C*SrSSKJr SSKJr SSKJr SSKrSSKrSSKJr SSKJ r SSK J r SS K J r SS K Jr SS K Jr SS KJr SS KJr SSKJr \ R."SSSS9r\ R."SS\R2"5SS9r\ R."S\R6"S5S9r\ R."SS\R:"\\\\\\\\\\\R2"SS9\R2"SS9\R2"SS9S. S9S \R6"S!5S"9r \ R."S#\R2"5S$\RBS%S&9r"\ R."S'S(\R2"5\RBS)S*9r#\ R."S+S,S-S.9r$S/r%\ RL4S0jr'S1r(g)2z8Flags definition specifically for gcloud ai custom-jobs.)absolute_import)division)unicode_literalsN) arg_parsers)base)concepts) constants)flags) region_util)custom_jobs_util) labels_util)concept_parsersz--display-nameTz)Display name of the custom job to create.)requiredhelpz--python-package-urisPYTHON_PACKAGE_URISzThe common Python package URIs to be used for training with a pre-built container image. e.g. `--python-package-uri=path1,path2` If you are using multiple worker pools and want to specify a different Python packag fo reach pool, use `--config` instead.metavartyperz--configa Path to the job configuration file. This file should be a YAML document containing a [`CustomJobSpec`](https://cloud.google.com/vertex-ai/docs/reference/rest/v1/CustomJobSpec). If an option is specified both in the configuration file **and** via command-line arguments, the command-line arguments override the configuration file. Note that keys with underscore are invalid. Example(YAML): workerPoolSpecs: machineSpec: machineType: n1-highmem-2 replicaCount: 1 containerSpec: imageUri: gcr.io/ucaip-test/ucaip-training-test args: - port=8500 command: - start)rz--worker-pool-specappend;)custom_delim_char) z replica-countz machine-typezaccelerator-typezaccelerator-countzcontainer-image-urizexecutor-image-urizoutput-image-uriz python-modulescriptzlocal-package-path requirementsz extra-dirszextra-packages)specWORKER_POOL_SPECa Define the worker pool configuration used by the custom job. You can specify multiple worker pool specs in order to create a custom job with multiple worker pools. The spec can contain the following fields: *machine-type*::: (Required): The type of the machine. see https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types for supported types. This is corresponding to the `machineSpec.machineType` field in `WorkerPoolSpec` API message. *replica-count*::: The number of worker replicas to use for this worker pool, by default the value is 1. This is corresponding to the `replicaCount` field in `WorkerPoolSpec` API message. *accelerator-type*::: The type of GPUs. see https://cloud.google.com/vertex-ai/docs/training/configure-compute#specifying_gpus for more requirements. This is corresponding to the `machineSpec.acceleratorType` field in `WorkerPoolSpec` API message. *accelerator-count*::: The number of GPUs for each VM in the worker pool to use, by default the value if 1. This is corresponding to the `machineSpec.acceleratorCount` field in `WorkerPoolSpec` API message. *container-image-uri*::: The URI of a container image to be directly run on each worker replica. This is corresponding to the `containerSpec.imageUri` field in `WorkerPoolSpec` API message. *executor-image-uri*::: The URI of a container image that will run the provided package. *output-image-uri*::: The URI of a custom container image to be built for autopackaged custom jobs. *python-module*::: The Python module name to run within the provided package. *local-package-path*::: The local path of a folder that contains training code. *script*::: The relative path under the `local-package-path` to a file to execute. It can be a Python file or an arbitrary bash script. *requirements*::: Python dependencies to be installed from PyPI, separated by ";". This is supposed to be used when some public packages are required by your training application but not in the base images. It has the same effect as editing a "requirements.txt" file under `local-package-path`. *extra-packages*::: Relative paths of local Python archives to be installed, separated by ";". This is supposed to be used when some custom packages are required by your training application but not in the base images. Every path should be relative to the `local-package-path`. *extra-dirs*::: Relative paths of the folders under `local-package-path` to be copied into the container, separated by ";". If not specified, only the parent directory that contains the main executable (`script` or `python-module`) will be copied. :::: Note that some of these fields are used for different job creation methods and are categorized as mutually exclusive groups listed below. Exactly one of these groups of fields must be specified: `container-image-uri`:::: Specify this field to use a custom container image for training. Together with the `--command` and `--args` flags, this field represents a [`WorkerPoolSpec.ContainerSpec`](https://cloud.google.com/vertex-ai/docs/reference/rest/v1/CustomJobSpec?#containerspec) message. In this case, the `--python-package-uris` flag is disallowed. Example: --worker-pool-spec=replica-count=1,machine-type=n1-highmem-2,container-image-uri=gcr.io/ucaip-test/ucaip-training-test `executor-image-uri, python-module`:::: Specify these fields to train using a pre-built container and Python packages that are already in Cloud Storage. Together with the `--python-package-uris` and `--args` flags, these fields represent a [`WorkerPoolSpec.PythonPackageSpec`](https://cloud.google.com/vertex-ai/docs/reference/rest/v1/CustomJobSpec#pythonpackagespec) message . Example: --worker-pool-spec=machine-type=e2-standard-4,executor-image-uri=us-docker.pkg.dev/vertex-ai/training/tf-cpu.2-4:latest,python-module=trainer.task `output-image-uri`:::: Specify this field to push the output custom container training image to a specific path in Container Registry or Artifact Registry for an autopackaged custom job. Example: --worker-pool-spec=machine-type=e2-standard-4,executor-image-uri=us-docker.pkg.dev/vertex-ai/training/tf-cpu.2-4:latest,output-image-uri='eu.gcr.io/projectName/imageName',python-module=trainer.task `local-package-path, executor-image-uri, output-image-uri, python-module|script`:::: Specify these fields, optionally with `requirements`, `extra-packages`, or `extra-dirs`, to train using a pre-built container and Python code from a local path. In this case, the `--python-package-uris` flag is disallowed. Example using `python-module`: --worker-pool-spec=machine-type=e2-standard-4,replica-count=1,executor-image-uri=us-docker.pkg.dev/vertex-ai/training/tf-cpu.2-4:latest,python-module=trainer.task,local-package-path=/usr/page/application Example using `script`: --worker-pool-spec=machine-type=e2-standard-4,replica-count=1,executor-image-uri=us-docker.pkg.dev/vertex-ai/training/tf-cpu.2-4:latest,script=my_run.sh,local-package-path=/usr/jeff/application )actionrrrz --commandCOMMANDz Command to be invoked when containers are started. It overrides the entrypoint instruction in Dockerfile when provided. )rrrrz--argsARGz?Comma-separated arguments passed to containers or python tasks.)rrrrz--persistent-resource-idPERSISTENT_RESOURCE_IDa The name of the persistent resource from the same project and region on which to run this custom job. If this is specified, the job will be run on existing machines held by the PersistentResource instead of on-demand short-lived machines. The network and CMEK configs on the job should be consistent with those on the PersistentResource, otherwise, the job will be rejected. rrc8[R"US[R"[R 5S9 [R RU5 [RRU5 [RRU5 [RRU5 [R"US5 [R"U5 [RU5 [RU5 [ RU5 ["RU5 [$RU5 [&R("SSS9nUR+[,5 UR+[.5 URU5 g)z*Adds flags related to create a custom job.zto create a custom job prompt_func custom jobzWorker pool specification.T)rrN) shared_flagsAddRegionResourceArgr GetPromptForRegionFuncr SUPPORTED_TRAINING_REGIONSTRAINING_SERVICE_ACCOUNT AddToParserNETWORKENABLE_WEB_ACCESSENABLE_DASHBOARD_ACCESSAddKmsKeyResourceArgr AddCreateLabelsFlags _DISPLAY_NAME_PYTHON_PACKAGE_URIS_CUSTOM_JOB_ARGS_CUSTOM_JOB_COMMAND_PERSISTENT_RESOURCE_IDr ArgumentGroup AddArgument_CUSTOM_JOB_CONFIG_WORKER_POOL_SPEC)parserworker_pool_spec_groups 6lib/googlecloudsdk/command_lib/ai/custom_jobs/flags.pyAddCreateCustomJobFlagsr<s%## 44  . .01 ''33F;""6*  ,,V4&&226:##FL9 ""6*F#""6*v&!!&)%%f--- '$8$$%78$$%67$$V,c 4[R"[RS[R "[ R"U5S9[RSS9n[RRSUSRU5SS9RU5 g ) a&Add a resource argument for a Vertex AI custom job. NOTE: Must be used only if it's the only resource arg in the command. Args: parser: the parser for the command. verb: str, the verb to describe the job resource, such as 'to update'. regions: list[str], the list of supported regions. r$r"F)resource_collection resource_name locationsId projectsIddisable_auto_completers custom_jobzThe custom job {}.T)rN)r ResourceSpecr CUSTOM_JOB_COLLECTIONr%RegionAttributeConfigr r' DEFAULT_PROJECT_ATTRIBUTE_CONFIGr ConceptParser ForResourceformatr*)r9verbregionsjob_resource_specs r;AddCustomJobResourceArgrOs++*@@ 44!88AC::# %++!!$' ,![(r=c UR5nURSS[R"S5S9 URSS[R"S5S9 URSS S /[R"S 5S 9 URS S[R "5[R"S5S9 URSSSS/[R"S5S9 URSS[R "5[R"S5S9 URSS[R "5[R"S5S9 URSS[R"S5S9 URS S!S"S#S$9 URS%S&S[R "5[R"S'5S(9 URS)S*[R"S+5S9 URS,[ R/S-S.S/9 g0)1z*Add local-run related flags to the parser.z--python-module PYTHON_MODULEz Name of the python module to execute, in 'trainer.train' or 'train' format. Its path should be relative to the `work_dir`. r z--scriptSCRIPTz The relative path of the file to execute. Accepets a Python file or an arbitrary bash script. This path should be relative to the `work_dir`. z--local-package-path LOCAL_PATHz --work-dira local path of the directory where the python-module or script exists. If not specified, it use the directory where you run the this command. Only the contents of this directory will be accessible to the built container image. )rsuggestion_aliasesrz --extra-dirs EXTRA_DIRa Extra directories under the working directory to include, besides the one that contains the main executable. By default, only the parent directory of the main script or python module is copied to the container. For example, if the module is "training.task" or the script is "training/task.py", the whole "training" directory, including its sub-directories, will always be copied to the container. You may specify this flag to also copy other directories if necessary. Note: if no parent is specified in 'python_module' or 'scirpt', the whole working directory is copied, then you don't need to specify this flag. rz--executor-image-uri IMAGE_URITz --base-imagea  URI or ID of the container image in either the Container Registry or local that will run the application. See https://cloud.google.com/vertex-ai/docs/training/pre-built-containers for available pre-built container images provided by Vertex AI for training. )rrrTrz--requirements REQUIREMENTSax Python dependencies from PyPI to be used when running the application. If this is not specified, and there is no "setup.py" or "requirements.txt" in the working directory, your application will only have access to what exists in the base image with on other dependencies. Example: 'tensorflow-cpu, pandas==1.2.0, matplotlib>=3.0.2' z--extra-packagesPACKAGEa8 Local paths to Python archives used as training dependencies in the image container. These can be absolute or relative paths. However, they have to be under the work_dir; Otherwise, this tool will not be able to access it. Example: 'dep1.tar.gz, ./downloads/dep2.whl' z--output-image-uri OUTPUT_IMAGEzf Uri of the custom container image to be built with the your application packed in. z--gpu store_trueFzEnable to use GPU.)rdefaultrz--docker-run-optionsDOCKER_RUN_OPTIONSz Custom Docker run options to pass to image during execution. For example, '--no-healthcheck, -a stdin'. See https://docs.docker.com/engine/reference/commandline/run/#options for more details. )rhiddenrrz--service-account-key-fileACCOUNT_KEY_FILEaG The JSON file of a Google Cloud service account private key. When specified, the corresponding service account will be used to authenticate the local container to access Google Cloud services. Note that the key file won't be copied to the container, it will be mounted during running time. argsz>Additional user arguments to be forwarded to your application.zX$ {command} --script=my_run.sh --base-image=gcr.io/my/image -- --my-arg bar --enable_foo)nargsr[rexampleN)add_mutually_exclusive_group add_argumenttextwrapdedentrArgListargparse REMAINDER)r9application_groups r;AddLocalRunCustomJobFlagsrj sV99;   ??  !    ??  !  & ??         ??   ( () ??         ??         ??      ??     lE8LN "     ??     " ??         O. 0r=))__doc__ __future__rrrrgrdgooglecloudsdk.callioperr googlecloudsdk.calliope.conceptsrgooglecloudsdk.command_lib.air r r%r )googlecloudsdk.command_lib.ai.custom_jobsr $googlecloudsdk.command_lib.util.argsr (googlecloudsdk.command_lib.util.conceptsrArgumentr0rfr1rer7ArgDictintstrr8 UpdateActionr3r2r4r<r(rOrjr=r;rys?&'/(53?5F<D   58 }} !     K M]]  *MM      #!$#&"% # "%'//#F%--D)11CH   \ \ )p dmm       # #   ==       # # J L-- $   -<%.$H$H)6N0r=