gcloud beta ml-engine jobs submit prediction - start an AI Platform batch prediction job
gcloud beta ml-engine jobs submit prediction JOB --data-format=DATA_FORMAT --input-paths=INPUT_PATH,[INPUT_PATH,...] --output-path=OUTPUT_PATH --region=REGION (--model=MODEL | --model-dir=MODEL_DIR) [--batch-size=BATCH_SIZE] [--labels=[KEY=VALUE,...]] [--max-worker-count=MAX_WORKER_COUNT] [--runtime-version=RUNTIME_VERSION] [--signature-name=SIGNATURE_NAME] [--version=VERSION] [GCLOUD_WIDE_FLAG ...]
(BETA) Start an AI Platform batch prediction job.
- JOB
Name of the batch prediction job.
- --data-format=DATA_FORMAT
Data format of the input files. DATA_FORMAT must be one of:
- text
Text and JSON files; for text files, see https://www.tensorflow.org/guide/datasets#consuming_text_data, for JSON files, see https://cloud.google.com/ai-platform/prediction/docs/overview#batch_prediction_input_data
- tf-record
TFRecord files; see https://www.tensorflow.org/guide/datasets#consuming_tfrecord_data
- tf-record-gzip
GZIP-compressed TFRecord files.
- --input-paths=INPUT_PATH,[INPUT_PATH,...]
Cloud Storage paths to the instances to run prediction on.
Wildcards (*) accepted at the end of a path. More than one path can be specified if multiple file patterns are needed. For example,
gs://my-bucket/instances*,gs://my-bucket/other-instances1
will match any objects whose names start with instances in my-bucket as well as the other-instances1 bucket, while
gs://my-bucket/instance-dir/*
will match any objects in the instance-dir "directory" (since directories aren't a first-class Cloud Storage concept) of my-bucket.
- --output-path=OUTPUT_PATH
Cloud Storage path to which to save the output. Example: gs://my-bucket/output.
- --region=REGION
The Compute Engine region to run the job in.
- Exactly one of these must be specified:
- --model=MODEL
Name of the model to use for prediction.
- --model-dir=MODEL_DIR
Cloud Storage location where the model files are located.
- --batch-size=BATCH_SIZE
The number of records per batch. The service will buffer batch_size number of records in memory before invoking TensorFlow. Defaults to 64 if not specified.
- --labels=[KEY=VALUE,...]
List of label KEY=VALUE pairs to add.
Keys must start with a lowercase character and contain only hyphens (-), underscores (_), lowercase characters, and numbers. Values must contain only hyphens (-), underscores (_), lowercase characters, and numbers.
- --max-worker-count=MAX_WORKER_COUNT
The maximum number of workers to be used for parallel processing. Defaults to 10 if not specified.
- --runtime-version=RUNTIME_VERSION
AI Platform runtime version for this job. Must be specified unless --master-image-uri is specified instead. It is defined in documentation along with the list of supported versions: https://cloud.google.com/ai-platform/prediction/docs/runtime-version-list
- --signature-name=SIGNATURE_NAME
Name of the signature defined in the SavedModel to use for this job. Defaults to DEFAULT_SERVING_SIGNATURE_DEF_KEY in https://www.tensorflow.org/api_docs/python/tf/compat/v1/saved_model/signature_constants, which is "serving_default". Only applies to TensorFlow models.
- --version=VERSION
Model version to be used.
This flag may only be given if --model is specified. If unspecified, the default version of the model will be used. To list versions for a model, run
$ gcloud ai-platform versions list
These flags are available to all commands: --access-token-file, --account, --billing-project, --configuration, --flags-file, --flatten, --format, --help, --impersonate-service-account, --log-http, --project, --quiet, --trace-token, --user-output-enabled, --verbosity.
Run $ gcloud help for details.
This command is currently in beta and might change without notice. These variants are also available:
$ gcloud ml-engine jobs submit prediction
$ gcloud alpha ml-engine jobs submit prediction