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Refactor ClearML Agent page
This commit is contained in:
alnoam
parent
c1b9b2e129
commit
94525b12c9
@ -135,162 +135,165 @@ In case a `clearml.conf` file already exists, add a few ClearML Agent specific c
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* Mac - `$HOME/clearml.conf`
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* Windows - `\User\<username>\clearml.conf`
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1. After the `api` section, add the following `agent` section:
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1. After the `api` section, add your `agent` section
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<details className="cml-expansion-panel configuration">
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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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# 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 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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# 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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# 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 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 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 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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# 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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# 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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# target folder for virtual environments builds, created when executing experiment
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venvs_dir = ~/.clearml/venvs-builds
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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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# 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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}
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</div></details>
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1. Save the configuration.
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## Execution
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### Simple Execution
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### Spinning up an Agent
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#### Executing an Agent
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To execute an agent, listening to a queue, run:
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@ -370,40 +373,6 @@ clearml-agent daemon --queue default --foreground
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```
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## Building Docker Containers
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### Task Container
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Build a Docker container that when launched executes a specific experiment, or a clone (copy) of that experiment.
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- Build a Docker container that at launch will execute a specific Task.
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```bash
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clearml-agent build --id <task-id> --docker --target <new-docker-name> --entry-point reuse_task
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```
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- Build a Docker container that at launch will clone a Task specified by Task ID, and will execute the newly cloned Task.
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```bash
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clearml-agent build --id <task-id> --docker --target <new-docker-name> --entry-point clone_task
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```
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- Run built Docker by executing:
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```bash
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docker run <new-docker-name>
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```
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### Base Docker Container
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Build a Docker container according to the execution environment of a specific Task.
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```bash
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clearml-agent build --id <task-id> --docker --target <new-docker-name>
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```
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It's possible to add the Docker container as the base Docker image to a Task (experiment), using one of the following methods:
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- Using the **ClearML Web UI** - See [Base Docker image](webapp/webapp_exp_tuning.md#base-docker-image) on the "Tuning
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Experiments" page.
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- In the **ClearML** configuration file - Use the **ClearML** configuration file [agent.default_docker](configs/clearml_conf.md#agentdefault_docker)
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options.
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## Execution Environments
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@ -490,6 +459,51 @@ venvs_cache: {
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},
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```
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## Dynamic GPU Allocation
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:::important
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Available with the ClearML Enterprise offering
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:::
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The ClearML Enterprise server supports dynamic allocation of GPUs based on queue properties.
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Agents can spin multiple Tasks from different queues based on the number of GPUs the queue
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needs.
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`dynamic-gpus` enables dynamic allocation of GPUs based on queue properties.
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To configure the number of GPUs for a queue, use the `--queue` flag and specify the queue name and number of GPUs:
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```console
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clearml-agent daemon --dynamic-gpus --queue dual_gpus=2 single_gpu=1
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```
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### Example
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Let's say there are three queues on a server, named:
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* `dual_gpu`
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* `quad_gpu`
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* `opportunistic`
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An agent can be spun on multiple GPUs (e.g. 8 GPUs, `--gpus 0-7`), and then attached to multiple
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queues that are configured to run with a certain amount of resources:
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```console
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clearml-agent daemon --dynamic-gpus --queues quad_gpu=4 dual_gpu=2
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```
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The agent can now spin multiple Tasks from the different queues based on the number of GPUs configured to the queue.
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The agent will pick a Task from the `quad_gpu` queue, use GPUs 0-3 and spin it. Then it will pick a Task from `dual_gpu`
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queue, look for available GPUs again and spin on GPUs 4-5.
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Another option for allocating GPUs:
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```console
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clearml-agent daemon --dynamic-gpus --queue dual=2 opportunistic=1-4
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```
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Notice that a minimum and maximum value of GPUs was specified for the `opportunistic` queue. This means the agent
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will pull a Task from the `opportunistic` queue and allocate up to 4 GPUs based on availability (i.e. GPUs not currently
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being used by other agents).
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## Services Mode
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The ClearML Agent Services Mode executes an Agent that can execute multiple Tasks. This is useful for Tasks that are mostly
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idling, such as periodic cleanup services, or a [pipeline controller](references/sdk/automation_controller_pipelinecontroller.md).
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@ -524,56 +538,47 @@ CLEARML_API_ACCESS_KEY
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CLEARML_API_SECRET_KEY
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```
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## Exporting a Task into a Standalone Docker Container
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### Task Container
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Build a Docker container that when launched executes a specific experiment, or a clone (copy) of that experiment.
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- Build a Docker container that at launch will execute a specific Task.
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```bash
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clearml-agent build --id <task-id> --docker --target <new-docker-name> --entry-point reuse_task
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```
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- Build a Docker container that at launch will clone a Task specified by Task ID, and will execute the newly cloned Task.
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```bash
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clearml-agent build --id <task-id> --docker --target <new-docker-name> --entry-point clone_task
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```
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- Run built Docker by executing:
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```bash
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docker run <new-docker-name>
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```
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### Base Docker Container
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Build a Docker container according to the execution environment of a specific Task.
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```bash
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clearml-agent build --id <task-id> --docker --target <new-docker-name>
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```
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It's possible to add the Docker container as the base Docker image to a Task (experiment), using one of the following methods:
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- Using the **ClearML Web UI** - See [Base Docker image](webapp/webapp_exp_tuning.md#base-docker-image) on the "Tuning
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Experiments" page.
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- In the **ClearML** configuration file - Use the **ClearML** configuration file [agent.default_docker](configs/clearml_conf.md#agentdefault_docker)
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options.
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## Google Colab
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ClearML Agent can run on a [google colab](https://colab.research.google.com/) instance. This helps users to leverage
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compute resources provided by google colab and send experiments for execution on it. <br/>
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Check out [this](guides/ide/google_colab.md) tutorial on how to run a ClearML Agent on Google Colab!
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## Dynamic GPU Allocation
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|
||||
:::important
|
||||
Available with the ClearML Enterprise offering
|
||||
:::
|
||||
|
||||
The ClearML Enterprise server supports dynamic allocation of GPUs based on queue properties.
|
||||
Agents can spin multiple Tasks from different queues based on the number of GPUs the queue
|
||||
needs.
|
||||
|
||||
`dynamic-gpus` enables dynamic allocation of GPUs based on queue properties.
|
||||
To configure the number of GPUs for a queue, use the `--queue` flag and specify the queue name and number of GPUs:
|
||||
|
||||
```console
|
||||
clearml-agent daemon --dynamic-gpus --queue dual_gpus=2 single_gpu=1
|
||||
```
|
||||
|
||||
### Example
|
||||
|
||||
Let's say there are three queues on a server, named:
|
||||
* `dual_gpu`
|
||||
* `quad_gpu`
|
||||
* `opportunistic`
|
||||
|
||||
An agent can be spun on multiple GPUs (e.g. 8 GPUs, `--gpus 0-7`), and then attached to multiple
|
||||
queues that are configured to run with a certain amount of resources:
|
||||
|
||||
```console
|
||||
clearml-agent daemon --dynamic-gpus --queues quad_gpu=4 dual_gpu=2
|
||||
```
|
||||
|
||||
The agent can now spin multiple Tasks from the different queues based on the number of GPUs configured to the queue.
|
||||
The agent will pick a Task from the `quad_gpu` queue, use GPUs 0-3 and spin it. Then it will pick a Task from `dual_gpu`
|
||||
queue, look for available GPUs again and spin on GPUs 4-5.
|
||||
|
||||
Another option for allocating GPUs:
|
||||
|
||||
```console
|
||||
clearml-agent daemon --dynamic-gpus --queue dual=2 opportunistic=1-4
|
||||
```
|
||||
|
||||
Notice that a minimum and maximum value of GPUs was specified for the `opportunistic` queue. This means the agent
|
||||
will pull a Task from the `opportunistic` queue and allocate up to 4 GPUs based on availability (i.e. GPUs not currently
|
||||
being used by other agents).
|
||||
|
||||
## Scheduling working hours
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user