关于“Structured data prediction using Vertex AI Platform”的评价

Gerald S. · 评论5 months之前

Andre G. · 评论5 months之前

GOPIKA C. · 评论5 months之前

Luis Miguel B. · 评论5 months之前

Mattia C. · 评论5 months之前

Diego A. · 评论5 months之前

Diego G. · 评论5 months之前

Sobhan N. · 评论5 months之前

Balaji r. · 评论5 months之前

took 16 minutes to train that keras model. too long. it's also a weird wide + deep neural net from 2017. probably deprecated in 2025

Alexei C. · 评论5 months之前

Sujay H. · 评论5 months之前

Facundo A. · 评论5 months之前

Camilo G. · 评论5 months之前

Thomas N. · 评论5 months之前

Asitho D. · 评论5 months之前

thanks

hicham h. · 评论5 months之前

Zahra M. · 评论5 months之前

Ashwin P. · 评论5 months之前

Yuchieh Cheng 鄭宇傑 E. · 评论5 months之前

Deepak B. · 评论5 months之前

Tristan L. · 评论5 months之前

ERROR: (gcloud.ai-platform.jobs.submit.training) FAILED_PRECONDITION: Constraint `constraints/gcp.resourceLocations` violated for `projects/qwiklabs-gcp-00-0ac1d33a0acf`: region `us-central1` not allowed. See https://cloud.google.com/resource-manager/docs/organization-policy/defining-locations for details. - '@type': type.googleapis.com/google.rpc.PreconditionFailure violations: - description: 'Constraint `constraints/gcp.resourceLocations` violated for `projects/qwiklabs-gcp-00-0ac1d33a0acf`: region `us-central1` not allowed. See https://cloud.google.com/resource-manager/docs/organization-policy/defining-locations for details.' subject: orgpolicy:projects/qwiklabs-gcp-00-0ac1d33a0acf type: constraints/gcp.resourceLocations --------------------------------------------------------------------------- CalledProcessError Traceback (most recent call last) Cell In[21], line 1 ----> 1 get_ipython().run_cell_magic('bash', '', '\nOUTDIR=gs://${BUCKET}/babyweight/trained_model\nJOBID=babyweight_$(date -u +%y%m%d_%H%M%S)\n\ngcloud ai-platform jobs submit training ${JOBID} \\\n --region=${REGION} \\\n --module-name=trainer.task \\\n --package-path=$(pwd)/babyweight/trainer \\\n --job-dir=${OUTDIR} \\\n --staging-bucket=gs://${BUCKET} \\\n --master-machine-type=n1-standard-8 \\\n --scale-tier=CUSTOM \\\n --runtime-version=${TFVERSION} \\\n --python-version=${PYTHONVERSION} \\\n -- \\\n --train_data_path=gs://${BUCKET}/babyweight/data/train*.csv \\\n --eval_data_path=gs://${BUCKET}/babyweight/data/eval*.csv \\\n --output_dir=${OUTDIR} \\\n --num_epochs=10 \\\n --train_examples=10000 \\\n --eval_steps=100 \\\n --batch_size=32 \\\n --nembeds=8\n') File /opt/conda/lib/python3.10/site-packages/IPython/core/interactiveshell.py:2517, in InteractiveShell.run_cell_magic(self, magic_name, line, cell) 2515 with self.builtin_trap: 2516 args = (magic_arg_s, cell) -> 2517 result = fn(*args, **kwargs) 2519 # The code below prevents the output from being displayed 2520 # when using magics with decorator @output_can_be_silenced 2521 # when the last Python token in the expression is a ';'. 2522 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'\nOUTDIR=gs://${BUCKET}/babyweight/trained_model\nJOBID=babyweight_$(date -u +%y%m%d_%H%M%S)\n\ngcloud ai-platform jobs submit training ${JOBID} \\\n --region=${REGION} \\\n --module-name=trainer.task \\\n --package-path=$(pwd)/babyweight/trainer \\\n --job-dir=${OUTDIR} \\\n --staging-bucket=gs://${BUCKET} \\\n --master-machine-type=n1-standard-8 \\\n --scale-tier=CUSTOM \\\n --runtime-version=${TFVERSION} \\\n --python-version=${PYTHONVERSION} \\\n -- \\\n --train_data_path=gs://${BUCKET}/babyweight/data/train*.csv \\\n --eval_data_path=gs://${BUCKET}/babyweight/data/eval*.csv \\\n --output_dir=${OUTDIR} \\\n --num_epochs=10 \\\n --train_examples=10000 \\\n --eval_steps=100 \\\n --batch_size=32 \\\n --nembeds=8\n'' returned non-zero exit status 1.

Carlos Fernando S. · 评论5 months之前

All graded tasks are completing. However, the issue is in the notebook, showing errors during the deployment..

Arnel Perez P. · 评论5 months之前

The deploy code had a problem with the region

Abimael A. · 评论5 months之前

Angeline J. · 评论5 months之前