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

Siegfried H. · Recensione inserita circa 2 anni fa

Too many errors

Rombout H. · Recensione inserita circa 2 anni fa

does not work

Lukas D. · Recensione inserita circa 2 anni fa

Lukas D. · Recensione inserita circa 2 anni fa

Naufer N. · Recensione inserita circa 2 anni fa

Naufer N. · Recensione inserita circa 2 anni fa

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

Blake C. · Recensione inserita circa 2 anni fa

Not understandable

Devpal S. · Recensione inserita oltre 2 anni fa

Asma B. · Recensione inserita oltre 2 anni fa

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. · Recensione inserita oltre 2 anni fa

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 and most of the dpendencies are outdated which makes it fail.

gerald g. · Recensione inserita oltre 2 anni fa

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

gerald g. · Recensione inserita oltre 2 anni fa

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

gerald g. · Recensione inserita oltre 2 anni fa

Wanming H. · Recensione inserita oltre 2 anni fa

gerald g. · Recensione inserita oltre 2 anni fa

Wanming H. · Recensione inserita oltre 2 anni fa

Shriram G. · Recensione inserita oltre 2 anni fa

Salvatore V. · Recensione inserita oltre 2 anni fa

Notebook failing build

Christer D. · Recensione inserita oltre 2 anni fa

K S. · Recensione inserita oltre 2 anni fa

SOUMOJIT G. · Recensione inserita oltre 2 anni fa

Farhan S. · Recensione inserita oltre 2 anni fa

Rich B. · Recensione inserita oltre 2 anni fa

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. · Recensione inserita oltre 2 anni fa

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. · Recensione inserita oltre 2 anni fa