clearml-agent/trains_agent/backend_api/config/default/agent.conf

{
    # unique name of this worker, if None, created based on hostname:process_id
    # Override with os environment: TRAINS_WORKER_ID
    # worker_id: "trains-agent-machine1:gpu0"
    worker_id: ""

    # worker name, replaces the hostname when creating a unique name for this worker
    # Override with os environment: TRAINS_WORKER_NAME
    # worker_name: "trains-agent-machine1"
    worker_name: ""

    # Set GIT user/pass credentials (if user/pass are set, GIT protocol will be set to https)
    # leave blank for GIT SSH credentials (set force_git_ssh_protocol=true to force SSH protocol)
    # git_user: ""
    # git_pass: ""

    # Force GIT protocol to use SSH regardless of the git url (Assumes GIT user/pass are blank)
    force_git_ssh_protocol: false

    # Set the python version to use when creating the virtual environment and launching the experiment
    # Example values: "/usr/bin/python3" or "/usr/local/bin/python3.6"
    # The default is the python executing the trains_agent
    python_binary: ""

    # select python package manager:
    # currently supported pip and conda
    # poetry is used if pip selected and repository contains poetry.lock file
    package_manager: {
        # supported options: pip, conda, poetry
        type: pip,

        # specify pip version to use (examples "<20", "==19.3.1", "", empty string will install the latest version)
        pip_version: "<20.2",

        # virtual environment inheres packages from system
        system_site_packages: false,

        # install with --upgrade
        force_upgrade: false,

        # additional artifact repositories to use when installing python packages
        # extra_index_url: ["https://allegroai.jfrog.io/trainsai/api/pypi/public/simple"]

        # additional conda channels to use when installing with conda package manager
        conda_channels: ["defaults", "conda-forge", "pytorch", ]

        # set the priority packages to be installed before the rest of the required packages
        # priority_packages: ["cython", "numpy", "setuptools", ]

        # set the optional priority packages to be installed before the rest of the required packages,
        # In case a package installation fails, the package will be ignored,
        # and the virtual environment process will continue
        # priority_optional_packages: ["pygobject", ]

        # set the post packages to be installed after all the rest of the required packages
        # post_packages: ["horovod", ]

        # set the optional post packages to be installed after all the rest of the required packages,
        # In case a package installation fails, the package will be ignored,
        # and the virtual environment process will continue
        # post_optional_packages: []

        # set to True to support torch nightly build installation,
        # notice: torch nightly builds are ephemeral and are deleted from time to time
        torch_nightly: false,
    },

    # target folder for virtual environments builds, created when executing experiment
    venvs_dir = ~/.trains/venvs-builds

    # cached git clone folder
    vcs_cache: {
        enabled: true,
        path: ~/.trains/vcs-cache
    },

    # use venv-update in order to accelerate python virtual environment building
    # Still in beta, turned off by default
    venv_update: {
        enabled: false,
    },

    # cached folder for specific python package download (used for pytorch package caching)
    pip_download_cache {
        enabled: true,
        path: ~/.trains/pip-download-cache
    },

    translate_ssh: true,
    # reload configuration file every daemon execution
    reload_config: false,

    # pip cache folder mapped into docker, used for python package caching
    docker_pip_cache = ~/.trains/pip-cache
    # apt cache folder mapped into docker, used for ubuntu package caching
    docker_apt_cache = ~/.trains/apt-cache

    # optional arguments to pass to docker image
    # these are local for this agent and will not be updated in the experiment's docker_cmd section
    # extra_docker_arguments: ["--ipc=host", ]

    # optional shell script to run in docker when started before the experiment is started
    # extra_docker_shell_script: ["apt-get install -y bindfs", ]

    # optional uptime configuration, make sure to use only one of 'uptime/downtime' and not both.
    # If uptime is specified, agent will actively poll (and execute) tasks in the time-spans defined here.
    # Outside of the specified time-spans, the agent will be idle.
    # Defined using a list of items of the format: "<hours> <days>".
    # hours - use values 0-23, single values would count as start hour and end at midnight.
    # days - use days in abbreviated format (SUN-SAT)
    # use '-' for ranges and ',' to separate singular values.
    # for example, to enable the workers every Sunday and Tuesday between 17:00-20:00 set uptime to:
    # uptime: ["17-20 SUN,TUE"]

    # optional downtime configuration, can be used only when uptime is not used.
    # If downtime is specified, agent will be idle in the time-spans defined here.
    # Outside of the specified time-spans, the agent will actively poll (and execute) tasks.
    # Use the same format as described above for uptime
    # downtime: []

    # set to true in order to force "docker pull" before running an experiment using a docker image.
    # This makes sure the docker image is updated.
    docker_force_pull: false

    default_docker: {
        # default docker image to use when running in docker mode
        image: "nvidia/cuda:10.1-runtime-ubuntu18.04"

        # optional arguments to pass to docker image
        # arguments: ["--ipc=host", ]
    }

    # set the initial bash script to execute at the startup of any docker.
    # all lines will be executed regardless of their exit code.
    # {python_single_digit} is translated to 'python3' or 'python2' according to requested python version
    # docker_init_bash_script = [
    #     "echo 'Binary::apt::APT::Keep-Downloaded-Packages \"true\";' > /etc/apt/apt.conf.d/docker-clean",
    #     "chown -R root /root/.cache/pip",
    #     "apt-get update",
    #     "apt-get install -y git libsm6 libxext6 libxrender-dev libglib2.0-0",
    #     "(which {python_single_digit} && {python_single_digit} -m pip --version) || apt-get install -y {python_single_digit}-pip",
    # ]

    # cuda versions used for solving pytorch wheel packages
    # should be detected automatically. Override with os environment CUDA_VERSION / CUDNN_VERSION
    # cuda_version: 10.1
    # cudnn_version: 7.6
}