gcloud alpha ai-platform jobs submit prediction

gcloud alpha ai-platform jobs submit prediction - start an AI Platform batch prediction job
gcloud alpha ai-platform 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] [--accelerator-count=ACCELERATOR_COUNT --accelerator-type=ACCELERATOR_TYPE] [GCLOUD_WIDE_FLAG]
(ALPHA) Start an AI Platform batch prediction job.
Name of the batch prediction job.
Data format of the input files. DATA_FORMAT must be one of:
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
TFRecord files; see https://www.tensorflow.org/guide/datasets#consuming_tfrecord_data
GZIP-compressed TFRecord files.
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,


will match any objects whose names start with instances in my-bucket as well as the other-instances1 bucket, while


will match any objects in the instance-dir "directory" (since directories aren't a first-class Cloud Storage concept) of my-bucket.

Google Cloud Storage path to which to save the output. Example: gs://my-bucket/output.
The Google Compute Engine region to run the job in.
Exactly one of these must be specified:
Name of the model to use for prediction.
Google Cloud Storage location where the model files are located.
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.
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.

The maximum number of workers to be used for parallel processing. Defaults to 10 if not specified.
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/ml-engine/docs/tensorflow/runtime-version-list
The 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/saved_model/signature_constants, which is "serving_default". Only applies to TensorFlow models.
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
Accelerator Configuration.
The number of accelerators to attach to the machines. Must be >= 1. This flag must be specified if any of the other arguments in this group are specified.
The available types of accelerators. ACCELERATOR_TYPE must be one of:
NVIDIA Tesla P100 GPU.

This flag must be specified if any of the other arguments in this group are specified.

These flags are available to all commands: --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 ALPHA and may change without notice. If this command fails with API permission errors despite specifying the right project, you may be trying to access an API with an invitation-only early access whitelist. These variants are also available:
gcloud ai-platform jobs submit prediction
gcloud beta ai-platform jobs submit prediction