Commands and Shells

In addition to structured model training workloads, which are handled using experiments, HPE Machine Learning Development Environment also supports free-form tasks using commands and shells. Commands and shells enable you to use an HPE Machine Learning Development Environment cluster and cluster GPUs without needing to write code that conforms to the trial APIs.

Commands execute a user-specified program on the cluster. Commands are useful for running existing code in batch mode.

Shells start SSH servers that let you use cluster resources interactively. Shells provide access to the cluster in the form of interactive SSH sessions.

This document describes the most common CLI and shell commands.


CLI commands start with det command, abbreviated as det cmd. The main subcommand is det cmd run, which runs a command in the cluster and streams its output. For example, the following CLI command uses nvidia-smi to display information about the GPUs available to tasks in the container:

det cmd run nvidia-smi

More complex commands including shell constructs can also be run provided they are quoted to prevent interpretation by the local shell:

det cmd run 'for x in a b c; do echo $x; done'

det cmd run streams output from the command until it finishes, but the command continues executing and occupying cluster resources even if the CLI is interrupted or killed, such as due to entering Ctrl-C. To stop the command or view additional output, you need the command UUID, which you can get from the output of the original det cmd run or det cmd list. After you have the UUID, run

  • det cmd logs <UUID> to view a snapshot of logs.

  • det cmd logs -f <UUID> to view the current logs and continue streaming future output.

  • det cmd kill <UUID> to stop the command.


The CLI is distributed as a Python wheel package. Each user should install a copy of the CLI on their local development machine.

The CLI requires Python >= 3.7. It is recommended that you install the CLI into a virtualenv, although this is optional. To install the CLI into a virtualenv, activate the virtualenv before entering the following command.

Installed the CLI using the pip utility:

pip install determined

After the CLI has been installed, it should be configured to connect to the HPE Machine Learning Development Environment master at the appropriate IP address. This is done by setting the DET_MASTER environment variable:

export DET_MASTER=<master IP>

You might want to place this into the appropriate configuration file for your login shell, such as .bashrc.


After the wheel is installed, the CLI is invoked with the det command. Use det --help for more information about the individual CLI commands.

CLI subcommands usually follow a <noun> <verb> form, similar to the paradigm of ip. Certain abbreviations are supported, and a missing verb is the same as list, when possible.

For example, the different commands within each of the blocks below all do the same thing:

# List all experiments.
$ det experiment list
$ det e list
$ det e
# List all agents.
$ det agent list
$ det a list
$ det a
# List all slots.
$ det slot list
$ det slot
$ det s

For a complete description of the available nouns and abbreviations, see the output of det help. Each noun also provides a help verb that describes the possible verbs for that noun. Or, you can provide the -h or --help argument anywhere, which causes the CLI to exit after printing a help message for the object or action specified to that point.

Environment Variables

  • DET_MASTER: The network address of the master of the HPE Machine Learning Development Environment installation. The value can be overridden using the -m flag.

  • DET_USER and DET_PASS: Specifies the current HPE Machine Learning Development Environment user and password for use when non-interactive behaviour is required such as scripts. det user login is preferred for normal usage. Both DET_USER and DET_PASS must be set together to take effect. These variables can be overridden by using the -u flag.




det e
det experiment
det experiment list

Show information about experiments in the cluster.

det -m e

Show information about experiments in the cluster at network address

det t logs -f 289

Show the logs for trial 289 and continue showing new logs as they arrive.

det e label add 17 foobar

Add the label foobar to experiment 17.

det e describe 493 --metrics --csv

Display information about experiment 493, including full metrics, in CSV format.

det e create -f --paused const.yaml .

Create an experiment with the configuration file const.yaml and the code contained in the current directory. The experiment is created in a paused state, which means that it is not scheduled on the cluster until it is activated.

det e set max-slots 85 4

Ensure that experiment 85 does not use more than 4 slots in the cluster.

det u create --admin hoid

Create a new user named hoid who has admin privileges.

det version

Show detailed information about the CLI and master. This command does not take both an object and an action.


Shell-related CLI commands start with det shell. To start a persistent SSH server container in the HPE Machine Learning Development Environment cluster and connect an interactive session to it, use det shell start:

det shell start

After starting a server with det shell start, you can make another independent connection to the same server by running det shell open <UUID>. You can get the UUID from the output of the original det shell start or det shell list command:

$ det shell list
 Id                                   | Owner      | Description                  | State   | Exit Status
 d75c3908-fb11-4fa5-852c-4c32ed30703b | determined | Shell (annually-alert-crane) | RUNNING | N/A
$ det shell open d75c3908-fb11-4fa5-852c-4c32ed30703b

Optionally, you can provide extra options to pass to the SSH client when using det shell start or det shell open by including them after --. For example, this command starts a new shell and forwards a port from the local machine to the container:

det shell start -- -L8080:localhost:8080

To stop the SSH server container and free cluster resources, run det shell kill <UUID>.

Command-line Interface (CLI) Reference

usage: det [-h] [-u username] [-m address] [-v] command ...

Determined command-line client

positional arguments:
    help                show help for this command
    auth                manage auth
    agent (a)           manage agents
    command (cmd)       manage commands
    checkpoint (c)      manage checkpoints
    deploy (d)          manage deployments
    experiment (e)      manage experiments
    job (j)             manage job
    master (m)          manage master
    model (m)           manage models
    notebook            manage notebooks
    oauth               manage OAuth
    preview-search      preview search
    resources (res)     query historical resource allocation
    shell               manage shells
    slot (s)            manage slots
    task                manage tasks (commands, experiments, notebooks,
                        shells, tensorboards)
    template (tpl)      manage config templates
    tensorboard         manage TensorBoard instances
    trial (t)           manage trials
    user (u)            manage users
    version             show version information

optional arguments:
  -h, --help            show this help message and exit
  -u username, --user username
                        run as the given user (default: None)
  -m address, --master address
                        master address (default: localhost:8080)
  -v, --version         print CLI version and exit