mirror of
https://github.com/imjasonh/gcloud-help
synced 2026-07-08 10:35:03 +00:00
111 lines
4.6 KiB
Text
111 lines
4.6 KiB
Text
NAME
|
|
gcloud alpha ai-platform local train - run an AI Platform training job
|
|
locally
|
|
|
|
SYNOPSIS
|
|
gcloud alpha ai-platform local train --module-name=MODULE_NAME
|
|
[--distributed] [--evaluator-count=EVALUATOR_COUNT] [--job-dir=JOB_DIR]
|
|
[--package-path=PACKAGE_PATH]
|
|
[--parameter-server-count=PARAMETER_SERVER_COUNT]
|
|
[--start-port=START_PORT; default=27182] [--worker-count=WORKER_COUNT]
|
|
[GCLOUD_WIDE_FLAG ...] [-- USER_ARGS ...]
|
|
|
|
DESCRIPTION
|
|
(ALPHA) This command runs the specified module in an environment similar to
|
|
that of a live AI Platform Training Job.
|
|
|
|
This is especially useful in the case of testing distributed models, as it
|
|
allows you to validate that you are properly interacting with the AI
|
|
Platform cluster configuration. If your model expects a specific number of
|
|
parameter servers or workers (i.e. you expect to use the CUSTOM machine
|
|
type), use the --parameter-server-count and --worker-count flags to further
|
|
specify the desired cluster configuration, just as you would in your cloud
|
|
training job configuration:
|
|
|
|
$ gcloud alpha ai-platform local train --module-name trainer.task \
|
|
--package-path /path/to/my/code/trainer \
|
|
--distributed \
|
|
--parameter-server-count 4 \
|
|
--worker-count 8
|
|
|
|
Unlike submitting a training job, the --package-path parameter can be
|
|
omitted, and will use your current working directory.
|
|
|
|
AI Platform Training sets a TF_CONFIG environment variable on each VM in
|
|
your training job. You can use TF_CONFIG to access the cluster description
|
|
and the task description for each VM.
|
|
|
|
Learn more about TF_CONFIG:
|
|
https://cloud.google.com/ai-platform/training/docs/distributed-training-details.
|
|
|
|
POSITIONAL ARGUMENTS
|
|
[-- USER_ARGS ...]
|
|
Additional user arguments to be forwarded to user code. Any relative
|
|
paths will be relative to the parent directory of --package-path.
|
|
|
|
The '--' argument must be specified between gcloud specific args on the
|
|
left and USER_ARGS on the right.
|
|
|
|
REQUIRED FLAGS
|
|
--module-name=MODULE_NAME
|
|
Name of the module to run.
|
|
|
|
OPTIONAL FLAGS
|
|
--distributed
|
|
Runs the provided code in distributed mode by providing cluster
|
|
configurations as environment variables to subprocesses
|
|
|
|
--evaluator-count=EVALUATOR_COUNT
|
|
Number of evaluators with which to run. Ignored if --distributed is not
|
|
specified. Default: 0
|
|
|
|
--job-dir=JOB_DIR
|
|
Cloud Storage path or local_directory 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.
|
|
|
|
--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
|
|
using setuptools (which must be installed) using its parent directory
|
|
as context. If the parent directory contains a setup.py file, the build
|
|
will use that; otherwise, it will use a simple built-in one.
|
|
|
|
--parameter-server-count=PARAMETER_SERVER_COUNT
|
|
Number of parameter servers with which to run. Ignored if --distributed
|
|
is not specified. Default: 2
|
|
|
|
--start-port=START_PORT; default=27182
|
|
Start of the range of ports reserved by the local cluster. This command
|
|
will use a contiguous block of ports equal to parameter-server-count +
|
|
worker-count + 1.
|
|
|
|
If --distributed is not specified, this flag is ignored.
|
|
|
|
--worker-count=WORKER_COUNT
|
|
Number of workers with which to run. Ignored if --distributed is not
|
|
specified. Default: 2
|
|
|
|
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
|
|
This command is currently in alpha and might change without notice. If this
|
|
command fails with API permission errors despite specifying the correct
|
|
project, you might be trying to access an API with an invitation-only early
|
|
access allowlist. These variants are also available:
|
|
|
|
$ gcloud ai-platform local train
|
|
|
|
$ gcloud beta ai-platform local train
|
|
|