{ # unique name of this worker, if None, created based on hostname:process_id # Override with os environment: CLEARML_WORKER_ID # worker_id: "clearml-agent-machine1:gpu0" worker_id: "" # worker name, replaces the hostname when creating a unique name for this worker # Override with os environment: CLEARML_WORKER_NAME # worker_name: "clearml-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) # Notice: GitHub personal token is equivalent to password, you can put it directly into `git_pass` # git_user: "" # git_pass: "" # git_host: "" # Force GIT protocol to use SSH regardless of the git url (Assumes GIT user/pass are blank) force_git_ssh_protocol: false # Force a specific SSH port when converting http to ssh links (the domain is kept the same) # force_git_ssh_port: 0 # Force a specific SSH username when converting http to ssh links (the default username is 'git') # force_git_ssh_user: git # 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 clearml_agent python_binary: "" # ignore any requested python version (Default: False, if a Task was using a # specific python version and the system supports multiple python the agent will use the requested python version) # ignore_requested_python_version: true # Force the root folder of the git repository (instead of the working directory) into the PYHTONPATH # default false, only the working directory will be added to the PYHTONPATH # force_git_root_python_path: false # select python package manager: # currently supported: pip, conda and poetry # if "pip" or "conda" are used, the agent installs the required packages # based on the "installed packages" section of the Task. If the "installed packages" is empty, # it will revert to using `requirements.txt` from the repository's root directory. # If Poetry is selected and the root repository contains `poetry.lock` or `pyproject.toml`, # the "installed packages" section is ignored, and poetry is used. # If Poetry is selected and no lock file is found, it reverts to "pip" package manager behaviour. 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", # specify poetry version to use (examples "<2", "==1.1.1", "", empty string will install the latest version) # poetry_version: "<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/clearmlai/api/pypi/public/simple"] # additional conda channels to use when installing with conda package manager conda_channels: ["pytorch", "conda-forge", "defaults", ] # If set to true, Task's "installed packages" are ignored, # and the repository's "requirements.txt" is used instead # force_repo_requirements_txt: false # 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 = ~/.clearml/venvs-builds # cached virtual environment folder venvs_cache: { # maximum number of cached venvs max_entries: 10 # minimum required free space to allow for cache entry, disable by passing 0 or negative value free_space_threshold_gb: 2.0 # unmark to enable virtual environment caching # path: ~/.clearml/venvs-cache }, # cached git clone folder vcs_cache: { enabled: true, path: ~/.clearml/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: ~/.clearml/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 = ~/.clearml/pip-cache # apt cache folder mapped into docker, used for ubuntu package caching docker_apt_cache = ~/.clearml/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", ] # Install the required packages for opencv libraries (libsm6 libxext6 libxrender-dev libglib2.0-0), # for backwards compatibility reasons, true as default, # change to false to skip installation and decrease docker spin up time # docker_install_opencv_libs: true # 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 - 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:10.2-cudnn7-runtime-ubuntu18.04" # optional arguments to pass to docker image # arguments: ["--ipc=host", ] } # set the OS environments based on the Task's Environment section before launching the Task process. enable_task_env: false # 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", # ] # set the preprocessing bash script to execute at the startup of any docker. # all lines will be executed regardless of their exit code. # docker_preprocess_bash_script = [ # "echo \"starting docker\"", #] # If False replace \r with \n and display full console output # default is True, report a single \r line in a sequence of consecutive lines, per 5 seconds. # suppress_carriage_return: true # 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 # Hide docker environment variables containing secrets when printing out the docker command by replacing their # values with "********". Turning this feature on will hide the following environment variables values: # CLEARML_API_SECRET_KEY, CLEARML_AGENT_GIT_PASS, AWS_SECRET_ACCESS_KEY, AZURE_STORAGE_KEY # To include more environment variables, add their keys to the "extra_keys" list. E.g. to make sure the value of # your custom environment variable named MY_SPECIAL_PASSWORD will not show in the logs when included in the # docker command, set: # extra_keys: ["MY_SPECIAL_PASSWORD"] hide_docker_command_env_vars { enabled: true extra_keys: [] } # allow to set internal mount points inside the docker, # especially useful for non-root docker container images. docker_internal_mounts { sdk_cache: "/clearml_agent_cache" apt_cache: "/var/cache/apt/archives" ssh_folder: "/root/.ssh" pip_cache: "/root/.cache/pip" poetry_cache: "/root/.cache/pypoetry" vcs_cache: "/root/.clearml/vcs-cache" venv_build: "/root/.clearml/venvs-builds" pip_download: "/root/.clearml/pip-download-cache" } # Name docker containers created by the daemon using the following string format (supported from Docker 0.6.5) # Allowed variables are task_id, worker_id and rand_string (random lower-case letters string, up to 32 characters) # Note: resulting name must start with an alphanumeric character and continue with alphanumeric characters, # underscores (_), dots (.) and/or dashes (-) #docker_container_name_format: "clearml-id-{task_id}-{rand_string:.8}" # Apply top-level environment section from configuration into os.environ apply_environment: true # Top-level environment section is in the form of: # environment { # key: value # ... # } # and is applied to the OS environment as `key=value` for each key/value pair # Apply top-level files section from configuration into local file system apply_files: true # Top-level files section allows auto-generating files at designated paths with a predefined contents # and target format. Options include: # contents: the target file's content, typically a string (or any base type int/float/list/dict etc.) # format: a custom format for the contents. Currently supported value is `base64` to automatically decode a # base64-encoded contents string, otherwise ignored # path: the target file's path, may include ~ and inplace env vars # target_format: format used to encode contents before writing into the target file. Supported values are json, # yaml, yml and bytes (in which case the file will be written in binary mode). Default is text mode. # overwrite: overwrite the target file in case it exists. Default is true. # # Example: # files { # myfile1 { # contents: "The quick brown fox jumped over the lazy dog" # path: "/tmp/fox.txt" # } # myjsonfile { # contents: { # some { # nested { # value: [1, 2, 3, 4] # } # } # } # path: "/tmp/test.json" # target_format: json # } # } }