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Update agent configuration file sample (#122)
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pollfly
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@ -152,154 +152,195 @@ In case a `clearml.conf` file already exists, add a few ClearML Agent specific c
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<summary className="cml-expansion-panel-summary">View sample agent section</summary>
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<div className="cml-expansion-panel-content">
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agent {
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# Set GIT user/pass credentials (if user/pass are set, GIT protocol will be set to https)
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# leave blank for GIT SSH credentials (set force_git_ssh_protocol=true to force SSH protocol)
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git_user: ""
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git_pass: ""
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# Limit credentials to a single domain, for example: github.com,
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# all other domains will use public access (no user/pass). Default: always send user/pass for any VCS domain
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git_host=""
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# Force GIT protocol to use SSH regardless of the git url (Assumes GIT user/pass are blank)
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force_git_ssh_protocol: false
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# Force a specific SSH port when converting http to ssh links (the domain is kept the same)
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# force_git_ssh_port: 0
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# Force a specific SSH username when converting http to ssh links (the default username is 'git')
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# force_git_ssh_user: git
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# unique name of this worker, if None, created based on hostname:process_id
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# Override with os environment: CLEARML_WORKER_ID
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# worker_id: "clearml-agent-machine1:gpu0"
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worker_id: ""
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# worker name, replaces the hostname when creating a unique name for this worker
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# Override with os environment: CLEARML_WORKER_ID
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# worker_name: "clearml-agent-machine1"
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worker_name: ""
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# Set the python version to use when creating the virtual environment and launching the experiment
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# Example values: "/usr/bin/python3" or "/usr/local/bin/python3.6"
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# The default is the python executing the clearml_agent
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python_binary: ""
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# select python package manager:
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# currently supported pip and conda
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# poetry is used if pip selected and repository contains poetry.lock file
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package_manager: {
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# supported options: pip, conda, poetry
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type: pip,
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# specify pip version to use (examples "<20", "==19.3.1", "", empty string will install the latest version)
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pip_version: "<20.2",
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# virtual environment inheres packages from system
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system_site_packages: false,
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# install with --upgrade
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force_upgrade: false,
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# additional artifact repositories to use when installing python packages
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# extra_index_url: ["https://allegroai.jfrog.io/clearmlai/api/pypi/public/simple"]
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extra_index_url: []
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agent {
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# Set GIT user/pass credentials (if user/pass are set, GIT protocol will be set to https)
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# leave blank for GIT SSH credentials (set force_git_ssh_protocol=true to force SSH protocol)
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git_user=""
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git_pass=""
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# Limit credentials to a single domain, for example: github.com,
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# all other domains will use public access (no user/pass). Default: always send user/pass for any VCS domain
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git_host=""
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# Force GIT protocol to use SSH regardless of the git url (Assumes GIT user/pass are blank)
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force_git_ssh_protocol: false
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# Force a specific SSH port when converting http to ssh links (the domain is kept the same)
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# force_git_ssh_port: 0
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# Force a specific SSH username when converting http to ssh links (the default username is 'git')
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# force_git_ssh_user: git
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# unique name of this worker, if None, created based on hostname:process_id
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# Overridden with os environment: CLEARML_WORKER_NAME
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# worker_id: "clearml-agent-machine1:gpu0"
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worker_id: ""
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# worker name, replaces the hostname when creating a unique name for this worker
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# Overridden with os environment: CLEARML_WORKER_ID
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# worker_name: "clearml-agent-machine1"
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worker_name: ""
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# Set the python version to use when creating the virtual environment and launching the experiment
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# Example values: "/usr/bin/python3" or "/usr/local/bin/python3.6"
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# The default is the python executing the clearml_agent
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python_binary: ""
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# ignore any requested python version (Default: False, if a Task was using a
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# specific python version and the system supports multiple python the agent will use the requested python version)
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# ignore_requested_python_version: true
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# select python package manager:
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# currently supported: pip, conda and poetry
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# if "pip" or "conda" are used, the agent installs the required packages
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# based on the "installed packages" section of the Task. If the "installed packages" is empty,
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# it will revert to using `requirements.txt` from the repository's root directory.
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# If Poetry is selected and the root repository contains `poetry.lock` or `pyproject.toml`,
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# the "installed packages" section is ignored, and poetry is used.
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# If Poetry is selected and no lock file is found, it reverts to "pip" package manager behaviour.
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package_manager: {
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# supported options: pip, conda, poetry
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type: pip,
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# additional conda channels to use when installing with conda package manager
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conda_channels: ["defaults", "conda-forge", "pytorch", ]
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# conda_full_env_update: false
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# conda_env_as_base_docker: false
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# set the priority packages to be installed before the rest of the required packages
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# priority_packages: ["cython", "numpy", "setuptools", ]
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# set the optional priority packages to be installed before the rest of the required packages,
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# In case a package installation fails, the package will be ignored,
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# and the virtual environment process will continue
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# priority_optional_packages: ["pygobject", ]
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# set the post packages to be installed after all the rest of the required packages
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# post_packages: ["horovod", ]
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# set the optional post packages to be installed after all the rest of the required packages,
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# In case a package installation fails, the package will be ignored,
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# and the virtual environment process will continue
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# post_optional_packages: []
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# set to True to support torch nightly build installation,
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# notice: torch nightly builds are ephemeral and are deleted from time to time
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torch_nightly: false,
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},
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# target folder for virtual environments builds, created when executing experiment
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venvs_dir = ~/.clearml/venvs-builds
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# cached virtual environment folder
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venvs_cache: {
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# maximum number of cached venvs
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max_entries: 10
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# minimum required free space to allow for cache entry, disable by passing 0 or negative value
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free_space_threshold_gb: 2.0
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# unmark to enable virtual environment caching
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# path: ~/.clearml/venvs-cache
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},
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# cached git clone folder
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vcs_cache: {
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enabled: true,
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path: ~/.clearml/vcs-cache
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},
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# use venv-update in order to accelerate python virtual environment building
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# Still in beta, turned off by default
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venv_update: {
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enabled: false,
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},
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# cached folder for specific python package download (mostly pytorch versions)
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pip_download_cache {
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enabled: true,
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path: ~/.clearml/pip-download-cache
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},
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translate_ssh: true,
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# reload configuration file every daemon execution
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reload_config: false,
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# pip cache folder mapped into docker, used for python package caching
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docker_pip_cache = ~/.clearml/pip-cache
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# apt cache folder mapped into docker, used for ubuntu package caching
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docker_apt_cache = ~/.clearml/apt-cache
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# optional arguments to pass to docker image
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# these are local for this agent and will not be updated in the experiment's docker_cmd section
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# extra_docker_arguments: ["--ipc=host", "-v", "/mnt/host/data:/mnt/data"]
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# optional shell script to run in docker when started before the experiment is started
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# extra_docker_shell_script: ["apt-get install -y bindfs", ]
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# Install the required packages for opencv libraries (libsm6 libxext6 libxrender-dev libglib2.0-0),
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# for backwards compatibility reasons, true as default,
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# change to false to skip installation and decrease docker spin up time
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# docker_install_opencv_libs: true
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# set to true in order to force "docker pull" before running an experiment using a docker image.
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# This makes sure the docker image is updated.
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docker_force_pull: false
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default_docker: {
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# default docker image to use when running in docker mode
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image: "nvidia/cuda:10.1-runtime-ubuntu18.04"
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# optional arguments to pass to docker image
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# arguments: ["--ipc=host", ]
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}
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# set the OS environments based on the Task's Environment section before launching the Task process.
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enable_task_env: false
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# CUDA versions used for Conda setup & solving PyTorch wheel packages
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# it Should be detected automatically. Override with os environment CUDA_VERSION / CUDNN_VERSION
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# cuda_version: 10.1
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# cudnn_version: 7.6
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# specify pip version to use (examples "<20", "==19.3.1", "", empty string will install the latest version)
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# pip_version: "<20"
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# virtual environment inheres packages from system
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system_site_packages: false,
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# install with --upgrade
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force_upgrade: false,
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# additional artifact repositories to use when installing python packages
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# extra_index_url: ["https://allegroai.jfrog.io/clearml/api/pypi/public/simple"]
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extra_index_url: []
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# additional conda channels to use when installing with conda package manager
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conda_channels: ["pytorch", "conda-forge", "defaults", ]
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# conda_full_env_update: false
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# conda_env_as_base_docker: false
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# set the priority packages to be installed before the rest of the required packages
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# priority_packages: ["cython", "numpy", "setuptools", ]
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# set the optional priority packages to be installed before the rest of the required packages,
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# In case a package installation fails, the package will be ignored,
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# and the virtual environment process will continue
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# priority_optional_packages: ["pygobject", ]
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# set the post packages to be installed after all the rest of the required packages
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# post_packages: ["horovod", ]
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# set the optional post packages to be installed after all the rest of the required packages,
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# In case a package installation fails, the package will be ignored,
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# and the virtual environment process will continue
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# post_optional_packages: []
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# set to True to support torch nightly build installation,
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# notice: torch nightly builds are ephemeral and are deleted from time to time
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torch_nightly: false,
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},
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# target folder for virtual environments builds, created when executing experiment
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venvs_dir = ~/.clearml/venvs-builds
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# cached virtual environment folder
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venvs_cache: {
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# maximum number of cached venvs
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max_entries: 10
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# minimum required free space to allow for cache entry, disable by passing 0 or negative value
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free_space_threshold_gb: 2.0
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# unmark to enable virtual environment caching
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# path: ~/.clearml/venvs-cache
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},
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# cached git clone folder
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vcs_cache: {
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enabled: true,
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path: ~/.clearml/vcs-cache
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},
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# DEPRECATED: please use `venvs_cache` and set `venvs_cache.path`
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# use venv-update in order to accelerate python virtual environment building
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# Still in beta, turned off by default
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# venv_update: {
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# enabled: false,
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# },
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# cached folder for specific python package download (mostly pytorch versions)
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pip_download_cache {
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enabled: true,
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path: ~/.clearml/pip-download-cache
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},
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translate_ssh: true,
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# reload configuration file every daemon execution
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reload_config: false,
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# pip cache folder mapped into docker, used for python package caching
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docker_pip_cache = ~/.clearml/pip-cache
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# apt cache folder mapped into docker, used for ubuntu package caching
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docker_apt_cache = ~/.clearml/apt-cache
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# optional arguments to pass to docker image
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# these are local for this agent and will not be updated in the experiment's docker_cmd section
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# extra_docker_arguments: ["--ipc=host", "-v", "/mnt/host/data:/mnt/data"]
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# optional shell script to run in docker when started before the experiment is started
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# extra_docker_shell_script: ["apt-get install -y bindfs", ]
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# Install the required packages for opencv libraries (libsm6 libxext6 libxrender-dev libglib2.0-0),
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# for backwards compatibility reasons, true as default,
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# change to false to skip installation and decrease docker spin up time
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# docker_install_opencv_libs: true
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# set to true in order to force "docker pull" before running an experiment using a docker image.
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# This makes sure the docker image is updated.
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docker_force_pull: false
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default_docker: {
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# default docker image to use when running in docker mode
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image: "nvidia/cuda:10.1-cudnn7-runtime-ubuntu18.04"
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# optional arguments to pass to docker image
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# arguments: ["--ipc=host"]
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}
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# set the OS environments based on the Task's Environment section before launching the Task process.
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enable_task_env: false
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# CUDA versions used for Conda setup & solving PyTorch wheel packages
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# it Should be detected automatically. Override with os environment CUDA_VERSION / CUDNN_VERSION
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# cuda_version: 10.1
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# cudnn_version: 7.6
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# Hide docker environment variables containing secrets when printing out the docker command by replacing their
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# values with "********". Turning this feature on will hide the following environment variables values:
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# CLEARML_API_SECRET_KEY, CLEARML_AGENT_GIT_PASS, AWS_SECRET_ACCESS_KEY, AZURE_STORAGE_KEY
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# To include more environment variables, add their keys to the "extra_keys" list. E.g. to make sure the value of
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# your custom environment variable named MY_SPECIAL_PASSWORD will not show in the logs when included in the
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# docker command, set:
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# extra_keys: ["MY_SPECIAL_PASSWORD"]
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hide_docker_command_env_vars {
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enabled: true
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extra_keys: []
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}
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# allow to set internal mount points inside the docker,
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# especially useful for non-root docker container images.
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# docker_internal_mounts {
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# sdk_cache: "/clearml_agent_cache"
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# apt_cache: "/var/cache/apt/archives"
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# ssh_folder: "/root/.ssh"
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# pip_cache: "/root/.cache/pip"
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# poetry_cache: "/root/.cache/pypoetry"
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# vcs_cache: "/root/.clearml/vcs-cache"
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# venv_build: "/root/.clearml/venvs-builds"
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# pip_download: "/root/.clearml/pip-download-cache"
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# }
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# Name docker containers created by the daemon using the following string format (supported from Docker 0.6.5)
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# Allowed variables are task_id, worker_id and rand_string (random lower-case letters string, up to 32 characters)
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# Note: resulting name must start with an alphanumeric character and
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# continue with alphanumeric characters, underscores (_), dots (.) and/or dashes (-)
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# docker_container_name_format: "clearml-id-{task_id}-{rand_string:.8}"
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}
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</div></details>
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1. Save the configuration.
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