- NAME
-
- gcloud ml-engine jobs submit training - submit an AI Platform training job
- SYNOPSIS
-
-
gcloud ml-engine jobs submit training
JOB
[--config
=CONFIG
] [--enable-web-access
] [--job-dir
=JOB_DIR
] [--labels
=[KEY
=VALUE
,…]] [--master-accelerator
=[count
=COUNT
],[type
=TYPE
]] [--master-image-uri
=MASTER_IMAGE_URI
] [--master-machine-type
=MASTER_MACHINE_TYPE
] [--module-name
=MODULE_NAME
] [--package-path
=PACKAGE_PATH
] [--packages
=[PACKAGE
,…]] [--parameter-server-accelerator
=[count
=COUNT
],[type
=TYPE
]] [--parameter-server-image-uri
=PARAMETER_SERVER_IMAGE_URI
] [--python-version
=PYTHON_VERSION
] [--region
=REGION
] [--runtime-version
=RUNTIME_VERSION
] [--scale-tier
=SCALE_TIER
] [--service-account
=SERVICE_ACCOUNT
] [--staging-bucket
=STAGING_BUCKET
] [--use-chief-in-tf-config
=USE_CHIEF_IN_TF_CONFIG
] [--worker-accelerator
=[count
=COUNT
],[type
=TYPE
]] [--worker-image-uri
=WORKER_IMAGE_URI
] [--async
|--stream-logs
] [--kms-key
=KMS_KEY
:--kms-keyring
=KMS_KEYRING
--kms-location
=KMS_LOCATION
--kms-project
=KMS_PROJECT
] [--parameter-server-count
=PARAMETER_SERVER_COUNT
--parameter-server-machine-type
=PARAMETER_SERVER_MACHINE_TYPE
] [--worker-count
=WORKER_COUNT
--worker-machine-type
=WORKER_MACHINE_TYPE
] [GCLOUD_WIDE_FLAG …
] [--USER_ARGS
…]
-
- DESCRIPTION
-
Submit an AI Platform training job.
This creates temporary files and executes Python code staged by a user on Cloud Storage. Model code can either be specified with a path, e.g.:
gcloud ml-engine jobs submit training my_job --module-name trainer.task --staging-bucket gs://my-bucket --package-path /my/code/path/trainer --packages additional-dep1.tar.gz,dep2.whl
Or by specifying an already built package:
gcloud ml-engine jobs submit training my_job --module-name trainer.task --staging-bucket gs://my-bucket --packages trainer-0.0.1.tar.gz,additional-dep1.tar.gz,dep2.whl
If
--package-path=/my/code/path/trainer
is specified and there is asetup.py
file at/my/code/path/setup.py
, the setup file will be invoked withsdist
and the generated tar files will be uploaded to Cloud Storage. Otherwise, a temporarysetup.py
file will be generated for the build.By default, this command runs asynchronously; it exits once the job is successfully submitted.
To follow the progress of your job, pass the
--stream-logs
flag (note that even with the--stream-logs
flag, the job will continue to run after this command exits and must be cancelled withgcloud ai-platform jobs cancel JOB_ID
).For more information, see: https://cloud.google.com/ai-platform/training/docs/overview
- POSITIONAL ARGUMENTS
-
JOB
- Name of the job.
- [--
USER_ARGS
…] -
Additional user arguments to be forwarded to user code
The '--' argument must be specified between gcloud specific args on the left and USER_ARGS on the right.
- FLAGS
-
--config
=CONFIG
-
Path to the job configuration file. This file should be a YAML document (JSON
also accepted) containing a Job resource as defined in the API (all fields are
optional): https://cloud.google.com/ml/reference/rest/v1/projects.jobs
EXAMPLES:
JSON:
{ "jobId": "my_job", "labels": { "type": "prod", "owner": "alice" }, "trainingInput": { "scaleTier": "BASIC", "packageUris": [ "gs://my/package/path" ], "region": "us-east1" } }
YAML:
If an option is specified both in the configuration file **and** via command line arguments, the command line arguments override the configuration file.jobId: my_job labels: type: prod owner: alice trainingInput: scaleTier: BASIC packageUris: - gs://my/package/path region: us-east1
--enable-web-access
-
Whether you want AI Platform Training to enable [interactive shell access]
(https://cloud.google.com/ai-platform/training/docs/monitor-debug-interactive-shell)
to training containers. If set to
true
, you can access interactive shells at the URIs given by TrainingOutput.web_access_uris or HyperparameterOutput.web_access_uris (within TrainingOutput.trials). --job-dir
=JOB_DIR
-
Cloud Storage path in which to store training outputs and other data needed for
training.
This path will be passed to your TensorFlow program as the
--job-dir
command-line arg. The benefit of specifying this field is that AI Platform will validate the path for use in training. However, note that your training program will need to parse the provided--job-dir
argument.If packages must be uploaded and
--staging-bucket
is not provided, this path will be used instead. --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. --master-accelerator
=[count
=COUNT
],[type
=TYPE
]-
Hardware accelerator config for the master worker. Must specify both the
accelerator type (TYPE) for each server and the number of accelerators to attach
to each server (COUNT).
type
- Type of the accelerator. Choices are nvidia-tesla-a100,nvidia-tesla-k80,nvidia-tesla-p100,nvidia-tesla-p4,nvidia-tesla-t4,nvidia-tesla-v100,tpu-v2,tpu-v2-pod,tpu-v3,tpu-v3-pod,tpu-v4-pod
count
- Number of accelerators to attach to each machine running the job. Must be greater than 0.
--master-image-uri
=MASTER_IMAGE_URI
-
Docker image to run on each master worker. This image must be in Container
Registry. Only one of
--master-image-uri
and--runtime-version
must be specified. --master-machine-type
=MASTER_MACHINE_TYPE
-
Specifies the type of virtual machine to use for training job's master worker.
You must set this value when
--scale-tier
is set toCUSTOM
. --module-name
=MODULE_NAME
- Name of the module to run.
--package-path
=PACKAGE_PATH
-
Path to a Python package to build. This should point to a
local
directory containing the Python source for the job. It will be built usingsetuptools
(which must be installed) using itsparent
directory as context. If the parent directory contains asetup.py
file, the build will use that; otherwise, it will use a simple built-in one. --packages
=[PACKAGE
,…]-
Path to Python archives used for training. These can be local paths (absolute or
relative), in which case they will be uploaded to the Cloud Storage bucket given
by
--staging-bucket
, or Cloud Storage URLs ('gs://bucket-name/path/to/package.tar.gz'). --parameter-server-accelerator
=[count
=COUNT
],[type
=TYPE
]-
Hardware accelerator config for the parameter servers. Must specify both the
accelerator type (TYPE) for each server and the number of accelerators to attach
to each server (COUNT).
type
- Type of the accelerator. Choices are nvidia-tesla-a100,nvidia-tesla-k80,nvidia-tesla-p100,nvidia-tesla-p4,nvidia-tesla-t4,nvidia-tesla-v100,tpu-v2,tpu-v2-pod,tpu-v3,tpu-v3-pod,tpu-v4-pod
count
- Number of accelerators to attach to each machine running the job. Must be greater than 0.
--parameter-server-image-uri
=PARAMETER_SERVER_IMAGE_URI
-
Docker image to run on each parameter server. This image must be in Container
Registry. If not specified, the value of
--master-image-uri
is used. --python-version
=PYTHON_VERSION
-
Version of Python used during training. Choices are 3.7, 3.5, and 2.7. However,
this value must be compatible with the chosen runtime version for the job.
Must be used with a compatible runtime version:
- 3.7 is compatible with runtime versions 1.15 and later.
- 3.5 is compatible with runtime versions 1.4 through 1.14.
- 2.7 is compatible with runtime versions 1.15 and earlier.
--region
=REGION
-
Region of the machine learning training job to submit. If not specified, you
might be prompted to select a region (interactive mode only).
To avoid prompting when this flag is omitted, you can set the
property:compute/region
gcloud config set compute/region REGION
A list of regions can be fetched by running:
gcloud compute regions list
To unset the property, run:
gcloud config unset compute/region
Alternatively, the region can be stored in the environment variable
.CLOUDSDK_COMPUTE_REGION
--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
--scale-tier
=SCALE_TIER
-
Specify the machine types, the number of replicas for workers, and parameter
servers.
SCALE_TIER
must be one of:basic
- Single worker instance. This tier is suitable for learning how to use AI Platform, and for experimenting with new models using small datasets.
basic-gpu
- Single worker instance with a GPU.
basic-tpu
- Single worker instance with a Cloud TPU.
custom
-
CUSTOM tier is not a set tier, but rather enables you to use your own cluster
specification. When you use this tier, set values to configure your processing
cluster according to these guidelines (using the
--config
flag):-
You
must
setTrainingInput.masterType
to specify the type of machine to use for your master node. This is the only required setting. -
You
may
setTrainingInput.workerCount
to specify the number of workers to use. If you specify one or more workers, youmust
also setTrainingInput.workerType
to specify the type of machine to use for your worker nodes. -
You
may
setTrainingInput.parameterServerCount
to specify the number of parameter servers to use. If you specify one or more parameter servers, youmust
also setTrainingInput.parameterServerType
to specify the type of machine to use for your parameter servers. Note that all of your workers must use the same machine type, which can be different from your parameter server type and master type. Your parameter servers must likewise use the same machine type, which can be different from your worker type and master type.
-
You
- Large number of workers with many parameter servers.
standard-1
- Many workers and a few parameter servers.
--service-account
=SERVICE_ACCOUNT
-
The email address of a service account to use when running the training
appplication. You must have the
iam.serviceAccounts.actAs
permission for the specified service account. In addition, the AI Platform Training Google-managed service account must have theroles/iam.serviceAccountAdmin
role for the specified service account. Learn more about configuring a service account. If not specified, the AI Platform Training Google-managed service account is used by default. --staging-bucket
=STAGING_BUCKET
-
Bucket in which to stage training archives.
Required only if a file upload is necessary (that is, other flags include local paths) and no other flags implicitly specify an upload path.
--use-chief-in-tf-config
=USE_CHIEF_IN_TF_CONFIG
- Use "chief" role in the cluster instead of "master". This is required for TensorFlow 2.0 and newer versions. Unlike "master" node, "chief" node does not run evaluation.
--worker-accelerator
=[count
=COUNT
],[type
=TYPE
]-
Hardware accelerator config for the worker nodes. Must specify both the
accelerator type (TYPE) for each server and the number of accelerators to attach
to each server (COUNT).
type
- Type of the accelerator. Choices are nvidia-tesla-a100,nvidia-tesla-k80,nvidia-tesla-p100,nvidia-tesla-p4,nvidia-tesla-t4,nvidia-tesla-v100,tpu-v2,tpu-v2-pod,tpu-v3,tpu-v3-pod,tpu-v4-pod
count
- Number of accelerators to attach to each machine running the job. Must be greater than 0.
--worker-image-uri
=WORKER_IMAGE_URI
-
Docker image to run on each worker node. This image must be in Container
Registry. If not specified, the value of
--master-image-uri
is used. -
At most one of these can be specified:
--async
-
(DEPRECATED) Display information about the operation in progress without waiting
for the operation to complete. Enabled by default and can be omitted; use
--stream-logs
to run synchronously. --stream-logs
-
Block until job completion and stream the logs while the job runs.
Note that even if command execution is halted, the job will still run until cancelled with
gcloud ai-platform jobs cancel JOB_ID
-
Key resource - The Cloud KMS (Key Management Service) cryptokey that will be
used to protect the job. The 'AI Platform Service Agent' service account must
hold permission 'Cloud KMS CryptoKey Encrypter/Decrypter'. The arguments in this
group can be used to specify the attributes of this resource.
--kms-key
=KMS_KEY
-
ID of the key or fully qualified identifier for the key.
To set the
kms-key
attribute:-
provide the argument
--kms-key
on the command line.
This flag argument must be specified if any of the other arguments in this group are specified.
-
provide the argument
--kms-keyring
=KMS_KEYRING
-
The KMS keyring of the key.
To set the
kms-keyring
attribute:-
provide the argument
--kms-key
on the command line with a fully specified name; -
provide the argument
--kms-keyring
on the command line.
-
provide the argument
--kms-location
=KMS_LOCATION
-
The Google Cloud location for the key.
To set the
kms-location
attribute:-
provide the argument
--kms-key
on the command line with a fully specified name; -
provide the argument
--kms-location
on the command line.
-
provide the argument
--kms-project
=KMS_PROJECT
-
The Google Cloud project for the key.
To set the
kms-project
attribute:-
provide the argument
--kms-key
on the command line with a fully specified name; -
provide the argument
--kms-project
on the command line; -
set the property
core/project
.
-
provide the argument
-
Configure parameter server machine type settings.
--parameter-server-count
=PARAMETER_SERVER_COUNT
-
Number of parameter servers to use for the training job.
This flag argument must be specified if any of the other arguments in this group are specified.
--parameter-server-machine-type
=PARAMETER_SERVER_MACHINE_TYPE
-
Type of virtual machine to use for training job's parameter servers. This flag
must be specified if any of the other arguments in this group are specified
machine to use for training job's parameter servers.
This flag argument must be specified if any of the other arguments in this group are specified.
-
Configure worker node machine type settings.
--worker-count
=WORKER_COUNT
-
Number of worker nodes to use for the training job.
This flag argument must be specified if any of the other arguments in this group are specified.
--worker-machine-type
=WORKER_MACHINE_TYPE
-
Type of virtual machine to use for training job's worker nodes.
This flag argument must be specified if any of the other arguments in this group are specified.
- GCLOUD WIDE FLAGS
-
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. - NOTES
-
These variants are also available:
gcloud alpha ml-engine jobs submit training
gcloud beta ml-engine jobs submit training
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Last updated 2024-02-06 UTC.