mirror of
https://github.com/clearml/clearml-agent
synced 2025-02-01 01:26:46 +00:00
88 lines
3.0 KiB
Plaintext
88 lines
3.0 KiB
Plaintext
{
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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: TRAINS_WORKER_ID
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# worker_id: "trains-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: TRAINS_WORKER_NAME
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# worker_name: "trains-agent-machine1"
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worker_name: ""
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# Set GIT user/pass credentials for cloning code, leave blank for GIT SSH credentials.
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# git_user: ""
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# git_pass: ""
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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 trains_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
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type: pip,
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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/trainsai/api/pypi/public/simple"]
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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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},
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# target folder for virtual environments builds, created when executing experiment
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venvs_dir = ~/.trains/venvs-builds
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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: ~/.trains/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 (used for pytorch package caching)
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pip_download_cache {
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enabled: true,
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path: ~/.trains/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 used mapped into docker, for python package caching
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docker_pip_cache = ~/.trains/pip-cache
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# apt cache folder used mapped into docker, for ubuntu package caching
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docker_apt_cache = ~/.trains/apt-cache
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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"
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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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# cuda versions used for solving pytorch wheel packages
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# 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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}
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