Machine Learning with TensorFlow in Vertex AI Reviews

Nooshin G. · Reviewed 10 months ago

its too good

2303C 5. · Reviewed 10 months ago

Berta P. · Reviewed 10 months ago

Alex M. · Reviewed 10 months ago

useful

Pankaja M. · Reviewed 10 months ago

Adarsh K. · Reviewed 10 months ago

MODEL_ID= Using endpoint [https://us-east4-aiplatform.googleapis.com/] ERROR: (gcloud.beta.ai.endpoints.deploy-model) could not parse resource [] --------------------------------------------------------------------------- CalledProcessError Traceback (most recent call last) Cell In[36], line 1 ----> 1 get_ipython().run_cell_magic('bash', '', '# note ENDPOINT_NAME is being changed\n\nENDPOINT_NAME=flights_xai\n\nTIMESTAMP=$(date +%Y%m%d-%H%M%S)\nMODEL_NAME=${ENDPOINT_NAME}-${TIMESTAMP}\nEXPORT_PATH=$(gsutil ls ${OUTDIR}/export | tail -1)\necho $EXPORT_PATH\n\n# create the model endpoint for deploying the model\nif [[ $(gcloud beta ai endpoints list --region=$REGION \\\n --format=\'value(DISPLAY_NAME)\' --filter=display_name=${ENDPOINT_NAME}) ]]; then\n echo "Endpoint for $MODEL_NAME already exists"\nelse\n # create model endpoint\n echo "Creating Endpoint for $MODEL_NAME"\n gcloud beta ai endpoints create --region=${REGION} --display-name=${ENDPOINT_NAME}\nfi\n\nENDPOINT_ID=$(gcloud beta ai endpoints list --region=$REGION \\\n --format=\'value(ENDPOINT_ID)\' --filter=display_name=${ENDPOINT_NAME})\necho "ENDPOINT_ID=$ENDPOINT_ID"\n\n\n# delete any existing models with this name\nfor MODEL_ID in $(gcloud beta ai models list --region=$REGION --format=\'value(MODEL_ID)\' --filter=display_name=${MODEL_NAME}); do\n echo "Deleting existing $MODEL_NAME ... $MODEL_ID "\n gcloud ai models delete --region=$REGION $MODEL_ID\ndone\n\n\n# upload the model using the parameters docker conatiner image, artifact URI, explanation method,\n# explanation path count and explanation metadata JSON file `explanation-metadata.json`.\n# Here, you keep number of feature permutations to `10` when approximating the Shapley values for explanation.\ngcloud beta ai models upload --region=$REGION --display-name=$MODEL_NAME \\\n --container-image-uri=us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.${TF_VERSION}:latest \\\n --artifact-uri=$EXPORT_PATH \\\n --explanation-method=sampled-shapley --explanation-path-count=10 --explanation-metadata-file=explanation-metadata.json\nMODEL_ID=$(gcloud beta ai models list --region=$REGION --format=\'value(MODEL_ID)\' --filter=display_name=${MODEL_NAME})\necho "MODEL_ID=$MODEL_ID"\n\n\n# deploy the model to the endpoint\ngcloud beta ai endpoints deploy-model $ENDPOINT_ID \\\n --region=$REGION \\\n --model=$MODEL_ID \\\n --display-name=$MODEL_NAME \\\n --machine-type=e2-standard-2 \\\n --min-replica-count=1 \\\n --max-replica-count=1 \\\n --traffic-split=0=100\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'# note ENDPOINT_NAME is being changed\n\nENDPOINT_NAME=flights_xai\n\nTIMESTAMP=$(date +%Y%m%d-%H%M%S)\nMODEL_NAME=${ENDPOINT_NAME}-${TIMESTAMP}\nEXPORT_PATH=$(gsutil ls ${OUTDIR}/export | tail -1)\necho $EXPORT_PATH\n\n# create the model endpoint for deploying the model\nif [[ $(gcloud beta ai endpoints list --region=$REGION \\\n --format=\'value(DISPLAY_NAME)\' --filter=display_name=${ENDPOINT_NAME}) ]]; then\n echo "Endpoint for $MODEL_NAME already exists"\nelse\n # create model endpoint\n echo "Creating Endpoint for $MODEL_NAME"\n gcloud beta ai endpoints create --region=${REGION} --display-name=${ENDPOINT_NAME}\nfi\n\nENDPOINT_ID=$(gcloud beta ai endpoints list --region=$REGION \\\n --format=\'value(ENDPOINT_ID)\' --filter=display_name=${ENDPOINT_NAME})\necho "ENDPOINT_ID=$ENDPOINT_ID"\n\n\n# delete any existing models with this name\nfor MODEL_ID in $(gcloud beta ai models list --region=$REGION --format=\'value(MODEL_ID)\' --filter=display_name=${MODEL_NAME}); do\n echo "Deleting existing $MODEL_NAME ... $MODEL_ID "\n gcloud ai models delete --region=$REGION $MODEL_ID\ndone\n\n\n# upload the model using the parameters docker conatiner image, artifact URI, explanation method,\n# explanation path count and explanation metadata JSON file `explanation-metadata.json`.\n# Here, you keep number of feature permutations to `10` when approximating the Shapley values for explanation.\ngcloud beta ai models upload --region=$REGION --display-name=$MODEL_NAME \\\n --container-image-uri=us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.${TF_VERSION}:latest \\\n --artifact-uri=$EXPORT_PATH \\\n --explanation-method=sampled-shapley --explanation-path-count=10 --explanation-metadata-file=explanation-metadata.json\nMODEL_ID=$(gcloud beta ai models list --region=$REGION --format=\'value(MODEL_ID)\' --filter=display_name=${MODEL_NAME})\necho "MODEL_ID=$MODEL_ID"\n\n\n# deploy the model to the endpoint\ngcloud beta ai endpoints deploy-model $ENDPOINT_ID \\\n --region=$REGION \\\n --model=$MODEL_ID \\\n --display-name=$MODEL_NAME \\\n --machine-type=e2-standard-2 \\\n --min-replica-count=1 \\\n --max-replica-count=1 \\\n --traffic-split=0=100\n'' returned non-zero exit status 1.

Ilyass S. · Reviewed 10 months ago

Primarily just navigating around GCP and then waiting for 40 minutes on inference endpoint deployment. Would have liked to see more interactivity.

Matt R. · Reviewed 10 months ago

Amolika B. · Reviewed 10 months ago

ok

Pascal B. · Reviewed 10 months ago

ramin m. · Reviewed 10 months ago

Jmarquez90 S. · Reviewed 10 months ago

MIT S. · Reviewed 10 months ago

Sangeeth G. · Reviewed 11 months ago

Juan M. · Reviewed 11 months ago

Shakun K. · Reviewed 11 months ago

Sebastián G. · Reviewed 11 months ago

Erika F. · Reviewed 11 months ago

Norbert I. · Reviewed 11 months ago

It takes so much time to run the deployment code blocks

Otto V. · Reviewed 11 months ago

Nicholas S. · Reviewed 11 months ago

kamol t. · Reviewed 11 months ago

Igor M. · Reviewed 11 months ago

LEONARD M. · Reviewed 11 months ago

Faisal K. · Reviewed 11 months ago