--- title: 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. ![ClearML Agent flow diagram](img/clearml_agent_flow_diagram.png) The diagram above demonstrates a typical flow where an agent executes a task: 1. Enqueue a task for execution on the queue. 1. The agent pulls the task from the queue. 1. The agent launches a docker container in which to run the task's code. 1. The task's execution environment is set up: 1. Execute any custom setup script configured. 1. Install any required system packages. 1. Clone the code from a git repository. 1. Apply any uncommitted changes recorded. 1. Set up the python environment and required packages. 1. 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 (using [`Task.enqueue`](references/sdk/task.md#taskenqueue) or [`Task.execute_remotely`](references/sdk/task.md#execute_remotely)) * Through the ClearML Web UI (without working directly with code), by cloning experiments and enqueuing them to the queue that a ClearML Agent is servicing. The agent facilitates [overriding task execution detail](webapp/webapp_exp_tuning.md) values through the UI without code modification. Modifying a task clone’s configuration will have the ClearML agent executing it override the original values: * Modified package requirements will have the experiment script run with updated packages * Modified recorded command line arguments will have the ClearML agent inject the new values in their stead * Code-level configuration instrumented with [`Task.connect`](references/sdk/task.md#connect) will be overridden by modified hyperparameters For more information, see [ClearML Agent Reference](clearml_agent/clearml_agent_ref.md), and [configuration options](configs/clearml_conf.md#agent-section). ## Installation :::note If ClearML was previously configured, follow [this](#adding-clearml-agent-to-a-configuration-file) to add ClearML Agent specific configurations ::: To install ClearML Agent, execute ```bash pip install clearml-agent ``` :::info Install ClearML Agent as a system Python package and not in a Python virtual environment. An agent that runs in Virtual Environment Mode or Conda Environment Mode needs to create virtual environments, and it can't do that when running from a virtual environment. ::: ## Configuration 1. In a terminal session, execute ```bash clearml-agent init ``` The setup wizard prompts for ClearML credentials (see [here](webapp/webapp_profile.md#clearml-credentials) about obtaining credentials). ``` Please create new clearml credentials through the settings page in your `clearml-server` web app, or create a free account at https://app.clear.ml/settings/webapp-configuration In the settings > workspace 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](#adding-clearml-agent-to-a-configuration-file). The wizard does not edit or overwrite existing configuration files. 1. 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="*******" ``` 1. **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.clear.ml] ``` :::note If you are using a self-hosted ClearML Server, the default URL will use your domain. ::: 1. Do as above for API, URL, and file servers. 1. The wizard responds with your configuration: ``` CLEARML Hosts configuration: Web App: https://app.clear.ml API: https://api.clear.ml File Store: https://files.clear.ml Verifying credentials ... Credentials verified! ``` 1. 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 '': ``` The setup wizard confirms your git credentials. ``` Git repository cloning will be using user= password= ``` 1. 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//clearml.conf CLEARML-AGENT setup completed successfully. ``` The configuration file location depends upon the operating system: * Linux - `~/clearml.conf` * Mac - `$HOME/clearml.conf` * Windows - `\User\\clearml.conf` 1. Optionally, configure ClearML options for **ClearML Agent** (default docker, package manager, etc.). See the [ClearML Configuration Reference](configs/clearml_conf.md) and the [ClearML Agent Environment Variables reference](clearml_agent/clearml_agent_env_var.md). :::note The ClearML Enterprise server provides a [configuration vault](webapp/webapp_profile.md#configuration-vault), the contents of which are categorically applied on top of the agent-local configuration ::: ### Adding ClearML Agent to a Configuration File In case a `clearml.conf` file already exists, add a few ClearML Agent specific configurations to it.
**Adding ClearML Agent to a ClearML configuration file:** 1. Open the ClearML configuration file for editing. Depending upon the operating system, it is: * Linux - `~/clearml.conf` * Mac - `$HOME/clearml.conf` * Windows - `\User\\clearml.conf` 1. After the `api` section, add your `agent` section. For example: ``` agent { # Set GIT user/pass credentials (if user/pass are set, GIT protocol will be set to https) git_user="" git_pass="" # 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 # unique name of this worker, if None, created based on hostname:process_id # Overridden with os environment: CLEARML_WORKER_NAME worker_id: "" } ``` View a complete ClearML Agent configuration file sample including an `agent` section [here](https://github.com/allegroai/clearml-agent/blob/master/docs/clearml.conf). 1. Save the configuration. ## Deployment ### Spinning Up an Agent You can spin up an agent on any machine: on-prem and/or cloud instance. When spinning up an agent, you assign it to service a queue(s). Utilize the machine by enqueuing tasks to the queue that the agent is servicing, and the agent will pull and execute the tasks. :::tip cross-platform execution ClearML Agent is platform agnostic. When using the ClearML Agent to execute experiments cross-platform, set platform specific environment variables before launching the agent. For example, to run an agent on an ARM device, set the core type environment variable before spinning up the agent: ```bash export OPENBLAS_CORETYPE=ARMV8 clearml-agent daemon --queue ``` ::: #### Executing an Agent To execute an agent, listening to a queue, run: ```bash clearml-agent daemon --queue ``` #### Executing in Background To execute an agent in the background, run: ```bash clearml-agent daemon --queue --detached ``` #### Stopping Agents To stop an agent running in the background, run: ```bash clearml-agent daemon --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: ```bash 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 ```bash 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. ```bash clearml-agent daemon --detached --queue high_q low_q --gpus 0 ``` This ensures the agent first tries to pull a Task from the `high_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. ```bash 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. #### SSH Access By default, ClearML Agent maps the host's `~/.ssh` into the container's `/root/.ssh` directory (configurable, see [clearml.conf](configs/clearml_conf.md#docker_internal_mounts)). If you want to use existing auth sockets with ssh-agent, you can verify your host ssh-agent is working correctly with: ```commandline echo $SSH_AUTH_SOCK ``` You should see a path to a temporary file, something like this: ```console /tmp/ssh-/agent. ``` Then run your `clearml-agent` in Docker mode, which will automatically detect the `SSH_AUTH_SOCK` environment variable, and mount the socket into any container it spins. You can also explicitly set the `SSH_AUTH_SOCK` environment variable when executing an agent. The command below will execute an agent in Docker mode and assign it to service a queue. The agent will have access to the SSH socket provided in the environment variable. ``` SSH_AUTH_SOCK= clearml-agent daemon --gpus --queue --docker ``` ### Kubernetes Agents can be deployed bare-metal or as dockers in a Kubernetes cluster. ClearML Agent adds the missing scheduling capabilities to Kubernetes, allows for more flexible automation from code, and gives access to all of ClearML Agent’s features (scheduling, job prioritization, and more). There are two options for deploying the ClearML Agent to a Kubernetes cluster: * Spin ClearML Agent as a long-lasting service pod * Map ClearML jobs directly to K8s jobs with Kubernetes Glue (available in the ClearML Enterprise plan) See more details [here](https://github.com/allegroai/clearml-agent#kubernetes-integration-optional). ### 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. ```bash clearml-agent execute --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. ```bash clearml-agent execute --id --clone ``` #### Execute Task inside a Docker Execute a Task with a `clearml-agent` worker using a Docker container without a queue. ```bash clearml-agent execute --id --docker ``` ### Debugging Run a `clearml-agent` daemon in foreground mode, sending all output to the console. ```bash 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](https://en.wikipedia.org/wiki/Pip_(package_manager)) as the package manager. When ClearML runs, it will create a virtual environment (or reuse an existing one, see [here](clearml_agent.md#virtual-environment-reuse)). Task dependencies (Python packages) will be installed in the virtual environment. ### Conda Mode This mode is similar to the PIP mode but uses [Conda](https://docs.conda.io/en/latest/) 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](https://python-poetry.org/) 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. :::note Using Poetry with Pyenv Some versions of poetry (using `install-poetry.py`) do not respect `pyenv global`. If you are using pyenv to control the environment where you use ClearML Agent, you can: * Use poetry v1.2 and above (which [fixes this issue](https://github.com/python-poetry/poetry/issues/5077)) * Install poetry with the deprecated `get-poetry.py` installer ::: ### 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 1. Install ClearML Agent in the container 1. 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: ```bash clearml-agent daemon --queue --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: ```bash 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 Enterprise Feature This feature is available under the ClearML Enterprise plan ::: 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 a server has three queues: * `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 --gpus 0-7 --queue 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 --gpus 0-7 --queue dual=2 opportunistic=1-4 ``` Notice that a minimum and maximum value of GPUs is 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 ClearML Agent supports a **Services Mode** where, as soon as a task is launched off of its queue, the agent moves on to the next task without waiting for the previous one to complete. This mode is intended for running resource-sparse tasks that are usually idling, such as periodic cleanup services or a [pipeline controller](references/sdk/automation_controller_pipelinecontroller.md). To run a `clearml-agent` in services mode, run: ```bash clearml-agent daemon --services-mode --queue services --create-queue --docker --cpu-only ``` To limit the number of simultaneous tasks run in services mode, pass the maximum number immediately after the `--services-mode` option (e.g. `--services-mode 5`) :::note Notes * `services-mode` currently only supports Docker mode. Each service spins on its own Docker image. * The default `clearml-server` configuration already runs a single `clearml-agent` in services mode that listens to the `services` queue. ::: Launch a service task like any other task, by enqueuing it to the appropriate queue. :::caution 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](deploying_clearml/clearml_server.md) 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](deploying_clearml/clearml_server_security.md#user-access-security). 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. ```bash clearml-agent build --id --docker --target --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. ```bash clearml-agent build --id --docker --target --entry-point clone_task ``` - Run built Docker by executing: ```bash docker run ``` Check out [this tutorial](guides/clearml_agent/executable_exp_containers.md) for building executable experiment containers. ### Base Docker Container Build a Docker container according to the execution environment of a specific task. ```bash clearml-agent build --id --docker --target ``` It's possible to add the Docker container as the base Docker image to a task (experiment), using one of the following methods: - Using the **ClearML Web UI** - See [Base Docker image](webapp/webapp_exp_tuning.md#base-docker-image) on the "Tuning Experiments" page. - In the ClearML configuration file - Use the ClearML configuration file [agent.default_docker](configs/clearml_conf.md#agentdefault_docker) options. Check out [this tutorial](guides/clearml_agent/exp_environment_containers.md) for building a Docker container replicating the execution environment of an existing task. ## Google Colab ClearML Agent can run on a [Google Colab](https://colab.research.google.com/) instance. This helps users to leverage compute resources provided by Google Colab and send experiments for execution on it. Check out [this tutorial](guides/ide/google_colab.md) on how to run a ClearML Agent on Google Colab! ## Scheduling Working Hours :::important Enterprise Feature This feature is available under the ClearML Enterprise plan ::: 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: :::caution 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 `" "`, where: * `` - A span of hours (`00-23`) or a single hour. A single hour defines a span from that hour to midnight. * `` - 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: :::caution Use only 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: :::caution 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":