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108 lines
4.4 KiB
Text
108 lines
4.4 KiB
Text
NAME
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gcloud beta ml-engine local train - run an AI Platform training job locally
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SYNOPSIS
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gcloud beta ml-engine local train --module-name=MODULE_NAME [--distributed]
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[--evaluator-count=EVALUATOR_COUNT] [--job-dir=JOB_DIR]
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[--package-path=PACKAGE_PATH]
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[--parameter-server-count=PARAMETER_SERVER_COUNT]
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[--start-port=START_PORT; default=27182] [--worker-count=WORKER_COUNT]
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[GCLOUD_WIDE_FLAG ...] [-- USER_ARGS ...]
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DESCRIPTION
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(BETA) This command runs the specified module in an environment similar to
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that of a live AI Platform Training Job.
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This is especially useful in the case of testing distributed models, as it
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allows you to validate that you are properly interacting with the AI
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Platform cluster configuration. If your model expects a specific number of
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parameter servers or workers (i.e. you expect to use the CUSTOM machine
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type), use the --parameter-server-count and --worker-count flags to further
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specify the desired cluster configuration, just as you would in your cloud
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training job configuration:
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$ gcloud beta ml-engine local train --module-name trainer.task \
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--package-path /path/to/my/code/trainer \
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--distributed \
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--parameter-server-count 4 \
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--worker-count 8
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Unlike submitting a training job, the --package-path parameter can be
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omitted, and will use your current working directory.
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AI Platform Training sets a TF_CONFIG environment variable on each VM in
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your training job. You can use TF_CONFIG to access the cluster description
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and the task description for each VM.
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Learn more about TF_CONFIG:
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https://cloud.google.com/ai-platform/training/docs/distributed-training-details.
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POSITIONAL ARGUMENTS
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[-- USER_ARGS ...]
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Additional user arguments to be forwarded to user code. Any relative
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paths will be relative to the parent directory of --package-path.
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The '--' argument must be specified between gcloud specific args on the
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left and USER_ARGS on the right.
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REQUIRED FLAGS
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--module-name=MODULE_NAME
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Name of the module to run.
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OPTIONAL FLAGS
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--distributed
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Runs the provided code in distributed mode by providing cluster
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configurations as environment variables to subprocesses
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--evaluator-count=EVALUATOR_COUNT
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Number of evaluators with which to run. Ignored if --distributed is not
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specified. Default: 0
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--job-dir=JOB_DIR
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Cloud Storage path or local_directory in which to store training
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outputs and other data needed for training.
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This path will be passed to your TensorFlow program as the --job-dir
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command-line arg. The benefit of specifying this field is that AI
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Platform will validate the path for use in training. However, note that
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your training program will need to parse the provided --job-dir
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argument.
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--package-path=PACKAGE_PATH
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Path to a Python package to build. This should point to a local
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directory containing the Python source for the job. It will be built
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using setuptools (which must be installed) using its parent directory
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as context. If the parent directory contains a setup.py file, the build
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will use that; otherwise, it will use a simple built-in one.
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--parameter-server-count=PARAMETER_SERVER_COUNT
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Number of parameter servers with which to run. Ignored if --distributed
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is not specified. Default: 2
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--start-port=START_PORT; default=27182
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Start of the range of ports reserved by the local cluster. This command
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will use a contiguous block of ports equal to parameter-server-count +
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worker-count + 1.
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If --distributed is not specified, this flag is ignored.
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--worker-count=WORKER_COUNT
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Number of workers with which to run. Ignored if --distributed is not
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specified. Default: 2
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GCLOUD WIDE FLAGS
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These flags are available to all commands: --access-token-file, --account,
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--billing-project, --configuration, --flags-file, --flatten, --format,
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--help, --impersonate-service-account, --log-http, --project, --quiet,
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--trace-token, --user-output-enabled, --verbosity.
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Run $ gcloud help for details.
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NOTES
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This command is currently in beta and might change without notice. These
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variants are also available:
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$ gcloud ml-engine local train
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$ gcloud alpha ml-engine local train
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