gcloud_beta_ml-engine_jobs_submit_prediction (1)

NAME

gcloud beta ml-engine jobs submit prediction - start a Cloud ML Engine batch prediction job

SYNOPSIS

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 ...]

DESCRIPTION

(BETA) Start a Cloud ML Engine batch prediction job.

POSITIONAL ARGUMENTS

JOB

Name of the batch prediction job.

REQUIRED FLAGS

--data-format=DATA_FORMAT

Data format of the input files. DATA_FORMAT must be one of:

text

Text files; see www.tensorflow.org/guide/datasets#consuming_text_data

tf-record

TFRecord files; see www.tensorflow.org/guide/datasets#consuming_tfrecord_data

tf-record-gzip

GZIP-compressed TFRecord files.

--input-paths=INPUT_PATH,[INPUT_PATH,...]

Google 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

Google Cloud Storage path to which to save the output. Example: gs://my-bucket/output.

--region=REGION

The Google 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

Google Cloud Storage location where the model files are located.

OPTIONAL FLAGS

--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

Google Cloud ML Engine runtime version for this job. Defaults to a stable version, which is defined in documentation along with the list of supported versions: cloud.google.com/ml-engine/docs/tensorflow/runtime-version-list

--signature-name=SIGNATURE_NAME

The name of the signature defined in the SavedModel to use for this job. Defaults to DEFAULT_SERVING_SIGNATURE_DEF_KEY in www.tensorflow.org/api_docs/python/tf/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 ml-engine versions list

GCLOUD WIDE FLAGS

These flags are available to all commands: --account, --configuration, --flags-file, --flatten, --format, --help, --log-http, --project, --quiet, --trace-token, --user-output-enabled, --verbosity. Run $ gcloud help for details.

NOTES

This command is currently in BETA and may change without notice. These variants are also available:

$ gcloud ml-engine jobs submit prediction $ gcloud alpha ml-engine jobs submit prediction