gcloud alpha ai custom-jobs local-run - run a custom training locally

gcloud alpha ai custom-jobs local-run--executor-image-uri=IMAGE_URI[--extra-dirs=[EXTRA_DIR,…]][--extra-packages=[PACKAGE,…]][--gpu][--local-package-path=LOCAL_PATH][--output-image-uri=OUTPUT_IMAGE][--requirements=[REQUIREMENTS,…]][--service-account-key-file=ACCOUNT_KEY_FILE][--python-module=PYTHON_MODULE    |--script=SCRIPT][GCLOUD_WIDE_FLAG …][--ARGS …]

You should execute this command in the top folder which includes all the codeand resources you want to pack and run, or specify the 'work-dir' flag to pointto it. Any other path you specified via flags should be a relative path to thework-dir and under it; otherwise it will be unaccessible.

Supposing your directories are like the following structures:

/root-my_project-my_training-task.py-util.py-setup.py-other_modules-some_module.py-dataset-small.dat-large.dat-config-dep-foo.tar.gz-bar.whl-requirements.txt-another_project-something

If you set 'my_project' as the package, then you should execute the task.py byspecifying "--script=my_training/task.py" or "--python-module=my_training.task",the 'requirements.txt' will be processed. And you will also be able to installextra packages by, e.g. specifying "--extra-packages=dep/foo.tar.gz,bar.whl" orinclude extra directories, e.g. specifying "--extra-dirs=dataset,config".

If you set 'my_training' as the package, then you should execute the task.py byspecifying "--script=task.py" or "--python-module=task", the 'setup.py' will beprocessed. However, you won't be able to access any other files or directoriesthat are not in 'my_training' folder.

See more details in the HELP info of the corresponding flags.

gcloudalphaaicustom-jobslocal-run--python-module=my_training.task--executor-image-uri=gcr.io/my/image--requirements=pandas,scipy>=1.3.0

To execute a python script using local GPU, run:

gcloudalphaaicustom-jobslocal-run--script=my_training/task.py--executor-image-uri=gcr.io/my/image--gpu

To execute an arbitrary script with custom arguments, run:

gcloudalphaaicustom-jobslocal-run--script=my_run.sh--executor-image-uri=gcr.io/my/image----my-argbar--enable_foo

To run an existing container training without building new image, run:

gcloudalphaaicustom-jobslocal-run--executor-image-uri=gcr.io/my/custom-training-image

[--ARGS …]

Additional user arguments to be forwarded to your application.

The '--' argument must be specified between gcloud specific args on the left andARGS on the right. Example:

gcloudalphaaicustom-jobslocal-run--script=my_run.sh--base-image=gcr.io/my/image----my-argbar--enable_foo

--executor-image-uri=IMAGE_URI

URI or ID of the container image in either the Container Registry or local thatwill run the application. Seehttps://cloud.google.com/vertex-ai/docs/training/pre-built-containersfor available pre-built container images provided by Vertex AI for training.

--extra-dirs=[EXTRA_DIR,…]

Extra directories under the working directory to include, besides the one thatcontains the main executable.

By default, only the parent directory of the main script or python module iscopied to the container. For example, if the module is "training.task" or thescript is "training/task.py", the whole "training" directory, including itssub-directories, will always be copied to the container. You may specify thisflag to also copy other directories if necessary.

Note: if no parent is specified in 'python_module' or 'scirpt', the wholeworking directory is copied, then you don't need to specify this flag.

--extra-packages=[PACKAGE,…]

Local paths to Python archives used as training dependencies in the imagecontainer. These can be absolute or relative paths. However, they have to beunder the work_dir; Otherwise, this tool will not be able to access it.

Example: 'dep1.tar.gz, ./downloads/dep2.whl'

--gpu

Enable to use GPU.

--local-package-path=LOCAL_PATH

local path of the directory where the python-module or script exists. If notspecified, it use the directory where you run the this command.

Only the contents of this directory will be accessible to the built containerimage.

--output-image-uri=OUTPUT_IMAGE

Uri of the custom container image to be built with the your application packedin.

--requirements=[REQUIREMENTS,…]

Python dependencies from PyPI to be used when running the application. If thisis not specified, and there is no "setup.py" or "requirements.txt" in theworking directory, your application will only have access to what exists in thebase image with on other dependencies.

Example: 'tensorflow-cpu, pandas==1.2.0, matplotlib>=3.0.2'

--service-account-key-file=ACCOUNT_KEY_FILE

The JSON file of a Google Cloud service account private key. When specified, thecorresponding service account will be used to authenticate the local containerto access Google Cloud services. Note that the key file won't be copied to thecontainer, it will be mounted during running time.

At most one of these can be specified:

--python-module=PYTHON_MODULE

Name of the python module to execute, in 'trainer.train' or 'train' format. Itspath should be relative to thework_dir.

--script=SCRIPT

The relative path of the file to execute. Accepets a Python file or an arbitrarybash script. This path should be relative to thework_dir.

Run$gcloud help for details.