runai tensorflow port-forward

forward one or more local ports to a tf training workload

runai tensorflow port-forward [WORKLOAD_NAME] [flags]

Examples

# Forward connections from localhost:8080 to a workload on port 8090:
runai training tf port-forward tf-01 --port 8080:8090 --address localhost

# Forward connections from 0.0.0.0:8080 to a workload on port 8080:
runai training tf port-forward tf-01 --port 8080 --address 0.0.0.0 [requires privileges]

# Forward multiple connections from localhost:8080 to a workload on port 8090 and from localhost:6443 to a workload on port 443:
runai training tf port-forward tf-01 --port 8080:8090 --port 6443:443 --address localhost

Options

      --address string                 Bind the workload to a specific local interface or host. E.g., --address localhost or --address 0.0.0.0. (default "localhost")
  -h, --help                           help for port-forward
      --pod string                     Workload pod ID for port-forward, default: distributed(master) otherwise(random)
      --pod-running-timeout duration   Timeout for pod to reach running state (e.g. 5s, 2m, 3h).
      --port stringArray               port
  -p, --project string                 Specify the project for the command to use. Defaults to the project set in the context, if any. Use 'runai project set <project>' to set the default.

Options inherited from parent commands

      --config-file string   config file name; can be set by environment variable RUNAI_CLI_CONFIG_FILE (default "config.json")
      --config-path string   config path; can be set by environment variable RUNAI_CLI_CONFIG_PATH
  -d, --debug                enable debug mode
  -q, --quiet                enable quiet mode, suppress all output except error messages
      --verbose              enable verbose mode

SEE ALSO

• runai tensorflow - alias for tensorflow management