Addestramento su larga scala con il servizio Vertex AI Training recensioni

Nelson F. · Recensione inserita oltre 2 anni fa

Seitkazin B. · Recensione inserita oltre 2 anni fa

Алихан С. · Recensione inserita oltre 2 anni fa

Karthick s. · Recensione inserita oltre 2 anni fa

Matti M. · Recensione inserita oltre 2 anni fa

URK21CO3026 S. · Recensione inserita oltre 2 anni fa

Gulbakhram K. · Recensione inserita oltre 2 anni fa

Mochammad Zava A. · Recensione inserita oltre 2 anni fa

Азат И. · Recensione inserita oltre 2 anni fa

直也 小. · Recensione inserita oltre 2 anni fa

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. · Recensione inserita oltre 2 anni fa

Medeu Z. · Recensione inserita oltre 2 anni fa

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. · Recensione inserita oltre 2 anni fa

Anwesha R. · Recensione inserita oltre 2 anni fa

Gowthaman K. · Recensione inserita oltre 2 anni fa

Craig P. · Recensione inserita oltre 2 anni fa

Danzler A. · Recensione inserita oltre 2 anni fa

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. · Recensione inserita oltre 2 anni fa

Togzhan B. · Recensione inserita oltre 2 anni fa

Eric P. · Recensione inserita oltre 2 anni fa

Yasser A. · Recensione inserita oltre 2 anni fa

Tongqing Q. · Recensione inserita oltre 2 anni fa

Will B. · Recensione inserita oltre 2 anni fa

Jefferson P. B. · Recensione inserita oltre 2 anni fa

Marián Ž. · Recensione inserita oltre 2 anni fa