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

Siegfried H. · Reviewed أكثر من سنتين ago

Too many errors

Rombout H. · Reviewed أكثر من سنتين ago

does not work

Lukas D. · Reviewed أكثر من سنتين ago

Lukas D. · Reviewed أكثر من سنتين ago

Naufer N. · Reviewed أكثر من سنتين ago

Naufer N. · Reviewed أكثر من سنتين ago

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

Blake C. · Reviewed أكثر من سنتين ago

Not understandable

Devpal S. · Reviewed أكثر من سنتين ago

Asma B. · Reviewed أكثر من سنتين 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 أكثر من سنتين 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 and most of the dpendencies are outdated which makes it fail.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 أكثر من سنتين ago

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

gerald g. · Reviewed أكثر من سنتين ago

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

gerald g. · Reviewed أكثر من سنتين ago

Wanming H. · Reviewed أكثر من سنتين ago

gerald g. · Reviewed أكثر من سنتين ago

Wanming H. · Reviewed أكثر من سنتين ago

Shriram G. · Reviewed أكثر من سنتين ago

Salvatore V. · Reviewed أكثر من سنتين ago

Notebook failing build

Christer D. · Reviewed أكثر من سنتين ago

K S. · Reviewed أكثر من سنتين ago

SOUMOJIT G. · Reviewed أكثر من سنتين ago

Farhan S. · Reviewed أكثر من سنتين ago

Rich B. · Reviewed أكثر من سنتين 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 أكثر من سنتين 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 أكثر من سنتين ago