Unusable lab due to deprecated TF 1.x code in "Using Neural Networks for Content-Based Filtering" (`content_based_using_neural_networks.ipynb`)

[Csaba Toth]

It was already a bad omen that the lab instructions (https://www.cloudskillsboost.google/course_sessions/2920313/labs/325069) tell me to pick a TensorFlow Enterprise > TensorFlow Enterprise 1.15 (with LTS) notebook. I could pick a 2.6 LTS.

The https://github.com/GoogleCloudPlatform/training-data-analyst/blob/master/courses/machine_learning/deepdive2/recommendation_systems/labs/content_based_preproc.ipynb was without a hickup, but then in the main notebook https://github.com/GoogleCloudPlatform/training-data-analyst/blob/master/courses/machine_learning/deepdive2/recommendation_systems/labs/content_based_using_neural_networks.ipynb I spotted os.environ['TFVERSION'] = '1.15.3' doubled down with !pip3 install --upgrade tensorflow==1.15.3 and also other versions are ancient as well.

I had a Star Wars like "I have a bad feeling about this!" feeling, and then the training and eval step crashed and I started to tumble down the rabbit hole.

• columns = tf.decode_csv(value_column,record_defaults=record_defaults) -> columns = tf.io.decode_csv(value_column,record_defaults=record_defaults)
• return dataset.make_one_shot_iterator().get_next() -> return tf.compat.v1.data.make_one_shot_iterator(dataset).get_next()
• net = tf.feature_column.input_layer(features, params['feature_columns']) -> net = tf.compat.v1.feature_column.input_layer(features, params['feature_columns'])
• net = tf.layers.dense(net, units=units, activation=tf.nn.relu) -> net = tf.compat.v1.layers.dense(net, units=units, activation=tf.nn.relu)
• logits = tf.layers.dense(net, params['n_classes'], activation=None) -> logits = tf.compat.v1.layers.dense(net, params['n_classes'], activation=None)

Aaand I ran out of time converting the tf.lookup.index_table_from_file(vocabulary_file="content_ids.txt"). Tried all kinds of tf.compat.v1 routes, tf.disable_v2_behavior() magic, etc.

Please convert this lab!

[Csaba Toth]

Good news: there's a workaround until the ipynb will be refreshed. I learned this trick in the next lab (#2441): you cannot select TF 1.15 LTS on the GUI any more, but the prepared Anaconda VM image still exists, so we can create the workbench instead via the Terminal with CLI. The lab instructions should be updated though, so just like the #2441 ipynb:

So instead of the User Managed Notebook creation block in the instructions, have the "Activate Cloud Shell" block, and then the CLI, just like in the next lab:

1. Activate Cloud Shell
2. gcloud auth list
3. gcloud config list project
4. And this is the workaround: gcloud notebooks instances create tensorflow-notebook --vm-image-project=deeplearning-platform-release --vm-image-family=tf-1-15-cpu --machine-type=e2-standard-4 --location=us-central1-a

I used e2-standard-4 because that's what the GUI defaults to right now. This VM provides a 1.15.5 TF. So I was a little worried that the prep notebook (https://github.com/GoogleCloudPlatform/training-data-analyst/blob/master/courses/machine_learning/deepdive2/recommendation_systems/labs/content_based_preproc.ipynb) we first have to run requires 2.1 TF, but it survived and completed.

After that I moved on to the main ipynb https://github.com/GoogleCloudPlatform/training-data-analyst/blob/master/courses/machine_learning/deepdive2/recommendation_systems/labs/content_based_using_neural_networks.ipynb which I was able to execute with this CLI created VM. So once the instructions are updated and this VM image still exists the lab can live without the ipynb TF code being upgraded to say TF 2.13. However the #2441 fails still.