrequest_id�[49m�[43m,�[49m�[43m �[49m�[43moutputs�[49m�[38;5;241;43m=�[39;49m�[43moutputs�[49m�[43m)�[49m

E           �[1;32m    143�[0m results �[38;5;241m=�[39m {}

E           �[1;32m    144�[0m �[38;5;28;01mfor�[39;00m col �[38;5;129;01min�[39;00m outputs_list:

E

E           File �[0;32m/usr/local/lib/python3.8/dist-packages/tritonclient/grpc/init.py:1322�[0m, in �[0;36mInferenceServerClient.infer�[0;34m(self, model_name, inputs, model_version, outputs, request_id, sequence_id, sequence_start, sequence_end, priority, timeout, client_timeout, headers, compression_algorithm)�[0m

E           �[1;32m   1320�[0m     �[38;5;28;01mreturn�[39;00m result

E           �[1;32m   1321�[0m �[38;5;28;01mexcept�[39;00m grpc�[38;5;241m.�[39mRpcError �[38;5;28;01mas�[39;00m rpc_error:

E           �[0;32m-> 1322�[0m     �[43mraise_error_grpc�[49m�[43m(�[49m�[43mrpc_error�[49m�[43m)�[49m

E

E           File �[0;32m/usr/local/lib/python3.8/dist-packages/tritonclient/grpc/init.py:62�[0m, in �[0;36mraise_error_grpc�[0;34m(rpc_error)�[0m

E           �[1;32m     61�[0m �[38;5;28;01mdef�[39;00m �[38;5;21mraise_error_grpc�[39m(rpc_error):

E           �[0;32m---> 62�[0m     �[38;5;28;01mraise�[39;00m get_error_grpc(rpc_error) �[38;5;28;01mfrom�[39;00m �[38;5;28mNone�[39m

E

E           �[0;31mInferenceServerException�[0m: [StatusCode.INTERNAL] in ensemble 'ensemble_model', Failed to process the request(s) for model instance '3_queryfeast', message: TypeError: init(): incompatible constructor arguments. The following argument types are supported:

E               1. c_python_backend_utils.InferenceResponse(output_tensors: List[c_python_backend_utils.Tensor], error: c_python_backend_utils.TritonError = None)

E

E           Invoked with: kwargs: tensors=[], error="<class 'TypeError'>, int() argument must be a string, a bytes-like object or a number, not 'NoneType', [<FrameSummary file /tmp/examples/poc_ensemble/3_queryfeast/1/model.py, line 105 in execute>, <FrameSummary file /usr/local/lib/python3.8/dist-packages/merlin/systems/dag/op_runner.py, line 38 in execute>, <FrameSummary file /usr/local/lib/python3.8/dist-packages/merlin/systems/dag/ops/feast.py, line 299 in transform>]"

E

E           At:

E             /tmp/examples/poc_ensemble/3_queryfeast/1/model.py(122): execute

E

E           InferenceServerException: [StatusCode.INTERNAL] in ensemble 'ensemble_model', Failed to process the request(s) for model instance '3_queryfeast', message: TypeError: init(): incompatible constructor arguments. The following argument types are supported:

E               1. c_python_backend_utils.InferenceResponse(output_tensors: List[c_python_backend_utils.Tensor], error: c_python_backend_utils.TritonError = None)

E

E           Invoked with: kwargs: tensors=[], error="<class 'TypeError'>, int() argument must be a string, a bytes-like object or a number, not 'NoneType', [<FrameSummary file /tmp/examples/poc_ensemble/3_queryfeast/1/model.py, line 105 in execute>, <FrameSummary file /usr/local/lib/python3.8/dist-packages/merlin/systems/dag/op_runner.py, line 38 in execute>, <FrameSummary file /usr/local/lib/python3.8/dist-packages/merlin/systems/dag/ops/feast.py, line 299 in transform>]"

E

E           At:

E             /tmp/examples/poc_ensemble/3_queryfeast/1/model.py(122): execute


/usr/local/lib/python3.8/dist-packages/nbclient/client.py:916: CellExecutionError


During handling of the above exception, another exception occurred:


def test_func():
    with testbook(
        REPO_ROOT
        / "examples"
        / "Building-and-deploying-multi-stage-RecSys"
        / "01-Building-Recommender-Systems-with-Merlin.ipynb",
        execute=False,
    ) as tb1:
        tb1.inject(
            """
            import os
            os.environ["DATA_FOLDER"] = "/tmp/data/"
            os.environ["NUM_ROWS"] = "10000"
            os.system("mkdir -p /tmp/examples")
            os.environ["BASE_DIR"] = "/tmp/examples/"
            """
        )
        tb1.execute()
        assert os.path.isdir("/tmp/examples/dlrm")
        assert os.path.isdir("/tmp/examples/feature_repo")
        assert os.path.isdir("/tmp/examples/query_tower")
        assert os.path.isfile("/tmp/examples/item_embeddings.parquet")
        assert os.path.isfile("/tmp/examples/feature_repo/user_features.py")
        assert os.path.isfile("/tmp/examples/feature_repo/item_features.py")

    with testbook(
        REPO_ROOT
        / "examples"
        / "Building-and-deploying-multi-stage-RecSys"
        / "02-Deploying-multi-stage-RecSys-with-Merlin-Systems.ipynb",
        execute=False,
    ) as tb2:
        tb2.inject(
            """
            import os
            os.environ["DATA_FOLDER"] = "/tmp/data/"
            os.environ["BASE_DIR"] = "/tmp/examples/"
            """
        )
        NUM_OF_CELLS = len(tb2.cells)
        tb2.execute_cell(list(range(0, NUM_OF_CELLS - 3)))
        top_k = tb2.ref("top_k")
        outputs = tb2.ref("outputs")
        assert outputs[0] == "ordered_ids"





      tb2.inject(





            """
            import shutil
            from merlin.models.loader.tf_utils import configure_tensorflow
            configure_tensorflow()
            from merlin.systems.triton.utils import run_ensemble_on_tritonserver
            response = run_ensemble_on_tritonserver(
                "/tmp/examples/poc_ensemble", outputs, request, "ensemble_model"
            )
            response = [x.tolist()[0] for x in response["ordered_ids"]]
            shutil.rmtree("/tmp/examples/", ignore_errors=True)
            """
        )



tests/unit/examples/test_building_deploying_multi_stage_RecSys.py:57:




/usr/local/lib/python3.8/dist-packages/testbook/client.py:237: in inject

cell = TestbookNode(self.execute_cell(inject_idx)) if run else TestbookNode(code_cell)




self = <testbook.client.TestbookNotebookClient object at 0x7fc0487e1610>

cell = [53], kwargs = {}, cell_indexes = [53], executed_cells = [], idx = 53


def execute_cell(self, cell, **kwargs) -> Union[Dict, List[Dict]]:
    """
    Executes a cell or list of cells
    """
    if isinstance(cell, slice):
        start, stop = self._cell_index(cell.start), self._cell_index(cell.stop)
        if cell.step is not None:
            raise TestbookError('testbook does not support step argument')

        cell = range(start, stop + 1)
    elif isinstance(cell, str) or isinstance(cell, int):
        cell = [cell]

    cell_indexes = cell

    if all(isinstance(x, str) for x in cell):
        cell_indexes = [self._cell_index(tag) for tag in cell]

    executed_cells = []
    for idx in cell_indexes:
        try:
            cell = super().execute_cell(self.nb['cells'][idx], idx, **kwargs)
        except CellExecutionError as ce:





          raise TestbookRuntimeError(ce.evalue, ce, self._get_error_class(ce.ename))





E               testbook.exceptions.TestbookRuntimeError: An error occurred while executing the following cell:

E               ------------------

E

E               import shutil

E               from merlin.models.loader.tf_utils import configure_tensorflow

E               configure_tensorflow()

E               from merlin.systems.triton.utils import run_ensemble_on_tritonserver

E               response = run_ensemble_on_tritonserver(

E                   "/tmp/examples/poc_ensemble", outputs, request, "ensemble_model"

E               )

E               response = [x.tolist()[0] for x in response["ordered_ids"]]

E               shutil.rmtree("/tmp/examples/", ignore_errors=True)

E

E               ------------------

E

E               �[0;31m---------------------------------------------------------------------------�[0m

E               �[0;31mInferenceServerException�[0m                  Traceback (most recent call last)

E               Input �[0;32mIn [32]�[0m, in �[0;36m<cell line: 5>�[0;34m()�[0m

E               �[1;32m      3�[0m configure_tensorflow()

E               �[1;32m      4�[0m �[38;5;28;01mfrom�[39;00m �[38;5;21;01mmerlin�[39;00m�[38;5;21;01m.�[39;00m�[38;5;21;01msystems�[39;00m�[38;5;21;01m.�[39;00m�[38;5;21;01mtriton�[39;00m�[38;5;21;01m.�[39;00m�[38;5;21;01mutils�[39;00m �[38;5;28;01mimport�[39;00m run_ensemble_on_tritonserver

E               �[0;32m----> 5�[0m response �[38;5;241m=�[39m �[43mrun_ensemble_on_tritonserver�[49m�[43m(�[49m

E               �[1;32m      6�[0m �[43m    �[49m�[38;5;124;43m"�[39;49m�[38;5;124;43m/tmp/examples/poc_ensemble�[39;49m�[38;5;124;43m"�[39;49m�[43m,�[49m�[43m �[49m�[43moutputs�[49m�[43m,�[49m�[43m �[49m�[43mrequest�[49m�[43m,�[49m�[43m �[49m�[38;5;124;43m"�[39;49m�[38;5;124;43mensemble_model�[39;49m�[38;5;124;43m"�[39;49m

E               �[1;32m      7�[0m �[43m)�[49m

E               �[1;32m      8�[0m response �[38;5;241m=�[39m [x�[38;5;241m.�[39mtolist()[�[38;5;241m0�[39m] �[38;5;28;01mfor�[39;00m x �[38;5;129;01min�[39;00m response[�[38;5;124m"�[39m�[38;5;124mordered_ids�[39m�[38;5;124m"�[39m]]

E               �[1;32m      9�[0m shutil�[38;5;241m.�[39mrmtree(�[38;5;124m"�[39m�[38;5;124m/tmp/examples/�[39m�[38;5;124m"�[39m, ignore_errors�[38;5;241m=�[39m�[38;5;28;01mTrue�[39;00m)

E

E               File �[0;32m/usr/local/lib/python3.8/dist-packages/merlin/systems/triton/utils.py:93�[0m, in �[0;36mrun_ensemble_on_tritonserver�[0;34m(tmpdir, output_columns, df, model_name)�[0m

E               �[1;32m     91�[0m response �[38;5;241m=�[39m �[38;5;28;01mNone�[39;00m

E               �[1;32m     92�[0m �[38;5;28;01mwith�[39;00m run_triton_server(tmpdir) �[38;5;28;01mas�[39;00m client:

E               �[0;32m---> 93�[0m     response �[38;5;241m=�[39m �[43msend_triton_request�[49m�[43m(�[49m�[43mdf�[49m�[43m,�[49m�[43m �[49m�[43moutput_columns�[49m�[43m,�[49m�[43m �[49m�[43mclient�[49m�[38;5;241;43m=�[39;49m�[43mclient�[49m�[43m,�[49m�[43m �[49m�[43mtriton_model�[49m�[38;5;241;43m=�[39;49m�[43mmodel_name�[49m�[43m)�[49m

E               �[1;32m     95�[0m �[38;5;28;01mreturn�[39;00m response

E

E               File �[0;32m/usr/local/lib/python3.8/dist-packages/merlin/systems/triton/utils.py:141�[0m, in �[0;36msend_triton_request�[0;34m(df, outputs_list, client, endpoint, request_id, triton_model)�[0m

E               �[1;32m    139�[0m outputs �[38;5;241m=�[39m [grpcclient�[38;5;241m.�[39mInferRequestedOutput(col) �[38;5;28;01mfor�[39;00m col �[38;5;129;01min�[39;00m outputs_list]

E               �[1;32m    140�[0m �[38;5;28;01mwith�[39;00m client:

E               �[0;32m--> 141�[0m     response �[38;5;241m=�[39m �[43mclient�[49m�[38;5;241;43m.�[39;49m�[43minfer�[49m�[43m(�[49m�[43mtriton_model�[49m�[43m,�[49m�[43m �[49m�[43minputs�[49m�[43m,�[49m�[43m �[49m�[43mrequest_id�[49m�[38;5;241;43m=�[39;49m�[43mrequest_id�[49m�[43m,�[49m�[43m �[49m�[43moutputs�[49m�[38;5;241;43m=�[39;49m�[43moutputs�[49m�[43m)�[49m

E               �[1;32m    143�[0m results �[38;5;241m=�[39m {}

E               �[1;32m    144�[0m �[38;5;28;01mfor�[39;00m col �[38;5;129;01min�[39;00m outputs_list:

E

E               File �[0;32m/usr/local/lib/python3.8/dist-packages/tritonclient/grpc/init.py:1322�[0m, in �[0;36mInferenceServerClient.infer�[0;34m(self, model_name, inputs, model_version, outputs, request_id, sequence_id, sequence_start, sequence_end, priority, timeout, client_timeout, headers, compression_algorithm)�[0m

E               �[1;32m   1320�[0m     �[38;5;28;01mreturn�[39;00m result

E               �[1;32m   1321�[0m �[38;5;28;01mexcept�[39;00m grpc�[38;5;241m.�[39mRpcError �[38;5;28;01mas�[39;00m rpc_error:

E               �[0;32m-> 1322�[0m     �[43mraise_error_grpc�[49m�[43m(�[49m�[43mrpc_error�[49m�[43m)�[49m

E

E               File �[0;32m/usr/local/lib/python3.8/dist-packages/tritonclient/grpc/init.py:62�[0m, in �[0;36mraise_error_grpc�[0;34m(rpc_error)�[0m

E               �[1;32m     61�[0m �[38;5;28;01mdef�[39;00m �[38;5;21mraise_error_grpc�[39m(rpc_error):

E               �[0;32m---> 62�[0m     �[38;5;28;01mraise�[39;00m get_error_grpc(rpc_error) �[38;5;28;01mfrom�[39;00m �[38;5;28mNone�[39m

E

E               �[0;31mInferenceServerException�[0m: [StatusCode.INTERNAL] in ensemble 'ensemble_model', Failed to process the request(s) for model instance '3_queryfeast', message: TypeError: init(): incompatible constructor arguments. The following argument types are supported:

E                   1. c_python_backend_utils.InferenceResponse(output_tensors: List[c_python_backend_utils.Tensor], error: c_python_backend_utils.TritonError = None)

E

E               Invoked with: kwargs: tensors=[], error="<class 'TypeError'>, int() argument must be a string, a bytes-like object or a number, not 'NoneType', [<FrameSummary file /tmp/examples/poc_ensemble/3_queryfeast/1/model.py, line 105 in execute>, <FrameSummary file /usr/local/lib/python3.8/dist-packages/merlin/systems/dag/op_runner.py, line 38 in execute>, <FrameSummary file /usr/local/lib/python3.8/dist-packages/merlin/systems/dag/ops/feast.py, line 299 in transform>]"

E

E               At:

E                 /tmp/examples/poc_ensemble/3_queryfeast/1/model.py(122): execute

E

E               InferenceServerException: [StatusCode.INTERNAL] in ensemble 'ensemble_model', Failed to process the request(s) for model instance '3_queryfeast', message: TypeError: init(): incompatible constructor arguments. The following argument types are supported:

E                   1. c_python_backend_utils.InferenceResponse(output_tensors: List[c_python_backend_utils.Tensor], error: c_python_backend_utils.TritonError = None)

E

E               Invoked with: kwargs: tensors=[], error="<class 'TypeError'>, int() argument must be a string, a bytes-like object or a number, not 'NoneType', [<FrameSummary file /tmp/examples/poc_ensemble/3_queryfeast/1/model.py, line 105 in execute>, <FrameSummary file /usr/local/lib/python3.8/dist-packages/merlin/systems/dag/op_runner.py, line 38 in execute>, <FrameSummary file /usr/local/lib/python3.8/dist-packages/merlin/systems/dag/ops/feast.py, line 299 in transform>]"

E

E               At:

E                 /tmp/examples/poc_ensemble/3_queryfeast/1/model.py(122): execute


/usr/local/lib/python3.8/dist-packages/testbook/client.py:135: TestbookRuntimeError

----------------------------- Captured stdout call -----------------------------

Signal (2) received.

----------------------------- Captured stderr call -----------------------------

2022-08-10 11:56:28.257461: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA

To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.

2022-08-10 11:56:30.270260: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 1627 MB memory:  -> device: 0, name: Tesla P100-DGXS-16GB, pci bus id: 0000:07:00.0, compute capability: 6.0

2022-08-10 11:56:30.271010: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 15153 MB memory:  -> device: 1, name: Tesla P100-DGXS-16GB, pci bus id: 0000:08:00.0, compute capability: 6.0

Error in atexit._run_exitfuncs:

Traceback (most recent call last):

File "/usr/lib/python3.8/logging/init.py", line 2127, in shutdown

h.close()

File "/usr/local/lib/python3.8/dist-packages/absl/logging/init.py", line 934, in close

self.stream.close()

File "/usr/local/lib/python3.8/dist-packages/ipykernel/iostream.py", line 438, in close

self.watch_fd_thread.join()

AttributeError: 'OutStream' object has no attribute 'watch_fd_thread'

WARNING clustering 239 points to 32 centroids: please provide at least 1248 training points

2022-08-10 11:57:41.627317: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA

To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.

2022-08-10 11:57:43.588038: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 1627 MB memory:  -> device: 0, name: Tesla P100-DGXS-16GB, pci bus id: 0000:07:00.0, compute capability: 6.0

2022-08-10 11:57:43.588765: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 15153 MB memory:  -> device: 1, name: Tesla P100-DGXS-16GB, pci bus id: 0000:08:00.0, compute capability: 6.0

I0810 11:57:48.716483 6047 pinned_memory_manager.cc:240] Pinned memory pool is created at '0x7fe1ce000000' with size 268435456

I0810 11:57:48.717271 6047 cuda_memory_manager.cc:105] CUDA memory pool is created on device 0 with size 67108864

I0810 11:57:48.724415 6047 model_repository_manager.cc:1191] loading: 0_queryfeast:1

I0810 11:57:48.824754 6047 model_repository_manager.cc:1191] loading: 1_predicttensorflow:1

I0810 11:57:48.831865 6047 python.cc:2388] TRITONBACKEND_ModelInstanceInitialize: 0_queryfeast (GPU device 0)

I0810 11:57:48.925155 6047 model_repository_manager.cc:1191] loading: 2_queryfaiss:1

I0810 11:57:49.025374 6047 model_repository_manager.cc:1191] loading: 3_queryfeast:1

I0810 11:57:49.125668 6047 model_repository_manager.cc:1191] loading: 4_unrollfeatures:1

I0810 11:57:49.225951 6047 model_repository_manager.cc:1191] loading: 5_predicttensorflow:1

I0810 11:57:49.326233 6047 model_repository_manager.cc:1191] loading: 6_softmaxsampling:1

I0810 11:57:51.136131 6047 model_repository_manager.cc:1345] successfully loaded '0_queryfeast' version 1

I0810 11:57:51.417250 6047 tensorflow.cc:2181] TRITONBACKEND_Initialize: tensorflow

I0810 11:57:51.417291 6047 tensorflow.cc:2191] Triton TRITONBACKEND API version: 1.9

I0810 11:57:51.417298 6047 tensorflow.cc:2197] 'tensorflow' TRITONBACKEND API version: 1.9

I0810 11:57:51.417304 6047 tensorflow.cc:2221] backend configuration:

{"cmdline":{"auto-complete-config":"false","backend-directory":"/opt/tritonserver/backends","min-compute-capability":"6.000000","version":"2","default-max-batch-size":"4"}}

I0810 11:57:51.417341 6047 tensorflow.cc:2281] TRITONBACKEND_ModelInitialize: 1_predicttensorflow (version 1)

I0810 11:57:51.419654 6047 tensorflow.cc:2281] TRITONBACKEND_ModelInitialize: 5_predicttensorflow (version 1)

I0810 11:57:51.422476 6047 tensorflow.cc:2330] TRITONBACKEND_ModelInstanceInitialize: 1_predicttensorflow (GPU device 0)

2022-08-10 11:57:51.764881: I tensorflow/cc/saved_model/reader.cc:43] Reading SavedModel from: /tmp/examples/poc_ensemble/1_predicttensorflow/1/model.savedmodel

2022-08-10 11:57:51.769186: I tensorflow/cc/saved_model/reader.cc:78] Reading meta graph with tags { serve }

2022-08-10 11:57:51.769217: I tensorflow/cc/saved_model/reader.cc:119] Reading SavedModel debug info (if present) from: /tmp/examples/poc_ensemble/1_predicttensorflow/1/model.savedmodel

2022-08-10 11:57:51.769325: I tensorflow/core/platform/cpu_feature_guard.cc:152] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE3 SSE4.1 SSE4.2 AVX

To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.

2022-08-10 11:57:51.806461: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 12648 MB memory:  -> device: 0, name: Tesla P100-DGXS-16GB, pci bus id: 0000:07:00.0, compute capability: 6.0

2022-08-10 11:57:51.848209: I tensorflow/cc/saved_model/loader.cc:230] Restoring SavedModel bundle.

2022-08-10 11:57:51.927593: I tensorflow/cc/saved_model/loader.cc:214] Running initialization op on SavedModel bundle at path: /tmp/examples/poc_ensemble/1_predicttensorflow/1/model.savedmodel

2022-08-10 11:57:51.951144: I tensorflow/cc/saved_model/loader.cc:321] SavedModel load for tags { serve }; Status: success: OK. Took 186281 microseconds.

I0810 11:57:51.951259 6047 python.cc:2388] TRITONBACKEND_ModelInstanceInitialize: 2_queryfaiss (GPU device 0)

I0810 11:57:51.951354 6047 model_repository_manager.cc:1345] successfully loaded '1_predicttensorflow' version 1

I0810 11:57:54.303539 6047 python.cc:2388] TRITONBACKEND_ModelInstanceInitialize: 3_queryfeast (GPU device 0)

I0810 11:57:54.305282 6047 model_repository_manager.cc:1345] successfully loaded '2_queryfaiss' version 1

I0810 11:57:56.663362 6047 python.cc:2388] TRITONBACKEND_ModelInstanceInitialize: 6_softmaxsampling (GPU device 0)

I0810 11:57:56.663529 6047 model_repository_manager.cc:1345] successfully loaded '3_queryfeast' version 1

I0810 11:57:58.737662 6047 tensorflow.cc:2330] TRITONBACKEND_ModelInstanceInitialize: 5_predicttensorflow (GPU device 0)

I0810 11:57:58.737867 6047 model_repository_manager.cc:1345] successfully loaded '6_softmaxsampling' version 1

2022-08-10 11:57:58.739301: I tensorflow/cc/saved_model/reader.cc:43] Reading SavedModel from: /tmp/examples/poc_ensemble/5_predicttensorflow/1/model.savedmodel

2022-08-10 11:57:58.757761: I tensorflow/cc/saved_model/reader.cc:78] Reading meta graph with tags { serve }

2022-08-10 11:57:58.757803: I tensorflow/cc/saved_model/reader.cc:119] Reading SavedModel debug info (if present) from: /tmp/examples/poc_ensemble/5_predicttensorflow/1/model.savedmodel

2022-08-10 11:57:58.759866: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 12648 MB memory:  -> device: 0, name: Tesla P100-DGXS-16GB, pci bus id: 0000:07:00.0, compute capability: 6.0

2022-08-10 11:57:58.782676: I tensorflow/cc/saved_model/loader.cc:230] Restoring SavedModel bundle.

2022-08-10 11:57:58.939538: I tensorflow/cc/saved_model/loader.cc:214] Running initialization op on SavedModel bundle at path: /tmp/examples/poc_ensemble/5_predicttensorflow/1/model.savedmodel

2022-08-10 11:57:58.992375: I tensorflow/cc/saved_model/loader.cc:321] SavedModel load for tags { serve }; Status: success: OK. Took 253087 microseconds.

I0810 11:57:58.992530 6047 python.cc:2388] TRITONBACKEND_ModelInstanceInitialize: 4_unrollfeatures (GPU device 0)

I0810 11:57:58.992611 6047 model_repository_manager.cc:1345] successfully loaded '5_predicttensorflow' version 1

I0810 11:58:01.097468 6047 model_repository_manager.cc:1345] successfully loaded '4_unrollfeatures' version 1

I0810 11:58:01.099991 6047 model_repository_manager.cc:1191] loading: ensemble_model:1

I0810 11:58:01.200805 6047 model_repository_manager.cc:1345] successfully loaded 'ensemble_model' version 1

I0810 11:58:01.200919 6047 server.cc:556]

+------------------+------+

| Repository Agent | Path |

+------------------+------+

+------------------+------+


I0810 11:58:01.200969 6047 server.cc:583]

+------------+-----------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

| Backend    | Path                                                            | Config                                                                                                                                                                       |

+------------+-----------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

| python     | /opt/tritonserver/backends/python/libtriton_python.so           | {"cmdline":{"auto-complete-config":"false","min-compute-capability":"6.000000","backend-directory":"/opt/tritonserver/backends","default-max-batch-size":"4"}}               |

| tensorflow | /opt/tritonserver/backends/tensorflow2/libtriton_tensorflow2.so | {"cmdline":{"auto-complete-config":"false","backend-directory":"/opt/tritonserver/backends","min-compute-capability":"6.000000","version":"2","default-max-batch-size":"4"}} |

+------------+-----------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+


I0810 11:58:01.201018 6047 server.cc:626]

+---------------------+---------+--------+

| Model               | Version | Status |

+---------------------+---------+--------+

| 0_queryfeast        | 1       | READY  |

| 1_predicttensorflow | 1       | READY  |

| 2_queryfaiss        | 1       | READY  |

| 3_queryfeast        | 1       | READY  |

| 4_unrollfeatures    | 1       | READY  |

| 5_predicttensorflow | 1       | READY  |

| 6_softmaxsampling   | 1       | READY  |

| ensemble_model      | 1       | READY  |

+---------------------+---------+--------+


I0810 11:58:01.261466 6047 metrics.cc:650] Collecting metrics for GPU 0: Tesla P100-DGXS-16GB

I0810 11:58:01.262304 6047 tritonserver.cc:2138]

+----------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

| Option                           | Value                                                                                                                                                                                        |

+----------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

| server_id                        | triton                                                                                                                                                                                       |

| server_version                   | 2.22.0                                                                                                                                                                                       |

| server_extensions                | classification sequence model_repository model_repository(unload_dependents) schedule_policy model_configuration system_shared_memory cuda_shared_memory binary_tensor_data statistics trace |

| model_repository_path[0]         | /tmp/examples/poc_ensemble                                                                                                                                                                   |

| model_control_mode               | MODE_NONE                                                                                                                                                                                    |

| strict_model_config              | 1                                                                                                                                                                                            |

| rate_limit                       | OFF                                                                                                                                                                                          |

| pinned_memory_pool_byte_size     | 268435456                                                                                                                                                                                    |

| cuda_memory_pool_byte_size{0}    | 67108864                                                                                                                                                                                     |

| response_cache_byte_size         | 0                                                                                                                                                                                            |

| min_supported_compute_capability | 6.0                                                                                                                                                                                          |

| strict_readiness                 | 1                                                                                                                                                                                            |

| exit_timeout                     | 30                                                                                                                                                                                           |

+----------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+


I0810 11:58:01.263481 6047 grpc_server.cc:4589] Started GRPCInferenceService at 0.0.0.0:8001

I0810 11:58:01.263708 6047 http_server.cc:3303] Started HTTPService at 0.0.0.0:8000

I0810 11:58:01.304602 6047 http_server.cc:178] Started Metrics Service at 0.0.0.0:8002

W0810 11:58:02.284252 6047 metrics.cc:468] Unable to get energy consumption for GPU 0. Status:Success, value:0

W0810 11:58:02.284324 6047 metrics.cc:507] Unable to get memory usage for GPU 0. Memory usage status:Success, value:0. Memory total status:Success, value:0

W0810 11:58:03.284489 6047 metrics.cc:468] Unable to get energy consumption for GPU 0. Status:Success, value:0

W0810 11:58:03.284546 6047 metrics.cc:507] Unable to get memory usage for GPU 0. Memory usage status:Success, value:0. Memory total status:Success, value:0

W0810 11:58:04.303521 6047 metrics.cc:468] Unable to get energy consumption for GPU 0. Status:Success, value:0

W0810 11:58:04.303574 6047 metrics.cc:507] Unable to get memory usage for GPU 0. Memory usage status:Success, value:0. Memory total status:Success, value:0

0810 11:58:04.984638 6304 pb_stub.cc:749] Failed to process the request(s) for model '3_queryfeast', message: TypeError: init(): incompatible constructor arguments. The following argument types are supported:

1. c_python_backend_utils.InferenceResponse(output_tensors: List[c_python_backend_utils.Tensor], error: c_python_backend_utils.TritonError = None)


Invoked with: kwargs: tensors=[], error="<class 'TypeError'>, int() argument must be a string, a bytes-like object or a number, not 'NoneType', [<FrameSummary file /tmp/examples/poc_ensemble/3_queryfeast/1/model.py, line 105 in execute>, <FrameSummary file /usr/local/lib/python3.8/dist-packages/merlin/systems/dag/op_runner.py, line 38 in execute>, <FrameSummary file /usr/local/lib/python3.8/dist-packages/merlin/systems/dag/ops/feast.py, line 299 in transform>]"


At:

/tmp/examples/poc_ensemble/3_queryfeast/1/model.py(122): execute


I0810 11:58:04.989219 6047 server.cc:257] Waiting for in-flight requests to complete.

I0810 11:58:04.989258 6047 server.cc:273] Timeout 30: Found 0 model versions that have in-flight inferences

I0810 11:58:04.989272 6047 model_repository_manager.cc:1223] unloading: ensemble_model:1

I0810 11:58:04.989355 6047 model_repository_manager.cc:1223] unloading: 6_softmaxsampling:1

I0810 11:58:04.989405 6047 model_repository_manager.cc:1223] unloading: 5_predicttensorflow:1

I0810 11:58:04.989464 6047 model_repository_manager.cc:1223] unloading: 4_unrollfeatures:1

I0810 11:58:04.989517 6047 model_repository_manager.cc:1328] successfully unloaded 'ensemble_model' version 1

I0810 11:58:04.989534 6047 model_repository_manager.cc:1223] unloading: 3_queryfeast:1

I0810 11:58:04.989567 6047 tensorflow.cc:2368] TRITONBACKEND_ModelInstanceFinalize: delete instance state

I0810 11:58:04.989621 6047 model_repository_manager.cc:1223] unloading: 2_queryfaiss:1

I0810 11:58:04.989653 6047 model_repository_manager.cc:1223] unloading: 1_predicttensorflow:1

I0810 11:58:04.989687 6047 model_repository_manager.cc:1223] unloading: 0_queryfeast:1

I0810 11:58:04.989710 6047 server.cc:288] All models are stopped, unloading models

I0810 11:58:04.989723 6047 server.cc:295] Timeout 30: Found 7 live models and 0 in-flight non-inference requests

I0810 11:58:04.989859 6047 tensorflow.cc:2368] TRITONBACKEND_ModelInstanceFinalize: delete instance state

I0810 11:58:04.989891 6047 tensorflow.cc:2307] TRITONBACKEND_ModelFinalize: delete model state

I0810 11:58:04.990039 6047 tensorflow.cc:2307] TRITONBACKEND_ModelFinalize: delete model state

I0810 11:58:05.005298 6047 model_repository_manager.cc:1328] successfully unloaded '1_predicttensorflow' version 1

I0810 11:58:05.014735 6047 model_repository_manager.cc:1328] successfully unloaded '5_predicttensorflow' version 1

I0810 11:58:05.989851 6047 server.cc:295] Timeout 29: Found 5 live models and 0 in-flight non-inference requests

/usr/local/lib/python3.8/dist-packages/merlin/systems/dag/ops/feast.py:15: DeprecationWarning: np.float is a deprecated alias for the builtin float. To silence this warning, use float by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use np.float64 here.

Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations

ValueType.FLOAT: (np.float, False, False),

/usr/local/lib/python3.8/dist-packages/merlin/systems/dag/ops/feast.py:15: DeprecationWarning: np.float is a deprecated alias for the builtin float. To silence this warning, use float by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use np.float64 here.

Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations

ValueType.FLOAT: (np.float, False, False),

I0810 11:58:06.357585 6047 model_repository_manager.cc:1328] successfully unloaded '4_unrollfeatures' version 1

I0810 11:58:06.386569 6047 model_repository_manager.cc:1328] successfully unloaded '3_queryfeast' version 1

I0810 11:58:06.414774 6047 model_repository_manager.cc:1328] successfully unloaded '6_softmaxsampling' version 1

I0810 11:58:06.441832 6047 model_repository_manager.cc:1328] successfully unloaded '0_queryfeast' version 1

I0810 11:58:06.550717 6047 model_repository_manager.cc:1328] successfully unloaded '2_queryfaiss' version 1

I0810 11:58:06.990029 6047 server.cc:295] Timeout 28: Found 0 live models and 0 in-flight non-inference requests

=========================== short test summary info ============================

FAILED tests/unit/examples/test_building_deploying_multi_stage_RecSys.py::test_func

=================== 1 failed, 2 passed in 208.02s (0:03:28) ====================

Build step 'Execute shell' marked build as failure

Performing Post build task...

Match found for : : True

Logical operation result is TRUE

Running script  : #!/bin/bash

cd /var/jenkins_home/

CUDA_VISIBLE_DEVICES=1 python test_res_push.py "https://api.GitHub.com/repos/NVIDIA-Merlin/Merlin/issues/$ghprbPullId/comments" "/var/jenkins_home/jobs/$JOB_NAME/builds/$BUILD_NUMBER/log"

[merlin_merlin] $ /bin/bash /tmp/jenkins12229265000334026508.sh

@karlhigley
Copy link
Contributor Author

rerun tests

@nvidia-merlin-bot
Copy link
Contributor

Click to view CI Results
GitHub pull request #524 of commit a1c95f262485c8f5d7f6e07d42b1a678d011f7bf, no merge conflicts.
Running as SYSTEM
Setting status of a1c95f262485c8f5d7f6e07d42b1a678d011f7bf to PENDING with url https://10.20.13.93:8080/job/merlin_merlin/340/console and message: 'Pending'
Using context: Jenkins
Building on master in workspace /var/jenkins_home/workspace/merlin_merlin
using credential systems-login
 > git rev-parse --is-inside-work-tree # timeout=10
Fetching changes from the remote Git repository
 > git config remote.origin.url https://github.com/NVIDIA-Merlin/Merlin # timeout=10
Fetching upstream changes from https://github.com/NVIDIA-Merlin/Merlin
 > git --version # timeout=10
using GIT_ASKPASS to set credentials login for merlin-systems
 > git fetch --tags --force --progress -- https://github.com/NVIDIA-Merlin/Merlin +refs/pull/524/*:refs/remotes/origin/pr/524/* # timeout=10
 > git rev-parse a1c95f262485c8f5d7f6e07d42b1a678d011f7bf^{commit} # timeout=10
Checking out Revision a1c95f262485c8f5d7f6e07d42b1a678d011f7bf (detached)
 > git config core.sparsecheckout # timeout=10
 > git checkout -f a1c95f262485c8f5d7f6e07d42b1a678d011f7bf # timeout=10
Commit message: "Merge branch 'main' into fix/cmake-install-v2"
 > git rev-list --no-walk a1c95f262485c8f5d7f6e07d42b1a678d011f7bf # timeout=10
[merlin_merlin] $ /bin/bash /tmp/jenkins16143319096547220262.sh
============================= test session starts ==============================
platform linux -- Python 3.8.10, pytest-7.1.2, pluggy-1.0.0
rootdir: /var/jenkins_home/workspace/merlin_merlin/merlin
plugins: anyio-3.6.1, xdist-2.5.0, forked-1.4.0, cov-3.0.0
collected 3 items

tests/unit/test_version.py .                                             [ 33%]

tests/unit/examples/test_building_deploying_multi_stage_RecSys.py F      [ 66%]

tests/unit/examples/test_scaling_criteo_merlin_models.py .               [100%]


=================================== FAILURES ===================================

__________________________________ test_func ___________________________________


self = <testbook.client.TestbookNotebookClient object at 0x7f4954f12580>

cell = [53], kwargs = {}, cell_indexes = [53], executed_cells = [], idx = 53


def execute_cell(self, cell, **kwargs) -> Union[Dict, List[Dict]]:
    """
    Executes a cell or list of cells
    """
    if isinstance(cell, slice):
        start, stop = self._cell_index(cell.start), self._cell_index(cell.stop)
        if cell.step is not None:
            raise TestbookError('testbook does not support step argument')

        cell = range(start, stop + 1)
    elif isinstance(cell, str) or isinstance(cell, int):
        cell = [cell]

    cell_indexes = cell

    if all(isinstance(x, str) for x in cell):
        cell_indexes = [self._cell_index(tag) for tag in cell]

    executed_cells = []
    for idx in cell_indexes:
        try:





          cell = super().execute_cell(self.nb['cells'][idx], idx, **kwargs)





/usr/local/lib/python3.8/dist-packages/testbook/client.py:133:




args = (<testbook.client.TestbookNotebookClient object at 0x7f4954f12580>, {'id': '9a3fd395', 'cell_type': 'code', 'metadata'...ast.py, line 299 in transform>]"\n\nAt:\n  /tmp/examples/poc_ensemble/3_queryfeast/1/model.py(122): execute\n']}]}, 53)

kwargs = {}


def wrapped(*args, **kwargs):





  return just_run(coro(*args, **kwargs))





/usr/local/lib/python3.8/dist-packages/nbclient/util.py:85:




coro = <coroutine object NotebookClient.async_execute_cell at 0x7f487c72e3c0>


def just_run(coro: Awaitable) -> Any:
    """Make the coroutine run, even if there is an event loop running (using nest_asyncio)"""
    try:
        loop = asyncio.get_running_loop()
    except RuntimeError:
        loop = None
    if loop is None:
        had_running_loop = False
        loop = asyncio.new_event_loop()
        asyncio.set_event_loop(loop)
    else:
        had_running_loop = True
    if had_running_loop:
        # if there is a running loop, we patch using nest_asyncio
        # to have reentrant event loops
        check_ipython()
        import nest_asyncio

        nest_asyncio.apply()
        check_patch_tornado()





  return loop.run_until_complete(coro)





/usr/local/lib/python3.8/dist-packages/nbclient/util.py:60:




self = <_UnixSelectorEventLoop running=False closed=False debug=False>

future = <Task finished name='Task-369' coro=<NotebookClient.async_execute_cell() done, defined at /usr/local/lib/python3.8/dis...ps/feast.py, line 299 in transform>]"\n\nAt:\n  /tmp/examples/poc_ensemble/3_queryfeast/1/model.py(122): execute\n\n')>


def run_until_complete(self, future):
    """Run until the Future is done.

    If the argument is a coroutine, it is wrapped in a Task.

    WARNING: It would be disastrous to call run_until_complete()
    with the same coroutine twice -- it would wrap it in two
    different Tasks and that can't be good.

    Return the Future's result, or raise its exception.
    """
    self._check_closed()
    self._check_running()

    new_task = not futures.isfuture(future)
    future = tasks.ensure_future(future, loop=self)
    if new_task:
        # An exception is raised if the future didn't complete, so there
        # is no need to log the "destroy pending task" message
        future._log_destroy_pending = False

    future.add_done_callback(_run_until_complete_cb)
    try:
        self.run_forever()
    except:
        if new_task and future.done() and not future.cancelled():
            # The coroutine raised a BaseException. Consume the exception
            # to not log a warning, the caller doesn't have access to the
            # local task.
            future.exception()
        raise
    finally:
        future.remove_done_callback(_run_until_complete_cb)
    if not future.done():
        raise RuntimeError('Event loop stopped before Future completed.')





  return future.result()





/usr/lib/python3.8/asyncio/base_events.py:616:




self = <testbook.client.TestbookNotebookClient object at 0x7f4954f12580>

cell = {'id': '9a3fd395', 'cell_type': 'code', 'metadata': {'execution': {'iopub.status.busy': '2022-08-10T12:04:14.222233Z',...ps/feast.py, line 299 in transform>]"\n\nAt:\n  /tmp/examples/poc_ensemble/3_queryfeast/1/model.py(122): execute\n']}]}

cell_index = 53, execution_count = None, store_history = True


async def async_execute_cell(
    self,
    cell: NotebookNode,
    cell_index: int,
    execution_count: t.Optional[int] = None,
    store_history: bool = True,
) -> NotebookNode:
    """
    Executes a single code cell.

    To execute all cells see :meth:`execute`.

    Parameters
    ----------
    cell : nbformat.NotebookNode
        The cell which is currently being processed.
    cell_index : int
        The position of the cell within the notebook object.
    execution_count : int
        The execution count to be assigned to the cell (default: Use kernel response)
    store_history : bool
        Determines if history should be stored in the kernel (default: False).
        Specific to ipython kernels, which can store command histories.

    Returns
    -------
    output : dict
        The execution output payload (or None for no output).

    Raises
    ------
    CellExecutionError
        If execution failed and should raise an exception, this will be raised
        with defaults about the failure.

    Returns
    -------
    cell : NotebookNode
        The cell which was just processed.
    """
    assert self.kc is not None

    await run_hook(self.on_cell_start, cell=cell, cell_index=cell_index)

    if cell.cell_type != 'code' or not cell.source.strip():
        self.log.debug("Skipping non-executing cell %s", cell_index)
        return cell

    if self.skip_cells_with_tag in cell.metadata.get("tags", []):
        self.log.debug("Skipping tagged cell %s", cell_index)
        return cell

    if self.record_timing:  # clear execution metadata prior to execution
        cell['metadata']['execution'] = {}

    self.log.debug("Executing cell:\n%s", cell.source)

    cell_allows_errors = (not self.force_raise_errors) and (
        self.allow_errors or "raises-exception" in cell.metadata.get("tags", [])
    )

    await run_hook(self.on_cell_execute, cell=cell, cell_index=cell_index)
    parent_msg_id = await ensure_async(
        self.kc.execute(
            cell.source, store_history=store_history, stop_on_error=not cell_allows_errors
        )
    )
    await run_hook(self.on_cell_complete, cell=cell, cell_index=cell_index)
    # We launched a code cell to execute
    self.code_cells_executed += 1
    exec_timeout = self._get_timeout(cell)

    cell.outputs = []
    self.clear_before_next_output = False

    task_poll_kernel_alive = asyncio.ensure_future(self._async_poll_kernel_alive())
    task_poll_output_msg = asyncio.ensure_future(
        self._async_poll_output_msg(parent_msg_id, cell, cell_index)
    )
    self.task_poll_for_reply = asyncio.ensure_future(
        self._async_poll_for_reply(
            parent_msg_id, cell, exec_timeout, task_poll_output_msg, task_poll_kernel_alive
        )
    )
    try:
        exec_reply = await self.task_poll_for_reply
    except asyncio.CancelledError:
        # can only be cancelled by task_poll_kernel_alive when the kernel is dead
        task_poll_output_msg.cancel()
        raise DeadKernelError("Kernel died")
    except Exception as e:
        # Best effort to cancel request if it hasn't been resolved
        try:
            # Check if the task_poll_output is doing the raising for us
            if not isinstance(e, CellControlSignal):
                task_poll_output_msg.cancel()
        finally:
            raise

    if execution_count:
        cell['execution_count'] = execution_count
    await run_hook(
        self.on_cell_executed, cell=cell, cell_index=cell_index, execute_reply=exec_reply
    )





  await self._check_raise_for_error(cell, cell_index, exec_reply)





/usr/local/lib/python3.8/dist-packages/nbclient/client.py:1022:




self = <testbook.client.TestbookNotebookClient object at 0x7f4954f12580>

cell = {'id': '9a3fd395', 'cell_type': 'code', 'metadata': {'execution': {'iopub.status.busy': '2022-08-10T12:04:14.222233Z',...ps/feast.py, line 299 in transform>]"\n\nAt:\n  /tmp/examples/poc_ensemble/3_queryfeast/1/model.py(122): execute\n']}]}

cell_index = 53

exec_reply = {'buffers': [], 'content': {'ename': 'InferenceServerException', 'engine_info': {'engine_id': -1, 'engine_uuid': '47d5...e, 'engine': '47d5f4ae-0271-43f6-b604-90757513c168', 'started': '2022-08-10T12:04:14.222548Z', 'status': 'error'}, ...}


async def _check_raise_for_error(
    self, cell: NotebookNode, cell_index: int, exec_reply: t.Optional[t.Dict]
) -> None:

    if exec_reply is None:
        return None

    exec_reply_content = exec_reply['content']
    if exec_reply_content['status'] != 'error':
        return None

    cell_allows_errors = (not self.force_raise_errors) and (
        self.allow_errors
        or exec_reply_content.get('ename') in self.allow_error_names
        or "raises-exception" in cell.metadata.get("tags", [])
    )
    await run_hook(
        self.on_cell_error, cell=cell, cell_index=cell_index, execute_reply=exec_reply
    )
    if not cell_allows_errors:





      raise CellExecutionError.from_cell_and_msg(cell, exec_reply_content)





E           nbclient.exceptions.CellExecutionError: An error occurred while executing the following cell:

E           ------------------

E

E           import shutil

E           from merlin.models.loader.tf_utils import configure_tensorflow

E           configure_tensorflow()

E           from merlin.systems.triton.utils import run_ensemble_on_tritonserver

E           response = run_ensemble_on_tritonserver(

E               "/tmp/examples/poc_ensemble", outputs, request, "ensemble_model"

E           )

E           response = [x.tolist()[0] for x in response["ordered_ids"]]

E           shutil.rmtree("/tmp/examples/", ignore_errors=True)

E

E           ------------------

E

E           �[0;31m---------------------------------------------------------------------------�[0m

E           �[0;31mInferenceServerException�[0m                  Traceback (most recent call last)

E           Input �[0;32mIn [32]�[0m, in �[0;36m<cell line: 5>�[0;34m()�[0m

E           �[1;32m      3�[0m configure_tensorflow()

E           �[1;32m      4�[0m �[38;5;28;01mfrom�[39;00m �[38;5;21;01mmerlin�[39;00m�[38;5;21;01m.�[39;00m�[38;5;21;01msystems�[39;00m�[38;5;21;01m.�[39;00m�[38;5;21;01mtriton�[39;00m�[38;5;21;01m.�[39;00m�[38;5;21;01mutils�[39;00m �[38;5;28;01mimport�[39;00m run_ensemble_on_tritonserver

E           �[0;32m----> 5�[0m response �[38;5;241m=�[39m �[43mrun_ensemble_on_tritonserver�[49m�[43m(�[49m

E           �[1;32m      6�[0m �[43m    �[49m�[38;5;124;43m"�[39;49m�[38;5;124;43m/tmp/examples/poc_ensemble�[39;49m�[38;5;124;43m"�[39;49m�[43m,�[49m�[43m �[49m�[43moutputs�[49m�[43m,�[49m�[43m �[49m�[43mrequest�[49m�[43m,�[49m�[43m �[49m�[38;5;124;43m"�[39;49m�[38;5;124;43mensemble_model�[39;49m�[38;5;124;43m"�[39;49m

E           �[1;32m      7�[0m �[43m)�[49m

E           �[1;32m      8�[0m response �[38;5;241m=�[39m [x�[38;5;241m.�[39mtolist()[�[38;5;241m0�[39m] �[38;5;28;01mfor�[39;00m x �[38;5;129;01min�[39;00m response[�[38;5;124m"�[39m�[38;5;124mordered_ids�[39m�[38;5;124m"�[39m]]

E           �[1;32m      9�[0m shutil�[38;5;241m.�[39mrmtree(�[38;5;124m"�[39m�[38;5;124m/tmp/examples/�[39m�[38;5;124m"�[39m, ignore_errors�[38;5;241m=�[39m�[38;5;28;01mTrue�[39;00m)

E

E           File �[0;32m/usr/local/lib/python3.8/dist-packages/merlin/systems/triton/utils.py:93�[0m, in �[0;36mrun_ensemble_on_tritonserver�[0;34m(tmpdir, output_columns, df, model_name)�[0m

E           �[1;32m     91�[0m response �[38;5;241m=�[39m �[38;5;28;01mNone�[39;00m

E           �[1;32m     92�[0m �[38;5;28;01mwith�[39;00m run_triton_server(tmpdir) �[38;5;28;01mas�[39;00m client:

E           �[0;32m---> 93�[0m     response �[38;5;241m=�[39m �[43msend_triton_request�[49m�[43m(�[49m�[43mdf�[49m�[43m,�[49m�[43m �[49m�[43moutput_columns�[49m�[43m,�[49m�[43m �[49m�[43mclient�[49m�[38;5;241;43m=�[39;49m�[43mclient�[49m�[43m,�[49m�[43m �[49m�[43mtriton_model�[49m�[38;5;241;43m=�[39;49m�[43mmodel_name�[49m�[43m)�[49m

E           �[1;32m     95�[0m �[38;5;28;01mreturn�[39;00m response

E

E           File �[0;32m/usr/local/lib/python3.8/dist-packages/merlin/systems/triton/utils.py:141�[0m, in �[0;36msend_triton_request�[0;34m(df, outputs_list, client, endpoint, request_id, triton_model)�[0m

E           �[1;32m    139�[0m outputs �[38;5;241m=�[39m [grpcclient�[38;5;241m.�[39mInferRequestedOutput(col) �[38;5;28;01mfor�[39;00m col �[38;5;129;01min�[39;00m outputs_list]

E           �[1;32m    140�[0m �[38;5;28;01mwith�[39;00m client:

E           �[0;32m--> 141�[0m     response �[38;5;241m=�[39m �[43mclient�[49m�[38;5;241;43m.�[39;49m�[43minfer�[49m�[43m(�[49m�[43mtriton_model�[49m�[43m,�[49m�[43m �[49m�[43minputs�[49m�[43m,�[49m�[43m �[49m�[43mrequest_id�[49m�[38;5;241;43m=�[39;49m�[43mrequest_id�[49m�[43m,�[49m�[43m �[49m�[43moutputs�[49m�[38;5;241;43m=�[39;49m�[43moutputs�[49m�[43m)�[49m

E           �[1;32m    143�[0m results �[38;5;241m=�[39m {}

E           �[1;32m    144�[0m �[38;5;28;01mfor�[39;00m col �[38;5;129;01min�[39;00m outputs_list:

E

E           File �[0;32m/usr/local/lib/python3.8/dist-packages/tritonclient/grpc/init.py:1322�[0m, in �[0;36mInferenceServerClient.infer�[0;34m(self, model_name, inputs, model_version, outputs, request_id, sequence_id, sequence_start, sequence_end, priority, timeout, client_timeout, headers, compression_algorithm)�[0m

E           �[1;32m   1320�[0m     �[38;5;28;01mreturn�[39;00m result

E           �[1;32m   1321�[0m �[38;5;28;01mexcept�[39;00m grpc�[38;5;241m.�[39mRpcError �[38;5;28;01mas�[39;00m rpc_error:

E           �[0;32m-> 1322�[0m     �[43mraise_error_grpc�[49m�[43m(�[49m�[43mrpc_error�[49m�[43m)�[49m

E

E           File �[0;32m/usr/local/lib/python3.8/dist-packages/tritonclient/grpc/init.py:62�[0m, in �[0;36mraise_error_grpc�[0;34m(rpc_error)�[0m

E           �[1;32m     61�[0m �[38;5;28;01mdef�[39;00m �[38;5;21mraise_error_grpc�[39m(rpc_error):

E           �[0;32m---> 62�[0m     �[38;5;28;01mraise�[39;00m get_error_grpc(rpc_error) �[38;5;28;01mfrom�[39;00m �[38;5;28mNone�[39m

E

E           �[0;31mInferenceServerException�[0m: [StatusCode.INTERNAL] in ensemble 'ensemble_model', Failed to process the request(s) for model instance '3_queryfeast', message: TypeError: init(): incompatible constructor arguments. The following argument types are supported:

E               1. c_python_backend_utils.InferenceResponse(output_tensors: List[c_python_backend_utils.Tensor], error: c_python_backend_utils.TritonError = None)

E

E           Invoked with: kwargs: tensors=[], error="<class 'TypeError'>, int() argument must be a string, a bytes-like object or a number, not 'NoneType', [<FrameSummary file /tmp/examples/poc_ensemble/3_queryfeast/1/model.py, line 105 in execute>, <FrameSummary file /usr/local/lib/python3.8/dist-packages/merlin/systems/dag/op_runner.py, line 38 in execute>, <FrameSummary file /usr/local/lib/python3.8/dist-packages/merlin/systems/dag/ops/feast.py, line 299 in transform>]"

E

E           At:

E             /tmp/examples/poc_ensemble/3_queryfeast/1/model.py(122): execute

E

E           InferenceServerException: [StatusCode.INTERNAL] in ensemble 'ensemble_model', Failed to process the request(s) for model instance '3_queryfeast', message: TypeError: init(): incompatible constructor arguments. The following argument types are supported:

E               1. c_python_backend_utils.InferenceResponse(output_tensors: List[c_python_backend_utils.Tensor], error: c_python_backend_utils.TritonError = None)

E

E           Invoked with: kwargs: tensors=[], error="<class 'TypeError'>, int() argument must be a string, a bytes-like object or a number, not 'NoneType', [<FrameSummary file /tmp/examples/poc_ensemble/3_queryfeast/1/model.py, line 105 in execute>, <FrameSummary file /usr/local/lib/python3.8/dist-packages/merlin/systems/dag/op_runner.py, line 38 in execute>, <FrameSummary file /usr/local/lib/python3.8/dist-packages/merlin/systems/dag/ops/feast.py, line 299 in transform>]"

E

E           At:

E             /tmp/examples/poc_ensemble/3_queryfeast/1/model.py(122): execute


/usr/local/lib/python3.8/dist-packages/nbclient/client.py:916: CellExecutionError


During handling of the above exception, another exception occurred:


def test_func():
    with testbook(
        REPO_ROOT
        / "examples"
        / "Building-and-deploying-multi-stage-RecSys"
        / "01-Building-Recommender-Systems-with-Merlin.ipynb",
        execute=False,
    ) as tb1:
        tb1.inject(
            """
            import os
            os.environ["DATA_FOLDER"] = "/tmp/data/"
            os.environ["NUM_ROWS"] = "10000"
            os.system("mkdir -p /tmp/examples")
            os.environ["BASE_DIR"] = "/tmp/examples/"
            """
        )
        tb1.execute()
        assert os.path.isdir("/tmp/examples/dlrm")
        assert os.path.isdir("/tmp/examples/feature_repo")
        assert os.path.isdir("/tmp/examples/query_tower")
        assert os.path.isfile("/tmp/examples/item_embeddings.parquet")
        assert os.path.isfile("/tmp/examples/feature_repo/user_features.py")
        assert os.path.isfile("/tmp/examples/feature_repo/item_features.py")

    with testbook(
        REPO_ROOT
        / "examples"
        / "Building-and-deploying-multi-stage-RecSys"
        / "02-Deploying-multi-stage-RecSys-with-Merlin-Systems.ipynb",
        execute=False,
    ) as tb2:
        tb2.inject(
            """
            import os
            os.environ["DATA_FOLDER"] = "/tmp/data/"
            os.environ["BASE_DIR"] = "/tmp/examples/"
            """
        )
        NUM_OF_CELLS = len(tb2.cells)
        tb2.execute_cell(list(range(0, NUM_OF_CELLS - 3)))
        top_k = tb2.ref("top_k")
        outputs = tb2.ref("outputs")
        assert outputs[0] == "ordered_ids"





      tb2.inject(





            """
            import shutil
            from merlin.models.loader.tf_utils import configure_tensorflow
            configure_tensorflow()
            from merlin.systems.triton.utils import run_ensemble_on_tritonserver
            response = run_ensemble_on_tritonserver(
                "/tmp/examples/poc_ensemble", outputs, request, "ensemble_model"
            )
            response = [x.tolist()[0] for x in response["ordered_ids"]]
            shutil.rmtree("/tmp/examples/", ignore_errors=True)
            """
        )



tests/unit/examples/test_building_deploying_multi_stage_RecSys.py:57:




/usr/local/lib/python3.8/dist-packages/testbook/client.py:237: in inject

cell = TestbookNode(self.execute_cell(inject_idx)) if run else TestbookNode(code_cell)




self = <testbook.client.TestbookNotebookClient object at 0x7f4954f12580>

cell = [53], kwargs = {}, cell_indexes = [53], executed_cells = [], idx = 53


def execute_cell(self, cell, **kwargs) -> Union[Dict, List[Dict]]:
    """
    Executes a cell or list of cells
    """
    if isinstance(cell, slice):
        start, stop = self._cell_index(cell.start), self._cell_index(cell.stop)
        if cell.step is not None:
            raise TestbookError('testbook does not support step argument')

        cell = range(start, stop + 1)
    elif isinstance(cell, str) or isinstance(cell, int):
        cell = [cell]

    cell_indexes = cell

    if all(isinstance(x, str) for x in cell):
        cell_indexes = [self._cell_index(tag) for tag in cell]

    executed_cells = []
    for idx in cell_indexes:
        try:
            cell = super().execute_cell(self.nb['cells'][idx], idx, **kwargs)
        except CellExecutionError as ce:





          raise TestbookRuntimeError(ce.evalue, ce, self._get_error_class(ce.ename))





E               testbook.exceptions.TestbookRuntimeError: An error occurred while executing the following cell:

E               ------------------

E

E               import shutil

E               from merlin.models.loader.tf_utils import configure_tensorflow

E               configure_tensorflow()

E               from merlin.systems.triton.utils import run_ensemble_on_tritonserver

E               response = run_ensemble_on_tritonserver(

E                   "/tmp/examples/poc_ensemble", outputs, request, "ensemble_model"

E               )

E               response = [x.tolist()[0] for x in response["ordered_ids"]]

E               shutil.rmtree("/tmp/examples/", ignore_errors=True)

E

E               ------------------

E

E               �[0;31m---------------------------------------------------------------------------�[0m

E               �[0;31mInferenceServerException�[0m                  Traceback (most recent call last)

E               Input �[0;32mIn [32]�[0m, in �[0;36m<cell line: 5>�[0;34m()�[0m

E               �[1;32m      3�[0m configure_tensorflow()

E               �[1;32m      4�[0m �[38;5;28;01mfrom�[39;00m �[38;5;21;01mmerlin�[39;00m�[38;5;21;01m.�[39;00m�[38;5;21;01msystems�[39;00m�[38;5;21;01m.�[39;00m�[38;5;21;01mtriton�[39;00m�[38;5;21;01m.�[39;00m�[38;5;21;01mutils�[39;00m �[38;5;28;01mimport�[39;00m run_ensemble_on_tritonserver

E               �[0;32m----> 5�[0m response �[38;5;241m=�[39m �[43mrun_ensemble_on_tritonserver�[49m�[43m(�[49m

E               �[1;32m      6�[0m �[43m    �[49m�[38;5;124;43m"�[39;49m�[38;5;124;43m/tmp/examples/poc_ensemble�[39;49m�[38;5;124;43m"�[39;49m�[43m,�[49m�[43m �[49m�[43moutputs�[49m�[43m,�[49m�[43m �[49m�[43mrequest�[49m�[43m,�[49m�[43m �[49m�[38;5;124;43m"�[39;49m�[38;5;124;43mensemble_model�[39;49m�[38;5;124;43m"�[39;49m

E               �[1;32m      7�[0m �[43m)�[49m

E               �[1;32m      8�[0m response �[38;5;241m=�[39m [x�[38;5;241m.�[39mtolist()[�[38;5;241m0�[39m] �[38;5;28;01mfor�[39;00m x �[38;5;129;01min�[39;00m response[�[38;5;124m"�[39m�[38;5;124mordered_ids�[39m�[38;5;124m"�[39m]]

E               �[1;32m      9�[0m shutil�[38;5;241m.�[39mrmtree(�[38;5;124m"�[39m�[38;5;124m/tmp/examples/�[39m�[38;5;124m"�[39m, ignore_errors�[38;5;241m=�[39m�[38;5;28;01mTrue�[39;00m)

E

E               File �[0;32m/usr/local/lib/python3.8/dist-packages/merlin/systems/triton/utils.py:93�[0m, in �[0;36mrun_ensemble_on_tritonserver�[0;34m(tmpdir, output_columns, df, model_name)�[0m

E               �[1;32m     91�[0m response �[38;5;241m=�[39m �[38;5;28;01mNone�[39;00m

E               �[1;32m     92�[0m �[38;5;28;01mwith�[39;00m run_triton_server(tmpdir) �[38;5;28;01mas�[39;00m client:

E               �[0;32m---> 93�[0m     response �[38;5;241m=�[39m �[43msend_triton_request�[49m�[43m(�[49m�[43mdf�[49m�[43m,�[49m�[43m �[49m�[43moutput_columns�[49m�[43m,�[49m�[43m �[49m�[43mclient�[49m�[38;5;241;43m=�[39;49m�[43mclient�[49m�[43m,�[49m�[43m �[49m�[43mtriton_model�[49m�[38;5;241;43m=�[39;49m�[43mmodel_name�[49m�[43m)�[49m

E               �[1;32m     95�[0m �[38;5;28;01mreturn�[39;00m response

E

E               File �[0;32m/usr/local/lib/python3.8/dist-packages/merlin/systems/triton/utils.py:141�[0m, in �[0;36msend_triton_request�[0;34m(df, outputs_list, client, endpoint, request_id, triton_model)�[0m

E               �[1;32m    139�[0m outputs �[38;5;241m=�[39m [grpcclient�[38;5;241m.�[39mInferRequestedOutput(col) �[38;5;28;01mfor�[39;00m col �[38;5;129;01min�[39;00m outputs_list]

E               �[1;32m    140�[0m �[38;5;28;01mwith�[39;00m client:

E               �[0;32m--> 141�[0m     response �[38;5;241m=�[39m �[43mclient�[49m�[38;5;241;43m.�[39;49m�[43minfer�[49m�[43m(�[49m�[43mtriton_model�[49m�[43m,�[49m�[43m �[49m�[43minputs�[49m�[43m,�[49m�[43m �[49m�[43mrequest_id�[49m�[38;5;241;43m=�[39;49m�[43mrequest_id�[49m�[43m,�[49m�[43m �[49m�[43moutputs�[49m�[38;5;241;43m=�[39;49m�[43moutputs�[49m�[43m)�[49m

E               �[1;32m    143�[0m results �[38;5;241m=�[39m {}

E               �[1;32m    144�[0m �[38;5;28;01mfor�[39;00m col �[38;5;129;01min�[39;00m outputs_list:

E

E               File �[0;32m/usr/local/lib/python3.8/dist-packages/tritonclient/grpc/init.py:1322�[0m, in �[0;36mInferenceServerClient.infer�[0;34m(self, model_name, inputs, model_version, outputs, request_id, sequence_id, sequence_start, sequence_end, priority, timeout, client_timeout, headers, compression_algorithm)�[0m

E               �[1;32m   1320�[0m     �[38;5;28;01mreturn�[39;00m result

E               �[1;32m   1321�[0m �[38;5;28;01mexcept�[39;00m grpc�[38;5;241m.�[39mRpcError �[38;5;28;01mas�[39;00m rpc_error:

E               �[0;32m-> 1322�[0m     �[43mraise_error_grpc�[49m�[43m(�[49m�[43mrpc_error�[49m�[43m)�[49m

E

E               File �[0;32m/usr/local/lib/python3.8/dist-packages/tritonclient/grpc/init.py:62�[0m, in �[0;36mraise_error_grpc�[0;34m(rpc_error)�[0m

E               �[1;32m     61�[0m �[38;5;28;01mdef�[39;00m �[38;5;21mraise_error_grpc�[39m(rpc_error):

E               �[0;32m---> 62�[0m     �[38;5;28;01mraise�[39;00m get_error_grpc(rpc_error) �[38;5;28;01mfrom�[39;00m �[38;5;28mNone�[39m

E

E               �[0;31mInferenceServerException�[0m: [StatusCode.INTERNAL] in ensemble 'ensemble_model', Failed to process the request(s) for model instance '3_queryfeast', message: TypeError: init(): incompatible constructor arguments. The following argument types are supported:

E                   1. c_python_backend_utils.InferenceResponse(output_tensors: List[c_python_backend_utils.Tensor], error: c_python_backend_utils.TritonError = None)

E

E               Invoked with: kwargs: tensors=[], error="<class 'TypeError'>, int() argument must be a string, a bytes-like object or a number, not 'NoneType', [<FrameSummary file /tmp/examples/poc_ensemble/3_queryfeast/1/model.py, line 105 in execute>, <FrameSummary file /usr/local/lib/python3.8/dist-packages/merlin/systems/dag/op_runner.py, line 38 in execute>, <FrameSummary file /usr/local/lib/python3.8/dist-packages/merlin/systems/dag/ops/feast.py, line 299 in transform>]"

E

E               At:

E                 /tmp/examples/poc_ensemble/3_queryfeast/1/model.py(122): execute

E

E               InferenceServerException: [StatusCode.INTERNAL] in ensemble 'ensemble_model', Failed to process the request(s) for model instance '3_queryfeast', message: TypeError: init(): incompatible constructor arguments. The following argument types are supported:

E                   1. c_python_backend_utils.InferenceResponse(output_tensors: List[c_python_backend_utils.Tensor], error: c_python_backend_utils.TritonError = None)

E

E               Invoked with: kwargs: tensors=[], error="<class 'TypeError'>, int() argument must be a string, a bytes-like object or a number, not 'NoneType', [<FrameSummary file /tmp/examples/poc_ensemble/3_queryfeast/1/model.py, line 105 in execute>, <FrameSummary file /usr/local/lib/python3.8/dist-packages/merlin/systems/dag/op_runner.py, line 38 in execute>, <FrameSummary file /usr/local/lib/python3.8/dist-packages/merlin/systems/dag/ops/feast.py, line 299 in transform>]"

E

E               At:

E                 /tmp/examples/poc_ensemble/3_queryfeast/1/model.py(122): execute


/usr/local/lib/python3.8/dist-packages/testbook/client.py:135: TestbookRuntimeError

----------------------------- Captured stdout call -----------------------------

Signal (2) received.

----------------------------- Captured stderr call -----------------------------

2022-08-10 12:03:13.020383: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA

To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.

2022-08-10 12:03:15.015563: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 1627 MB memory:  -> device: 0, name: Tesla P100-DGXS-16GB, pci bus id: 0000:07:00.0, compute capability: 6.0

2022-08-10 12:03:15.016307: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 15153 MB memory:  -> device: 1, name: Tesla P100-DGXS-16GB, pci bus id: 0000:08:00.0, compute capability: 6.0

Error in atexit._run_exitfuncs:

Traceback (most recent call last):

File "/usr/lib/python3.8/logging/init.py", line 2127, in shutdown

h.close()

File "/usr/local/lib/python3.8/dist-packages/absl/logging/init.py", line 934, in close

self.stream.close()

File "/usr/local/lib/python3.8/dist-packages/ipykernel/iostream.py", line 438, in close

self.watch_fd_thread.join()

AttributeError: 'OutStream' object has no attribute 'watch_fd_thread'

WARNING clustering 265 points to 32 centroids: please provide at least 1248 training points

2022-08-10 12:04:07.334570: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA

To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.

2022-08-10 12:04:09.313719: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 1627 MB memory:  -> device: 0, name: Tesla P100-DGXS-16GB, pci bus id: 0000:07:00.0, compute capability: 6.0

2022-08-10 12:04:09.314472: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 15153 MB memory:  -> device: 1, name: Tesla P100-DGXS-16GB, pci bus id: 0000:08:00.0, compute capability: 6.0

I0810 12:04:14.492271 7928 pinned_memory_manager.cc:240] Pinned memory pool is created at '0x7f3cb4000000' with size 268435456

I0810 12:04:14.493047 7928 cuda_memory_manager.cc:105] CUDA memory pool is created on device 0 with size 67108864

I0810 12:04:14.515661 7928 model_repository_manager.cc:1191] loading: 1_predicttensorflow:1

I0810 12:04:14.616016 7928 model_repository_manager.cc:1191] loading: 0_queryfeast:1

I0810 12:04:14.716323 7928 model_repository_manager.cc:1191] loading: 2_queryfaiss:1

I0810 12:04:14.816624 7928 model_repository_manager.cc:1191] loading: 3_queryfeast:1

I0810 12:04:14.889446 7928 tensorflow.cc:2181] TRITONBACKEND_Initialize: tensorflow

I0810 12:04:14.889483 7928 tensorflow.cc:2191] Triton TRITONBACKEND API version: 1.9

I0810 12:04:14.889491 7928 tensorflow.cc:2197] 'tensorflow' TRITONBACKEND API version: 1.9

I0810 12:04:14.889497 7928 tensorflow.cc:2221] backend configuration:

{"cmdline":{"auto-complete-config":"false","backend-directory":"/opt/tritonserver/backends","min-compute-capability":"6.000000","version":"2","default-max-batch-size":"4"}}

I0810 12:04:14.889532 7928 tensorflow.cc:2281] TRITONBACKEND_ModelInitialize: 1_predicttensorflow (version 1)

I0810 12:04:14.894448 7928 tensorflow.cc:2330] TRITONBACKEND_ModelInstanceInitialize: 1_predicttensorflow (GPU device 0)

I0810 12:04:14.916938 7928 model_repository_manager.cc:1191] loading: 4_unrollfeatures:1

I0810 12:04:15.017230 7928 model_repository_manager.cc:1191] loading: 5_predicttensorflow:1

I0810 12:04:15.117527 7928 model_repository_manager.cc:1191] loading: 6_softmaxsampling:1

2022-08-10 12:04:15.237802: I tensorflow/cc/saved_model/reader.cc:43] Reading SavedModel from: /tmp/examples/poc_ensemble/1_predicttensorflow/1/model.savedmodel

2022-08-10 12:04:15.242110: I tensorflow/cc/saved_model/reader.cc:78] Reading meta graph with tags { serve }

2022-08-10 12:04:15.242162: I tensorflow/cc/saved_model/reader.cc:119] Reading SavedModel debug info (if present) from: /tmp/examples/poc_ensemble/1_predicttensorflow/1/model.savedmodel

2022-08-10 12:04:15.242260: I tensorflow/core/platform/cpu_feature_guard.cc:152] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE3 SSE4.1 SSE4.2 AVX

To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.

2022-08-10 12:04:15.289928: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 12901 MB memory:  -> device: 0, name: Tesla P100-DGXS-16GB, pci bus id: 0000:07:00.0, compute capability: 6.0

2022-08-10 12:04:15.324981: I tensorflow/cc/saved_model/loader.cc:230] Restoring SavedModel bundle.

2022-08-10 12:04:15.403023: I tensorflow/cc/saved_model/loader.cc:214] Running initialization op on SavedModel bundle at path: /tmp/examples/poc_ensemble/1_predicttensorflow/1/model.savedmodel

2022-08-10 12:04:15.426443: I tensorflow/cc/saved_model/loader.cc:321] SavedModel load for tags { serve }; Status: success: OK. Took 188661 microseconds.

I0810 12:04:15.426688 7928 model_repository_manager.cc:1345] successfully loaded '1_predicttensorflow' version 1

I0810 12:04:15.431110 7928 tensorflow.cc:2281] TRITONBACKEND_ModelInitialize: 5_predicttensorflow (version 1)

I0810 12:04:15.433215 7928 python.cc:2388] TRITONBACKEND_ModelInstanceInitialize: 0_queryfeast (GPU device 0)

I0810 12:04:17.733093 7928 python.cc:2388] TRITONBACKEND_ModelInstanceInitialize: 2_queryfaiss (GPU device 0)

I0810 12:04:17.733325 7928 model_repository_manager.cc:1345] successfully loaded '0_queryfeast' version 1

I0810 12:04:20.109877 7928 python.cc:2388] TRITONBACKEND_ModelInstanceInitialize: 3_queryfeast (GPU device 0)

I0810 12:04:20.111593 7928 model_repository_manager.cc:1345] successfully loaded '2_queryfaiss' version 1

I0810 12:04:22.481582 7928 python.cc:2388] TRITONBACKEND_ModelInstanceInitialize: 4_unrollfeatures (GPU device 0)

I0810 12:04:22.481774 7928 model_repository_manager.cc:1345] successfully loaded '3_queryfeast' version 1

I0810 12:04:24.549993 7928 tensorflow.cc:2330] TRITONBACKEND_ModelInstanceInitialize: 5_predicttensorflow (GPU device 0)

I0810 12:04:24.550241 7928 model_repository_manager.cc:1345] successfully loaded '4_unrollfeatures' version 1

2022-08-10 12:04:24.551608: I tensorflow/cc/saved_model/reader.cc:43] Reading SavedModel from: /tmp/examples/poc_ensemble/5_predicttensorflow/1/model.savedmodel

2022-08-10 12:04:24.570295: I tensorflow/cc/saved_model/reader.cc:78] Reading meta graph with tags { serve }

2022-08-10 12:04:24.570335: I tensorflow/cc/saved_model/reader.cc:119] Reading SavedModel debug info (if present) from: /tmp/examples/poc_ensemble/5_predicttensorflow/1/model.savedmodel

2022-08-10 12:04:24.572468: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 12901 MB memory:  -> device: 0, name: Tesla P100-DGXS-16GB, pci bus id: 0000:07:00.0, compute capability: 6.0

2022-08-10 12:04:24.593884: I tensorflow/cc/saved_model/loader.cc:230] Restoring SavedModel bundle.

2022-08-10 12:04:24.748550: I tensorflow/cc/saved_model/loader.cc:214] Running initialization op on SavedModel bundle at path: /tmp/examples/poc_ensemble/5_predicttensorflow/1/model.savedmodel

2022-08-10 12:04:24.798686: I tensorflow/cc/saved_model/loader.cc:321] SavedModel load for tags { serve }; Status: success: OK. Took 247092 microseconds.

I0810 12:04:24.798835 7928 python.cc:2388] TRITONBACKEND_ModelInstanceInitialize: 6_softmaxsampling (GPU device 0)

I0810 12:04:24.798910 7928 model_repository_manager.cc:1345] successfully loaded '5_predicttensorflow' version 1

I0810 12:04:26.914725 7928 model_repository_manager.cc:1345] successfully loaded '6_softmaxsampling' version 1

I0810 12:04:26.916969 7928 model_repository_manager.cc:1191] loading: ensemble_model:1

I0810 12:04:27.017769 7928 model_repository_manager.cc:1345] successfully loaded 'ensemble_model' version 1

I0810 12:04:27.017942 7928 server.cc:556]

+------------------+------+

| Repository Agent | Path |

+------------------+------+

+------------------+------+


I0810 12:04:27.018053 7928 server.cc:583]

+------------+-----------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

| Backend    | Path                                                            | Config                                                                                                                                                                       |

+------------+-----------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

| tensorflow | /opt/tritonserver/backends/tensorflow2/libtriton_tensorflow2.so | {"cmdline":{"auto-complete-config":"false","backend-directory":"/opt/tritonserver/backends","min-compute-capability":"6.000000","version":"2","default-max-batch-size":"4"}} |

| python     | /opt/tritonserver/backends/python/libtriton_python.so           | {"cmdline":{"auto-complete-config":"false","min-compute-capability":"6.000000","backend-directory":"/opt/tritonserver/backends","default-max-batch-size":"4"}}               |

+------------+-----------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+


I0810 12:04:27.018168 7928 server.cc:626]

+---------------------+---------+--------+

| Model               | Version | Status |

+---------------------+---------+--------+

| 0_queryfeast        | 1       | READY  |

| 1_predicttensorflow | 1       | READY  |

| 2_queryfaiss        | 1       | READY  |

| 3_queryfeast        | 1       | READY  |

| 4_unrollfeatures    | 1       | READY  |

| 5_predicttensorflow | 1       | READY  |

| 6_softmaxsampling   | 1       | READY  |

| ensemble_model      | 1       | READY  |

+---------------------+---------+--------+


I0810 12:04:27.081255 7928 metrics.cc:650] Collecting metrics for GPU 0: Tesla P100-DGXS-16GB

I0810 12:04:27.082089 7928 tritonserver.cc:2138]

+----------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

| Option                           | Value                                                                                                                                                                                        |

+----------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

| server_id                        | triton                                                                                                                                                                                       |

| server_version                   | 2.22.0                                                                                                                                                                                       |

| server_extensions                | classification sequence model_repository model_repository(unload_dependents) schedule_policy model_configuration system_shared_memory cuda_shared_memory binary_tensor_data statistics trace |

| model_repository_path[0]         | /tmp/examples/poc_ensemble                                                                                                                                                                   |

| model_control_mode               | MODE_NONE                                                                                                                                                                                    |

| strict_model_config              | 1                                                                                                                                                                                            |

| rate_limit                       | OFF                                                                                                                                                                                          |

| pinned_memory_pool_byte_size     | 268435456                                                                                                                                                                                    |

| cuda_memory_pool_byte_size{0}    | 67108864                                                                                                                                                                                     |

| response_cache_byte_size         | 0                                                                                                                                                                                            |

| min_supported_compute_capability | 6.0                                                                                                                                                                                          |

| strict_readiness                 | 1                                                                                                                                                                                            |

| exit_timeout                     | 30                                                                                                                                                                                           |

+----------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+


I0810 12:04:27.082854 7928 grpc_server.cc:4589] Started GRPCInferenceService at 0.0.0.0:8001

I0810 12:04:27.083417 7928 http_server.cc:3303] Started HTTPService at 0.0.0.0:8000

I0810 12:04:27.124616 7928 http_server.cc:178] Started Metrics Service at 0.0.0.0:8002

W0810 12:04:28.105460 7928 metrics.cc:468] Unable to get energy consumption for GPU 0. Status:Success, value:0

W0810 12:04:28.105509 7928 metrics.cc:507] Unable to get memory usage for GPU 0. Memory usage status:Success, value:0. Memory total status:Success, value:0

W0810 12:04:29.105658 7928 metrics.cc:468] Unable to get energy consumption for GPU 0. Status:Success, value:0

W0810 12:04:29.105713 7928 metrics.cc:507] Unable to get memory usage for GPU 0. Memory usage status:Success, value:0. Memory total status:Success, value:0

W0810 12:04:30.126814 7928 metrics.cc:468] Unable to get energy consumption for GPU 0. Status:Success, value:0

W0810 12:04:30.126868 7928 metrics.cc:507] Unable to get memory usage for GPU 0. Memory usage status:Success, value:0. Memory total status:Success, value:0

0810 12:04:31.758129 8185 pb_stub.cc:749] Failed to process the request(s) for model '3_queryfeast', message: TypeError: init(): incompatible constructor arguments. The following argument types are supported:

1. c_python_backend_utils.InferenceResponse(output_tensors: List[c_python_backend_utils.Tensor], error: c_python_backend_utils.TritonError = None)


Invoked with: kwargs: tensors=[], error="<class 'TypeError'>, int() argument must be a string, a bytes-like object or a number, not 'NoneType', [<FrameSummary file /tmp/examples/poc_ensemble/3_queryfeast/1/model.py, line 105 in execute>, <FrameSummary file /usr/local/lib/python3.8/dist-packages/merlin/systems/dag/op_runner.py, line 38 in execute>, <FrameSummary file /usr/local/lib/python3.8/dist-packages/merlin/systems/dag/ops/feast.py, line 299 in transform>]"


At:

/tmp/examples/poc_ensemble/3_queryfeast/1/model.py(122): execute


I0810 12:04:31.759013 7928 server.cc:257] Waiting for in-flight requests to complete.

I0810 12:04:31.759066 7928 server.cc:273] Timeout 30: Found 0 model versions that have in-flight inferences

I0810 12:04:31.759085 7928 model_repository_manager.cc:1223] unloading: ensemble_model:1

I0810 12:04:31.759183 7928 model_repository_manager.cc:1223] unloading: 6_softmaxsampling:1

I0810 12:04:31.759255 7928 model_repository_manager.cc:1223] unloading: 5_predicttensorflow:1

I0810 12:04:31.759337 7928 model_repository_manager.cc:1223] unloading: 4_unrollfeatures:1

I0810 12:04:31.759354 7928 model_repository_manager.cc:1328] successfully unloaded 'ensemble_model' version 1

I0810 12:04:31.759398 7928 model_repository_manager.cc:1223] unloading: 3_queryfeast:1

I0810 12:04:31.759469 7928 model_repository_manager.cc:1223] unloading: 2_queryfaiss:1

I0810 12:04:31.759473 7928 tensorflow.cc:2368] TRITONBACKEND_ModelInstanceFinalize: delete instance state

I0810 12:04:31.759550 7928 model_repository_manager.cc:1223] unloading: 1_predicttensorflow:1

I0810 12:04:31.759624 7928 model_repository_manager.cc:1223] unloading: 0_queryfeast:1

I0810 12:04:31.759683 7928 server.cc:288] All models are stopped, unloading models

I0810 12:04:31.759708 7928 server.cc:295] Timeout 30: Found 7 live models and 0 in-flight non-inference requests

I0810 12:04:31.759761 7928 tensorflow.cc:2307] TRITONBACKEND_ModelFinalize: delete model state

I0810 12:04:31.759774 7928 tensorflow.cc:2368] TRITONBACKEND_ModelInstanceFinalize: delete instance state

I0810 12:04:31.759897 7928 tensorflow.cc:2307] TRITONBACKEND_ModelFinalize: delete model state

I0810 12:04:31.767756 7928 model_repository_manager.cc:1328] successfully unloaded '1_predicttensorflow' version 1

I0810 12:04:31.782895 7928 model_repository_manager.cc:1328] successfully unloaded '5_predicttensorflow' version 1

I0810 12:04:32.759851 7928 server.cc:295] Timeout 29: Found 5 live models and 0 in-flight non-inference requests

/usr/local/lib/python3.8/dist-packages/merlin/systems/dag/ops/feast.py:15: DeprecationWarning: np.float is a deprecated alias for the builtin float. To silence this warning, use float by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use np.float64 here.

Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations

ValueType.FLOAT: (np.float, False, False),

I0810 12:04:33.105202 7928 model_repository_manager.cc:1328] successfully unloaded '4_unrollfeatures' version 1

I0810 12:04:33.267467 7928 model_repository_manager.cc:1328] successfully unloaded '2_queryfaiss' version 1

I0810 12:04:33.315888 7928 model_repository_manager.cc:1328] successfully unloaded '6_softmaxsampling' version 1

I0810 12:04:33.347930 7928 model_repository_manager.cc:1328] successfully unloaded '0_queryfeast' version 1

I0810 12:04:33.760018 7928 server.cc:295] Timeout 28: Found 1 live models and 0 in-flight non-inference requests

I0810 12:04:34.760164 7928 server.cc:295] Timeout 27: Found 1 live models and 0 in-flight non-inference requests

I0810 12:04:35.760303 7928 server.cc:295] Timeout 26: Found 1 live models and 0 in-flight non-inference requests

I0810 12:04:36.760445 7928 server.cc:295] Timeout 25: Found 1 live models and 0 in-flight non-inference requests

I0810 12:04:37.760581 7928 server.cc:295] Timeout 24: Found 1 live models and 0 in-flight non-inference requests

I0810 12:04:38.760722 7928 server.cc:295] Timeout 23: Found 1 live models and 0 in-flight non-inference requests

I0810 12:04:39.760857 7928 server.cc:295] Timeout 22: Found 1 live models and 0 in-flight non-inference requests

I0810 12:04:40.760995 7928 server.cc:295] Timeout 21: Found 1 live models and 0 in-flight non-inference requests

/usr/local/lib/python3.8/dist-packages/merlin/systems/dag/ops/feast.py:15: DeprecationWarning: np.float is a deprecated alias for the builtin float. To silence this warning, use float by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use np.float64 here.

Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations

ValueType.FLOAT: (np.float, False, False),

I0810 12:04:41.741594 7928 model_repository_manager.cc:1328] successfully unloaded '3_queryfeast' version 1

I0810 12:04:41.761119 7928 server.cc:295] Timeout 20: Found 0 live models and 0 in-flight non-inference requests

Error in atexit._run_exitfuncs:

Traceback (most recent call last):

File "/usr/lib/python3.8/logging/init.py", line 2127, in shutdown

h.close()

File "/usr/local/lib/python3.8/dist-packages/absl/logging/init.py", line 934, in close

self.stream.close()

File "/usr/local/lib/python3.8/dist-packages/ipykernel/iostream.py", line 438, in close

self.watch_fd_thread.join()

AttributeError: 'OutStream' object has no attribute 'watch_fd_thread'

=========================== short test summary info ============================

FAILED tests/unit/examples/test_building_deploying_multi_stage_RecSys.py::test_func

=================== 1 failed, 2 passed in 195.07s (0:03:15) ====================

Build step 'Execute shell' marked build as failure

Performing Post build task...

Match found for : : True

Logical operation result is TRUE

Running script  : #!/bin/bash

cd /var/jenkins_home/

CUDA_VISIBLE_DEVICES=1 python test_res_push.py "https://api.GitHub.com/repos/NVIDIA-Merlin/Merlin/issues/$ghprbPullId/comments" "/var/jenkins_home/jobs/$JOB_NAME/builds/$BUILD_NUMBER/log"

[merlin_merlin] $ /bin/bash /tmp/jenkins4027391082230061583.sh

@karlhigley
Copy link
Contributor Author

rerun tests

@nvidia-merlin-bot
Copy link
Contributor

Click to view CI Results
GitHub pull request #524 of commit a1c95f262485c8f5d7f6e07d42b1a678d011f7bf, no merge conflicts.
Running as SYSTEM
Setting status of a1c95f262485c8f5d7f6e07d42b1a678d011f7bf to PENDING with url https://10.20.13.93:8080/job/merlin_merlin/341/console and message: 'Pending'
Using context: Jenkins
Building on master in workspace /var/jenkins_home/workspace/merlin_merlin
using credential systems-login
 > git rev-parse --is-inside-work-tree # timeout=10
Fetching changes from the remote Git repository
 > git config remote.origin.url https://github.com/NVIDIA-Merlin/Merlin # timeout=10
Fetching upstream changes from https://github.com/NVIDIA-Merlin/Merlin
 > git --version # timeout=10
using GIT_ASKPASS to set credentials login for merlin-systems
 > git fetch --tags --force --progress -- https://github.com/NVIDIA-Merlin/Merlin +refs/pull/524/*:refs/remotes/origin/pr/524/* # timeout=10
 > git rev-parse a1c95f262485c8f5d7f6e07d42b1a678d011f7bf^{commit} # timeout=10
Checking out Revision a1c95f262485c8f5d7f6e07d42b1a678d011f7bf (detached)
 > git config core.sparsecheckout # timeout=10
 > git checkout -f a1c95f262485c8f5d7f6e07d42b1a678d011f7bf # timeout=10
Commit message: "Merge branch 'main' into fix/cmake-install-v2"
 > git rev-list --no-walk a1c95f262485c8f5d7f6e07d42b1a678d011f7bf # timeout=10
[merlin_merlin] $ /bin/bash /tmp/jenkins1401953661412466205.sh
============================= test session starts ==============================
platform linux -- Python 3.8.10, pytest-7.1.2, pluggy-1.0.0
rootdir: /var/jenkins_home/workspace/merlin_merlin/merlin
plugins: anyio-3.6.1, xdist-2.5.0, forked-1.4.0, cov-3.0.0
collected 3 items

tests/unit/test_version.py .                                             [ 33%]

tests/unit/examples/test_building_deploying_multi_stage_RecSys.py F      [ 66%]

tests/unit/examples/test_scaling_criteo_merlin_models.py .               [100%]


=================================== FAILURES ===================================

__________________________________ test_func ___________________________________


self = <testbook.client.TestbookNotebookClient object at 0x7fee37c5f4c0>

cell = [53], kwargs = {}, cell_indexes = [53], executed_cells = [], idx = 53


def execute_cell(self, cell, **kwargs) -> Union[Dict, List[Dict]]:
    """
    Executes a cell or list of cells
    """
    if isinstance(cell, slice):
        start, stop = self._cell_index(cell.start), self._cell_index(cell.stop)
        if cell.step is not None:
            raise TestbookError('testbook does not support step argument')

        cell = range(start, stop + 1)
    elif isinstance(cell, str) or isinstance(cell, int):
        cell = [cell]

    cell_indexes = cell

    if all(isinstance(x, str) for x in cell):
        cell_indexes = [self._cell_index(tag) for tag in cell]

    executed_cells = []
    for idx in cell_indexes:
        try:





          cell = super().execute_cell(self.nb['cells'][idx], idx, **kwargs)





/usr/local/lib/python3.8/dist-packages/testbook/client.py:133:




args = (<testbook.client.TestbookNotebookClient object at 0x7fee37c5f4c0>, {'id': '256a3ff7', 'cell_type': 'code', 'metadata'...ast.py, line 299 in transform>]"\n\nAt:\n  /tmp/examples/poc_ensemble/3_queryfeast/1/model.py(122): execute\n']}]}, 53)

kwargs = {}


def wrapped(*args, **kwargs):





  return just_run(coro(*args, **kwargs))





/usr/local/lib/python3.8/dist-packages/nbclient/util.py:85:




coro = <coroutine object NotebookClient.async_execute_cell at 0x7fed5f4863c0>


def just_run(coro: Awaitable) -> Any:
    """Make the coroutine run, even if there is an event loop running (using nest_asyncio)"""
    try:
        loop = asyncio.get_running_loop()
    except RuntimeError:
        loop = None
    if loop is None:
        had_running_loop = False
        loop = asyncio.new_event_loop()
        asyncio.set_event_loop(loop)
    else:
        had_running_loop = True
    if had_running_loop:
        # if there is a running loop, we patch using nest_asyncio
        # to have reentrant event loops
        check_ipython()
        import nest_asyncio

        nest_asyncio.apply()
        check_patch_tornado()





  return loop.run_until_complete(coro)





/usr/local/lib/python3.8/dist-packages/nbclient/util.py:60:




self = <_UnixSelectorEventLoop running=False closed=False debug=False>

future = <Task finished name='Task-369' coro=<NotebookClient.async_execute_cell() done, defined at /usr/local/lib/python3.8/dis...ps/feast.py, line 299 in transform>]"\n\nAt:\n  /tmp/examples/poc_ensemble/3_queryfeast/1/model.py(122): execute\n\n')>


def run_until_complete(self, future):
    """Run until the Future is done.

    If the argument is a coroutine, it is wrapped in a Task.

    WARNING: It would be disastrous to call run_until_complete()
    with the same coroutine twice -- it would wrap it in two
    different Tasks and that can't be good.

    Return the Future's result, or raise its exception.
    """
    self._check_closed()
    self._check_running()

    new_task = not futures.isfuture(future)
    future = tasks.ensure_future(future, loop=self)
    if new_task:
        # An exception is raised if the future didn't complete, so there
        # is no need to log the "destroy pending task" message
        future._log_destroy_pending = False

    future.add_done_callback(_run_until_complete_cb)
    try:
        self.run_forever()
    except:
        if new_task and future.done() and not future.cancelled():
            # The coroutine raised a BaseException. Consume the exception
            # to not log a warning, the caller doesn't have access to the
            # local task.
            future.exception()
        raise
    finally:
        future.remove_done_callback(_run_until_complete_cb)
    if not future.done():
        raise RuntimeError('Event loop stopped before Future completed.')





  return future.result()





/usr/lib/python3.8/asyncio/base_events.py:616:




self = <testbook.client.TestbookNotebookClient object at 0x7fee37c5f4c0>

cell = {'id': '256a3ff7', 'cell_type': 'code', 'metadata': {'execution': {'iopub.status.busy': '2022-08-10T12:11:44.605170Z',...ps/feast.py, line 299 in transform>]"\n\nAt:\n  /tmp/examples/poc_ensemble/3_queryfeast/1/model.py(122): execute\n']}]}

cell_index = 53, execution_count = None, store_history = True


async def async_execute_cell(
    self,
    cell: NotebookNode,
    cell_index: int,
    execution_count: t.Optional[int] = None,
    store_history: bool = True,
) -> NotebookNode:
    """
    Executes a single code cell.

    To execute all cells see :meth:`execute`.

    Parameters
    ----------
    cell : nbformat.NotebookNode
        The cell which is currently being processed.
    cell_index : int
        The position of the cell within the notebook object.
    execution_count : int
        The execution count to be assigned to the cell (default: Use kernel response)
    store_history : bool
        Determines if history should be stored in the kernel (default: False).
        Specific to ipython kernels, which can store command histories.

    Returns
    -------
    output : dict
        The execution output payload (or None for no output).

    Raises
    ------
    CellExecutionError
        If execution failed and should raise an exception, this will be raised
        with defaults about the failure.

    Returns
    -------
    cell : NotebookNode
        The cell which was just processed.
    """
    assert self.kc is not None

    await run_hook(self.on_cell_start, cell=cell, cell_index=cell_index)

    if cell.cell_type != 'code' or not cell.source.strip():
        self.log.debug("Skipping non-executing cell %s", cell_index)
        return cell

    if self.skip_cells_with_tag in cell.metadata.get("tags", []):
        self.log.debug("Skipping tagged cell %s", cell_index)
        return cell

    if self.record_timing:  # clear execution metadata prior to execution
        cell['metadata']['execution'] = {}

    self.log.debug("Executing cell:\n%s", cell.source)

    cell_allows_errors = (not self.force_raise_errors) and (
        self.allow_errors or "raises-exception" in cell.metadata.get("tags", [])
    )

    await run_hook(self.on_cell_execute, cell=cell, cell_index=cell_index)
    parent_msg_id = await ensure_async(
        self.kc.execute(
            cell.source, store_history=store_history, stop_on_error=not cell_allows_errors
        )
    )
    await run_hook(self.on_cell_complete, cell=cell, cell_index=cell_index)
    # We launched a code cell to execute
    self.code_cells_executed += 1
    exec_timeout = self._get_timeout(cell)

    cell.outputs = []
    self.clear_before_next_output = False

    task_poll_kernel_alive = asyncio.ensure_future(self._async_poll_kernel_alive())
    task_poll_output_msg = asyncio.ensure_future(
        self._async_poll_output_msg(parent_msg_id, cell, cell_index)
    )
    self.task_poll_for_reply = asyncio.ensure_future(
        self._async_poll_for_reply(
            parent_msg_id, cell, exec_timeout, task_poll_output_msg, task_poll_kernel_alive
        )
    )
    try:
        exec_reply = await self.task_poll_for_reply
    except asyncio.CancelledError:
        # can only be cancelled by task_poll_kernel_alive when the kernel is dead
        task_poll_output_msg.cancel()
        raise DeadKernelError("Kernel died")
    except Exception as e:
        # Best effort to cancel request if it hasn't been resolved
        try:
            # Check if the task_poll_output is doing the raising for us
            if not isinstance(e, CellControlSignal):
                task_poll_output_msg.cancel()
        finally:
            raise

    if execution_count:
        cell['execution_count'] = execution_count
    await run_hook(
        self.on_cell_executed, cell=cell, cell_index=cell_index, execute_reply=exec_reply
    )





  await self._check_raise_for_error(cell, cell_index, exec_reply)





/usr/local/lib/python3.8/dist-packages/nbclient/client.py:1022:




self = <testbook.client.TestbookNotebookClient object at 0x7fee37c5f4c0>

cell = {'id': '256a3ff7', 'cell_type': 'code', 'metadata': {'execution': {'iopub.status.busy': '2022-08-10T12:11:44.605170Z',...ps/feast.py, line 299 in transform>]"\n\nAt:\n  /tmp/examples/poc_ensemble/3_queryfeast/1/model.py(122): execute\n']}]}

cell_index = 53

exec_reply = {'buffers': [], 'content': {'ename': 'InferenceServerException', 'engine_info': {'engine_id': -1, 'engine_uuid': '79c9...e, 'engine': '79c941be-755b-4c6c-9ccd-474da1b4b879', 'started': '2022-08-10T12:11:44.605454Z', 'status': 'error'}, ...}


async def _check_raise_for_error(
    self, cell: NotebookNode, cell_index: int, exec_reply: t.Optional[t.Dict]
) -> None:

    if exec_reply is None:
        return None

    exec_reply_content = exec_reply['content']
    if exec_reply_content['status'] != 'error':
        return None

    cell_allows_errors = (not self.force_raise_errors) and (
        self.allow_errors
        or exec_reply_content.get('ename') in self.allow_error_names
        or "raises-exception" in cell.metadata.get("tags", [])
    )
    await run_hook(
        self.on_cell_error, cell=cell, cell_index=cell_index, execute_reply=exec_reply
    )
    if not cell_allows_errors:





      raise CellExecutionError.from_cell_and_msg(cell, exec_reply_content)





E           nbclient.exceptions.CellExecutionError: An error occurred while executing the following cell:

E           ------------------

E

E           import shutil

E           from merlin.models.loader.tf_utils import configure_tensorflow

E           configure_tensorflow()

E           from merlin.systems.triton.utils import run_ensemble_on_tritonserver

E           response = run_ensemble_on_tritonserver(

E               "/tmp/examples/poc_ensemble", outputs, request, "ensemble_model"

E           )

E           response = [x.tolist()[0] for x in response["ordered_ids"]]

E           shutil.rmtree("/tmp/examples/", ignore_errors=True)

E

E           ------------------

E

E           �[0;31m---------------------------------------------------------------------------�[0m

E           �[0;31mInferenceServerException�[0m                  Traceback (most recent call last)

E           Input �[0;32mIn [32]�[0m, in �[0;36m<cell line: 5>�[0;34m()�[0m

E           �[1;32m      3�[0m configure_tensorflow()

E           �[1;32m      4�[0m �[38;5;28;01mfrom�[39;00m �[38;5;21;01mmerlin�[39;00m�[38;5;21;01m.�[39;00m�[38;5;21;01msystems�[39;00m�[38;5;21;01m.�[39;00m�[38;5;21;01mtriton�[39;00m�[38;5;21;01m.�[39;00m�[38;5;21;01mutils�[39;00m �[38;5;28;01mimport�[39;00m run_ensemble_on_tritonserver

E           �[0;32m----> 5�[0m response �[38;5;241m=�[39m �[43mrun_ensemble_on_tritonserver�[49m�[43m(�[49m

E           �[1;32m      6�[0m �[43m    �[49m�[38;5;124;43m"�[39;49m�[38;5;124;43m/tmp/examples/poc_ensemble�[39;49m�[38;5;124;43m"�[39;49m�[43m,�[49m�[43m �[49m�[43moutputs�[49m�[43m,�[49m�[43m �[49m�[43mrequest�[49m�[43m,�[49m�[43m �[49m�[38;5;124;43m"�[39;49m�[38;5;124;43mensemble_model�[39;49m�[38;5;124;43m"�[39;49m

E           �[1;32m      7�[0m �[43m)�[49m

E           �[1;32m      8�[0m response �[38;5;241m=�[39m [x�[38;5;241m.�[39mtolist()[�[38;5;241m0�[39m] �[38;5;28;01mfor�[39;00m x �[38;5;129;01min�[39;00m response[�[38;5;124m"�[39m�[38;5;124mordered_ids�[39m�[38;5;124m"�[39m]]

E           �[1;32m      9�[0m shutil�[38;5;241m.�[39mrmtree(�[38;5;124m"�[39m�[38;5;124m/tmp/examples/�[39m�[38;5;124m"�[39m, ignore_errors�[38;5;241m=�[39m�[38;5;28;01mTrue�[39;00m)

E

E           File �[0;32m/usr/local/lib/python3.8/dist-packages/merlin/systems/triton/utils.py:93�[0m, in �[0;36mrun_ensemble_on_tritonserver�[0;34m(tmpdir, output_columns, df, model_name)�[0m

E           �[1;32m     91�[0m response �[38;5;241m=�[39m �[38;5;28;01mNone�[39;00m

E           �[1;32m     92�[0m �[38;5;28;01mwith�[39;00m run_triton_server(tmpdir) �[38;5;28;01mas�[39;00m client:

E           �[0;32m---> 93�[0m     response �[38;5;241m=�[39m �[43msend_triton_request�[49m�[43m(�[49m�[43mdf�[49m�[43m,�[49m�[43m �[49m�[43moutput_columns�[49m�[43m,�[49m�[43m �[49m�[43mclient�[49m�[38;5;241;43m=�[39;49m�[43mclient�[49m�[43m,�[49m�[43m �[49m�[43mtriton_model�[49m�[38;5;241;43m=�[39;49m�[43mmodel_name�[49m�[43m)�[49m

E           �[1;32m     95�[0m �[38;5;28;01mreturn�[39;00m response

E

E           File �[0;32m/usr/local/lib/python3.8/dist-packages/merlin/systems/triton/utils.py:141�[0m, in �[0;36msend_triton_request�[0;34m(df, outputs_list, client, endpoint, request_id, triton_model)�[0m

E           �[1;32m    139�[0m outputs �[38;5;241m=�[39m [grpcclient�[38;5;241m.�[39mInferRequestedOutput(col) �[38;5;28;01mfor�[39;00m col �[38;5;129;01min�[39;00m outputs_list]

E           �[1;32m    140�[0m �[38;5;28;01mwith�[39;00m client:

E           �[0;32m--> 141�[0m     response �[38;5;241m=�[39m �[43mclient�[49m�[38;5;241;43m.�[39;49m�[43minfer�[49m�[43m(�[49m�[43mtriton_model�[49m�[43m,�[49m�[43m �[49m�[43minputs�[49m�[43m,�[49m�[43m �[49m�[43mrequest_id�[49m�[38;5;241;43m=�[39;49m�[43mrequest_id�[49m�[43m,�[49m�[43m �[49m�[43moutputs�[49m�[38;5;241;43m=�[39;49m�[43moutputs�[49m�[43m)�[49m

E           �[1;32m    143�[0m results �[38;5;241m=�[39m {}

E           �[1;32m    144�[0m �[38;5;28;01mfor�[39;00m col �[38;5;129;01min�[39;00m outputs_list:

E

E           File �[0;32m/usr/local/lib/python3.8/dist-packages/tritonclient/grpc/init.py:1322�[0m, in �[0;36mInferenceServerClient.infer�[0;34m(self, model_name, inputs, model_version, outputs, request_id, sequence_id, sequence_start, sequence_end, priority, timeout, client_timeout, headers, compression_algorithm)�[0m

E           �[1;32m   1320�[0m     �[38;5;28;01mreturn�[39;00m result

E           �[1;32m   1321�[0m �[38;5;28;01mexcept�[39;00m grpc�[38;5;241m.�[39mRpcError �[38;5;28;01mas�[39;00m rpc_error:

E           �[0;32m-> 1322�[0m     �[43mraise_error_grpc�[49m�[43m(�[49m�[43mrpc_error�[49m�[43m)�[49m

E

E           File �[0;32m/usr/local/lib/python3.8/dist-packages/tritonclient/grpc/init.py:62�[0m, in �[0;36mraise_error_grpc�[0;34m(rpc_error)�[0m

E           �[1;32m     61�[0m �[38;5;28;01mdef�[39;00m �[38;5;21mraise_error_grpc�[39m(rpc_error):

E           �[0;32m---> 62�[0m     �[38;5;28;01mraise�[39;00m get_error_grpc(rpc_error) �[38;5;28;01mfrom�[39;00m �[38;5;28mNone�[39m

E

E           �[0;31mInferenceServerException�[0m: [StatusCode.INTERNAL] in ensemble 'ensemble_model', Failed to process the request(s) for model instance '3_queryfeast', message: TypeError: init(): incompatible constructor arguments. The following argument types are supported:

E               1. c_python_backend_utils.InferenceResponse(output_tensors: List[c_python_backend_utils.Tensor], error: c_python_backend_utils.TritonError = None)

E

E           Invoked with: kwargs: tensors=[], error="<class 'TypeError'>, int() argument must be a string, a bytes-like object or a number, not 'NoneType', [<FrameSummary file /tmp/examples/poc_ensemble/3_queryfeast/1/model.py, line 105 in execute>, <FrameSummary file /usr/local/lib/python3.8/dist-packages/merlin/systems/dag/op_runner.py, line 38 in execute>, <FrameSummary file /usr/local/lib/python3.8/dist-packages/merlin/systems/dag/ops/feast.py, line 299 in transform>]"

E

E           At:

E             /tmp/examples/poc_ensemble/3_queryfeast/1/model.py(122): execute

E

E           InferenceServerException: [StatusCode.INTERNAL] in ensemble 'ensemble_model', Failed to process the request(s) for model instance '3_queryfeast', message: TypeError: init(): incompatible constructor arguments. The following argument types are supported:

E               1. c_python_backend_utils.InferenceResponse(output_tensors: List[c_python_backend_utils.Tensor], error: c_python_backend_utils.TritonError = None)

E

E           Invoked with: kwargs: tensors=[], error="<class 'TypeError'>, int() argument must be a string, a bytes-like object or a number, not 'NoneType', [<FrameSummary file /tmp/examples/poc_ensemble/3_queryfeast/1/model.py, line 105 in execute>, <FrameSummary file /usr/local/lib/python3.8/dist-packages/merlin/systems/dag/op_runner.py, line 38 in execute>, <FrameSummary file /usr/local/lib/python3.8/dist-packages/merlin/systems/dag/ops/feast.py, line 299 in transform>]"

E

E           At:

E             /tmp/examples/poc_ensemble/3_queryfeast/1/model.py(122): execute


/usr/local/lib/python3.8/dist-packages/nbclient/client.py:916: CellExecutionError


During handling of the above exception, another exception occurred:


def test_func():
    with testbook(
        REPO_ROOT
        / "examples"
        / "Building-and-deploying-multi-stage-RecSys"
        / "01-Building-Recommender-Systems-with-Merlin.ipynb",
        execute=False,
    ) as tb1:
        tb1.inject(
            """
            import os
            os.environ["DATA_FOLDER"] = "/tmp/data/"
            os.environ["NUM_ROWS"] = "10000"
            os.system("mkdir -p /tmp/examples")
            os.environ["BASE_DIR"] = "/tmp/examples/"
            """
        )
        tb1.execute()
        assert os.path.isdir("/tmp/examples/dlrm")
        assert os.path.isdir("/tmp/examples/feature_repo")
        assert os.path.isdir("/tmp/examples/query_tower")
        assert os.path.isfile("/tmp/examples/item_embeddings.parquet")
        assert os.path.isfile("/tmp/examples/feature_repo/user_features.py")
        assert os.path.isfile("/tmp/examples/feature_repo/item_features.py")

    with testbook(
        REPO_ROOT
        / "examples"
        / "Building-and-deploying-multi-stage-RecSys"
        / "02-Deploying-multi-stage-RecSys-with-Merlin-Systems.ipynb",
        execute=False,
    ) as tb2:
        tb2.inject(
            """
            import os
            os.environ["DATA_FOLDER"] = "/tmp/data/"
            os.environ["BASE_DIR"] = "/tmp/examples/"
            """
        )
        NUM_OF_CELLS = len(tb2.cells)
        tb2.execute_cell(list(range(0, NUM_OF_CELLS - 3)))
        top_k = tb2.ref("top_k")
        outputs = tb2.ref("outputs")
        assert outputs[0] == "ordered_ids"





      tb2.inject(





            """
            import shutil
            from merlin.models.loader.tf_utils import configure_tensorflow
            configure_tensorflow()
            from merlin.systems.triton.utils import run_ensemble_on_tritonserver
            response = run_ensemble_on_tritonserver(
                "/tmp/examples/poc_ensemble", outputs, request, "ensemble_model"
            )
            response = [x.tolist()[0] for x in response["ordered_ids"]]
            shutil.rmtree("/tmp/examples/", ignore_errors=True)
            """
        )



tests/unit/examples/test_building_deploying_multi_stage_RecSys.py:57:




/usr/local/lib/python3.8/dist-packages/testbook/client.py:237: in inject

cell = TestbookNode(self.execute_cell(inject_idx)) if run else TestbookNode(code_cell)




self = <testbook.client.TestbookNotebookClient object at 0x7fee37c5f4c0>

cell = [53], kwargs = {}, cell_indexes = [53], executed_cells = [], idx = 53


def execute_cell(self, cell, **kwargs) -> Union[Dict, List[Dict]]:
    """
    Executes a cell or list of cells
    """
    if isinstance(cell, slice):
        start, stop = self._cell_index(cell.start), self._cell_index(cell.stop)
        if cell.step is not None:
            raise TestbookError('testbook does not support step argument')

        cell = range(start, stop + 1)
    elif isinstance(cell, str) or isinstance(cell, int):
        cell = [cell]

    cell_indexes = cell

    if all(isinstance(x, str) for x in cell):
        cell_indexes = [self._cell_index(tag) for tag in cell]

    executed_cells = []
    for idx in cell_indexes:
        try:
            cell = super().execute_cell(self.nb['cells'][idx], idx, **kwargs)
        except CellExecutionError as ce:





          raise TestbookRuntimeError(ce.evalue, ce, self._get_error_class(ce.ename))





E               testbook.exceptions.TestbookRuntimeError: An error occurred while executing the following cell:

E               ------------------

E

E               import shutil

E               from merlin.models.loader.tf_utils import configure_tensorflow

E               configure_tensorflow()

E               from merlin.systems.triton.utils import run_ensemble_on_tritonserver

E               response = run_ensemble_on_tritonserver(

E                   "/tmp/examples/poc_ensemble", outputs, request, "ensemble_model"

E               )

E               response = [x.tolist()[0] for x in response["ordered_ids"]]

E               shutil.rmtree("/tmp/examples/", ignore_errors=True)

E

E               ------------------

E

E               �[0;31m---------------------------------------------------------------------------�[0m

E               �[0;31mInferenceServerException�[0m                  Traceback (most recent call last)

E               Input �[0;32mIn [32]�[0m, in �[0;36m<cell line: 5>�[0;34m()�[0m

E               �[1;32m      3�[0m configure_tensorflow()

E               �[1;32m      4�[0m �[38;5;28;01mfrom�[39;00m �[38;5;21;01mmerlin�[39;00m�[38;5;21;01m.�[39;00m�[38;5;21;01msystems�[39;00m�[38;5;21;01m.�[39;00m�[38;5;21;01mtriton�[39;00m�[38;5;21;01m.�[39;00m�[38;5;21;01mutils�[39;00m �[38;5;28;01mimport�[39;00m run_ensemble_on_tritonserver

E               �[0;32m----> 5�[0m response �[38;5;241m=�[39m �[43mrun_ensemble_on_tritonserver�[49m�[43m(�[49m

E               �[1;32m      6�[0m �[43m    �[49m�[38;5;124;43m"�[39;49m�[38;5;124;43m/tmp/examples/poc_ensemble�[39;49m�[38;5;124;43m"�[39;49m�[43m,�[49m�[43m �[49m�[43moutputs�[49m�[43m,�[49m�[43m �[49m�[43mrequest�[49m�[43m,�[49m�[43m �[49m�[38;5;124;43m"�[39;49m�[38;5;124;43mensemble_model�[39;49m�[38;5;124;43m"�[39;49m

E               �[1;32m      7�[0m �[43m)�[49m

E               �[1;32m      8�[0m response �[38;5;241m=�[39m [x�[38;5;241m.�[39mtolist()[�[38;5;241m0�[39m] �[38;5;28;01mfor�[39;00m x �[38;5;129;01min�[39;00m response[�[38;5;124m"�[39m�[38;5;124mordered_ids�[39m�[38;5;124m"�[39m]]

E               �[1;32m      9�[0m shutil�[38;5;241m.�[39mrmtree(�[38;5;124m"�[39m�[38;5;124m/tmp/examples/�[39m�[38;5;124m"�[39m, ignore_errors�[38;5;241m=�[39m�[38;5;28;01mTrue�[39;00m)

E

E               File �[0;32m/usr/local/lib/python3.8/dist-packages/merlin/systems/triton/utils.py:93�[0m, in �[0;36mrun_ensemble_on_tritonserver�[0;34m(tmpdir, output_columns, df, model_name)�[0m

E               �[1;32m     91�[0m response �[38;5;241m=�[39m �[38;5;28;01mNone�[39;00m

E               �[1;32m     92�[0m �[38;5;28;01mwith�[39;00m run_triton_server(tmpdir) �[38;5;28;01mas�[39;00m client:

E               �[0;32m---> 93�[0m     response �[38;5;241m=�[39m �[43msend_triton_request�[49m�[43m(�[49m�[43mdf�[49m�[43m,�[49m�[43m �[49m�[43moutput_columns�[49m�[43m,�[49m�[43m �[49m�[43mclient�[49m�[38;5;241;43m=�[39;49m�[43mclient�[49m�[43m,�[49m�[43m �[49m�[43mtriton_model�[49m�[38;5;241;43m=�[39;49m�[43mmodel_name�[49m�[43m)�[49m

E               �[1;32m     95�[0m �[38;5;28;01mreturn�[39;00m response

E

E               File �[0;32m/usr/local/lib/python3.8/dist-packages/merlin/systems/triton/utils.py:141�[0m, in �[0;36msend_triton_request�[0;34m(df, outputs_list, client, endpoint, request_id, triton_model)�[0m

E               �[1;32m    139�[0m outputs �[38;5;241m=�[39m [grpcclient�[38;5;241m.�[39mInferRequestedOutput(col) �[38;5;28;01mfor�[39;00m col �[38;5;129;01min�[39;00m outputs_list]

E               �[1;32m    140�[0m �[38;5;28;01mwith�[39;00m client:

E               �[0;32m--> 141�[0m     response �[38;5;241m=�[39m �[43mclient�[49m�[38;5;241;43m.�[39;49m�[43minfer�[49m�[43m(�[49m�[43mtriton_model�[49m�[43m,�[49m�[43m �[49m�[43minputs�[49m�[43m,�[49m�[43m �[49m�[43mrequest_id�[49m�[38;5;241;43m=�[39;49m�[43mrequest_id�[49m�[43m,�[49m�[43m �[49m�[43moutputs�[49m�[38;5;241;43m=�[39;49m�[43moutputs�[49m�[43m)�[49m

E               �[1;32m    143�[0m results �[38;5;241m=�[39m {}

E               �[1;32m    144�[0m �[38;5;28;01mfor�[39;00m col �[38;5;129;01min�[39;00m outputs_list:

E

E               File �[0;32m/usr/local/lib/python3.8/dist-packages/tritonclient/grpc/init.py:1322�[0m, in �[0;36mInferenceServerClient.infer�[0;34m(self, model_name, inputs, model_version, outputs, request_id, sequence_id, sequence_start, sequence_end, priority, timeout, client_timeout, headers, compression_algorithm)�[0m

E               �[1;32m   1320�[0m     �[38;5;28;01mreturn�[39;00m result

E               �[1;32m   1321�[0m �[38;5;28;01mexcept�[39;00m grpc�[38;5;241m.�[39mRpcError �[38;5;28;01mas�[39;00m rpc_error:

E               �[0;32m-> 1322�[0m     �[43mraise_error_grpc�[49m�[43m(�[49m�[43mrpc_error�[49m�[43m)�[49m

E

E               File �[0;32m/usr/local/lib/python3.8/dist-packages/tritonclient/grpc/init.py:62�[0m, in �[0;36mraise_error_grpc�[0;34m(rpc_error)�[0m

E               �[1;32m     61�[0m �[38;5;28;01mdef�[39;00m �[38;5;21mraise_error_grpc�[39m(rpc_error):

E               �[0;32m---> 62�[0m     �[38;5;28;01mraise�[39;00m get_error_grpc(rpc_error) �[38;5;28;01mfrom�[39;00m �[38;5;28mNone�[39m

E

E               �[0;31mInferenceServerException�[0m: [StatusCode.INTERNAL] in ensemble 'ensemble_model', Failed to process the request(s) for model instance '3_queryfeast', message: TypeError: init(): incompatible constructor arguments. The following argument types are supported:

E                   1. c_python_backend_utils.InferenceResponse(output_tensors: List[c_python_backend_utils.Tensor], error: c_python_backend_utils.TritonError = None)

E

E               Invoked with: kwargs: tensors=[], error="<class 'TypeError'>, int() argument must be a string, a bytes-like object or a number, not 'NoneType', [<FrameSummary file /tmp/examples/poc_ensemble/3_queryfeast/1/model.py, line 105 in execute>, <FrameSummary file /usr/local/lib/python3.8/dist-packages/merlin/systems/dag/op_runner.py, line 38 in execute>, <FrameSummary file /usr/local/lib/python3.8/dist-packages/merlin/systems/dag/ops/feast.py, line 299 in transform>]"

E

E               At:

E                 /tmp/examples/poc_ensemble/3_queryfeast/1/model.py(122): execute

E

E               InferenceServerException: [StatusCode.INTERNAL] in ensemble 'ensemble_model', Failed to process the request(s) for model instance '3_queryfeast', message: TypeError: init(): incompatible constructor arguments. The following argument types are supported:

E                   1. c_python_backend_utils.InferenceResponse(output_tensors: List[c_python_backend_utils.Tensor], error: c_python_backend_utils.TritonError = None)

E

E               Invoked with: kwargs: tensors=[], error="<class 'TypeError'>, int() argument must be a string, a bytes-like object or a number, not 'NoneType', [<FrameSummary file /tmp/examples/poc_ensemble/3_queryfeast/1/model.py, line 105 in execute>, <FrameSummary file /usr/local/lib/python3.8/dist-packages/merlin/systems/dag/op_runner.py, line 38 in execute>, <FrameSummary file /usr/local/lib/python3.8/dist-packages/merlin/systems/dag/ops/feast.py, line 299 in transform>]"

E

E               At:

E                 /tmp/examples/poc_ensemble/3_queryfeast/1/model.py(122): execute


/usr/local/lib/python3.8/dist-packages/testbook/client.py:135: TestbookRuntimeError

----------------------------- Captured stdout call -----------------------------

Signal (2) received.

----------------------------- Captured stderr call -----------------------------

2022-08-10 12:10:38.345477: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA

To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.

2022-08-10 12:10:40.326850: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 1627 MB memory:  -> device: 0, name: Tesla P100-DGXS-16GB, pci bus id: 0000:07:00.0, compute capability: 6.0

2022-08-10 12:10:40.327554: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 15153 MB memory:  -> device: 1, name: Tesla P100-DGXS-16GB, pci bus id: 0000:08:00.0, compute capability: 6.0

Error in atexit._run_exitfuncs:

Traceback (most recent call last):

File "/usr/lib/python3.8/logging/init.py", line 2127, in shutdown

h.close()

File "/usr/local/lib/python3.8/dist-packages/absl/logging/init.py", line 934, in close

self.stream.close()

File "/usr/local/lib/python3.8/dist-packages/ipykernel/iostream.py", line 438, in close

self.watch_fd_thread.join()

AttributeError: 'OutStream' object has no attribute 'watch_fd_thread'

WARNING clustering 250 points to 32 centroids: please provide at least 1248 training points

2022-08-10 12:11:37.359027: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA

To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.

2022-08-10 12:11:39.337530: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 1627 MB memory:  -> device: 0, name: Tesla P100-DGXS-16GB, pci bus id: 0000:07:00.0, compute capability: 6.0

2022-08-10 12:11:39.338287: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 15153 MB memory:  -> device: 1, name: Tesla P100-DGXS-16GB, pci bus id: 0000:08:00.0, compute capability: 6.0

I0810 12:11:44.869358 9834 pinned_memory_manager.cc:240] Pinned memory pool is created at '0x7f7f36000000' with size 268435456

I0810 12:11:44.870106 9834 cuda_memory_manager.cc:105] CUDA memory pool is created on device 0 with size 67108864

I0810 12:11:44.877408 9834 model_repository_manager.cc:1191] loading: 1_predicttensorflow:1

I0810 12:11:44.977754 9834 model_repository_manager.cc:1191] loading: 0_queryfeast:1

I0810 12:11:45.078028 9834 model_repository_manager.cc:1191] loading: 2_queryfaiss:1

I0810 12:11:45.178309 9834 model_repository_manager.cc:1191] loading: 3_queryfeast:1

I0810 12:11:45.267192 9834 tensorflow.cc:2181] TRITONBACKEND_Initialize: tensorflow

I0810 12:11:45.267228 9834 tensorflow.cc:2191] Triton TRITONBACKEND API version: 1.9

I0810 12:11:45.267235 9834 tensorflow.cc:2197] 'tensorflow' TRITONBACKEND API version: 1.9

I0810 12:11:45.267241 9834 tensorflow.cc:2221] backend configuration:

{"cmdline":{"auto-complete-config":"false","backend-directory":"/opt/tritonserver/backends","min-compute-capability":"6.000000","version":"2","default-max-batch-size":"4"}}

I0810 12:11:45.267278 9834 tensorflow.cc:2281] TRITONBACKEND_ModelInitialize: 1_predicttensorflow (version 1)

I0810 12:11:45.271236 9834 tensorflow.cc:2330] TRITONBACKEND_ModelInstanceInitialize: 1_predicttensorflow (GPU device 0)

I0810 12:11:45.278611 9834 model_repository_manager.cc:1191] loading: 4_unrollfeatures:1

I0810 12:11:45.378913 9834 model_repository_manager.cc:1191] loading: 5_predicttensorflow:1

I0810 12:11:45.479207 9834 model_repository_manager.cc:1191] loading: 6_softmaxsampling:1

2022-08-10 12:11:45.612830: I tensorflow/cc/saved_model/reader.cc:43] Reading SavedModel from: /tmp/examples/poc_ensemble/1_predicttensorflow/1/model.savedmodel

2022-08-10 12:11:45.616379: I tensorflow/cc/saved_model/reader.cc:78] Reading meta graph with tags { serve }

2022-08-10 12:11:45.616427: I tensorflow/cc/saved_model/reader.cc:119] Reading SavedModel debug info (if present) from: /tmp/examples/poc_ensemble/1_predicttensorflow/1/model.savedmodel

2022-08-10 12:11:45.616523: I tensorflow/core/platform/cpu_feature_guard.cc:152] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE3 SSE4.1 SSE4.2 AVX

To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.

2022-08-10 12:11:45.665413: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 12901 MB memory:  -> device: 0, name: Tesla P100-DGXS-16GB, pci bus id: 0000:07:00.0, compute capability: 6.0

2022-08-10 12:11:45.700881: I tensorflow/cc/saved_model/loader.cc:230] Restoring SavedModel bundle.

2022-08-10 12:11:45.780941: I tensorflow/cc/saved_model/loader.cc:214] Running initialization op on SavedModel bundle at path: /tmp/examples/poc_ensemble/1_predicttensorflow/1/model.savedmodel

2022-08-10 12:11:45.805806: I tensorflow/cc/saved_model/loader.cc:321] SavedModel load for tags { serve }; Status: success: OK. Took 192995 microseconds.

I0810 12:11:45.806058 9834 model_repository_manager.cc:1345] successfully loaded '1_predicttensorflow' version 1

I0810 12:11:45.810335 9834 tensorflow.cc:2281] TRITONBACKEND_ModelInitialize: 5_predicttensorflow (version 1)

I0810 12:11:45.812367 9834 python.cc:2388] TRITONBACKEND_ModelInstanceInitialize: 0_queryfeast (GPU device 0)

I0810 12:11:48.171118 9834 python.cc:2388] TRITONBACKEND_ModelInstanceInitialize: 2_queryfaiss (GPU device 0)

I0810 12:11:48.171481 9834 model_repository_manager.cc:1345] successfully loaded '0_queryfeast' version 1

I0810 12:11:50.523481 9834 python.cc:2388] TRITONBACKEND_ModelInstanceInitialize: 3_queryfeast (GPU device 0)

I0810 12:11:50.525134 9834 model_repository_manager.cc:1345] successfully loaded '2_queryfaiss' version 1

I0810 12:11:52.821517 9834 python.cc:2388] TRITONBACKEND_ModelInstanceInitialize: 4_unrollfeatures (GPU device 0)

I0810 12:11:52.821750 9834 model_repository_manager.cc:1345] successfully loaded '3_queryfeast' version 1

I0810 12:11:54.911027 9834 tensorflow.cc:2330] TRITONBACKEND_ModelInstanceInitialize: 5_predicttensorflow (GPU device 0)

I0810 12:11:54.911142 9834 model_repository_manager.cc:1345] successfully loaded '4_unrollfeatures' version 1

2022-08-10 12:11:54.912663: I tensorflow/cc/saved_model/reader.cc:43] Reading SavedModel from: /tmp/examples/poc_ensemble/5_predicttensorflow/1/model.savedmodel

2022-08-10 12:11:54.923368: I tensorflow/cc/saved_model/reader.cc:78] Reading meta graph with tags { serve }

2022-08-10 12:11:54.923400: I tensorflow/cc/saved_model/reader.cc:119] Reading SavedModel debug info (if present) from: /tmp/examples/poc_ensemble/5_predicttensorflow/1/model.savedmodel

2022-08-10 12:11:54.925521: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 12901 MB memory:  -> device: 0, name: Tesla P100-DGXS-16GB, pci bus id: 0000:07:00.0, compute capability: 6.0

2022-08-10 12:11:54.947707: I tensorflow/cc/saved_model/loader.cc:230] Restoring SavedModel bundle.

2022-08-10 12:11:55.099334: I tensorflow/cc/saved_model/loader.cc:214] Running initialization op on SavedModel bundle at path: /tmp/examples/poc_ensemble/5_predicttensorflow/1/model.savedmodel

2022-08-10 12:11:55.149991: I tensorflow/cc/saved_model/loader.cc:321] SavedModel load for tags { serve }; Status: success: OK. Took 237335 microseconds.

I0810 12:11:55.150127 9834 python.cc:2388] TRITONBACKEND_ModelInstanceInitialize: 6_softmaxsampling (GPU device 0)

I0810 12:11:55.150212 9834 model_repository_manager.cc:1345] successfully loaded '5_predicttensorflow' version 1

I0810 12:11:57.270436 9834 model_repository_manager.cc:1345] successfully loaded '6_softmaxsampling' version 1

I0810 12:11:57.271563 9834 model_repository_manager.cc:1191] loading: ensemble_model:1

I0810 12:11:57.372422 9834 model_repository_manager.cc:1345] successfully loaded 'ensemble_model' version 1

I0810 12:11:57.372571 9834 server.cc:556]

+------------------+------+

| Repository Agent | Path |

+------------------+------+

+------------------+------+


I0810 12:11:57.372655 9834 server.cc:583]

+------------+-----------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

| Backend    | Path                                                            | Config                                                                                                                                                                       |

+------------+-----------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

| tensorflow | /opt/tritonserver/backends/tensorflow2/libtriton_tensorflow2.so | {"cmdline":{"auto-complete-config":"false","backend-directory":"/opt/tritonserver/backends","min-compute-capability":"6.000000","version":"2","default-max-batch-size":"4"}} |

| python     | /opt/tritonserver/backends/python/libtriton_python.so           | {"cmdline":{"auto-complete-config":"false","min-compute-capability":"6.000000","backend-directory":"/opt/tritonserver/backends","default-max-batch-size":"4"}}               |

+------------+-----------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+


I0810 12:11:57.372741 9834 server.cc:626]

+---------------------+---------+--------+

| Model               | Version | Status |

+---------------------+---------+--------+

| 0_queryfeast        | 1       | READY  |

| 1_predicttensorflow | 1       | READY  |

| 2_queryfaiss        | 1       | READY  |

| 3_queryfeast        | 1       | READY  |

| 4_unrollfeatures    | 1       | READY  |

| 5_predicttensorflow | 1       | READY  |

| 6_softmaxsampling   | 1       | READY  |

| ensemble_model      | 1       | READY  |

+---------------------+---------+--------+


I0810 12:11:57.435006 9834 metrics.cc:650] Collecting metrics for GPU 0: Tesla P100-DGXS-16GB

I0810 12:11:57.435852 9834 tritonserver.cc:2138]

+----------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

| Option                           | Value                                                                                                                                                                                        |

+----------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

| server_id                        | triton                                                                                                                                                                                       |

| server_version                   | 2.22.0                                                                                                                                                                                       |

| server_extensions                | classification sequence model_repository model_repository(unload_dependents) schedule_policy model_configuration system_shared_memory cuda_shared_memory binary_tensor_data statistics trace |

| model_repository_path[0]         | /tmp/examples/poc_ensemble                                                                                                                                                                   |

| model_control_mode               | MODE_NONE                                                                                                                                                                                    |

| strict_model_config              | 1                                                                                                                                                                                            |

| rate_limit                       | OFF                                                                                                                                                                                          |

| pinned_memory_pool_byte_size     | 268435456                                                                                                                                                                                    |

| cuda_memory_pool_byte_size{0}    | 67108864                                                                                                                                                                                     |

| response_cache_byte_size         | 0                                                                                                                                                                                            |

| min_supported_compute_capability | 6.0                                                                                                                                                                                          |

| strict_readiness                 | 1                                                                                                                                                                                            |

| exit_timeout                     | 30                                                                                                                                                                                           |

+----------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+


I0810 12:11:57.436642 9834 grpc_server.cc:4589] Started GRPCInferenceService at 0.0.0.0:8001

I0810 12:11:57.437202 9834 http_server.cc:3303] Started HTTPService at 0.0.0.0:8000

I0810 12:11:57.478418 9834 http_server.cc:178] Started Metrics Service at 0.0.0.0:8002

W0810 12:11:58.452231 9834 metrics.cc:468] Unable to get energy consumption for GPU 0. Status:Success, value:0

W0810 12:11:58.452294 9834 metrics.cc:507] Unable to get memory usage for GPU 0. Memory usage status:Success, value:0. Memory total status:Success, value:0

0810 12:11:59.130124 10091 pb_stub.cc:749] Failed to process the request(s) for model '3_queryfeast', message: TypeError: init(): incompatible constructor arguments. The following argument types are supported:

1. c_python_backend_utils.InferenceResponse(output_tensors: List[c_python_backend_utils.Tensor], error: c_python_backend_utils.TritonError = None)


Invoked with: kwargs: tensors=[], error="<class 'TypeError'>, int() argument must be a string, a bytes-like object or a number, not 'NoneType', [<FrameSummary file /tmp/examples/poc_ensemble/3_queryfeast/1/model.py, line 105 in execute>, <FrameSummary file /usr/local/lib/python3.8/dist-packages/merlin/systems/dag/op_runner.py, line 38 in execute>, <FrameSummary file /usr/local/lib/python3.8/dist-packages/merlin/systems/dag/ops/feast.py, line 299 in transform>]"


At:

/tmp/examples/poc_ensemble/3_queryfeast/1/model.py(122): execute


I0810 12:11:59.134568 9834 server.cc:257] Waiting for in-flight requests to complete.

I0810 12:11:59.134622 9834 server.cc:273] Timeout 30: Found 0 model versions that have in-flight inferences

I0810 12:11:59.134641 9834 model_repository_manager.cc:1223] unloading: ensemble_model:1

I0810 12:11:59.134733 9834 model_repository_manager.cc:1223] unloading: 6_softmaxsampling:1

I0810 12:11:59.134824 9834 model_repository_manager.cc:1223] unloading: 5_predicttensorflow:1

I0810 12:11:59.134888 9834 model_repository_manager.cc:1328] successfully unloaded 'ensemble_model' version 1

I0810 12:11:59.134925 9834 model_repository_manager.cc:1223] unloading: 4_unrollfeatures:1

I0810 12:11:59.135008 9834 model_repository_manager.cc:1223] unloading: 3_queryfeast:1

I0810 12:11:59.135011 9834 tensorflow.cc:2368] TRITONBACKEND_ModelInstanceFinalize: delete instance state

I0810 12:11:59.135063 9834 model_repository_manager.cc:1223] unloading: 2_queryfaiss:1

I0810 12:11:59.135112 9834 model_repository_manager.cc:1223] unloading: 1_predicttensorflow:1

I0810 12:11:59.135169 9834 model_repository_manager.cc:1223] unloading: 0_queryfeast:1

I0810 12:11:59.135256 9834 server.cc:288] All models are stopped, unloading models

I0810 12:11:59.135282 9834 server.cc:295] Timeout 30: Found 7 live models and 0 in-flight non-inference requests

I0810 12:11:59.135293 9834 tensorflow.cc:2307] TRITONBACKEND_ModelFinalize: delete model state

I0810 12:11:59.135408 9834 tensorflow.cc:2368] TRITONBACKEND_ModelInstanceFinalize: delete instance state

I0810 12:11:59.135520 9834 tensorflow.cc:2307] TRITONBACKEND_ModelFinalize: delete model state

I0810 12:11:59.143887 9834 model_repository_manager.cc:1328] successfully unloaded '1_predicttensorflow' version 1

I0810 12:11:59.159362 9834 model_repository_manager.cc:1328] successfully unloaded '5_predicttensorflow' version 1

W0810 12:11:59.452450 9834 metrics.cc:468] Unable to get energy consumption for GPU 0. Status:Success, value:0

W0810 12:11:59.452504 9834 metrics.cc:507] Unable to get memory usage for GPU 0. Memory usage status:Success, value:0. Memory total status:Success, value:0

I0810 12:12:00.135410 9834 server.cc:295] Timeout 29: Found 5 live models and 0 in-flight non-inference requests

I0810 12:12:00.427812 9834 model_repository_manager.cc:1328] successfully unloaded '4_unrollfeatures' version 1

W0810 12:12:00.468809 9834 metrics.cc:468] Unable to get energy consumption for GPU 0. Status:Success, value:0

W0810 12:12:00.468860 9834 metrics.cc:507] Unable to get memory usage for GPU 0. Memory usage status:Success, value:0. Memory total status:Success, value:0

I0810 12:12:00.674850 9834 model_repository_manager.cc:1328] successfully unloaded '2_queryfaiss' version 1

I0810 12:12:00.700680 9834 model_repository_manager.cc:1328] successfully unloaded '6_softmaxsampling' version 1

/usr/local/lib/python3.8/dist-packages/merlin/systems/dag/ops/feast.py:15: DeprecationWarning: np.float is a deprecated alias for the builtin float. To silence this warning, use float by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use np.float64 here.

Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations

ValueType.FLOAT: (np.float, False, False),

I0810 12:12:01.135591 9834 server.cc:295] Timeout 28: Found 2 live models and 0 in-flight non-inference requests

I0810 12:12:01.391002 9834 model_repository_manager.cc:1328] successfully unloaded '0_queryfeast' version 1

I0810 12:12:02.135717 9834 server.cc:295] Timeout 27: Found 1 live models and 0 in-flight non-inference requests

I0810 12:12:03.135847 9834 server.cc:295] Timeout 26: Found 1 live models and 0 in-flight non-inference requests

I0810 12:12:04.135985 9834 server.cc:295] Timeout 25: Found 1 live models and 0 in-flight non-inference requests

I0810 12:12:05.136157 9834 server.cc:295] Timeout 24: Found 1 live models and 0 in-flight non-inference requests

I0810 12:12:06.136291 9834 server.cc:295] Timeout 23: Found 1 live models and 0 in-flight non-inference requests

I0810 12:12:07.136430 9834 server.cc:295] Timeout 22: Found 1 live models and 0 in-flight non-inference requests

I0810 12:12:08.136563 9834 server.cc:295] Timeout 21: Found 1 live models and 0 in-flight non-inference requests

I0810 12:12:09.136695 9834 server.cc:295] Timeout 20: Found 1 live models and 0 in-flight non-inference requests

I0810 12:12:10.136843 9834 server.cc:295] Timeout 19: Found 1 live models and 0 in-flight non-inference requests

/usr/local/lib/python3.8/dist-packages/merlin/systems/dag/ops/feast.py:15: DeprecationWarning: np.float is a deprecated alias for the builtin float. To silence this warning, use float by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use np.float64 here.

Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations

ValueType.FLOAT: (np.float, False, False),

I0810 12:12:10.989312 9834 model_repository_manager.cc:1328] successfully unloaded '3_queryfeast' version 1

I0810 12:12:11.136960 9834 server.cc:295] Timeout 18: Found 0 live models and 0 in-flight non-inference requests

Error in atexit._run_exitfuncs:

Traceback (most recent call last):

File "/usr/lib/python3.8/logging/init.py", line 2127, in shutdown

h.close()

File "/usr/local/lib/python3.8/dist-packages/absl/logging/init.py", line 934, in close

self.stream.close()

File "/usr/local/lib/python3.8/dist-packages/ipykernel/iostream.py", line 438, in close

self.watch_fd_thread.join()

AttributeError: 'OutStream' object has no attribute 'watch_fd_thread'

=========================== short test summary info ============================

FAILED tests/unit/examples/test_building_deploying_multi_stage_RecSys.py::test_func

=================== 1 failed, 2 passed in 199.34s (0:03:19) ====================

Build step 'Execute shell' marked build as failure

Performing Post build task...

Match found for : : True

Logical operation result is TRUE

Running script  : #!/bin/bash

cd /var/jenkins_home/

CUDA_VISIBLE_DEVICES=1 python test_res_push.py "https://api.GitHub.com/repos/NVIDIA-Merlin/Merlin/issues/$ghprbPullId/comments" "/var/jenkins_home/jobs/$JOB_NAME/builds/$BUILD_NUMBER/log"

[merlin_merlin] $ /bin/bash /tmp/jenkins3494075882680291007.sh

@karlhigley karlhigley merged commit e9c7523 into main Aug 10, 2022
@nvidia-merlin-bot
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Click to view CI Results
GitHub pull request #524 of commit eb0789cb30033872c2d0aa82b722fc6e94b24da8, no merge conflicts.
Running as SYSTEM
Setting status of eb0789cb30033872c2d0aa82b722fc6e94b24da8 to PENDING with url https://10.20.13.93:8080/job/merlin_merlin/342/console and message: 'Pending'
Using context: Jenkins
Building on master in workspace /var/jenkins_home/workspace/merlin_merlin
using credential systems-login
 > git rev-parse --is-inside-work-tree # timeout=10
Fetching changes from the remote Git repository
 > git config remote.origin.url https://github.com/NVIDIA-Merlin/Merlin # timeout=10
Fetching upstream changes from https://github.com/NVIDIA-Merlin/Merlin
 > git --version # timeout=10
using GIT_ASKPASS to set credentials login for merlin-systems
 > git fetch --tags --force --progress -- https://github.com/NVIDIA-Merlin/Merlin +refs/pull/524/*:refs/remotes/origin/pr/524/* # timeout=10
 > git rev-parse eb0789cb30033872c2d0aa82b722fc6e94b24da8^{commit} # timeout=10
Checking out Revision eb0789cb30033872c2d0aa82b722fc6e94b24da8 (detached)
 > git config core.sparsecheckout # timeout=10
 > git checkout -f eb0789cb30033872c2d0aa82b722fc6e94b24da8 # timeout=10
Commit message: "Avoid copying CMake libraries over the top of install"
 > git rev-list --no-walk a1c95f262485c8f5d7f6e07d42b1a678d011f7bf # timeout=10
[merlin_merlin] $ /bin/bash /tmp/jenkins7322710968896341975.sh
============================= test session starts ==============================
platform linux -- Python 3.8.10, pytest-7.1.2, pluggy-1.0.0
rootdir: /var/jenkins_home/workspace/merlin_merlin/merlin
plugins: anyio-3.6.1, xdist-2.5.0, forked-1.4.0, cov-3.0.0
collected 3 items

tests/unit/test_version.py .                                             [ 33%]

tests/unit/examples/test_building_deploying_multi_stage_RecSys.py .      [ 66%]

tests/unit/examples/test_scaling_criteo_merlin_models.py .               [100%]


======================== 3 passed in 221.79s (0:03:41) =========================

Performing Post build task...

Match found for : : True

Logical operation result is TRUE

Running script  : #!/bin/bash

cd /var/jenkins_home/

CUDA_VISIBLE_DEVICES=1 python test_res_push.py "https://api.GitHub.com/repos/NVIDIA-Merlin/Merlin/issues/$ghprbPullId/comments" "/var/jenkins_home/jobs/$JOB_NAME/builds/$BUILD_NUMBER/log"

[merlin_merlin] $ /bin/bash /tmp/jenkins8893977752556108718.sh

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Reviewers

@oliverholworthy oliverholworthy oliverholworthy approved these changes

@nv-alaiacano nv-alaiacano Awaiting requested review from nv-alaiacano

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@karlhigley karlhigley

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Merlin 22.08
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@karlhigley @nvidia-merlin-bot @oliverholworthy