Vertex AI Training サービスによる大規模トレーニング のレビュー

Bakytzhan Seitkazin · 2年以上前にレビュー済み

Сабитов Алихан · 2年以上前にレビュー済み

selvaraj Karthick · 2年以上前にレビュー済み

Manninen Matti · 2年以上前にレビュー済み

SUNIL MOSES SAMRAJ D URK21CO3026 · 2年以上前にレビュー済み

Karazhakova Gulbakhram · 2年以上前にレビュー済み

Abbiyansyah Mochammad Zava · 2年以上前にレビュー済み

Искаков Азат · 2年以上前にレビュー済み

小磯 直也 · 2年以上前にレビュー済み

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.

Concha Alvarez Sebastián · 2年以上前にレビュー済み

Zhaxilikov Medeu · 2年以上前にレビュー済み

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.

Lawrence Leonardo · 2年以上前にレビュー済み

Roy Anwesha · 2年以上前にレビュー済み

Krishna Pillai Gowthaman · 2年以上前にレビュー済み

Parker Craig · 2年以上前にレビュー済み

Alan Smith Danzler · 2年以上前にレビュー済み

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!

Calin Denis · 2年以上前にレビュー済み

Beldeubayeva Togzhan · 2年以上前にレビュー済み

PIRI Eric · 2年以上前にレビュー済み

Al-Eryani Yasser · 2年以上前にレビュー済み

Qiu Tongqing · 2年以上前にレビュー済み

Bunker Will · 2年以上前にレビュー済み

Barros Jefferson P. · 2年以上前にレビュー済み

Žabka Marián · 2年以上前にレビュー済み

Абай Мадина · 2年以上前にレビュー済み