Training at Scale with Vertex AI Training Service Reviews

Seitkazin B. · Reviewed больше 2 лет ago

Алихан С. · Reviewed больше 2 лет ago

Karthick s. · Reviewed больше 2 лет ago

Matti M. · Reviewed больше 2 лет ago

URK21CO3026 S. · Reviewed больше 2 лет ago

Gulbakhram K. · Reviewed больше 2 лет ago

Mochammad Zava A. · Reviewed больше 2 лет ago

Азат И. · Reviewed больше 2 лет ago

直也 小. · Reviewed больше 2 лет ago

too much showing, and so low explaining, i mean i know what the results are going to be, but how and why?... we need more of an explanation than just showing what is happening.

Sebastián C. · Reviewed больше 2 лет ago

Medeu Z. · Reviewed больше 2 лет ago

BucketNotFoundException: 404 gs://qwiklabs-gcp-00-d4c1bc16f963 bucket does not exist. --------------------------------------------------------------------------- CalledProcessError Traceback (most recent call last) Cell In[24], line 1 ----> 1 get_ipython().run_cell_magic('bash', '', '# TODO 1 and TODO 2\n\necho "Deleting current contents of $OUTDIR"\ngsutil -m -q rm -rf $OUTDIR\n\necho "Extracting training data to $OUTDIR"\nbq --location=US extract \\\n --destination_format CSV \\\n --field_delimiter "," --noprint_header \\\n taxifare.feateng_training_data \\\n $OUTDIR/taxi-train-*.csv\n\necho "Extracting validation data to $OUTDIR"\nbq --location=US extract \\\n --destination_format CSV \\\n --field_delimiter "," --noprint_header \\\n taxifare.feateng_valid_data \\\n $OUTDIR/taxi-valid-*.csv\n\ngsutil ls -l $OUTDIR\n') File /opt/conda/lib/python3.10/site-packages/IPython/core/interactiveshell.py:2515, in InteractiveShell.run_cell_magic(self, magic_name, line, cell) 2513 with self.builtin_trap: 2514 args = (magic_arg_s, cell) -> 2515 result = fn(*args, **kwargs) 2517 # The code below prevents the output from being displayed 2518 # when using magics with decorator @output_can_be_silenced 2519 # when the last Python token in the expression is a ';'. 2520 if getattr(fn, magic.MAGIC_OUTPUT_CAN_BE_SILENCED, False): File /opt/conda/lib/python3.10/site-packages/IPython/core/magics/script.py:154, in ScriptMagics._make_script_magic.<locals>.named_script_magic(line, cell) 152 else: 153 line = script --> 154 return self.shebang(line, cell) File /opt/conda/lib/python3.10/site-packages/IPython/core/magics/script.py:314, in ScriptMagics.shebang(self, line, cell) 309 if args.raise_error and p.returncode != 0: 310 # If we get here and p.returncode is still None, we must have 311 # killed it but not yet seen its return code. We don't wait for it, 312 # in case it's stuck in uninterruptible sleep. -9 = SIGKILL 313 rc = p.returncode or -9 --> 314 raise CalledProcessError(rc, cell) CalledProcessError: Command 'b'# TODO 1 and TODO 2\n\necho "Deleting current contents of $OUTDIR"\ngsutil -m -q rm -rf $OUTDIR\n\necho "Extracting training data to $OUTDIR"\nbq --location=US extract \\\n --destination_format CSV \\\n --field_delimiter "," --noprint_header \\\n taxifare.feateng_training_data \\\n $OUTDIR/taxi-train-*.csv\n\necho "Extracting validation data to $OUTDIR"\nbq --location=US extract \\\n --destination_format CSV \\\n --field_delimiter "," --noprint_header \\\n taxifare.feateng_valid_data \\\n $OUTDIR/taxi-valid-*.csv\n\ngsutil ls -l $OUTDIR\n'' returned non-zero exit status 1.

Leonardo L. · Reviewed больше 2 лет ago

Anwesha R. · Reviewed больше 2 лет ago

Gowthaman K. · Reviewed больше 2 лет ago

Craig P. · Reviewed больше 2 лет ago

Danzler A. · Reviewed больше 2 лет ago

This is an excellent lab, the best one in the entire course. It would be great if other labs were of this quality. In the other labs of the course, the main thing that was missing for me is the clear and intuitive explanation of doing feature encoding and feature engineering in TF. The flow in those labs is very abstract and unintuitive, and quite confusing. A clear explanation of how numeric and categorical columns get converted into dense vectors (embeddings) would be super useful, especially if you focus on a simple example from scratch (let's say 5 features and 1 label) and look under the hood throughout the process and just make it more clear. Also, using pre-processing layers was quite unclear as well. Overall, I am very excited about these labs and ML courses on our GCP in general, definitely looking forward to more content in the future!

Denis C. · Reviewed больше 2 лет ago

Togzhan B. · Reviewed больше 2 лет ago

Eric P. · Reviewed больше 2 лет ago

Yasser A. · Reviewed больше 2 лет ago

Tongqing Q. · Reviewed больше 2 лет ago

Will B. · Reviewed больше 2 лет ago

Jefferson P. B. · Reviewed больше 2 лет ago

Marián Ž. · Reviewed больше 2 лет ago

Мадина А. · Reviewed больше 2 лет ago