[DEPRECATED] Continuous Training with TensorFlow, PyTorch, XGBoost, and Scikit Learn Models with Kubeflow and AI Platform Pipelines Reviews

Siegfried H. · Reviewed about 2 years ago

Too many errors

Rombout H. · Reviewed about 2 years ago

does not work

Lukas D. · Reviewed about 2 years ago

Lukas D. · Reviewed about 2 years ago

Naufer N. · Reviewed about 2 years ago

Naufer N. · Reviewed about 2 years ago

Very buggy, workarounds must be completed in a certain order or the lab will fail.

Blake C. · Reviewed about 2 years ago

Not understandable

Devpal S. · Reviewed about 2 years ago

Asma B. · Reviewed about 2 years ago

There were errors while running the notebook, so I could not create and run the pipeline: 1) None of the 4 Docker images were created successfully. 2) There is some problem with importing Iterable from collections - it seems like a Python version problem. In general this lab is ALMOST USELESS now, since the PIPELINE CANNOT BE CREATED AND EXECUTED, and the notebook can be found and accessed on GCP's official github page without paying.

Michał H. · Reviewed about 2 years ago

Lab does not work! Jupyter notebook badly initiated and missing kernel... and most of the dpendencies are outdated which makes it fail.

gerald g. · Reviewed about 2 years ago

Lab does not work! Jupyter notebook badly initiated and missing kernel... which makes it fail.

gerald g. · Reviewed about 2 years ago

Lab does not work! Jupyter notebook badly initiated and missing kernel... which makes it fail.

gerald g. · Reviewed about 2 years ago

Wanming H. · Reviewed about 2 years ago

gerald g. · Reviewed about 2 years ago

Wanming H. · Reviewed about 2 years ago

Shriram G. · Reviewed about 2 years ago

Salvatore V. · Reviewed about 2 years ago

Notebook failing build

Christer D. · Reviewed about 2 years ago

K S. · Reviewed about 2 years ago

SOUMOJIT G. · Reviewed about 2 years ago

Farhan S. · Reviewed about 2 years ago

Rich B. · Reviewed about 2 years ago

To make it work, I had to remove the library versions from each Dockerfile. After that, I added num_retries = 3 .set_retry(num_retries) after each .set_display_name, and update kfp: !pip install --upgrade kfp I did this for every artifact in the pipeline because at first, it would fail, but subsequent runs executed correctly. and change in scikit_trainer_image/train.py SGDClassifier(loss='log') -> SGDClassifier(loss='log_loss')

Carlos V. · Reviewed about 2 years ago

To make it work, I had to remove the library versions from each Dockerfile. After that, I added num_retries = 3 .set_retry(num_retries, policy=RetryPolicy.ALWAYS) after each .set_display_name. I did this for every artifact in the pipeline because at first, it would fail, but subsequent runs executed correctly.

Carlos V. · Reviewed about 2 years ago