Comments (14)
lakshmanok
commented on May 25, 2024
You seem to have a mismatch in tensor flow versions between the notebook
and docker image.
I see both 2-6 and 2-9 referenced in the error log. Could you check?
thanks,
Lak
…On Wed, Aug 3, 2022, 3:41 PM Chris Shin ***@***.***> wrote:
Chapter 9 within "Deploy model to Vertex AI" section in
flights_model_tf2.ipynb. The deployment crashes when you try to execute the
first cell:
...
# upload model
gcloud beta ai models upload --region=$REGION --display-name=$MODEL_NAME \
--container-image-uri=us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.${TF_VERSION}:latest <http://us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.$%7BTF_VERSION%7D:latest> \
--artifact-uri=$EXPORT_PATH
MODEL_ID=$(gcloud ai models list --region=$REGION --format='value(MODEL_ID)' --filter=display_name=${MODEL_NAME})
echo "MODEL_ID=$MODEL_ID"
# deploy model to endpoint
gcloud ai endpoints deploy-model $ENDPOINT_ID \
--region=$REGION \
--model=$MODEL_ID \
--display-name=$MODEL_NAME \
--machine-type=n1-standard-2 \
--min-replica-count=1 \
--max-replica-count=1 \
--traffic-split=0=100
When I check the Vertex Endpoints, one was created but something else
seems to have gone wrong.
Output:
gs://tribbute-ml-central/ch9/trained_model/export/flights_20220803-222758/
Creating Endpoint for flights-20220803-223154
ENDPOINT_ID=974809417000157184
MODEL_ID=
followed by very long error (the error was too long so I pasted part of
it):
Using endpoint [https://us-central1-aiplatform.googleapis.com/]
WARNING: The following filter keys were not present in any resource : display_name
Using endpoint [https://us-central1-aiplatform.googleapis.com/]
Waiting for operation [7706081518493368320]...
.....done.
Created Vertex AI endpoint: projects/591020730428/locations/us-central1/endpoints/974809417000157184.
Using endpoint [https://us-central1-aiplatform.googleapis.com/]
Using endpoint [https://us-central1-aiplatform.googleapis.com/]
ERROR: gcloud crashed (InvalidDataFromServerError): Error decoding response "{
"models": [
{
"name": "projects/591020730428/locations/us-central1/models/1316788319564070912",
"displayName": "flights-20220803-223002",
"predictSchemata": {},
"containerSpec": {
"imageUri": "us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-9:latest"
},
"supportedDeploymentResourcesTypes": [
"DEDICATED_RESOURCES"
],
"supportedInputStorageFormats": [
"jsonl",
"bigquery",
"csv",
"tf-record",
"tf-record-gzip",
"file-list"
],
"supportedOutputStorageFormats": [
"jsonl",
"bigquery"
],
"createTime": "2022-08-03T22:30:12.377079Z",
"updateTime": "2022-08-03T22:30:14.993220Z",
"etag": "AMEw9yOIRZqfqqO_ngaA77Jw8Fs9E_kcI8tkqAIsTzFViX-aIrRbHfc0d2HRBihT32rp",
"supportedExportFormats": [
{
"id": "custom-trained",
"exportableContents": [
"ARTIFACT"
]
}
],
...
If you would like to report this issue, please run the following command:
gcloud feedback
To check gcloud for common problems, please run the following command:
gcloud info --run-diagnostics
Using endpoint [https://us-central1-aiplatform.googleapis.com/]
ERROR: (gcloud.ai.endpoints.deploy-model) could not parse resource []
---------------------------------------------------------------------------
CalledProcessError Traceback (most recent call last)
/tmp/ipykernel_1/3503756464.py in <module>
----> 1 get_ipython().run_cell_magic('bash', '', '# note TF_VERSION and ENDPOINT_NAME set in 1st cell\n# TF_VERSION=2-6\n# ENDPOINT_NAME=flights\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\nif [[ $(gcloud ai endpoints list --region=$REGION \\\n --format=\'value(DISPLAY_NAME)\' --filter=display_name=${ENDPOINT_NAME}) ]]; then\n echo "Endpoint for $MODEL_NAME already exists"\nelse\n # create model\n echo "Creating Endpoint for $MODEL_NAME"\n gcloud ai endpoints create --region=${REGION} --display-name=${ENDPOINT_NAME}\nfi\n\nENDPOINT_ID=$(gcloud ai endpoints list --region=$REGION \\\n --format=\'value(ENDPOINT_ID)\' --filter=display_name=${ENDPOINT_NAME})\necho "ENDPOINT_ID=$ENDPOINT_ID"\n\n# delete any existing models with this name\nfor MODEL_ID in $(gcloud ai models list --region=$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 $MODEL_ID\ndone\n\n# upload model\ngcloud beta ai models upload --region=$REGION --display-name=$MODEL_NAME \\\n --container-image-uri=us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.${TF_VERSION}:latest <http://us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.$%7BTF_VERSION%7D:latest> \\\n --artifact-uri=$EXPORT_PATH\nMODEL_ID=$(gcloud ai models list --region=$REGION --format=\'value(MODEL_ID)\' --filter=display_name=${MODEL_NAME})\necho "MODEL_ID=$MODEL_ID"\n\n# deploy model to endpoint\ngcloud 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')
/opt/conda/lib/python3.7/site-packages/IPython/core/interactiveshell.py in run_cell_magic(self, magic_name, line, cell)
2470 with self.builtin_trap:
2471 args = (magic_arg_s, cell)
-> 2472 result = fn(*args, **kwargs)
2473 return result
2474
/opt/conda/lib/python3.7/site-packages/IPython/core/magics/script.py in named_script_magic(line, cell)
140 else:
141 line = script
--> 142 return self.shebang(line, cell)
143
144 # write a basic docstring:
/opt/conda/lib/python3.7/site-packages/decorator.py in fun(*args, **kw)
230 if not kwsyntax:
231 args, kw = fix(args, kw, sig)
--> 232 return caller(func, *(extras + args), **kw)
233 fun.__name__ = func.__name__
234 fun.__doc__ = func.__doc__
/opt/conda/lib/python3.7/site-packages/IPython/core/magic.py in <lambda>(f, *a, **k)
185 # but it's overkill for just that one bit of state.
186 def magic_deco(arg):
--> 187 call = lambda f, *a, **k: f(*a, **k)
188
189 if callable(arg):
/opt/conda/lib/python3.7/site-packages/IPython/core/magics/script.py in shebang(self, line, cell)
243 sys.stderr.flush()
244 if args.raise_error and p.returncode!=0:
--> 245 raise CalledProcessError(p.returncode, cell, output=out, stderr=err)
246
247 def _run_script(self, p, cell, to_close):
CalledProcessError: Command 'b'# note TF_VERSION and ENDPOINT_NAME set in 1st cell\n# TF_VERSION=2-6\n# ENDPOINT_NAME=flights\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\nif [[ $(gcloud ai endpoints list --region=$REGION \\\n --format=\'value(DISPLAY_NAME)\' --filter=display_name=${ENDPOINT_NAME}) ]]; then\n echo "Endpoint for $MODEL_NAME already exists"\nelse\n # create model\n echo "Creating Endpoint for $MODEL_NAME"\n gcloud ai endpoints create --region=${REGION} --display-name=${ENDPOINT_NAME}\nfi\n\nENDPOINT_ID=$(gcloud ai endpoints list --region=$REGION \\\n --format=\'value(ENDPOINT_ID)\' --filter=display_name=${ENDPOINT_NAME})\necho "ENDPOINT_ID=$ENDPOINT_ID"\n\n# delete any existing models with this name\nfor MODEL_ID in $(gcloud ai models list --region=$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 $MODEL_ID\ndone\n\n# upload model\ngcloud beta ai models upload --region=$REGION --display-name=$MODEL_NAME \\\n --container-image-uri=us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.${TF_VERSION}:latest <http://us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.$%7BTF_VERSION%7D:latest> \\\n --artifact-uri=$EXPORT_PATH\nMODEL_ID=$(gcloud ai models list --region=$REGION --format=\'value(MODEL_ID)\' --filter=display_name=${MODEL_NAME})\necho "MODEL_ID=$MODEL_ID"\n\n# deploy model to endpoint\ngcloud 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.
—
Reply to this email directly, view it on GitHub
<#154>,
or unsubscribe
<https://github.com/notifications/unsubscribe-auth/AANJPZ7HJN32NDLY3ZZC2S3VXLYRLANCNFSM55QPAVDA>
.
You are receiving this because you are subscribed to this thread.Message
ID: ***@***.***>
from data-science-on-gcp.
shinchri
commented on May 25, 2024
@lakshmanok
2-6 was part of the comment inside the bash. The version I am using is 2.9.0-rc2.
After os.environ['TF_VERSION']='2-' + tf.__version__[2:3], the TF_VERSION is 2-9. I tried hard coding the version to "2-6" or "2.9.0-rc2" as well, but none worked.
I am only using one version so I don't know where the other versions are coming from.
The complete error message is:
click to see entire error message
Using endpoint [https://us-central1-aiplatform.googleapis.com/]
WARNING: The following filter keys were not present in any resource : display_name
Using endpoint [https://us-central1-aiplatform.googleapis.com/]
Waiting for operation [476678216656879616]...
.....done.
Created Vertex AI endpoint: projects/591020730428/locations/us-central1/endpoints/2473382193007689728.
Using endpoint [https://us-central1-aiplatform.googleapis.com/]
Using endpoint [https://us-central1-aiplatform.googleapis.com/]
ERROR: gcloud crashed (InvalidDataFromServerError): Error decoding response "{
"models": [
{
"name": "projects/591020730428/locations/us-central1/models/9103512075287658496",
"displayName": "flights-20220804-012641",
"predictSchemata": {},
"containerSpec": {
"imageUri": "us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2.9.0-rc2:latest"
},
"supportedDeploymentResourcesTypes": [
"DEDICATED_RESOURCES"
],
"supportedInputStorageFormats": [
"jsonl",
"bigquery",
"csv",
"tf-record",
"tf-record-gzip",
"file-list"
],
"supportedOutputStorageFormats": [
"jsonl",
"bigquery"
],
"createTime": "2022-08-04T01:26:50.402637Z",
"updateTime": "2022-08-04T01:26:52.926706Z",
"etag": "AMEw9yM4rd6hO-VtoplqIeRMGz4t2Qa_JhGFUrlvxwDoGkPy8LWau6DkyWWf0VBAms9Y",
"supportedExportFormats": [
{
"id": "custom-trained",
"exportableContents": [
"ARTIFACT"
]
}
],
"artifactUri": "gs://tribbute-ml-central/ch9/trained_model/export/flights_20220804-012017/",
"versionId": "1",
"versionAliases": [
"default"
],
"versionCreateTime": "2022-08-04T01:26:50.402637Z",
"versionUpdateTime": "2022-08-04T01:26:52.926706Z"
},
{
"name": "projects/591020730428/locations/us-central1/models/842784458783326208",
"displayName": "flights-20220804-012530",
"predictSchemata": {},
"containerSpec": {
"imageUri": "us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-6:latest"
},
"supportedDeploymentResourcesTypes": [
"DEDICATED_RESOURCES",
"SHARED_RESOURCES"
],
"supportedInputStorageFormats": [
"jsonl",
"bigquery",
"csv",
"tf-record",
"tf-record-gzip",
"file-list"
],
"supportedOutputStorageFormats": [
"jsonl",
"bigquery"
],
"createTime": "2022-08-04T01:25:39.057762Z",
"updateTime": "2022-08-04T01:25:41.645650Z",
"etag": "AMEw9yOJYffsNNc6kJ8mW3bmtVQhhrTYoYJIngsLlvxhdhjclPNdo-cDTJyrsPqI0W0B",
"supportedExportFormats": [
{
"id": "custom-trained",
"exportableContents": [
"ARTIFACT"
]
}
],
"artifactUri": "gs://tribbute-ml-central/ch9/trained_model/export/flights_20220804-012017/",
"versionId": "1",
"versionAliases": [
"default"
],
"versionCreateTime": "2022-08-04T01:25:39.057762Z",
"versionUpdateTime": "2022-08-04T01:25:41.645650Z"
},
{
"name": "projects/591020730428/locations/us-central1/models/8432475730809454592",
"displayName": "flights-20220804-012417",
"predictSchemata": {},
"containerSpec": {
"imageUri": "us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-6:latest"
},
"supportedDeploymentResourcesTypes": [
"DEDICATED_RESOURCES",
"SHARED_RESOURCES"
],
"supportedInputStorageFormats": [
"jsonl",
"bigquery",
"csv",
"tf-record",
"tf-record-gzip",
"file-list"
],
"supportedOutputStorageFormats": [
"jsonl",
"bigquery"
],
"createTime": "2022-08-04T01:24:26.339107Z",
"updateTime": "2022-08-04T01:24:28.949204Z",
"etag": "AMEw9yOBQ4swa06T9UIAMDEO9iI7SKKcSi26CgPQ3pjBh8CHgd36cNk1OZD1-gg0MUY1",
"supportedExportFormats": [
{
"id": "custom-trained",
"exportableContents": [
"ARTIFACT"
]
}
],
"artifactUri": "gs://tribbute-ml-central/ch9/trained_model/export/flights_20220804-012017/",
"versionId": "1",
"versionAliases": [
"default"
],
"versionCreateTime": "2022-08-04T01:24:26.339107Z",
"versionUpdateTime": "2022-08-04T01:24:28.949204Z"
},
{
"name": "projects/591020730428/locations/us-central1/models/8810778099508576256",
"displayName": "flights-20220804-012255",
"predictSchemata": {},
"containerSpec": {
"imageUri": "us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-6:latest"
},
"supportedDeploymentResourcesTypes": [
"DEDICATED_RESOURCES",
"SHARED_RESOURCES"
],
"supportedInputStorageFormats": [
"jsonl",
"bigquery",
"csv",
"tf-record",
"tf-record-gzip",
"file-list"
],
"supportedOutputStorageFormats": [
"jsonl",
"bigquery"
],
"createTime": "2022-08-04T01:23:04.426138Z",
"updateTime": "2022-08-04T01:23:06.954014Z",
"etag": "AMEw9yPd43l0ruUmuukBzwK4j8mfL312w_QfK_XeQQCj77lwK3xmApR3i0RRYxT74pH9",
"supportedExportFormats": [
{
"id": "custom-trained",
"exportableContents": [
"ARTIFACT"
]
}
],
"artifactUri": "gs://tribbute-ml-central/ch9/trained_model/export/flights_20220804-012017/",
"versionId": "1",
"versionAliases": [
"default"
],
"versionCreateTime": "2022-08-04T01:23:04.426138Z",
"versionUpdateTime": "2022-08-04T01:23:06.954014Z"
},
{
"name": "projects/591020730428/locations/us-central1/models/3820789712382066688",
"displayName": "flights-20220804-012133",
"predictSchemata": {},
"containerSpec": {
"imageUri": "us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-6:latest"
},
"supportedDeploymentResourcesTypes": [
"DEDICATED_RESOURCES",
"SHARED_RESOURCES"
],
"supportedInputStorageFormats": [
"jsonl",
"bigquery",
"csv",
"tf-record",
"tf-record-gzip",
"file-list"
],
"supportedOutputStorageFormats": [
"jsonl",
"bigquery"
],
"createTime": "2022-08-04T01:21:39.996004Z",
"updateTime": "2022-08-04T01:21:42.602720Z",
"etag": "AMEw9yO2F-RCkQUJE4gkcZ6EYkiHday9IsNsBJEl7yCpumOoN2nj4M0RURvD7dFgFg9y",
"supportedExportFormats": [
{
"id": "custom-trained",
"exportableContents": [
"ARTIFACT"
]
}
],
"artifactUri": "gs://tribbute-ml-central/ch9/trained_model/export/flights_20220804-012017/",
"versionId": "1",
"versionAliases": [
"default"
],
"versionCreateTime": "2022-08-04T01:21:39.996004Z",
"versionUpdateTime": "2022-08-04T01:21:42.602720Z"
},
{
"name": "projects/591020730428/locations/us-central1/models/738075767446962176",
"displayName": "flights-20220804-012101",
"predictSchemata": {},
"containerSpec": {
"imageUri": "us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-9:latest"
},
"supportedDeploymentResourcesTypes": [
"DEDICATED_RESOURCES"
],
"supportedInputStorageFormats": [
"jsonl",
"bigquery",
"csv",
"tf-record",
"tf-record-gzip",
"file-list"
],
"supportedOutputStorageFormats": [
"jsonl",
"bigquery"
],
"createTime": "2022-08-04T01:21:08.743501Z",
"updateTime": "2022-08-04T01:21:11.421906Z",
"etag": "AMEw9yNtBvF2fcwdOSdeqc9D1iPWHKikNFJMgQaDaicyW_YlxcbQodkjobhyRVubZF0E",
"supportedExportFormats": [
{
"id": "custom-trained",
"exportableContents": [
"ARTIFACT"
]
}
],
"artifactUri": "gs://tribbute-ml-central/ch9/trained_model/export/flights_20220804-012017/",
"versionId": "1",
"versionAliases": [
"default"
],
"versionCreateTime": "2022-08-04T01:21:08.743501Z",
"versionUpdateTime": "2022-08-04T01:21:11.421906Z"
},
{
"name": "projects/591020730428/locations/us-central1/models/3622631328777764864",
"displayName": "flights-20220803-223154",
"predictSchemata": {},
"containerSpec": {
"imageUri": "us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-9:latest"
},
"supportedDeploymentResourcesTypes": [
"DEDICATED_RESOURCES"
],
"supportedInputStorageFormats": [
"jsonl",
"bigquery",
"csv",
"tf-record",
"tf-record-gzip",
"file-list"
],
"supportedOutputStorageFormats": [
"jsonl",
"bigquery"
],
"createTime": "2022-08-03T22:32:04.481570Z",
"updateTime": "2022-08-03T22:32:07.084890Z",
"etag": "AMEw9yM1z5M_3p1RJ3E-Xdm0q8zAALfdkLk0xr8ydSFn_TaVEFb3bCcERIXpKu1S6Ag=",
"supportedExportFormats": [
{
"id": "custom-trained",
"exportableContents": [
"ARTIFACT"
]
}
],
"artifactUri": "gs://tribbute-ml-central/ch9/trained_model/export/flights_20220803-222758/",
"versionId": "1",
"versionAliases": [
"default"
],
"versionCreateTime": "2022-08-03T22:32:04.481570Z",
"versionUpdateTime": "2022-08-03T22:32:07.084890Z"
},
{
"name": "projects/591020730428/locations/us-central1/models/1316788319564070912",
"displayName": "flights-20220803-223002",
"predictSchemata": {},
"containerSpec": {
"imageUri": "us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-9:latest"
},
"supportedDeploymentResourcesTypes": [
"DEDICATED_RESOURCES"
],
"supportedInputStorageFormats": [
"jsonl",
"bigquery",
"csv",
"tf-record",
"tf-record-gzip",
"file-list"
],
"supportedOutputStorageFormats": [
"jsonl",
"bigquery"
],
"createTime": "2022-08-03T22:30:12.377079Z",
"updateTime": "2022-08-03T22:30:14.993220Z",
"etag": "AMEw9yOMfSljOWQw5aAZ7WKrLwmFQDgLqkiBBoMXg4s3MyG_5x9Qnv4BhoB5sM3dAxjy",
"supportedExportFormats": [
{
"id": "custom-trained",
"exportableContents": [
"ARTIFACT"
]
}
],
"artifactUri": "gs://tribbute-ml-central/ch9/trained_model/export/flights_20220803-222758/",
"versionId": "1",
"versionAliases": [
"default"
],
"versionCreateTime": "2022-08-03T22:30:12.377079Z",
"versionUpdateTime": "2022-08-03T22:30:14.993220Z"
},
{
"name": "projects/591020730428/locations/us-central1/models/4988910865731289088",
"displayName": "flights-20220803-222904",
"predictSchemata": {},
"containerSpec": {
"imageUri": "us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-9:latest"
},
"supportedDeploymentResourcesTypes": [
"DEDICATED_RESOURCES"
],
"supportedInputStorageFormats": [
"jsonl",
"bigquery",
"csv",
"tf-record",
"tf-record-gzip",
"file-list"
],
"supportedOutputStorageFormats": [
"jsonl",
"bigquery"
],
"createTime": "2022-08-03T22:29:11.348035Z",
"updateTime": "2022-08-03T22:29:13.961294Z",
"etag": "AMEw9yMsc4_EAziRd8lPcZyeor2W90ShMn0WfFLP4_Axane_7HhdLArIKJGN6kKAdZdD",
"supportedExportFormats": [
{
"id": "custom-trained",
"exportableContents": [
"ARTIFACT"
]
}
],
"artifactUri": "gs://tribbute-ml-central/ch9/trained_model/export/flights_20220803-222758/",
"versionId": "1",
"versionAliases": [
"default"
],
"versionCreateTime": "2022-08-03T22:29:11.348035Z",
"versionUpdateTime": "2022-08-03T22:29:13.961294Z"
},
{
"name": "projects/591020730428/locations/us-central1/models/4445664160679723008",
"displayName": "flights-20220803-222609",
"predictSchemata": {},
"containerSpec": {
"imageUri": "us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-9:latest"
},
"supportedDeploymentResourcesTypes": [
"DEDICATED_RESOURCES"
],
"supportedInputStorageFormats": [
"jsonl",
"bigquery",
"csv",
"tf-record",
"tf-record-gzip",
"file-list"
],
"supportedOutputStorageFormats": [
"jsonl",
"bigquery"
],
"createTime": "2022-08-03T22:26:16.737076Z",
"updateTime": "2022-08-03T22:26:19.654852Z",
"etag": "AMEw9yO6zVO9G5Vo8QM5n3Ao3s3n6qCCTklmLWlSIwgFOaD-PXqGbfoi5h1DodIlAlox",
"supportedExportFormats": [
{
"id": "custom-trained",
"exportableContents": [
"ARTIFACT"
]
}
],
"artifactUri": "gs://tribbute-ml-central/ch9/trained_model/export/flights_20220803-212637/",
"versionId": "1",
"versionAliases": [
"default"
],
"versionCreateTime": "2022-08-03T22:26:16.737076Z",
"versionUpdateTime": "2022-08-03T22:26:19.654852Z"
},
{
"name": "projects/591020730428/locations/us-central1/models/7088151242039361536",
"displayName": "flights-20220803-222539",
"predictSchemata": {},
"containerSpec": {
"imageUri": "us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-9:latest"
},
"supportedDeploymentResourcesTypes": [
"DEDICATED_RESOURCES"
],
"supportedInputStorageFormats": [
"jsonl",
"bigquery",
"csv",
"tf-record",
"tf-record-gzip",
"file-list"
],
"supportedOutputStorageFormats": [
"jsonl",
"bigquery"
],
"createTime": "2022-08-03T22:25:47.111845Z",
"updateTime": "2022-08-03T22:25:49.675406Z",
"etag": "AMEw9yOM6BuUFV_OLwkVdELeNw3i1HHVIMB0nqKJ0ZZV6P9xbpkiGy-xbYNQ4B87l5Ys",
"supportedExportFormats": [
{
"id": "custom-trained",
"exportableContents": [
"ARTIFACT"
]
}
],
"artifactUri": "gs://tribbute-ml-central/ch9/trained_model/export/flights_20220803-212637/",
"versionId": "1",
"versionAliases": [
"default"
],
"versionCreateTime": "2022-08-03T22:25:47.111845Z",
"versionUpdateTime": "2022-08-03T22:25:49.675406Z"
},
{
"name": "projects/591020730428/locations/us-central1/models/377224847303901184",
"displayName": "flights-20220803-222459",
"predictSchemata": {},
"containerSpec": {
"imageUri": "us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-9:latest"
},
"supportedDeploymentResourcesTypes": [
"DEDICATED_RESOURCES"
],
"supportedInputStorageFormats": [
"jsonl",
"bigquery",
"csv",
"tf-record",
"tf-record-gzip",
"file-list"
],
"supportedOutputStorageFormats": [
"jsonl",
"bigquery"
],
"createTime": "2022-08-03T22:25:07.431618Z",
"updateTime": "2022-08-03T22:25:10.129002Z",
"etag": "AMEw9yNW87y8hoSOnoReSw0BiZoaUAIv7tp1ph1d1MRd3vAUQkLsIbR77bmTAOchw_U=",
"supportedExportFormats": [
{
"id": "custom-trained",
"exportableContents": [
"ARTIFACT"
]
}
],
"artifactUri": "gs://tribbute-ml-central/ch9/trained_model/export/flights_20220803-212637/",
"versionId": "1",
"versionAliases": [
"default"
],
"versionCreateTime": "2022-08-03T22:25:07.431618Z",
"versionUpdateTime": "2022-08-03T22:25:10.129002Z"
},
{
"name": "projects/591020730428/locations/us-central1/models/3915365304556847104",
"displayName": "flights-20220803-222330",
"predictSchemata": {},
"containerSpec": {
"imageUri": "us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-6:latest"
},
"supportedDeploymentResourcesTypes": [
"DEDICATED_RESOURCES",
"SHARED_RESOURCES"
],
"supportedInputStorageFormats": [
"jsonl",
"bigquery",
"csv",
"tf-record",
"tf-record-gzip",
"file-list"
],
"supportedOutputStorageFormats": [
"jsonl",
"bigquery"
],
"createTime": "2022-08-03T22:23:37.884370Z",
"updateTime": "2022-08-03T22:23:40.524363Z",
"etag": "AMEw9yMVm_CE9bftw-qceZk8LeHaNhdLx-cgTA5PjZ3e07_BFqwRwgPd3ty_3xlnE82v",
"supportedExportFormats": [
{
"id": "custom-trained",
"exportableContents": [
"ARTIFACT"
]
}
],
"artifactUri": "gs://tribbute-ml-central/ch9/trained_model/export/flights_20220803-212637/",
"versionId": "1",
"versionAliases": [
"default"
],
"versionCreateTime": "2022-08-03T22:23:37.884370Z",
"versionUpdateTime": "2022-08-03T22:23:40.524363Z"
},
{
"name": "projects/591020730428/locations/us-central1/models/6414863097747472384",
"displayName": "flights-20220803-221346",
"predictSchemata": {},
"containerSpec": {
"imageUri": "us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-9:latest"
},
"supportedDeploymentResourcesTypes": [
"DEDICATED_RESOURCES"
],
"supportedInputStorageFormats": [
"jsonl",
"bigquery",
"csv",
"tf-record",
"tf-record-gzip",
"file-list"
],
"supportedOutputStorageFormats": [
"jsonl",
"bigquery"
],
"createTime": "2022-08-03T22:14:03.490710Z",
"updateTime": "2022-08-03T22:14:05.659596Z",
"etag": "AMEw9yPFHHccVFPzhIe7ZluZ7E39sSJF4ViqODAl1jpewlOLy-UJjQcFTIbNk3yiJtmT",
"supportedExportFormats": [
{
"id": "custom-trained",
"exportableContents": [
"ARTIFACT"
]
}
],
"artifactUri": "gs://tribbute-ml-central/ch9/trained_model/export/flights_20220803-212637/",
"versionId": "1",
"versionAliases": [
"default"
],
"versionCreateTime": "2022-08-03T22:14:03.490710Z",
"versionUpdateTime": "2022-08-03T22:14:05.659596Z"
}
]
}
" as type GoogleCloudAiplatformV1ListModelsResponse: Repeated values for field supportedDeploymentResourcesTypes may not be None
If you would like to report this issue, please run the following command:
gcloud feedback
To check gcloud for common problems, please run the following command:
gcloud info --run-diagnostics
Using endpoint [https://us-central1-aiplatform.googleapis.com/]
Waiting for operation [4491637284457676800]...
.................done.
Using endpoint [https://us-central1-aiplatform.googleapis.com/]
ERROR: gcloud crashed (InvalidDataFromServerError): Error decoding response "{
"models": [
{
"name": "projects/591020730428/locations/us-central1/models/96312820546666496",
"displayName": "flights-20220804-013301",
"predictSchemata": {},
"containerSpec": {
"imageUri": "us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-9:latest"
},
"supportedDeploymentResourcesTypes": [
"DEDICATED_RESOURCES"
],
"supportedInputStorageFormats": [
"jsonl",
"bigquery",
"csv",
"tf-record",
"tf-record-gzip",
"file-list"
],
"supportedOutputStorageFormats": [
"jsonl",
"bigquery"
],
"createTime": "2022-08-04T01:33:10.171939Z",
"updateTime": "2022-08-04T01:33:12.776819Z",
"etag": "AMEw9yOCbuBK63h94z4a6QxtkSxu8bUmvfYbplO6UrrL58i1_DsVtLqEQIUFFo9-_Ghi",
"supportedExportFormats": [
{
"id": "custom-trained",
"exportableContents": [
"ARTIFACT"
]
}
],
"artifactUri": "gs://tribbute-ml-central/ch9/trained_model/export/flights_20220804-013242/",
"versionId": "1",
"versionAliases": [
"default"
],
"versionCreateTime": "2022-08-04T01:33:10.171939Z",
"versionUpdateTime": "2022-08-04T01:33:12.776819Z"
},
{
"name": "projects/591020730428/locations/us-central1/models/9103512075287658496",
"displayName": "flights-20220804-012641",
"predictSchemata": {},
"containerSpec": {
"imageUri": "us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2.9.0-rc2:latest"
},
"supportedDeploymentResourcesTypes": [
"DEDICATED_RESOURCES"
],
"supportedInputStorageFormats": [
"jsonl",
"bigquery",
"csv",
"tf-record",
"tf-record-gzip",
"file-list"
],
"supportedOutputStorageFormats": [
"jsonl",
"bigquery"
],
"createTime": "2022-08-04T01:26:50.402637Z",
"updateTime": "2022-08-04T01:26:52.926706Z",
"etag": "AMEw9yOjYl7oco3RqtEusWtpce6--5bI4DAsHRdIUbpcPhvTmF58PhSFiHMzUKXU609g",
"supportedExportFormats": [
{
"id": "custom-trained",
"exportableContents": [
"ARTIFACT"
]
}
],
"artifactUri": "gs://tribbute-ml-central/ch9/trained_model/export/flights_20220804-012017/",
"versionId": "1",
"versionAliases": [
"default"
],
"versionCreateTime": "2022-08-04T01:26:50.402637Z",
"versionUpdateTime": "2022-08-04T01:26:52.926706Z"
},
{
"name": "projects/591020730428/locations/us-central1/models/842784458783326208",
"displayName": "flights-20220804-012530",
"predictSchemata": {},
"containerSpec": {
"imageUri": "us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-6:latest"
},
"supportedDeploymentResourcesTypes": [
"DEDICATED_RESOURCES",
"SHARED_RESOURCES"
],
"supportedInputStorageFormats": [
"jsonl",
"bigquery",
"csv",
"tf-record",
"tf-record-gzip",
"file-list"
],
"supportedOutputStorageFormats": [
"jsonl",
"bigquery"
],
"createTime": "2022-08-04T01:25:39.057762Z",
"updateTime": "2022-08-04T01:25:41.645650Z",
"etag": "AMEw9yMuhhglqjf7bs-WXLBDQKQaLq0Xk8rURA2WEWaB1TcHxJKgmGVJ3Kv6kidotPp7",
"supportedExportFormats": [
{
"id": "custom-trained",
"exportableContents": [
"ARTIFACT"
]
}
],
"artifactUri": "gs://tribbute-ml-central/ch9/trained_model/export/flights_20220804-012017/",
"versionId": "1",
"versionAliases": [
"default"
],
"versionCreateTime": "2022-08-04T01:25:39.057762Z",
"versionUpdateTime": "2022-08-04T01:25:41.645650Z"
},
{
"name": "projects/591020730428/locations/us-central1/models/8432475730809454592",
"displayName": "flights-20220804-012417",
"predictSchemata": {},
"containerSpec": {
"imageUri": "us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-6:latest"
},
"supportedDeploymentResourcesTypes": [
"DEDICATED_RESOURCES",
"SHARED_RESOURCES"
],
"supportedInputStorageFormats": [
"jsonl",
"bigquery",
"csv",
"tf-record",
"tf-record-gzip",
"file-list"
],
"supportedOutputStorageFormats": [
"jsonl",
"bigquery"
],
"createTime": "2022-08-04T01:24:26.339107Z",
"updateTime": "2022-08-04T01:24:28.949204Z",
"etag": "AMEw9yNVmNnvgY3isGkEb-vY66FKRPB9sRusJxvqPiu_5lBVJOtsmvlpJAOAPm8pSs2r",
"supportedExportFormats": [
{
"id": "custom-trained",
"exportableContents": [
"ARTIFACT"
]
}
],
"artifactUri": "gs://tribbute-ml-central/ch9/trained_model/export/flights_20220804-012017/",
"versionId": "1",
"versionAliases": [
"default"
],
"versionCreateTime": "2022-08-04T01:24:26.339107Z",
"versionUpdateTime": "2022-08-04T01:24:28.949204Z"
},
{
"name": "projects/591020730428/locations/us-central1/models/8810778099508576256",
"displayName": "flights-20220804-012255",
"predictSchemata": {},
"containerSpec": {
"imageUri": "us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-6:latest"
},
"supportedDeploymentResourcesTypes": [
"DEDICATED_RESOURCES",
"SHARED_RESOURCES"
],
"supportedInputStorageFormats": [
"jsonl",
"bigquery",
"csv",
"tf-record",
"tf-record-gzip",
"file-list"
],
"supportedOutputStorageFormats": [
"jsonl",
"bigquery"
],
"createTime": "2022-08-04T01:23:04.426138Z",
"updateTime": "2022-08-04T01:23:06.954014Z",
"etag": "AMEw9yPOlQQsPbSDqF2hR4FQJ0KTR4iqsrCgvT9LJN3MWwlJo4ACSzoC4Mkmjoj1ZpaC",
"supportedExportFormats": [
{
"id": "custom-trained",
"exportableContents": [
"ARTIFACT"
]
}
],
"artifactUri": "gs://tribbute-ml-central/ch9/trained_model/export/flights_20220804-012017/",
"versionId": "1",
"versionAliases": [
"default"
],
"versionCreateTime": "2022-08-04T01:23:04.426138Z",
"versionUpdateTime": "2022-08-04T01:23:06.954014Z"
},
{
"name": "projects/591020730428/locations/us-central1/models/3820789712382066688",
"displayName": "flights-20220804-012133",
"predictSchemata": {},
"containerSpec": {
"imageUri": "us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-6:latest"
},
"supportedDeploymentResourcesTypes": [
"DEDICATED_RESOURCES",
"SHARED_RESOURCES"
],
"supportedInputStorageFormats": [
"jsonl",
"bigquery",
"csv",
"tf-record",
"tf-record-gzip",
"file-list"
],
"supportedOutputStorageFormats": [
"jsonl",
"bigquery"
],
"createTime": "2022-08-04T01:21:39.996004Z",
"updateTime": "2022-08-04T01:21:42.602720Z",
"etag": "AMEw9yNd5w4pV05qq0hymVKu4QY3R6i3tyLvtEgAfgR9hWUJ3qQEpTeoyDMJN_5k37oK",
"supportedExportFormats": [
{
"id": "custom-trained",
"exportableContents": [
"ARTIFACT"
]
}
],
"artifactUri": "gs://tribbute-ml-central/ch9/trained_model/export/flights_20220804-012017/",
"versionId": "1",
"versionAliases": [
"default"
],
"versionCreateTime": "2022-08-04T01:21:39.996004Z",
"versionUpdateTime": "2022-08-04T01:21:42.602720Z"
},
{
"name": "projects/591020730428/locations/us-central1/models/738075767446962176",
"displayName": "flights-20220804-012101",
"predictSchemata": {},
"containerSpec": {
"imageUri": "us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-9:latest"
},
"supportedDeploymentResourcesTypes": [
"DEDICATED_RESOURCES"
],
"supportedInputStorageFormats": [
"jsonl",
"bigquery",
"csv",
"tf-record",
"tf-record-gzip",
"file-list"
],
"supportedOutputStorageFormats": [
"jsonl",
"bigquery"
],
"createTime": "2022-08-04T01:21:08.743501Z",
"updateTime": "2022-08-04T01:21:11.421906Z",
"etag": "AMEw9yOIDT_twcwlEAk9SJ_uDC44JhNzYTiu6e-Mg0gOimzUpknc6kWBmqxzAhW4-7E7",
"supportedExportFormats": [
{
"id": "custom-trained",
"exportableContents": [
"ARTIFACT"
]
}
],
"artifactUri": "gs://tribbute-ml-central/ch9/trained_model/export/flights_20220804-012017/",
"versionId": "1",
"versionAliases": [
"default"
],
"versionCreateTime": "2022-08-04T01:21:08.743501Z",
"versionUpdateTime": "2022-08-04T01:21:11.421906Z"
},
{
"name": "projects/591020730428/locations/us-central1/models/3622631328777764864",
"displayName": "flights-20220803-223154",
"predictSchemata": {},
"containerSpec": {
"imageUri": "us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-9:latest"
},
"supportedDeploymentResourcesTypes": [
"DEDICATED_RESOURCES"
],
"supportedInputStorageFormats": [
"jsonl",
"bigquery",
"csv",
"tf-record",
"tf-record-gzip",
"file-list"
],
"supportedOutputStorageFormats": [
"jsonl",
"bigquery"
],
"createTime": "2022-08-03T22:32:04.481570Z",
"updateTime": "2022-08-03T22:32:07.084890Z",
"etag": "AMEw9yN62Xe0wnLagCf7fTaHzG6rbo8EKpB0lIqKO3JVzlDJN1rEb9zLH4eHsp-0lJk=",
"supportedExportFormats": [
{
"id": "custom-trained",
"exportableContents": [
"ARTIFACT"
]
}
],
"artifactUri": "gs://tribbute-ml-central/ch9/trained_model/export/flights_20220803-222758/",
"versionId": "1",
"versionAliases": [
"default"
],
"versionCreateTime": "2022-08-03T22:32:04.481570Z",
"versionUpdateTime": "2022-08-03T22:32:07.084890Z"
},
{
"name": "projects/591020730428/locations/us-central1/models/1316788319564070912",
"displayName": "flights-20220803-223002",
"predictSchemata": {},
"containerSpec": {
"imageUri": "us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-9:latest"
},
"supportedDeploymentResourcesTypes": [
"DEDICATED_RESOURCES"
],
"supportedInputStorageFormats": [
"jsonl",
"bigquery",
"csv",
"tf-record",
"tf-record-gzip",
"file-list"
],
"supportedOutputStorageFormats": [
"jsonl",
"bigquery"
],
"createTime": "2022-08-03T22:30:12.377079Z",
"updateTime": "2022-08-03T22:30:14.993220Z",
"etag": "AMEw9yO6ubkb9WbTSAi1K1DfBMVHAt-pKJ8UCkSij-gW8ExrWsKLHhaokVD7dZV07U2E",
"supportedExportFormats": [
{
"id": "custom-trained",
"exportableContents": [
"ARTIFACT"
]
}
],
"artifactUri": "gs://tribbute-ml-central/ch9/trained_model/export/flights_20220803-222758/",
"versionId": "1",
"versionAliases": [
"default"
],
"versionCreateTime": "2022-08-03T22:30:12.377079Z",
"versionUpdateTime": "2022-08-03T22:30:14.993220Z"
},
{
"name": "projects/591020730428/locations/us-central1/models/4988910865731289088",
"displayName": "flights-20220803-222904",
"predictSchemata": {},
"containerSpec": {
"imageUri": "us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-9:latest"
},
"supportedDeploymentResourcesTypes": [
"DEDICATED_RESOURCES"
],
"supportedInputStorageFormats": [
"jsonl",
"bigquery",
"csv",
"tf-record",
"tf-record-gzip",
"file-list"
],
"supportedOutputStorageFormats": [
"jsonl",
"bigquery"
],
"createTime": "2022-08-03T22:29:11.348035Z",
"updateTime": "2022-08-03T22:29:13.961294Z",
"etag": "AMEw9yMD4O81h3HVrMUSzoRku4BaN8Td8EDwUBsp9bGZUu9H9vb_Aae7QUrV99vinWTT",
"supportedExportFormats": [
{
"id": "custom-trained",
"exportableContents": [
"ARTIFACT"
]
}
],
"artifactUri": "gs://tribbute-ml-central/ch9/trained_model/export/flights_20220803-222758/",
"versionId": "1",
"versionAliases": [
"default"
],
"versionCreateTime": "2022-08-03T22:29:11.348035Z",
"versionUpdateTime": "2022-08-03T22:29:13.961294Z"
},
{
"name": "projects/591020730428/locations/us-central1/models/4445664160679723008",
"displayName": "flights-20220803-222609",
"predictSchemata": {},
"containerSpec": {
"imageUri": "us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-9:latest"
},
"supportedDeploymentResourcesTypes": [
"DEDICATED_RESOURCES"
],
"supportedInputStorageFormats": [
"jsonl",
"bigquery",
"csv",
"tf-record",
"tf-record-gzip",
"file-list"
],
"supportedOutputStorageFormats": [
"jsonl",
"bigquery"
],
"createTime": "2022-08-03T22:26:16.737076Z",
"updateTime": "2022-08-03T22:26:19.654852Z",
"etag": "AMEw9yMxqTeQvMxLe8ADkN61tZHxrecnyOQZq5rFSRQ-MPMw7hL8xXuSUZn6biiFb7If",
"supportedExportFormats": [
{
"id": "custom-trained",
"exportableContents": [
"ARTIFACT"
]
}
],
"artifactUri": "gs://tribbute-ml-central/ch9/trained_model/export/flights_20220803-212637/",
"versionId": "1",
"versionAliases": [
"default"
],
"versionCreateTime": "2022-08-03T22:26:16.737076Z",
"versionUpdateTime": "2022-08-03T22:26:19.654852Z"
},
{
"name": "projects/591020730428/locations/us-central1/models/7088151242039361536",
"displayName": "flights-20220803-222539",
"predictSchemata": {},
"containerSpec": {
"imageUri": "us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-9:latest"
},
"supportedDeploymentResourcesTypes": [
"DEDICATED_RESOURCES"
],
"supportedInputStorageFormats": [
"jsonl",
"bigquery",
"csv",
"tf-record",
"tf-record-gzip",
"file-list"
],
"supportedOutputStorageFormats": [
"jsonl",
"bigquery"
],
"createTime": "2022-08-03T22:25:47.111845Z",
"updateTime": "2022-08-03T22:25:49.675406Z",
"etag": "AMEw9yNCj4O7ymtGrFBkBNmY6hNQ5aFLH_jRELpdtPM68eyYaKXaeSBztEkCK5XoDKcO",
"supportedExportFormats": [
{
"id": "custom-trained",
"exportableContents": [
"ARTIFACT"
]
}
],
"artifactUri": "gs://tribbute-ml-central/ch9/trained_model/export/flights_20220803-212637/",
"versionId": "1",
"versionAliases": [
"default"
],
"versionCreateTime": "2022-08-03T22:25:47.111845Z",
"versionUpdateTime": "2022-08-03T22:25:49.675406Z"
},
{
"name": "projects/591020730428/locations/us-central1/models/377224847303901184",
"displayName": "flights-20220803-222459",
"predictSchemata": {},
"containerSpec": {
"imageUri": "us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-9:latest"
},
"supportedDeploymentResourcesTypes": [
"DEDICATED_RESOURCES"
],
"supportedInputStorageFormats": [
"jsonl",
"bigquery",
"csv",
"tf-record",
"tf-record-gzip",
"file-list"
],
"supportedOutputStorageFormats": [
"jsonl",
"bigquery"
],
"createTime": "2022-08-03T22:25:07.431618Z",
"updateTime": "2022-08-03T22:25:10.129002Z",
"etag": "AMEw9yMpqzewL95pvf4uUXqfV1C2Tyzg-lI-0CS1l23dnXGgCuCA7zL_hgJ2TigeIWQ=",
"supportedExportFormats": [
{
"id": "custom-trained",
"exportableContents": [
"ARTIFACT"
]
}
],
"artifactUri": "gs://tribbute-ml-central/ch9/trained_model/export/flights_20220803-212637/",
"versionId": "1",
"versionAliases": [
"default"
],
"versionCreateTime": "2022-08-03T22:25:07.431618Z",
"versionUpdateTime": "2022-08-03T22:25:10.129002Z"
},
{
"name": "projects/591020730428/locations/us-central1/models/3915365304556847104",
"displayName": "flights-20220803-222330",
"predictSchemata": {},
"containerSpec": {
"imageUri": "us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-6:latest"
},
"supportedDeploymentResourcesTypes": [
"DEDICATED_RESOURCES",
"SHARED_RESOURCES"
],
"supportedInputStorageFormats": [
"jsonl",
"bigquery",
"csv",
"tf-record",
"tf-record-gzip",
"file-list"
],
"supportedOutputStorageFormats": [
"jsonl",
"bigquery"
],
"createTime": "2022-08-03T22:23:37.884370Z",
"updateTime": "2022-08-03T22:23:40.524363Z",
"etag": "AMEw9yOPNa9CqLX8zmkSrxSuUb14pUCiQwXFbEqWCjEfrwUJ-IJfmgKw-HU4e7iiN7Av",
"supportedExportFormats": [
{
"id": "custom-trained",
"exportableContents": [
"ARTIFACT"
]
}
],
"artifactUri": "gs://tribbute-ml-central/ch9/trained_model/export/flights_20220803-212637/",
"versionId": "1",
"versionAliases": [
"default"
],
"versionCreateTime": "2022-08-03T22:23:37.884370Z",
"versionUpdateTime": "2022-08-03T22:23:40.524363Z"
},
{
"name": "projects/591020730428/locations/us-central1/models/6414863097747472384",
"displayName": "flights-20220803-221346",
"predictSchemata": {},
"containerSpec": {
"imageUri": "us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-9:latest"
},
"supportedDeploymentResourcesTypes": [
"DEDICATED_RESOURCES"
],
"supportedInputStorageFormats": [
"jsonl",
"bigquery",
"csv",
"tf-record",
"tf-record-gzip",
"file-list"
],
"supportedOutputStorageFormats": [
"jsonl",
"bigquery"
],
"createTime": "2022-08-03T22:14:03.490710Z",
"updateTime": "2022-08-03T22:14:05.659596Z",
"etag": "AMEw9yOAGZ7o-sld7iqDg1utX90aP0nOV96Eoz3J60GAFoUO_JorDGQ3eg9Iur6swTJe",
"supportedExportFormats": [
{
"id": "custom-trained",
"exportableContents": [
"ARTIFACT"
]
}
],
"artifactUri": "gs://tribbute-ml-central/ch9/trained_model/export/flights_20220803-212637/",
"versionId": "1",
"versionAliases": [
"default"
],
"versionCreateTime": "2022-08-03T22:14:03.490710Z",
"versionUpdateTime": "2022-08-03T22:14:05.659596Z"
}
]
}
" as type GoogleCloudAiplatformV1ListModelsResponse: Repeated values for field supportedDeploymentResourcesTypes may not be None
If you would like to report this issue, please run the following command:
gcloud feedback
To check gcloud for common problems, please run the following command:
gcloud info --run-diagnostics
Using endpoint [https://us-central1-aiplatform.googleapis.com/]
ERROR: (gcloud.ai.endpoints.deploy-model) could not parse resource []
CalledProcessError Traceback (most recent call last)
/tmp/ipykernel_1/2211222481.py in
----> 1 get_ipython().run_cell_magic('bash', '', '\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\nif [[ $(gcloud ai endpoints list --region=$REGION \\n --format='value(DISPLAY_NAME)' --filter=display_name=${ENDPOINT_NAME}) ]]; then\n echo "Endpoint for $MODEL_NAME already exists"\nelse\n # create model\n echo "Creating Endpoint for $MODEL_NAME"\n gcloud ai endpoints create --region=${REGION} --display-name=${ENDPOINT_NAME}\nfi\n\nENDPOINT_ID=$(gcloud ai endpoints list --region=$REGION \\n --format='value(ENDPOINT_ID)' --filter=display_name=${ENDPOINT_NAME})\necho "ENDPOINT_ID=$ENDPOINT_ID"\n\n# delete any existing models with this name\nfor MODEL_ID in $(gcloud ai models list --region=$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 $MODEL_ID\ndone\n\n# upload model\ngcloud beta ai models upload --region=$REGION --display-name=$MODEL_NAME \\n --container-image-uri=us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.${TF_VERSION}:latest \\n --artifact-uri=$EXPORT_PATH\nMODEL_ID=$(gcloud ai models list --region=$REGION --format='value(MODEL_ID)' --filter=display_name=${MODEL_NAME})\necho "MODEL_ID=$MODEL_ID"\n\n# deploy model to endpoint\ngcloud 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')
/opt/conda/lib/python3.7/site-packages/IPython/core/interactiveshell.py in run_cell_magic(self, magic_name, line, cell)
2470 with self.builtin_trap:
2471 args = (magic_arg_s, cell)
-> 2472 result = fn(*args, **kwargs)
2473 return result
2474
/opt/conda/lib/python3.7/site-packages/IPython/core/magics/script.py in named_script_magic(line, cell)
140 else:
141 line = script
--> 142 return self.shebang(line, cell)
143
144 # write a basic docstring:
/opt/conda/lib/python3.7/site-packages/decorator.py in fun(*args, **kw)
230 if not kwsyntax:
231 args, kw = fix(args, kw, sig)
--> 232 return caller(func, *(extras + args), **kw)
233 fun.name = func.name
234 fun.doc = func.doc
/opt/conda/lib/python3.7/site-packages/IPython/core/magic.py in (f, *a, **k)
185 # but it's overkill for just that one bit of state.
186 def magic_deco(arg):
--> 187 call = lambda f, *a, **k: f(*a, **k)
188
189 if callable(arg):
/opt/conda/lib/python3.7/site-packages/IPython/core/magics/script.py in shebang(self, line, cell)
243 sys.stderr.flush()
244 if args.raise_error and p.returncode!=0:
--> 245 raise CalledProcessError(p.returncode, cell, output=out, stderr=err)
246
247 def _run_script(self, p, cell, to_close):
CalledProcessError: Command 'b'\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\nif [[ $(gcloud ai endpoints list --region=$REGION \\n --format='value(DISPLAY_NAME)' --filter=display_name=${ENDPOINT_NAME}) ]]; then\n echo "Endpoint for $MODEL_NAME already exists"\nelse\n # create model\n echo "Creating Endpoint for $MODEL_NAME"\n gcloud ai endpoints create --region=${REGION} --display-name=${ENDPOINT_NAME}\nfi\n\nENDPOINT_ID=$(gcloud ai endpoints list --region=$REGION \\n --format='value(ENDPOINT_ID)' --filter=display_name=${ENDPOINT_NAME})\necho "ENDPOINT_ID=$ENDPOINT_ID"\n\n# delete any existing models with this name\nfor MODEL_ID in $(gcloud ai models list --region=$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 $MODEL_ID\ndone\n\n# upload model\ngcloud beta ai models upload --region=$REGION --display-name=$MODEL_NAME \\n --container-image-uri=us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.${TF_VERSION}:latest \\n --artifact-uri=$EXPORT_PATH\nMODEL_ID=$(gcloud ai models list --region=$REGION --format='value(MODEL_ID)' --filter=display_name=${MODEL_NAME})\necho "MODEL_ID=$MODEL_ID"\n\n# deploy model to endpoint\ngcloud 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.
from data-science-on-gcp.
shinchri
commented on May 25, 2024
@lakshmanok I found out why I kept getting different versions. For some reason the code to delete any existing models doesn't work. The code to get MODEL_ID: $(gcloud ai models list --region=$REGION --format='value(MODEL_ID)' --filter=display_name=${MODEL_NAME}) doesn't seem to return anything. So I had to manually delete the models that were registered (and there were many).
Once I fixed that I stopped getting multiple models but I still had a problem. Here is the error code that I have now (I tried both 2-6 and 2-9 but they give the same error message but with different version):
Using endpoint [https://us-central1-aiplatform.googleapis.com/]
WARNING: The following filter keys were not present in any resource : display_name
Using endpoint [https://us-central1-aiplatform.googleapis.com/]
Waiting for operation [1000784623292121088]...
.....done.
Created Vertex AI endpoint: projects/591020730428/locations/us-central1/endpoints/7596789719095050240.
Using endpoint [https://us-central1-aiplatform.googleapis.com/]
Using endpoint [https://us-central1-aiplatform.googleapis.com/]
WARNING: The following filter keys were not present in any resource : display_name
Using endpoint [https://us-central1-aiplatform.googleapis.com/]
Waiting for operation [5612470641719508992]...
....................done.
Using endpoint [https://us-central1-aiplatform.googleapis.com/]
Using endpoint [https://us-central1-aiplatform.googleapis.com/]
ERROR: (gcloud.ai.endpoints.deploy-model) INVALID_ARGUMENT: Invalid image "us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-9:latest" for deployment. Please use a Model with a valid image.
---------------------------------------------------------------------------
CalledProcessError Traceback (most recent call last)
/tmp/ipykernel_1/1002185504.py in <module>
----> 1 get_ipython().run_cell_magic('bash', '', '\nTIMESTAMP=$(date +%Y%m%d-%H%M%S)\nMODEL_NAME=${ENDPOINT_NAME}-${TIMESTAMP}\nEXPORT_PATH=$(gsutil ls ${OUTDIR}/export | tail -1)\necho $EXPORT_PATH\n\nif [[ $(gcloud ai endpoints list --region=$REGION \\\n --format=\'value(DISPLAY_NAME)\' --filter=display_name=${ENDPOINT_NAME}) ]]; then\n echo "Endpoint for $MODEL_NAME already exists"\nelse\n # create model\n echo "Creating Endpoint for $MODEL_NAME"\n gcloud ai endpoints create --region=${REGION} --display-name=${ENDPOINT_NAME}\nfi\n\nENDPOINT_ID=$(gcloud ai endpoints list --region=$REGION \\\n --format=\'value(ENDPOINT_ID)\' --filter=display_name=${ENDPOINT_NAME})\necho "ENDPOINT_ID=$ENDPOINT_ID"\n\n# delete any existing models with this name\nfor MODEL_ID in $(gcloud ai models list --region=$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 $MODEL_ID\ndone\n\n# upload model\ngcloud beta ai models upload --region=$REGION --display-name=$MODEL_NAME \\\n --container-image-uri=us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.${TF_VERSION}:latest \\\n --artifact-uri=$EXPORT_PATH\nMODEL_ID=$(gcloud ai models list --region=$REGION --format=\'value(MODEL_ID)\' --filter=display_name=${MODEL_NAME})\necho "MODEL_ID=$MODEL_ID"\n\n# deploy model to endpoint\ngcloud 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\n')
/opt/conda/lib/python3.7/site-packages/IPython/core/interactiveshell.py in run_cell_magic(self, magic_name, line, cell)
2470 with self.builtin_trap:
2471 args = (magic_arg_s, cell)
-> 2472 result = fn(*args, **kwargs)
2473 return result
2474
/opt/conda/lib/python3.7/site-packages/IPython/core/magics/script.py in named_script_magic(line, cell)
140 else:
141 line = script
--> 142 return self.shebang(line, cell)
143
144 # write a basic docstring:
/opt/conda/lib/python3.7/site-packages/decorator.py in fun(*args, **kw)
230 if not kwsyntax:
231 args, kw = fix(args, kw, sig)
--> 232 return caller(func, *(extras + args), **kw)
233 fun.__name__ = func.__name__
234 fun.__doc__ = func.__doc__
/opt/conda/lib/python3.7/site-packages/IPython/core/magic.py in <lambda>(f, *a, **k)
185 # but it's overkill for just that one bit of state.
186 def magic_deco(arg):
--> 187 call = lambda f, *a, **k: f(*a, **k)
188
189 if callable(arg):
/opt/conda/lib/python3.7/site-packages/IPython/core/magics/script.py in shebang(self, line, cell)
243 sys.stderr.flush()
244 if args.raise_error and p.returncode!=0:
--> 245 raise CalledProcessError(p.returncode, cell, output=out, stderr=err)
246
247 def _run_script(self, p, cell, to_close):
CalledProcessError: Command 'b'\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\nif [[ $(gcloud ai endpoints list --region=$REGION \\\n --format=\'value(DISPLAY_NAME)\' --filter=display_name=${ENDPOINT_NAME}) ]]; then\n echo "Endpoint for $MODEL_NAME already exists"\nelse\n # create model\n echo "Creating Endpoint for $MODEL_NAME"\n gcloud ai endpoints create --region=${REGION} --display-name=${ENDPOINT_NAME}\nfi\n\nENDPOINT_ID=$(gcloud ai endpoints list --region=$REGION \\\n --format=\'value(ENDPOINT_ID)\' --filter=display_name=${ENDPOINT_NAME})\necho "ENDPOINT_ID=$ENDPOINT_ID"\n\n# delete any existing models with this name\nfor MODEL_ID in $(gcloud ai models list --region=$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 $MODEL_ID\ndone\n\n# upload model\ngcloud beta ai models upload --region=$REGION --display-name=$MODEL_NAME \\\n --container-image-uri=us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.${TF_VERSION}:latest \\\n --artifact-uri=$EXPORT_PATH\nMODEL_ID=$(gcloud ai models list --region=$REGION --format=\'value(MODEL_ID)\' --filter=display_name=${MODEL_NAME})\necho "MODEL_ID=$MODEL_ID"\n\n# deploy model to endpoint\ngcloud 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\n'' returned non-zero exit status 1.
The problem seems to be the docker image, but not sure where the image is coming from.
Thanks,
Chris
from data-science-on-gcp.
lakshmanok
commented on May 25, 2024
These are the supported docker images in 2.9
Is the name that the script generates correct?
thanks,
Lak
…On Wed, Aug 3, 2022, 7:35 PM Chris Shin ***@***.***> wrote:
@lakshmanok <https://github.com/lakshmanok> I found out why I kept
getting different versions. For some reason the code to delete any existing
models doesn't work. So I had to manually delete the models that were
registered (and there were many).
Once I fixed that I stopped getting multiple models but I still had a
problem. Here is the error code that I have now (I tried both 2-6 and 2-9
but they give the same error message but with different version):
Using endpoint [https://us-central1-aiplatform.googleapis.com/]
WARNING: The following filter keys were not present in any resource : display_name
Using endpoint [https://us-central1-aiplatform.googleapis.com/]
Waiting for operation [1000784623292121088]...
.....done.
Created Vertex AI endpoint: projects/591020730428/locations/us-central1/endpoints/7596789719095050240.
Using endpoint [https://us-central1-aiplatform.googleapis.com/]
Using endpoint [https://us-central1-aiplatform.googleapis.com/]
WARNING: The following filter keys were not present in any resource : display_name
Using endpoint [https://us-central1-aiplatform.googleapis.com/]
Waiting for operation [5612470641719508992]...
....................done.
Using endpoint [https://us-central1-aiplatform.googleapis.com/]
Using endpoint [https://us-central1-aiplatform.googleapis.com/]
ERROR: (gcloud.ai.endpoints.deploy-model) INVALID_ARGUMENT: Invalid image "us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-9:latest" for deployment. Please use a Model with a valid image.
---------------------------------------------------------------------------
CalledProcessError Traceback (most recent call last)
/tmp/ipykernel_1/1002185504.py in <module>
----> 1 get_ipython().run_cell_magic('bash', '', '\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\nif [[ $(gcloud ai endpoints list --region=$REGION \\\n --format=\'value(DISPLAY_NAME)\' --filter=display_name=${ENDPOINT_NAME}) ]]; then\n echo "Endpoint for $MODEL_NAME already exists"\nelse\n # create model\n echo "Creating Endpoint for $MODEL_NAME"\n gcloud ai endpoints create --region=${REGION} --display-name=${ENDPOINT_NAME}\nfi\n\nENDPOINT_ID=$(gcloud ai endpoints list --region=$REGION \\\n --format=\'value(ENDPOINT_ID)\' --filter=display_name=${ENDPOINT_NAME})\necho "ENDPOINT_ID=$ENDPOINT_ID"\n\n# delete any existing models with this name\nfor MODEL_ID in $(gcloud ai models list --region=$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 $MODEL_ID\ndone\n\n# upload model\ngcloud beta ai models upload --region=$REGION --display-name=$MODEL_NAME \\\n --container-image-uri=us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.${TF_VERSION}:latest <http://us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.$%7BTF_VERSION%7D:latest> \\\n --artifact-uri=$EXPORT_PATH\nMODEL_ID=$(gcloud ai models list --region=$REGION --format=\'value(MODEL_ID)\' --filter=display_name=${MODEL_NAME})\necho "MODEL_ID=$MODEL_ID"\n\n# deploy model to endpoint\ngcloud 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\n')
/opt/conda/lib/python3.7/site-packages/IPython/core/interactiveshell.py in run_cell_magic(self, magic_name, line, cell)
2470 with self.builtin_trap:
2471 args = (magic_arg_s, cell)
-> 2472 result = fn(*args, **kwargs)
2473 return result
2474
/opt/conda/lib/python3.7/site-packages/IPython/core/magics/script.py in named_script_magic(line, cell)
140 else:
141 line = script
--> 142 return self.shebang(line, cell)
143
144 # write a basic docstring:
/opt/conda/lib/python3.7/site-packages/decorator.py in fun(*args, **kw)
230 if not kwsyntax:
231 args, kw = fix(args, kw, sig)
--> 232 return caller(func, *(extras + args), **kw)
233 fun.__name__ = func.__name__
234 fun.__doc__ = func.__doc__
/opt/conda/lib/python3.7/site-packages/IPython/core/magic.py in <lambda>(f, *a, **k)
185 # but it's overkill for just that one bit of state.
186 def magic_deco(arg):
--> 187 call = lambda f, *a, **k: f(*a, **k)
188
189 if callable(arg):
/opt/conda/lib/python3.7/site-packages/IPython/core/magics/script.py in shebang(self, line, cell)
243 sys.stderr.flush()
244 if args.raise_error and p.returncode!=0:
--> 245 raise CalledProcessError(p.returncode, cell, output=out, stderr=err)
246
247 def _run_script(self, p, cell, to_close):
CalledProcessError: Command 'b'\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\nif [[ $(gcloud ai endpoints list --region=$REGION \\\n --format=\'value(DISPLAY_NAME)\' --filter=display_name=${ENDPOINT_NAME}) ]]; then\n echo "Endpoint for $MODEL_NAME already exists"\nelse\n # create model\n echo "Creating Endpoint for $MODEL_NAME"\n gcloud ai endpoints create --region=${REGION} --display-name=${ENDPOINT_NAME}\nfi\n\nENDPOINT_ID=$(gcloud ai endpoints list --region=$REGION \\\n --format=\'value(ENDPOINT_ID)\' --filter=display_name=${ENDPOINT_NAME})\necho "ENDPOINT_ID=$ENDPOINT_ID"\n\n# delete any existing models with this name\nfor MODEL_ID in $(gcloud ai models list --region=$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 $MODEL_ID\ndone\n\n# upload model\ngcloud beta ai models upload --region=$REGION --display-name=$MODEL_NAME \\\n --container-image-uri=us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.${TF_VERSION}:latest <http://us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.$%7BTF_VERSION%7D:latest> \\\n --artifact-uri=$EXPORT_PATH\nMODEL_ID=$(gcloud ai models list --region=$REGION --format=\'value(MODEL_ID)\' --filter=display_name=${MODEL_NAME})\necho "MODEL_ID=$MODEL_ID"\n\n# deploy model to endpoint\ngcloud 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\n'' returned non-zero exit status 1.
The problem seems to be the docker image, but not sure where the image is
coming from.
Thanks,
Chris
—
Reply to this email directly, view it on GitHub
<#154 (comment)>,
or unsubscribe
<https://github.com/notifications/unsubscribe-auth/AANJPZ7RYC7WNRSMF7CYWETVXMT5LANCNFSM55QPAVDA>
.
You are receiving this because you were mentioned.Message ID:
***@***.***>
from data-science-on-gcp.
shinchri
commented on May 25, 2024
@lakshmanok Do you mean the model?
The name of the Model is "flights-20220804-023315" and the endpoint is named "flights".
I tried to deploy by going to Model Registry within Vertex AI and tried to manually deploy the model, but it still returns same error (actually this one uses different image but still doesn't work):
Failed to deploy model "flights-20220804-023315" to endpoint "flights".
Invalid image "us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-9:latest" for deployment. Please use a Model with a valid image.
Response code: 400
Status: INVALID_ARGUMENT
Tracking number: c5109049753637665
from data-science-on-gcp.
lakshmanok
commented on May 25, 2024
Chris,
Please see this page for tensorflow versions that are supported by vertex
AI prediction:
https://cloud.google.com/vertex-ai/docs/predictions/pre-built-containers
Notice that 2.9 is not (yet?) supported. This is usually a matter of time.
Perhaps a couple of weeks.
For now, the way to resolve this problem is to train *and* deploy using 2.8
or earlier. You can do this if you do this chapter in a Vertex AI notebook
that is built for tensorflow 2.8.
thanks,
Lak
…On Wed, Aug 3, 2022, 7:43 PM Chris Shin ***@***.***> wrote:
@lakshmanok <https://github.com/lakshmanok> Do you mean the model?
The name of the Model is "flights-20220804-023315" and the endpoint is
named "flights".
I tried to deploy by going to Model Registry within Vertex AI and tried to
manually deploy the model, but it still returns same error:
Failed to deploy model "flights-20220804-023315" to endpoint "flights".
Invalid image "us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-9:latest" for deployment. Please use a Model with a valid image.
Response code: 400
Status: INVALID_ARGUMENT
Tracking number: c5109049753637665
—
Reply to this email directly, view it on GitHub
<#154 (comment)>,
or unsubscribe
<https://github.com/notifications/unsubscribe-auth/AANJPZYFX2XKDO5T42HXE6TVXMU4ZANCNFSM55QPAVDA>
.
You are receiving this because you were mentioned.Message ID:
***@***.***>
from data-science-on-gcp.
shinchri
commented on May 25, 2024
@lakshmanok I tried using TensorFlow 2.8 but it still seem to cause issue.
This is what I tried:
Instead of using TensorFlow 2 (Local) kernel, I changed it to Python (Local) since TensorFlow 2 only seem to have version 2.9.
On very top of the notebook i install TensorFlow 2.8 using !pip install "tensorflow>=2.8,<2.9" which downloads 2.8.0.
Until I get to this part:
%%bash
TIMESTAMP=$(date +%Y%m%d-%H%M%S)
MODEL_NAME=${ENDPOINT_NAME}-${TIMESTAMP}
EXPORT_PATH=$(gsutil ls ${OUTDIR}/export | tail -1)
echo $EXPORT_PATH
if [[ $(gcloud ai endpoints list --region=$REGION \
--format='value(DISPLAY_NAME)' --filter=display_name=${ENDPOINT_NAME}) ]]; then
echo "Endpoint for $MODEL_NAME already exists"
else
# create model
echo "Creating Endpoint for $MODEL_NAME"
gcloud ai endpoints create --region=${REGION} --display-name=${ENDPOINT_NAME}
fi
ENDPOINT_ID=$(gcloud ai endpoints list --region=$REGION \
--format='value(ENDPOINT_ID)' --filter=display_name=${ENDPOINT_NAME})
echo "ENDPOINT_ID=$ENDPOINT_ID"
# delete any existing models with this name
for MODEL_ID in $(gcloud ai models list --region=$REGION --format='value(MODEL_ID)' --filter=display_name=${MODEL_NAME}); do
echo "Deleting existing $MODEL_NAME ... $MODEL_ID "
gcloud ai models delete --region=$REGION $MODEL_ID
done
# upload model
gcloud beta ai models upload --region=$REGION --display-name=$MODEL_NAME \
--container-image-uri=us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.${TF_VERSION}:latest \
--artifact-uri=$EXPORT_PATH
MODEL_ID=$(gcloud ai models list --region=$REGION --format='value(MODEL_ID)' --filter=display_name=${MODEL_NAME})
echo "MODEL_ID=$MODEL_ID"
# deploy model to endpoint
gcloud ai endpoints deploy-model $ENDPOINT_ID \
--region=$REGION \
--model=$MODEL_ID \
--display-name=$MODEL_NAME \
--machine-type=n1-standard-2 \
--min-replica-count=1 \
--max-replica-count=1 \
--traffic-split=0=100
The output is:
gs://tribbute-ml-central/ch9/trained_model/export/flights_20220804-141057/
Creating Endpoint for flights-20220804-141148
ENDPOINT_ID=5065203778559410176
MODEL_ID=
Followed by:
Using endpoint [https://us-central1-aiplatform.googleapis.com/]
WARNING: The following filter keys were not present in any resource : display_name
Using endpoint [https://us-central1-aiplatform.googleapis.com/]
Waiting for operation [6652731786897915904]...
.....done.
Created Vertex AI endpoint: projects/591020730428/locations/us-central1/endpoints/5065203778559410176.
Using endpoint [https://us-central1-aiplatform.googleapis.com/]
Using endpoint [https://us-central1-aiplatform.googleapis.com/]
WARNING: The following filter keys were not present in any resource : display_name
Using endpoint [https://us-central1-aiplatform.googleapis.com/]
Waiting for operation [8710876816606232576]...
.....................done.
Using endpoint [https://us-central1-aiplatform.googleapis.com/]
ERROR: gcloud crashed (InvalidDataFromServerError): Error decoding response "{
"models": [
{
"name": "projects/591020730428/locations/us-central1/models/4091005690024296448",
"displayName": "flights-20220804-141148",
"predictSchemata": {},
"containerSpec": {
"imageUri": "us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-8:latest"
},
"supportedDeploymentResourcesTypes": [
"DEDICATED_RESOURCES",
"SHARED_RESOURCES"
],
"supportedInputStorageFormats": [
"jsonl",
"bigquery",
"csv",
"tf-record",
"tf-record-gzip",
"file-list"
],
"supportedOutputStorageFormats": [
"jsonl",
"bigquery"
],
"createTime": "2022-08-04T14:11:58.599736Z",
"updateTime": "2022-08-04T14:12:01.166117Z",
"etag": "AMEw9yNjmCwSyM4YLnftSHzzrI6Q3FWiIjTuQLd54oKZ-wGaHroIIX5Z5jB8l-Pauxk=",
"supportedExportFormats": [
{
"id": "custom-trained",
"exportableContents": [
"ARTIFACT"
]
}
],
"artifactUri": "gs://tribbute-ml-central/ch9/trained_model/export/flights_20220804-141057/",
"versionId": "1",
"versionAliases": [
"default"
],
"versionCreateTime": "2022-08-04T14:11:58.599736Z",
"versionUpdateTime": "2022-08-04T14:12:01.166117Z"
}
]
}
" as type GoogleCloudAiplatformV1ListModelsResponse: Repeated values for field supportedDeploymentResourcesTypes may not be None
If you would like to report this issue, please run the following command:
gcloud feedback
To check gcloud for common problems, please run the following command:
gcloud info --run-diagnostics
Using endpoint [https://us-central1-aiplatform.googleapis.com/]
ERROR: (gcloud.ai.endpoints.deploy-model) could not parse resource []
---------------------------------------------------------------------------
CalledProcessError Traceback (most recent call last)
/tmp/ipykernel_1/1002185504.py in <module>
----> 1 get_ipython().run_cell_magic('bash', '', '\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\nif [[ $(gcloud ai endpoints list --region=$REGION \\\n --format=\'value(DISPLAY_NAME)\' --filter=display_name=${ENDPOINT_NAME}) ]]; then\n echo "Endpoint for $MODEL_NAME already exists"\nelse\n # create model\n echo "Creating Endpoint for $MODEL_NAME"\n gcloud ai endpoints create --region=${REGION} --display-name=${ENDPOINT_NAME}\nfi\n\nENDPOINT_ID=$(gcloud ai endpoints list --region=$REGION \\\n --format=\'value(ENDPOINT_ID)\' --filter=display_name=${ENDPOINT_NAME})\necho "ENDPOINT_ID=$ENDPOINT_ID"\n\n# delete any existing models with this name\nfor MODEL_ID in $(gcloud ai models list --region=$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 $MODEL_ID\ndone\n\n# upload model\ngcloud beta ai models upload --region=$REGION --display-name=$MODEL_NAME \\\n --container-image-uri=us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.${TF_VERSION}:latest \\\n --artifact-uri=$EXPORT_PATH\nMODEL_ID=$(gcloud ai models list --region=$REGION --format=\'value(MODEL_ID)\' --filter=display_name=${MODEL_NAME})\necho "MODEL_ID=$MODEL_ID"\n\n# deploy model to endpoint\ngcloud 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\n')
/opt/conda/lib/python3.7/site-packages/IPython/core/interactiveshell.py in run_cell_magic(self, magic_name, line, cell)
2470 with self.builtin_trap:
2471 args = (magic_arg_s, cell)
-> 2472 result = fn(*args, **kwargs)
2473 return result
2474
/opt/conda/lib/python3.7/site-packages/IPython/core/magics/script.py in named_script_magic(line, cell)
140 else:
141 line = script
--> 142 return self.shebang(line, cell)
143
144 # write a basic docstring:
/opt/conda/lib/python3.7/site-packages/decorator.py in fun(*args, **kw)
230 if not kwsyntax:
231 args, kw = fix(args, kw, sig)
--> 232 return caller(func, *(extras + args), **kw)
233 fun.__name__ = func.__name__
234 fun.__doc__ = func.__doc__
/opt/conda/lib/python3.7/site-packages/IPython/core/magic.py in <lambda>(f, *a, **k)
185 # but it's overkill for just that one bit of state.
186 def magic_deco(arg):
--> 187 call = lambda f, *a, **k: f(*a, **k)
188
189 if callable(arg):
/opt/conda/lib/python3.7/site-packages/IPython/core/magics/script.py in shebang(self, line, cell)
243 sys.stderr.flush()
244 if args.raise_error and p.returncode!=0:
--> 245 raise CalledProcessError(p.returncode, cell, output=out, stderr=err)
246
247 def _run_script(self, p, cell, to_close):
CalledProcessError: Command 'b'\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\nif [[ $(gcloud ai endpoints list --region=$REGION \\\n --format=\'value(DISPLAY_NAME)\' --filter=display_name=${ENDPOINT_NAME}) ]]; then\n echo "Endpoint for $MODEL_NAME already exists"\nelse\n # create model\n echo "Creating Endpoint for $MODEL_NAME"\n gcloud ai endpoints create --region=${REGION} --display-name=${ENDPOINT_NAME}\nfi\n\nENDPOINT_ID=$(gcloud ai endpoints list --region=$REGION \\\n --format=\'value(ENDPOINT_ID)\' --filter=display_name=${ENDPOINT_NAME})\necho "ENDPOINT_ID=$ENDPOINT_ID"\n\n# delete any existing models with this name\nfor MODEL_ID in $(gcloud ai models list --region=$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 $MODEL_ID\ndone\n\n# upload model\ngcloud beta ai models upload --region=$REGION --display-name=$MODEL_NAME \\\n --container-image-uri=us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.${TF_VERSION}:latest \\\n --artifact-uri=$EXPORT_PATH\nMODEL_ID=$(gcloud ai models list --region=$REGION --format=\'value(MODEL_ID)\' --filter=display_name=${MODEL_NAME})\necho "MODEL_ID=$MODEL_ID"\n\n# deploy model to endpoint\ngcloud 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\n'' returned non-zero exit status 1.
For some reason, the Model ID is not retrieved. I went to check if the model was created and flights-20220804-141148 had been created (which is the correct model created)
Thanks,
Chris
from data-science-on-gcp.
shinchri
commented on May 25, 2024
While I was looking for a solution, I found that other people seem to be having the same issue with gcloud ai models list:
https://issuetracker.google.com/issues/237019264
It seems like one of them solved issue by updating gcloud to most recent version so I tried this at the top of the notebook:
%%bash
pip install --upgrade gcloud
However, that still didn't seem to work for me.
So far the only workaround seems to be manually deploying model to the endpoint. You can do that by creating first endpoint and uploading model. Go the Vertex AI Model Registry. Click the round hamburger dropdown menu and select "Deploy to Endpoint". You can see the progress by going to Endpoints and selecting the endpoint.
from data-science-on-gcp.
lakshmanok
commented on May 25, 2024
On Wed, Aug 3, 2022, 7:37 PM Lakshmanan Valliappa ***@***.***> wrote:
These are the supported docker images in 2.9
Is the name that the script generates correct?
thanks,
Lak
On Wed, Aug 3, 2022, 7:35 PM Chris Shin ***@***.***> wrote:
> @lakshmanok <https://github.com/lakshmanok> I found out why I kept
> getting different versions. For some reason the code to delete any existing
> models doesn't work. So I had to manually delete the models that were
> registered (and there were many).
>
> Once I fixed that I stopped getting multiple models but I still had a
> problem. Here is the error code that I have now (I tried both 2-6 and 2-9
> but they give the same error message but with different version):
>
> Using endpoint [https://us-central1-aiplatform.googleapis.com/]
> WARNING: The following filter keys were not present in any resource : display_name
> Using endpoint [https://us-central1-aiplatform.googleapis.com/]
> Waiting for operation [1000784623292121088]...
> .....done.
> Created Vertex AI endpoint: projects/591020730428/locations/us-central1/endpoints/7596789719095050240.
> Using endpoint [https://us-central1-aiplatform.googleapis.com/]
> Using endpoint [https://us-central1-aiplatform.googleapis.com/]
> WARNING: The following filter keys were not present in any resource : display_name
> Using endpoint [https://us-central1-aiplatform.googleapis.com/]
> Waiting for operation [5612470641719508992]...
> ....................done.
> Using endpoint [https://us-central1-aiplatform.googleapis.com/]
> Using endpoint [https://us-central1-aiplatform.googleapis.com/]
> ERROR: (gcloud.ai.endpoints.deploy-model) INVALID_ARGUMENT: Invalid image "us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-9:latest" for deployment. Please use a Model with a valid image.
> ---------------------------------------------------------------------------
> CalledProcessError Traceback (most recent call last)
> /tmp/ipykernel_1/1002185504.py in <module>
> ----> 1 get_ipython().run_cell_magic('bash', '', '\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\nif [[ $(gcloud ai endpoints list --region=$REGION \\\n --format=\'value(DISPLAY_NAME)\' --filter=display_name=${ENDPOINT_NAME}) ]]; then\n echo "Endpoint for $MODEL_NAME already exists"\nelse\n # create model\n echo "Creating Endpoint for $MODEL_NAME"\n gcloud ai endpoints create --region=${REGION} --display-name=${ENDPOINT_NAME}\nfi\n\nENDPOINT_ID=$(gcloud ai endpoints list --region=$REGION \\\n --format=\'value(ENDPOINT_ID)\' --filter=display_name=${ENDPOINT_NAME})\necho "ENDPOINT_ID=$ENDPOINT_ID"\n\n# delete any existing models with this name\nfor MODEL_ID in $(gcloud ai models list --region=$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 $MODEL_ID\ndone\n\n# upload model\ngcloud beta ai models upload --region=$REGION --display-name=$MODEL_NAME \\\n --container-image-uri=us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.${TF_VERSION}:latest <http://us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.$%7BTF_VERSION%7D:latest> \\\n --artifact-uri=$EXPORT_PATH\nMODEL_ID=$(gcloud ai models list --region=$REGION --format=\'value(MODEL_ID)\' --filter=display_name=${MODEL_NAME})\necho "MODEL_ID=$MODEL_ID"\n\n# deploy model to endpoint\ngcloud 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\n')
>
> /opt/conda/lib/python3.7/site-packages/IPython/core/interactiveshell.py in run_cell_magic(self, magic_name, line, cell)
> 2470 with self.builtin_trap:
> 2471 args = (magic_arg_s, cell)
> -> 2472 result = fn(*args, **kwargs)
> 2473 return result
> 2474
>
> /opt/conda/lib/python3.7/site-packages/IPython/core/magics/script.py in named_script_magic(line, cell)
> 140 else:
> 141 line = script
> --> 142 return self.shebang(line, cell)
> 143
> 144 # write a basic docstring:
>
> /opt/conda/lib/python3.7/site-packages/decorator.py in fun(*args, **kw)
> 230 if not kwsyntax:
> 231 args, kw = fix(args, kw, sig)
> --> 232 return caller(func, *(extras + args), **kw)
> 233 fun.__name__ = func.__name__
> 234 fun.__doc__ = func.__doc__
>
> /opt/conda/lib/python3.7/site-packages/IPython/core/magic.py in <lambda>(f, *a, **k)
> 185 # but it's overkill for just that one bit of state.
> 186 def magic_deco(arg):
> --> 187 call = lambda f, *a, **k: f(*a, **k)
> 188
> 189 if callable(arg):
>
> /opt/conda/lib/python3.7/site-packages/IPython/core/magics/script.py in shebang(self, line, cell)
> 243 sys.stderr.flush()
> 244 if args.raise_error and p.returncode!=0:
> --> 245 raise CalledProcessError(p.returncode, cell, output=out, stderr=err)
> 246
> 247 def _run_script(self, p, cell, to_close):
>
> CalledProcessError: Command 'b'\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\nif [[ $(gcloud ai endpoints list --region=$REGION \\\n --format=\'value(DISPLAY_NAME)\' --filter=display_name=${ENDPOINT_NAME}) ]]; then\n echo "Endpoint for $MODEL_NAME already exists"\nelse\n # create model\n echo "Creating Endpoint for $MODEL_NAME"\n gcloud ai endpoints create --region=${REGION} --display-name=${ENDPOINT_NAME}\nfi\n\nENDPOINT_ID=$(gcloud ai endpoints list --region=$REGION \\\n --format=\'value(ENDPOINT_ID)\' --filter=display_name=${ENDPOINT_NAME})\necho "ENDPOINT_ID=$ENDPOINT_ID"\n\n# delete any existing models with this name\nfor MODEL_ID in $(gcloud ai models list --region=$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 $MODEL_ID\ndone\n\n# upload model\ngcloud beta ai models upload --region=$REGION --display-name=$MODEL_NAME \\\n --container-image-uri=us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.${TF_VERSION}:latest <http://us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.$%7BTF_VERSION%7D:latest> \\\n --artifact-uri=$EXPORT_PATH\nMODEL_ID=$(gcloud ai models list --region=$REGION --format=\'value(MODEL_ID)\' --filter=display_name=${MODEL_NAME})\necho "MODEL_ID=$MODEL_ID"\n\n# deploy model to endpoint\ngcloud 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\n'' returned non-zero exit status 1.
>
> The problem seems to be the docker image, but not sure where the image is
> coming from.
>
> Thanks,
> Chris
>
> —
> Reply to this email directly, view it on GitHub
> <#154 (comment)>,
> or unsubscribe
> <https://github.com/notifications/unsubscribe-auth/AANJPZ7RYC7WNRSMF7CYWETVXMT5LANCNFSM55QPAVDA>
> .
> You are receiving this because you were mentioned.Message ID:
> ***@***.***
> com>
>
from data-science-on-gcp.
jgammerman
commented on May 25, 2024
I'm getting the same error as @shinchri . Tried using his workaround of manually deploying the model to a new endpoint using the GUI but unfortunately it still doesn't work.
from data-science-on-gcp.
lakshmanok
commented on May 25, 2024
James,
Could you check the TensorFlow version you are using, and the list of
supported versions at
https://cloud.google.com/vertex-ai/docs/predictions/pre-built-containers
thanks
Lak
…On Mon, Feb 13, 2023 at 4:20 AM James ***@***.***> wrote:
I'm getting the same error as @shinchri <https://github.com/shinchri> .
Tried using his workaround of manually deploying the model to a new
endpoint using the GUI but unfortunately it still doesn't work.
—
Reply to this email directly, view it on GitHub
<#154 (comment)>,
or unsubscribe
<https://github.com/notifications/unsubscribe-auth/AANJPZ6EWTAJTGHRZBO6QG3WXIRJZANCNFSM55QPAVDA>
.
You are receiving this because you were mentioned.Message ID:
***@***.***>
from data-science-on-gcp.
jgammerman
commented on May 25, 2024
Thanks! I downgraded my TF version from 2.11 to 2.9 and that fixed the error :)
Interestingly the returned predictions don't sum to 1:
{
"predictions": [
[
0.582659423
],
[
0.973581493
]
],
"deployedModelId": "2397282794226057216",
"model": "projects/506913857436/locations/us-central1/models/372200079164964864",
"modelDisplayName": "flights-v2-20230213-141845",
"modelVersionId": "1"
}
whereas yours were 0.22877 and 0.76613 respectively, which sum to ~1. Any idea why that might be @lakshmanok?
For anyone who needs to fix the deployment error - I opened a terminal, ran pip uninstall tensorflow to uninstall TF (also run pip show tensorflow to check it worked) then ran pip install tensorflow==2.9.0 --user.
Restart the kernel, change your endpoint name (I changed mine from flights to flights-v2) and the notebook should work now.
from data-science-on-gcp.
lakshmanok
commented on May 25, 2024
Lol. That's extremely unlikely coincidence. Note that the predictions are
for two different inputs, so there is no reason whatsoever that they should
sum to 1.
{"instances": [
{"dep_hour": 2, "is_weekday": 1, "dep_delay": 40, "taxi_out": 17,
"distance": 41, "carrier": "AS", "dep_airport_lat": 58.42527778,
"dep_airport_lon": -135.7075, "arr_airport_lat": 58.35472222,
"arr_airport_lon": -134.57472222, "origin": "GST", "dest": "JNU"},
{"dep_hour": 22, "is_weekday": 0, "dep_delay": -7, "taxi_out": 7,
"distance": 201, "carrier": "HA", "dep_airport_lat": 21.97611111,
"dep_airport_lon": -159.33888889, "arr_airport_lat": 20.89861111,
"arr_airport_lon": -156.43055556, "origin": "LIH", "dest": "OGG"}]}
…On Mon, Feb 13, 2023 at 6:44 AM James ***@***.***> wrote:
Thanks! I downgraded my TF version from 2.11 to 2.9 and that fixed the
error :)
Interestingly the returned predictions don't sum to 1:
{
"predictions": [
[
0.582659423
],
[
0.973581493
]
],
"deployedModelId": "2397282794226057216",
"model": "projects/506913857436/locations/us-central1/models/372200079164964864",
"modelDisplayName": "flights-v2-20230213-141845",
"modelVersionId": "1"
}
whereas yours were 0.22877 and 0.76613 respectively, which sum to ~1. Any
idea why that might be @lakshmanok <https://github.com/lakshmanok>?
For anyone who needs to fix the deployment error - I opened a terminal,
ran pip uninstall tensorflow to uninstall TF (also run pip show tensorflow
to check it worked) then ran pip install tensorflow==2.9.0 --user.
Restart the kernel, change your endpoint name (I changed mine from flights
to flights-v2) and the notebook should work now.
—
Reply to this email directly, view it on GitHub
<#154 (comment)>,
or unsubscribe
<https://github.com/notifications/unsubscribe-auth/AANJPZ6VYQXAVLWEWTGAEIDWXJCF5ANCNFSM55QPAVDA>
.
You are receiving this because you were mentioned.Message ID:
***@***.***>
from data-science-on-gcp.
jgammerman
commented on May 25, 2024
Ah yes, good point!
from data-science-on-gcp.
Related Issues (20)
- download.sh does not download the data HOT 1
- There is no Table Explorer in BQ HOT 1
- chapter 04: df07.py - Unable to open file: gs://BUCKETNAME/flights/staging/ch04timecorr.1656567385.996847/pipeline.pb. HOT 3
- hello
- problem extracting bigquery table data to cloud storage HOT 2
- Chapter 6 create_cluster.sh script problem
- Chapter 4: tz_correct KeyError: "CRS_ARR_TIME [while running 'flights:tzcorr-ptransform-157']" HOT 2
- I cannot make the table definition for the federated source in chapter 4 HOT 2
- AttributeError: tzinfo HOT 2
- README improvement for chapters 2 and 3 regarding upload to BQ HOT 4
- Ch.4 - Time Correction - input/output error when running df02.py HOT 3
- Ch7 - can't increase quota of Dataproc cluster; workaround not working HOT 1
- Ch.9 - can't export data to GCS - wrong project number? HOT 4
- Ch9 - training deep neural network - how to attach GPU? HOT 7
- Ch10 - can't run model.py - error with hypertune HOT 11
- Ch11 - error when running AutoML on full dataset HOT 1
- CH10 code to set TensorFlow level is broken now that there are 2 digits second level of current TensorFlow HOT 2
- Chapter 2 - ingest_flights.py - Access denied to gs file on bqload function HOT 4
- Ch9 - Call predictions using Flask webserver HOT 1
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from data-science-on-gcp.