ValueError: A Concatenate layer requires inputs with matching shapes except for the concat axis. Got inputs shapes: [(None, 2), (None, 4), (7,), (None, 3), (None, 1), (None, 1), (6,), (None, 3), (None, 3), (None, 1), (None, 10)]

Input Features dropoff_grid_xf <dtype: 'int64'>: [0, 0, 0] euclidean_xf <dtype: 'float32'>: [0.669279932975769, -0.8318284749984741, -0.8318284749984741] loc_cross_xf <dtype: 'int64'>: [0, 0, 0] payment_type_xf <dtype: 'int64'>: [2, 0, 0] pickup_grid_xf <dtype: 'int64'>: [0, 0, 0] trip_day_of_week_xf <dtype: 'int64'>: [0, 6, 2] trip_day_xf <dtype: 'int64'>: [8, 30, 1] trip_hour_xf <dtype: 'int64'>: [1, 13, 6] trip_miles_xf <dtype: 'float32'>: [2.3255326747894287, -0.22459185123443604, -0.4029441475868225] trip_month_xf <dtype: 'int64'>: [3, 1, 0] trip_seconds_xf <dtype: 'float32'>: [0.9550504088401794, -0.2630620300769806, -0.24356801807880402] target: [0, 0, 0]

ModuleNotFoundError: No module named 'user_module_0' During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/usr/local/lib/python3.7/site-packages/dataflow_worker/batchworker.py", line 649, in do_work work_executor.execute() File "/usr/local/lib/python3.7/site-packages/dataflow_worker/executor.py", line 179, in execute op.start() File "apache_beam/runners/worker/operations.py", line 710, in apache_beam.runners.worker.operations.DoOperation.start File "apache_beam/runners/worker/operations.py", line 712, in apache_beam.runners.worker.operations.DoOperation.start File "apache_beam/runners/worker/operations.py", line 713, in apache_beam.runners.worker.operations.DoOperation.start File "apache_beam/runners/worker/operations.py", line 311, in apache_beam.runners.worker.operations.Operation.start File "apache_beam/runners/worker/operations.py", line 317, in apache_beam.runners.worker.operations.Operation.start File "apache_beam/runners/worker/operations.py", line 659, in apache_beam.runners.worker.operations.DoOperation.setup File "apache_beam/runners/worker/operations.py", line 660, in apache_beam.runners.worker.operations.DoOperation.setup File "apache_beam/runners/worker/operations.py", line 292, in apache_beam.runners.worker.operations.Operation.setup File "apache_beam/runners/worker/operations.py", line 306, in apache_beam.runners.worker.operations.Operation.setup File "apache_beam/runners/worker/operations.py", line 799, in apache_beam.runners.worker.operations.DoOperation._get_runtime_performance_hints File "/usr/local/lib/python3.7/site-packages/apache_beam/internal/pickler.py", line 294, in loads return dill.loads(s) File "/usr/local/lib/python3.7/site-packages/dill/_dill.py", line 275, in loads return load(file, ignore, **kwds) File "/usr/local/lib/python3.7/site-packages/dill/_dill.py", line 270, in load return Unpickler(file, ignore=ignore, **kwds).load() File "/usr/local/lib/python3.7/site-packages/dill/_dill.py", line 472, in load obj = StockUnpickler.load(self) File "/usr/local/lib/python3.7/site-packages/dill/_dill.py", line 826, in _import_module return __import__(import_name) ModuleNotFoundError: No module named 'user_module_0'

Running the transform component of this repository in Vertex AI hits the following error (in the 04-pipeline-deployment.ipynb notebook). Does anyone have a quick fix for this? Have tried specifying setup.py and "save_main_session": True so far with no luck.

Hi I'm running the tutorial with TFX 1.4.0 and TF 2.7.0.

When I run the cell classifier = model.create_binary_classifier(tft_output, hyperparams) classifier.summary()

I get the error:

---

ValueError Traceback (most recent call last) in ----> 1 classifier = model.create_binary_classifier(tft_output, hyperparams) 2 classifier.summary()

~/mlops-with-vertex-ai/src/model_training/model.py in create_binary_classifier(tft_output, hyperparams) 83 ) 84 ---> 85 return _create_binary_classifier(feature_vocab_sizes, hyperparams)

~/mlops-with-vertex-ai/src/model_training/model.py in _create_binary_classifier(feature_vocab_sizes, hyperparams) 62 pass 63 ---> 64 joined = keras.layers.Concatenate(name="combines_inputs")(layers) 65 feedforward_output = keras.Sequential( 66 [

/opt/conda/lib/python3.7/site-packages/keras/utils/traceback_utils.py in error_handler(*args, **kwargs) 65 except Exception as e: # pylint: disable=broad-except 66 filtered_tb = _process_traceback_frames(e.traceback) ---> 67 raise e.with_traceback(filtered_tb) from None 68 finally: 69 del filtered_tb

/opt/conda/lib/python3.7/site-packages/keras/layers/merge.py in build(self, input_shape) 514 ranks = set(len(shape) for shape in shape_set) 515 if len(ranks) != 1: --> 516 raise ValueError(err_msg) 517 # Get the only rank for the set. 518 (rank,) = ranks

ValueError: A Concatenate layer requires inputs with matching shapes except for the concatenation axis. Received: input_shape=[(None, 2), (None, 4), (7,), (None, 3), (None, 1), (None, 1), (6,), (None, 3), (None, 3), (None, 1), (None, 10)]

During the training pipeline compilation phase with Cloud Build I am facing a weird error.

Here's the entire compilation log. log-41d61716-bd25-434c-ae3c-745540d398b8.log.zip

Hi @ksalama.

Thank you very much for this amazing resource. It's a mini-book in itself.

I am referring to the statement written just below the continuous training section:

The end-to-end TFX training pipeline implementation is in the src/pipelines directory, which covers the following steps:

Are these pipelines demonstrated in any of the notebooks?

Hi, everyone!

I trained a model using scikit-learn, and saved it as a pickle file, stored in a GCS bucket. I was wondering... how can I version this model using model registry since it is already trained?

In my case, I have the option of using Cloud Run for this (a cloud run container that runs a retraining task every week), but I want to start doing it on Vertex AI.

After reading some articles about Vertex AI Model Registry, I concluded that the model must be firstly trained using a custom training job, and, just after that, we can begin its versioning on Model Registry. Is this correct?

I literally created a bucket with a random BTC wallet address to make it unique, which I was told to do by the GCP guide, and still i am hitting the same error while executing gcs-bucket.tf

google_storage_bucket.bc1qxy2kgdygjrsqtzq2n0yrf2493p83kkfjhx0wlh: Creating... ╷ │ Error: googleapi: Error 409: The requested bucket name is not available. The bucket namespace is shared by all users of the system. Please select a different name and try again., conflict │ │ with google_storage_bucket.bc1qxy2kgdygjrsqtzq2n0yrf2493p83kkfjhx0wlh, │ on gcs-bucket.tf line 17, in resource "google_storage_bucket" "bc1qxy2kgdygjrsqtzq2n0yrf2493p83kkfjhx0wlh": │ 17: resource "google_storage_bucket" "bc1qxy2kgdygjrsqtzq2n0yrf2493p83kkfjhx0wlh" { │

I've noticed that the TensorBoard instance that is being produced in Notebook 2 uses a lot of credits. Before January 22 this was free, once enabled this instance , disabling the Vertex AI API won't stop the billing. See response from GCP - https://www.googlecloudcommunity.com/gc/AI-ML/How-can-I-avoid-being-charged-for-Tensorboard/td-p/180658

The latest Python 3 package markupsafe is not compatible with from tfx.orchestration.experimental.interactive.interactive_context import InteractiveContext:

import ml_metadata as mlmd
from ml_metadata.proto import metadata_store_pb2
from tfx.orchestration.experimental.interactive.interactive_context import InteractiveContext

Error:

ImportError Traceback (most recent call last) /tmp/ipykernel_1/3645963042.py in 1 import ml_metadata as mlmd 2 from ml_metadata.proto import metadata_store_pb2 ----> 3 from tfx.orchestration.experimental.interactive.interactive_context import InteractiveContext 4 5 connection_config = metadata_store_pb2.ConnectionConfig()

~/.local/lib/python3.7/site-packages/tfx/orchestration/experimental/interactive/interactive_context.py in 35 36 import absl ---> 37 import jinja2 38 import nbformat 39 from tfx import types

~/.local/lib/python3.7/site-packages/jinja2/init.py in 10 from .bccache import FileSystemBytecodeCache 11 from .bccache import MemcachedBytecodeCache ---> 12 from .environment import Environment 13 from .environment import Template 14 from .exceptions import TemplateAssertionError

~/.local/lib/python3.7/site-packages/jinja2/environment.py in 23 from .compiler import CodeGenerator 24 from .compiler import generate ---> 25 from .defaults import BLOCK_END_STRING 26 from .defaults import BLOCK_START_STRING 27 from .defaults import COMMENT_END_STRING

~/.local/lib/python3.7/site-packages/jinja2/defaults.py in 1 # -- coding: utf-8 -- 2 from ._compat import range_type ----> 3 from .filters import FILTERS as DEFAULT_FILTERS # noqa: F401 4 from .tests import TESTS as DEFAULT_TESTS # noqa: F401 5 from .utils import Cycler

~/.local/lib/python3.7/site-packages/jinja2/filters.py in 11 from markupsafe import escape 12 from markupsafe import Markup ---> 13 from markupsafe import soft_unicode 14 15 from ._compat import abc

ImportError: cannot import name 'soft_unicode' from 'markupsafe' (/home/jupyter/.local/lib/python3.7/site-packages/markupsafe/init.py)

The workaround is to install an older version by pip install markupsafe==2.0.1 and restart kernel

Python error encountered executing the following line at [Extract train and eval splits]:

sql_query = datasource_utils.get_training_source_query(

sql_query = datasource_utils.get_training_source_query(
    PROJECT, REGION, DATASET_DISPLAY_NAME, ml_use='UNASSIGNED', limit=5000)

Observed error:

IndexError Traceback (most recent call last) /tmp/ipykernel_1/1584844956.py in 1 print(DATASET_DISPLAY_NAME) 2 sql_query = datasource_utils.get_training_source_query( ----> 3 PROJECT, REGION, DATASET_DISPLAY_NAME, ml_use='UNASSIGNED', limit=5000) 4 5 output_config = example_gen_pb2.Output(

~/mlops-with-vertex-ai/src/common/datasource_utils.py in get_training_source_query(project, region, dataset_display_name, ml_use, limit) 55 dataset = vertex_ai.TabularDataset.list( 56 filter=f"display_name={dataset_display_name}", order_by="update_time" ---> 57 )[-1] 58 bq_source_uri = dataset.gca_resource.metadata"inputConfig"]["bigquerySource"][ 59 "uri"

IndexError: list index out of range

I can't find .list method for google.cloud.aiplatform's TabularDataset in datasource_utils.py