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ClearML Agent

ClearML Agent is a virtual environment and execution manager for DL / ML solutions on GPU machines. It integrates with the ClearML Python Package and ClearML Server to provide a full AI cluster solution.
Its main focus is around:

  • Reproducing experiments, including their complete environments.
  • Scaling workflows on multiple target machines.

ClearML Agent executes an experiment or other workflow by reproducing the state of the code from the original machine to a remote machine.

image

The diagram above demonstrates a typical flow where an agent executes a task:

  1. Enqueue a task for execution on the queue.
  2. The agent pulls the task from the queue.
  3. The agent launches a docker container in which to run the task's code.
  4. The task's execution environment is set up:
    1. Execute any custom setup script configured.
    2. Install any required system packages.
    3. Clone the code from a git repository.
    4. Apply any uncommitted changes recorded.
    5. Set up the python environment and required packages.
  5. The task's script/code is executed.

While the agent is running, it continuously reports system metrics to the ClearML Server (These can be monitored in the Workers and Queues page).

Continue using ClearML Agent once it is running on a target machine. Reproduce experiments and execute automated workflows in one (or both) of the following ways:

  • Programmatically
  • By using the ClearML Web UI (without directly working with code), by enqueuing experiments to the queue that a ClearML Agent is listening to.

For more information, see ClearML Agent Reference, and configuration options.

Installation#

note

If ClearML was previously configured, follow this to add ClearML Agent specific configurations

To install ClearML Agent execute

pip install clearml-agent

Configuration#

  1. In a terminal session, execute

    clearml-agent init

    The setup wizard prompts for ClearML credentials (see here about obtaining credentials).

    CLEARML-AGENT setup process
    Please create new clearml credentials through the profile page in your clearml web app (e.g., https://demoapp.demo.clear.ml/profile)
    In the profile page, press "Create new credentials", then press "Copy to clipboard".
    Paste copied configuration here:

    If the setup wizard's response indicates that a configuration file already exists, follow the instructions here. The wizard does not edit or overwrite existing configuration files.

  2. At the command prompt Paste copied configuration here:, copy and paste the ClearML credentials and press Enter. The setup wizard confirms the credentials.

    Detected credentials key="********************" secret="*******"
  3. Enter to accept default server URL, which is detected from the credentials or Enter a ClearML web server URL.

    A secure protocol, https, must be used. Do not use http.

    WEB Host configured to: [https://app.community.clear.ml]
    note

    If you are using a self-hosted ClearML Server, the default URL will use your domain.

  4. Do as above for API, URL, and file servers.

  5. The wizard responds with your configuration:

    CLEARML Hosts configuration:
    Web App: https://app.community.clear.ml
    API: https://demoapi.clearml.allegro.ai
    File Store: https://demofiles.clearml.allegro.ai
    Verifying credentials ...
    Credentials verified!
  6. Enter your Git username and password. Leave blank for SSH key authentication or when only using public repositories.
    This is needed for cloning repositories by the agent.

    Enter git username for repository cloning (leave blank for SSH key authentication): []
    Enter password for user '<username>':

    The setup wizard confirms your git credentials.

    Git repository cloning will be using user=<username> password=<password>
  7. Enter an additional artifact repository, or press Enter if not required.
    This is needed for installing Python packages not found in pypi.

    Enter additional artifact repository (extra-index-url) to use when installing python packages (leave blank if not required):

    The setup wizard completes.

    New configuration stored in /home/<username>/clearml.conf
    CLEARML-AGENT setup completed successfully.

    The configuration file location depends upon the operating system:

    • Linux - ~/clearml.conf
    • Mac - $HOME/clearml.conf
    • Windows - \User\<username>\clearml.conf
  8. Optionally, configure ClearML options for ClearML Agent (default docker, package manager, etc.). See the ClearML Configuration Reference.

Adding ClearML Agent to a configuration file#

In case a clearml.conf file already exists, add a few ClearML Agent specific configurations to it.

Adding ClearML Agent to a ClearML configuration file:

  1. Open the ClearML configuration file for editing. Depending upon the operating system, it is:

    • Linux - ~/clearml.conf
    • Mac - $HOME/clearml.conf
    • Windows - \User\<username>\clearml.conf
  2. After the api section, add your agent section

    View sample agent section
    agent {
    # 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: ""
    # Limit credentials to a single domain, for example: github.com,
    # all other domains will use public access (no user/pass). Default: always send user/pass for any VCS domain
    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
    # 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_ID
    # worker_name: "clearml-agent-machine1"
    worker_name: ""
    # 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: ""
    # 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/clearmlai/api/pypi/public/simple"]
    extra_index_url: []
    # additional conda channels to use when installing with conda package manager
    conda_channels: ["defaults", "conda-forge", "pytorch", ]
    # conda_full_env_update: false
    # conda_env_as_base_docker: 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 (mostly pytorch versions)
    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", "-v", "/mnt/host/data:/mnt/data"]
    # 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
    # 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 OS environments based on the Task's Environment section before launching the Task process.
    enable_task_env: false
    # CUDA versions used for Conda setup & solving PyTorch wheel packages
    # it Should be detected automatically. Override with os environment CUDA_VERSION / CUDNN_VERSION
    # cuda_version: 10.1
    # cudnn_version: 7.6
    }
  3. Save the configuration.

Execution#

Spinning up an Agent#

Executing an Agent#

To execute an agent, listening to a queue, run:

clearml-agent daemon --queue <queue_name>

Executing in Background#

To execute an agent in the background, run:

clearml-agent daemon --queue <execution_queue_to_pull_from> --detached

Stopping Agents#

To stop an agent running in the background, run:

clearml-agent daemon <arguments> --stop

Allocating Resources#

To specify GPUs associated with the agent, add the --gpus flag. To execute multiple agents on the same machine (usually assigning GPU for the different agents), run:

clearml-agent daemon --detached --queue default --gpus 0
clearml-agent daemon --detached --queue default --gpus 1

To allocate more than one GPU, provide a list of allocated GPUs

clearml-agent daemon --gpus 0,1 --queue dual_gpu &

Queue Prioritization#

A single agent can listen to multiple queues. The priority is set by their order.

clearml-agent daemon --detached --queue high_q low_q --gpus 0

This ensures the agent first tries to pull a Task from the “hiqh_q” queue, and only if it is empty, the agent will try to pull from the “low_q” queue.

To make sure an agent pulls from all queues equally, add the --order-fairness flag.

clearml-agent daemon --detached --queue group_a group_b --order-fairness --gpus 0

It will make sure the agent will pull from the “group_a” queue, then from “group_b”, then back to “group_a”, etc. This ensures that “group A” or ”group_b” will not be able to starve one another of resources.

Explicit Task execution#

ClearML Agent can also execute specific tasks directly, without listening to a queue.

Execute a Task without queue#

Execute a Task with a clearml-agent worker without a queue.

clearml-agent execute --id <task-id>

Clone a Task and execute the cloned Task#

Clone the specified Task and execute the cloned Task with a clearml-agent worker without a queue.

clearml-agent execute --id <task-id> --clone

Execute Task inside a Docker#

Execute a Task with a clearml-agent worker using a Docker container without a queue.

clearml-agent execute --id <task-id> --docker

Debugging#

  • Run a clearml-agent daemon in foreground mode, sending all output to the console.
clearml-agent daemon --queue default --foreground

Execution Environments#

ClearML Agent supports executing tasks in multiple environments.

PIP Mode#

By default, ClearML Agent works in PIP Mode, in which it uses pip as the package manager. When ClearML runs, it will create a virtual environment (or reuse an exisitng one, see here). Task dependencies (Python packages) will be installed in the virtual environment.

Conda Mode#

This mode is similar to the PIP mode but uses Conda as the package manager. To enable Conda mode, edit the clearml.conf file, and modify the type: pip to type: conda in the “package_manager” section. If extra conda channels are needed, look for “conda_channels” under “package_manager”, and add the missing channel.

Poetry Mode#

This mode is similar to the PIP mode but uses Poetry as the package manager. To enable Poetry mode, edit the clearml.conf file, and modify the type: pip to type: poetry in the “package_manager” section.

Docker Mode#

note

Docker Mode is only supported in linux.
Docker Mode requires docker service v19.03 or higher installed.

When executing the ClearML Agent in Docker mode, it will:

  1. Run the provided Docker container
  2. Install ClearML Agent in the container
  3. Execute the Task in the container, and monitor the process.

ClearML Agent uses the provided default Docker container, which can be overridden from the UI.

All ClearML Agent flags (Such as --gpus and --foreground) are applicable to Docker mode as well.

To execute ClearML Agent in Docker mode, run:

clearml-agent daemon --queue <execution_queue_to_pull_from> --docker [optional default docker image to use]

To use the current clearml-agent version in the Docker container, instead of the latest clearml-agent version that is automatically installed, run:

clearml-agent daemon --queue default --docker --force-current-version

For Kubernetes, specify a host mount on the daemon host. Do not use the host mount inside the Docker container. Set the environment variable CLEARML_AGENT_K8S_HOST_MOUNT. For example:

CLEARML_AGENT_K8S_HOST_MOUNT=/mnt/host/data:/root/.clearml

Environment Caching#

ClearML Agent caches virtual environments so when running experiments multiple times, there's no need to spend time reinstalling pre-installed packages. To make use of the cached virtual environments, enable the virtual environment reuse mechanism.

Virtual Environment Reuse#

The virtual environment reuse feature may reduce experiment startup time dramatically.

By default, ClearML uses the package manager's environment caching. This means that even if no new packages need to be installed, checking the list of packages can take a long time.

ClearML has a virtual environment reuse mechanism which, when enabled, allows using environments as-is without resolving installed packages. This means that when executing multiple experiments with the same package dependencies, the same environment will be used.

note

ClearML does not support environment reuse when using Poetry package manager

To enable environment reuse, modify the clearml.conf file and unmark the venvs_cache section.

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
},

Dynamic GPU Allocation#

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:

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:

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:

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).

Services Mode#

The ClearML Agent Services Mode executes an Agent that can execute multiple Tasks. This is useful for Tasks that are mostly idling, such as periodic cleanup services, or a pipeline controller.

Launch a service Task like any other Task, by enqueuing it to the appropriate queue.

note

The default clearml-server configuration already runs a single clearml-agent in services mode that listens to the “services” queue.

To run a clearml-agent in services mode, run:

clearml-agent daemon --services-mode --queue services --create-queue --docker <docker_name> --cpu-only
note

services-mode currently only supports Docker mode. Each service spins on its own Docker image.

warning

Do not enqueue training or inference Tasks into the services queue. They will put an unnecessary load on the server.

Setting Server Credentials#

Self hosted ClearML Server comes by default with a services queue. By default, the server is open and does not require username and password, but it can be password protected. In case it is password protected the services agent will need to be configured with server credentials (associated with a user).

To do that, set these environment variables on the ClearML Server machine with the appropriate credentials:

CLEARML_API_ACCESS_KEY
CLEARML_API_SECRET_KEY

Exporting a Task into a Standalone Docker Container#

Task Container#

Build a Docker container that when launched executes a specific experiment, or a clone (copy) of that experiment.

  • Build a Docker container that at launch will execute a specific Task.
    clearml-agent build --id <task-id> --docker --target <new-docker-name> --entry-point reuse_task
  • Build a Docker container that at launch will clone a Task specified by Task ID, and will execute the newly cloned Task.
    clearml-agent build --id <task-id> --docker --target <new-docker-name> --entry-point clone_task
  • Run built Docker by executing:
    docker run <new-docker-name>

Base Docker Container#

Build a Docker container according to the execution environment of a specific Task.

clearml-agent build --id <task-id> --docker --target <new-docker-name>

It's possible to add the Docker container as the base Docker image to a Task (experiment), using one of the following methods:

Google Colab#

ClearML Agent can run on a google colab instance. This helps users to leverage compute resources provided by google colab and send experiments for execution on it.
Check out this tutorial on how to run a ClearML Agent on Google Colab!

Scheduling working hours#

important

Available with the ClearML Enterprise offering

The Agent scheduler enables scheduling working hours for each Agent. During working hours, a worker will actively poll queues for Tasks, fetch and execute them. Outside working hours, a worker will be idle.

Schedule workers by:

  • Setting configuration file options
  • Running clearml-agent from the command line (overrides configuration file options)

Override worker schedules by:

  • Setting runtime properties to force a worker on or off
  • Tagging a queue on or off

Running clearml-agent with a schedule (command line)#

Set a schedule for a worker from the command line when running clearml-agent. Two properties enable setting working hours:

warning

Use only one of these properties

  • uptime - Time span during which a worker will actively poll a queue(s) for Tasks, and execute them. Outside this time span, the worker will be idle.
  • downtime - Time span during which a worker will be idle. Outside this time span, the worker will actively poll and execute Tasks.

Define uptime or downtime as "<hours> <days>", where:

  • <hours> - A span of hours (00-23) or a single hour. A single hour defines a span from that hour to midnight.
  • <days> - A span of days (SUN-SAT) or a single day.

Use - for a span, and , to separate individual values. To span before midnight to after midnight, use two spans.

For example:

  • "20-23 SUN" - 8 PM to 11 PM on Sundays.
  • "20-23 SUN,TUE" - 8 PM to 11 PM on Sundays and Tuesdays.
  • "20-23 SUN-TUE" - 8 PM to 11 PM on Sundays, Mondays, and Tuesdays.
  • "20 SUN" - 8 PM to midnight on Sundays.
  • "20-00,00-08 SUN" - 8 PM to midnight and midnight to 8 AM on Sundays
  • "20-00 SUN", "00-08 MON" - 8 PM on Sundays to 8 AM on Mondays (spans from before midnight to after midnight).

Setting worker schedules in the configuration file#

Set a schedule for a worker using configuration file options. The options are:

warning

Only use one of these properties

  • agent.uptime
  • agent.downtime

Use the same time span format for days and hours as is used in the command line.

For example, set a worker's schedule from 5 PM to 8 PM on Sunday through Tuesday, and 1 PM to 10 PM on Wednesday.

agent.uptime: ["17-20 SUN-TUE", "13-22 WED"]

Overriding worker schedules using runtime properties#

Runtime properties override the command line uptime / downtime properties. The runtime properties are:

warning

Use only one of these properties

  • force:on - Pull and execute Tasks until the property expires.
  • force:off - Prevent pulling and execution of Tasks until the property expires.

Currently, these runtime properties can only be set using an ClearML REST API call to the workers.set_runtime_properties endpoint, as follows:

  • The body of the request must contain the worker-id, and the runtime property to add.
  • An expiry date is optional. Use the format ”expiry”:<time>. For example, ”expiry”:86400 will set an expiry of 24 hours.
  • To delete the property, set the expiry date to zero, 'expiry:0'.

For example, to force a worker on for 24 hours:

curl --user <key>:<secret> --header "Content-Type: application/json" --data '{"worker":"<worker_id>","runtime_properties":[{"key": "force", "value": "on", "expiry": 86400}]}' http://<api-server-hostname-or-ip>:8008/workers.set_runtime_properties

Overriding worker schedules using queue tags#

Queue tags override command line and runtime properties. The queue tags are the following:

warning

Use only one of these properties

  • force_workers:on - Any worker listening to the queue will keep pulling Tasks from the queue.
  • force_workers:off - Prevent all workers listening to the queue from pulling Tasks from the queue.

Currently, you can set queue tags using an ClearML REST API call to the queues.update endpoint, or the APIClient. The body of the call must contain the queue-id and the tags to add.

For example, force workers on for a queue using the APIClient:

from trains.backend_api.session.client import APIClient
client = APIClient()
client.queues.update(queue=”<queue_id>”, tags=["force_workers:on"]

Or, force workers on for a queue using the REST API:

curl --user <key>:<secret> --header "Content-Type: application/json" --data '{"queue":"<queue_id>","tags":["force_workers:on"]}' http://<api-server-hostname-or-ip>:8008/queues.update