---
title: Manual 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 tasks 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 <queue_name>
```
:::

### Executing an Agent
To execute an agent, listening to a queue, run:

```bash
clearml-agent daemon --queue <queue_name>
```

### Executing in Background
To execute an agent in the background, run:
```bash
clearml-agent daemon --queue <execution_queue_to_pull_from> --detached
```
### Stopping Agents
To stop an agent running in the background, run:
```bash
clearml-agent daemon <arguments> --stop
```

### Allocating Resources
To specify GPUs associated with the agent, add the `--gpus` flag.

:::info Docker Mode
Make sure to include the `--docker` flag, as GPU management through the agent is only supported in [Docker Mode](clearml_agent_execution_env.md#docker-mode).
:::

To execute multiple agents on the same machine (usually assigning GPU for the different agents), run:
```bash
clearml-agent daemon --gpus 0 --queue default --docker
clearml-agent daemon --gpus 1 --queue default --docker
```
To allocate more than one GPU, provide a list of allocated GPUs
```bash
clearml-agent daemon --gpus 0,1 --queue dual_gpu --docker
```

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

```bash
clearml-agent daemon --queue high_q low_q
```
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 --queue group_a group_b --order-fairness
```
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-<random>/agent.<random>
```

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=<file_socket> clearml-agent daemon --gpus <your config> --queue <your queue name>  --docker
```

## 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 tasks for execution on it. 

Check out [this tutorial](../guides/ide/google_colab.md) on how to run a ClearML Agent on Google Colab!

## 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 <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.
```bash
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.
```bash
clearml-agent execute --id <task-id> --docker
```

## Debugging

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