Machine Learning with TensorFlow in Vertex AI Rezensionen
13791 Rezensionen
Lakshmi N. · Vor etwa ein Jahr überprüft
Need more explaination on code. Correct typo: At the beggining, we are using 5millions examples, and the comments say that it is a very low number.
Laura B. · Vor etwa ein Jahr überprüft
vahid a. · Vor etwa ein Jahr überprüft
Documentation should be updated.
Swapnil A. · Vor etwa ein Jahr überprüft
Paul C. · Vor etwa ein Jahr überprüft
Jyoti P. · Vor etwa ein Jahr überprüft
fixed
Suppadate T. · Vor etwa ein Jahr überprüft
problem in creating notebook
Frendy C. · Vor etwa ein Jahr überprüft
iVan D. · Vor etwa ein Jahr überprüft
Some complicated to follow
Jeongho J. · Vor etwa ein Jahr überprüft
Its broken! It doesnt work!
Justin H. · Vor etwa ein Jahr überprüft
could only get a score of 80% as task 6 kept failing due to error listed below: Google Cloud Self-Paced Labs Machine Learning with TensorFlow in Vertex AI - GSP273 Task 6 gs://qwiklabs-gcp-00-905ba1094efe-dsongcp/ch9/trained_model/export/flights_20230726-210005/ Using endpoint [https://us-central1-aiplatform.googleapis.com/] Endpoint for flights_xai-20230726-215121 already exists Using endpoint [https://us-central1-aiplatform.googleapis.com/] ENDPOINT_ID=6499675026667601920 Using endpoint [https://us-central1-aiplatform.googleapis.com/] Using endpoint [https://us-central1-aiplatform.googleapis.com/] Waiting for operation [7021920166275448832]... ..................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................failed. ERROR: (gcloud.beta.ai.models.upload) Error occurred in Explanation preprocessing. <class 'ValueError'> NodeDef mentions attr 'Tsegmentids' not in Op<name=SparseSegmentMean; signature=data:T, indices:Tidx, segment_ids:int32 -> output:T; attr=T:type,allowed=[DT_FLOAT, DT_DOUBLE]; attr=Tidx:type,default=DT_INT32,allowed=[DT_INT32, DT_INT64]>; NodeDef: {{node model_3/deep_inputs/arr_airport_lat_bucketized_X_arr_airport_lon_bucketized_X_dep_airport_lat_bucketized_X_dep_airport_lon_bucketized_embedding/arr_airport_lat_bucketized_X_arr_airport_lon_bucketized_X_dep_airport_lat_bucketized_X_dep_airport_lon_bucketized_embedding_weights/embedding_lookup_sparse}}. (Check whether your GraphDef-interpreting binary is up to date with your GraphDef-generating binary.). Using endpoint [https://us-central1-aiplatform.googleapis.com/] MODEL_ID= Using endpoint [https://us-central1-aiplatform.googleapis.com/] ERROR: (gcloud.beta.ai.endpoints.deploy-model) could not parse resource [] --------------------------------------------------------------------------- CalledProcessError Traceback (most recent call last) Cell In[42], line 1 ----> 1 get_ipython().run_cell_magic('bash', '', '# note TF_VERSION set in 1st cell, but ENDPOINT_NAME is being changed\n# TF_VERSION=2-6\nENDPOINT_NAME=flights_xai\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# 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=${ENDPOINT_NAME}\nfi\nENDPOINT_ID=$(gcloud beta ai endpoints list --region=$REGION \\\n --format=\'value(ENDPOINT_ID)\' --filter=display_name=${ENDPOINT_NAME})\necho "ENDPOINT_ID=$ENDPOINT_ID"\n# 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 $MODEL_ID\ndone\n# upload the model using the parameters docker conatiner image, artifact URI, explanation method, \n# explanation path count and explanation metadata JSON file `explanation-metadata.json`. \n# Here, you keep number of feature permutations to `10` when approximating the Shapley values for explanation.\ngcloud beta ai models upload --region=$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# 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=n1-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:2478, in InteractiveShell.run_cell_magic(self, magic_name, line, cell) 2476 with self.builtin_trap: 2477 args = (magic_arg_s, cell) -> 2478 result = fn(*args, **kwargs) 2480 # The code below prevents the output from being displayed 2481 # when using magics with decodator @output_can_be_silenced 2482 # when the last Python token in the expression is a ';'. 2483 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 TF_VERSION set in 1st cell, but ENDPOINT_NAME is being changed\n# TF_VERSION=2-6\nENDPOINT_NAME=flights_xai\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# 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=${ENDPOINT_NAME}\nfi\nENDPOINT_ID=$(gcloud beta ai endpoints list --region=$REGION \\\n --format=\'value(ENDPOINT_ID)\' --filter=display_name=${ENDPOINT_NAME})\necho "ENDPOINT_ID=$ENDPOINT_ID"\n# 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 $MODEL_ID\ndone\n# upload the model using the parameters docker conatiner image, artifact URI, explanation method, \n# explanation path count and explanation metadata JSON file `explanation-metadata.json`. \n# Here, you keep number of feature permutations to `10` when approximating the Shapley values for explanation.\ngcloud beta ai models upload --region=$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# 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=n1-standard-2 \\\n --min-replica-count=1 \\\n --max-replica-count=1 \\\n --traffic-split=0=100\n'' returned non-zero exit status 1.
Paul C. · Vor etwa ein Jahr überprüft
highly
Anna A. · Vor etwa ein Jahr überprüft
Anna A. · Vor etwa ein Jahr überprüft
Felype d. · Vor etwa ein Jahr überprüft
Notebook instance not creating in us-central1 region due to resources not being available in any zone in that region.
Sanjay S. · Vor etwa ein Jahr überprüft
can't create notebook
Suppadate T. · Vor etwa ein Jahr überprüft
Could not create the notebook. The directions are outdated.
Jeongho J. · Vor etwa ein Jahr überprüft
Could not actually instantiate a notebook from the coursera instructions. The error provided was no help.
Pat B. · Vor etwa ein Jahr überprüft
Resource issues
Gábor K. · Vor etwa ein Jahr überprüft
Santos B. · Vor etwa ein Jahr überprüft
David G. · Vor etwa ein Jahr überprüft
Full of errors. The environment cannot even run predetermined code. What a shame.
Rajesh R. · Vor etwa ein Jahr überprüft
Hard to get to resources to be able to even start the notebook.
Viktor S. · Vor etwa ein Jahr überprüft
The code provided contains a lot of bugs and errors
Aziz B. · Vor mehr als ein Jahr überprüft
Wir können nicht garantieren, dass die veröffentlichten Rezensionen von Verbrauchern stammen, die die Produkte gekauft oder genutzt haben. Die Rezensionen werden von Google nicht überprüft.