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112 lines
6.3 KiB
Markdown
112 lines
6.3 KiB
Markdown
---
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title: Agent & Queue
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---
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Two major components of MLOps is experiment reproducibility, and the ability to scale work to multiple machines. ClearML Agent,
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coupled with execution queues, addresses both these needs.
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The ClearML Agent is the base for **Automation** in ClearML and can be leveraged to build automated pipelines, launch custom services
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(e.g. a [monitor and alert service](https://github.com/allegroai/clearml/tree/master/examples/services/monitoring)) and more.
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## What Does a ClearML Agent Do?
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An agent (also referred to as a worker) allows users to execute code on any machine it's installed on, thus facilitating the
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scaling of data science work beyond one's own machine.
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The agent takes care of deploying the code to the target machine as well as setting up the entire execution environment:
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from installing required packages to setting environment variables,
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all leading to executing the code (supporting both virtual environment or flexible docker container configurations).
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The agent also supports overriding parameter values on-the-fly without code modification, thus enabling no-code experimentation (this is also the foundation on which
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ClearML [Hyperparameter Optimization](hpo.md) is implemented).
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An agent can be associated with specific GPUs, enabling workload distribution. For example, on a machine with 8 GPUs you
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can allocate several GPUs to an agent and use the rest for a different workload, even through another agent (see [Dynamic GPU Allocation](../clearml_agent.md#dynamic-gpu-allocation)).
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## What is a Queue?
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A ClearML queue is an ordered list of Tasks scheduled for execution. One or multiple agents can service a queue.
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Agents servicing a queue pull the queued tasks in order and execute them.
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A ClearML Agent can service multiple queues in either of the following modes:
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* Strict priority: The agent services the higher priority queue before servicing lower priority ones.
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* Round robin: The agent pulls a single task from a queue then moves to service the next queue.
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## Agent and Queue Workflow
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The diagram above demonstrates a typical flow where an agent executes a task:
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1. Enqueue a task for execution on the queue.
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1. The agent pulls the task from the queue.
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1. The agent launches a docker container in which to run the task's code.
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1. The task's execution environment is set up:
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1. Execute any custom setup script configured.
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1. Install any required system packages.
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1. Clone the code from a git repository.
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1. Apply any uncommitted changes recorded.
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1. Set up the python environment and required packages.
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1. The task's script/code is executed.
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While the agent is running, it continuously reports system metrics to the ClearML Server. You can monitor these metrics
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in the [**Workers and Queues**](../webapp/webapp_workers_queues.md) page.
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## Resource Management
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Installing an Agent on machines allows it to monitor all the machine's status (GPU / CPU / Memory / Network / Disk IO).
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When managing multiple machines, this allows users to have an overview of their HW resources: the status of each machine,
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the expected workload on each machine, etc.
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You can organize your queues according to resource usage. Say you have a single-GPU machine. You can create a queue called
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`single-gpu-queue` and assign the machine's agent, as well as other single-GPU agents to that queue. This way you will know
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that Tasks assigned to that queue will be executed by a single GPU machine.
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While the agents are up and running in your machines, you can access these resources from any machine by enqueueing a
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Task to one of your queues, according to the amount of resources you want to allocate to the Task.
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With queues and ClearML Agent, you can easily add and remove machines from the cluster, and you can
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reuse machines without the need for any dedicated containers or images.
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## Additional Features
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Agents can be deployed bare-metal, with multiple instances allocating
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specific GPUs to the agents. They can also be deployed as dockers in a Kubernetes cluster.
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The Agent has three running modes:
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- Docker Mode: The agent spins a docker image based on the Task’s definition then inside the docker the agent will clone
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the specified repository/code, apply the original execution’s uncommitted changes, install the required python packages
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and start executing the code while monitoring it.
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- Virtual Environment Mode: The agent creates a new virtual environment for the experiment, installs the required python
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packages based on the Task specification, clones the code repository, applies the uncommitted changes and finally
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executes the code while monitoring it.
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- Conda Environment Mode: Similar to the Virtual Environment mode, only instead of using pip, it uses conda install and
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pip combination. Notice this mode is quite brittle due to the Conda package version support table.
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## Services Mode
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In its default mode, a ClearML Agent executes a single task at a time, since training tasks typically require all resources
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available to them. Some tasks are mostly idling and require less computation power, such as controller tasks (e.g.
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a pipeline controller) or service tasks (e.g. cleanup service).
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This is where the `services-modes` comes into play. An agent running in `services-mode` will let multiple tasks execute
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in parallel (each task will register itself as a sub-agent, visible in the [Workers & Queues](../webapp/webapp_workers_queues.md) tab in the UI).
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This mode is intended for running maintenance tasks. Some suitable tasks include:
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- [Pipeline controller](../guides/pipeline/pipeline_controller.md) - Implementing the pipeline scheduling and logic
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- [Hyper-Parameter Optimization](../guides/optimization/hyper-parameter-optimization/examples_hyperparam_opt.md) - Implementing an active selection of experiments
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- [Control Service](../guides/services/aws_autoscaler.md) - AWS Autoscaler for example
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- [External services](../guides/services/slack_alerts.md) - Such as Slack integration alert service
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:::warning
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Do not enqueue training or inference tasks into the services queue. They will put an unnecessary load on the server.
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:::
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By default, the open source [ClearML Server](../deploying_clearml/clearml_server.md) runs a single clearml-agent in
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services mode that listens to the `services` queue.
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