Terminology update (#1035)

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@ -41,7 +41,7 @@ error, you are good to go.
clearml-session
```
You can add flags to set a Docker image, the remote SSH port, JupyterLab/VS Code versions, and more. See [CLI options](#command-line-options)
You can add flags to set a container image, the remote SSH port, JupyterLab/VS Code versions, and more. See [CLI options](#command-line-options)
for all configuration options.
`clearml-session` creates a new [Task](../fundamentals/task.md) that is responsible for setting up the SSH and
@ -57,8 +57,8 @@ error, you are good to go.
launches it.
1. Once the agent finishes the initial setup of the interactive Task, the local `cleaml-session` connects to the host
machine via SSH, and tunnels both SSH and IDE over the SSH connection. If a Docker is specified, the
IDE environment runs inside the Docker.
machine via SSH, and tunnels both SSH and IDE over the SSH connection. If a container is specified, the
IDE environment runs inside of it.
1. The CLI outputs access links to the remote JupyterLab and VS Code sessions:
@ -106,8 +106,8 @@ To connect to an existing session:
1. Click on the JupyterLab / VS Code link that is outputted, or connect directly to the SSH session
## Features
### Running in Docker
To run a session inside a Docker container, use the `--docker` flag and enter the docker image to use in the interactive
### Running in a Container
To run a session inside a container, use the `--docker` flag and enter the image to use in the interactive
session.
### Kubernetes Support
@ -224,8 +224,8 @@ clearml-session --continue-session <session_id> --store-workspace ~/workspace
| `--disable-fingerprint-check` | If set, bypass the remote SSH server fingerprint verification process | `none` |
| `--disable-session-cleanup` | If `True`, previous interactive sessions are not deleted | `false`|
| `--disable-store-defaults` | If set, do not store current setup as new default configuration| `none`|
| `--docker`| Select the docker image to use in the interactive session |`nvidia/cuda:11.6.2-runtime-ubuntu20.04` or previously used docker image|
| `--docker-args` | Add additional arguments for the docker image to use in the interactive session | `none` or the previously used docker-args |
| `--docker`| Select the image to use in the interactive session |`nvidia/cuda:11.6.2-runtime-ubuntu20.04` or previously used image|
| `--docker-args` | Add additional arguments for the docker image to use in the interactive session | `none` or the previously used `docker-args` |
| `--force_dropbear`| Force using `dropbear` instead of SSHd |`none`|
| `--git-credentials` | If `True`, local `.git-credentials` file is sent to the interactive session.| `false`|
| `--init-script` | Specify a BASH init script file to be executed when the interactive session is being set up | `none` or previously entered BASH script |

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@ -18,7 +18,7 @@ line arguments, Python module dependencies, and a requirements.txt file!
## How Does ClearML Task Work?
1. Execute `clearml-task`, specifying the ClearML target project and task name, along with your script (and repository / commit / branch).
Optionally, specify an execution queue and Docker image to use.
Optionally, specify an execution queue and container image to use.
1. `clearml-task` does its magic! It creates a new [ClearML Task](../fundamentals/task.md),
and, if so directed, enqueues it for execution by a ClearML Agent.
1. While the Task is running on the remote machine, all its console outputs are logged in real-time, alongside your
@ -26,9 +26,9 @@ line arguments, Python module dependencies, and a requirements.txt file!
(a link to your task details page in the ClearML Web UI is printed as ClearML Task creates the task).
## Execution Configuration
### Docker
Specify a Docker container to run the code in with the `--docker <docker_image>` option.
The ClearML Agent pulls it from Docker Hub or a Docker artifactory automatically.
### Container
Specify a container to run the code in with the `--docker <image>` option.
The ClearML Agent pulls it from Docker Hub or a container artifactory automatically.
### Package Dependencies
`clearml-task` automatically finds the `requirements.txt` file in remote repositories.
@ -61,8 +61,8 @@ errors in identifying the correct default branch.
| `--branch` | Select repository branch / tag. By default, latest commit from the master branch | <img src="/docs/latest/icons/ico-optional-no.svg" alt="No" className="icon size-md center-md" /> |
| `--commit` | Select commit ID to use. By default, latest commit, or local commit ID when using local repository | <img src="/docs/latest/icons/ico-optional-no.svg" alt="No" className="icon size-md center-md" /> |
| `--cwd` | Working directory to launch the script from. Relative to repo root or local `--folder` | <img src="/docs/latest/icons/ico-optional-no.svg" alt="No" className="icon size-md center-md" /> |
| `--docker` | Select the Docker image to use in the remote task | <img src="/docs/latest/icons/ico-optional-no.svg" alt="No" className="icon size-md center-md" /> |
| `--docker_bash_setup_script` | Add a bash script to be executed inside the Docker before setting up the task's environment | <img src="/docs/latest/icons/ico-optional-no.svg" alt="No" className="icon size-md center-md" /> |
| `--docker` | Select the container image to use in the remote task | <img src="/docs/latest/icons/ico-optional-no.svg" alt="No" className="icon size-md center-md" /> |
| `--docker_bash_setup_script` | Add a bash script to be executed inside the container before setting up the task's environment | <img src="/docs/latest/icons/ico-optional-no.svg" alt="No" className="icon size-md center-md" /> |
| `--docker_args` | Add Docker arguments. Pass a single string in the following format: `--docker_args "<argument_string>"`. For example: `--docker_args "-v some_dir_1:other_dir_1 -v some_dir_2:other_dir_2"` | <img src="/docs/latest/icons/ico-optional-no.svg" alt="No" className="icon size-md center-md" /> |
| `--folder` | Execute the code from a local folder. Notice, it assumes a git repository already exists. Current state of the repo (commit ID and uncommitted changes) is logged and replicated on the remote machine | <img src="/docs/latest/icons/ico-optional-no.svg" alt="No" className="icon size-md center-md" /> |
| `--import-offline-session`| Specify the path to the offline session you want to import.| <img src="/docs/latest/icons/ico-optional-no.svg" alt="No" className="icon size-md center-md" /> |

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@ -29,7 +29,7 @@ The preceding diagram 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 agent launches a 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.
@ -39,8 +39,8 @@ The preceding diagram demonstrates a typical flow where an agent executes a task
1. The task's script/code is executed.
:::note Python Version
ClearML Agent uses the Python version available in the environment or docker in which it executes the code. It does not
install Python, so make sure to use a docker or environment with the version you need.
ClearML Agent uses the Python version available in the environment or container in which it executes the code. It does not
install Python, so make sure to use a container or environment with the version you need.
:::
While the agent is running, it continuously reports system metrics to the ClearML Server (these can be monitored in the

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@ -39,8 +39,7 @@ clearml-agent build --id <task-id> --docker --target <new-docker-name>
You can add the Docker container as the base Docker image to a task, 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
Tasks" page.
- Using the **ClearML Web UI** - See [Default Container](../webapp/webapp_exp_tuning.md#default-container).
- In the ClearML configuration file - Use the ClearML configuration file [`agent.default_docker`](../configs/clearml_conf.md#agentdefault_docker)
options.

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@ -26,7 +26,7 @@ If you are using pyenv to control the environment where you use ClearML Agent, y
* Install poetry with the deprecated `get-poetry.py` installer
:::
## Docker Mode
## Docker Mode
:::note notes
* Docker Mode is only supported in Linux.
* Docker Mode requires docker service v19.03 or higher installed.

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@ -3,7 +3,7 @@ title: Executable Task Containers
---
This tutorial demonstrates using [`clearml-agent`](../../clearml_agent.md)'s [`build`](../../clearml_agent/clearml_agent_ref.md#build)
command to package a task into an executable container. In this example, you will build a Docker image that, when
command to package a task into an executable container. In this example, you will build a Container image that, when
run, will automatically execute the [keras_tensorboard.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/keras/keras_tensorboard.py)
script.
@ -44,8 +44,8 @@ script.
If the container will not make use of a GPU, add the `--cpu-only` flag.
:::
This command will create a Docker container, set up with the execution environment for this task in the
specified `--target` folder. When the Docker container is launched, it will clone the task specified with `id` and
This command will create a container, set up with the execution environment for this task in the
specified `--target` folder. When the container is launched, it will clone the task specified with `id` and
execute the clone (as designated by the `--entry-point` parameter).
1. Run the Docker, pointing to the new container:

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@ -3,7 +3,7 @@ title: Task Environment Containers
---
This tutorial demonstrates using [`clearml-agent`](../../clearml_agent.md)'s [`build`](../../clearml_agent/clearml_agent_ref.md#build)
command to build a Docker container replicating the execution environment of an existing task. ClearML Agents can make
command to build a container replicating the execution environment of an existing task. ClearML Agents can make
use of such containers to execute tasks without having to set up their environment every time.
A use case for this would be manual hyperparameter optimization, where a base task can be used to create a container to
@ -36,7 +36,7 @@ be used when running optimization tasks.
```
This ID will be used in the following section.
## Building the Docker Container
## Building the Container
Execute the following command to build the container. Input the ID of the task created above.
```console
@ -56,7 +56,7 @@ Committing docker container to: new_docker
sha256:460453b93ct1989fd1c6637c236e544031c4d378581433fc0b961103ce206af1
```
## Using the New Docker Container
## Using the New Container
Make use of the container you've just built by having a ClearML agent make use of it for executing a new task:
1. In the [ClearML Web UI](../../webapp/webapp_overview.md), go to the "examples" project, "Keras with TensorBoard

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@ -2,7 +2,7 @@
title: Extra Docker Shell Script
---
When using `clearml-agent`, an agent recreates an entire execution environment, be it by pulling the docker container or
When using `clearml-agent`, an agent recreates an entire execution environment, be it by pulling a container or
installing specified packages, and then executes the code on a remote machine. The Agent takes into account required Python packages,
but sometimes, when using a Docker container, a user may need to use additional, non-Python tools.

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@ -27,7 +27,7 @@ clearml-session --docker nvidia/cuda:10.1-cudnn7-runtime-ubuntu18.04 --packages
This sets the following arguments:
* `--docker nvidia/cuda:10.1-cudnn7-runtime-ubuntu18.04` - Docker image
* `--docker nvidia/cuda:10.1-cudnn7-runtime-ubuntu18.04` - Container image
* `--packages "clearml" "tensorflow>=2.2" "keras"` - Required Python packages
@ -39,7 +39,7 @@ name is `DevOps`.
:::
After launching the command, the `clearml-agent` listening to the `default` queue spins a remote Jupyter environment with
the specifications. It will automatically connect to the docker on the remote machine.
the specifications. It will automatically connect to the container on the remote machine.
The console should display the session's configuration details:

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@ -51,8 +51,8 @@ to open the app's instance launch form.
* Repository
* Branch
* Commit
* **Docker**
* Image - Docker image used to run the IDE in
* **Container**
* Image - Container image used to run the IDE in
* Docker arguments - `docker run` arguments, as a single string
* **Extra Packages** - Extra Python packages to be installed
* **Queue** - The queue serviced by the ClearML Agent that will execute the Jupyter Lab session

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@ -68,8 +68,8 @@ values from the file, which can be modified before launching the app instance
* Repository
* Branch
* Commit
* **Docker** - Input details to run the session in Docker container
* Image - Docker image to launch
* **Container** - Input details to run the session in Docker container
* Image - Container image to launch
* Docker Arguments - Additional arguments for the Docker image
* Init Script - Bash script that is executed upon container boot (comments are supported only at the beginning of the
line)

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@ -51,8 +51,8 @@ to open the app's instance launch form.
* Repository
* Branch
* Commit
* **Docker**
* Image - Docker image used to run the IDE in
* **Container**
* Image - container image used to run the IDE in
* Docker arguments - `docker run` arguments, as a single string
* **Queue** - The queue serviced by the ClearML Agent that will execute the VS Code session
* **Maximum idle time** (hours) - Maximum time of inactivity, after which the session will shut down. Configure idleness

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@ -27,7 +27,7 @@ Disable for default desktop scale.
* **Block running user's scripts in the browser** - Block any user and 3rd party scripts from running anywhere in the
WebApp. Note that if enabled, the WebApp will not display debug samples, [Hyper-Dataset frame previews](../../hyperdatasets/previews.md),
and embedded resources in [reports](../webapp_reports.md).
* **Hide specific container arguments** - Specify which Docker environment variable values should be hidden in logs.
* **Hide specific container arguments** - Specify which container environment variable values should be hidden in logs.
When printed, the variable values are replaced with `********`. By default, `CLEARML_API_SECRET_KEY`, `CLEARML_AGENT_GIT_PASS`,
`AWS_SECRET_ACCESS_KEY`, and `AZURE_STORAGE_KEY` values are redacted. To modify the hidden container argument list, click **Edit**.

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@ -39,7 +39,7 @@ Web UI**, edit any of the following
* [Source code](#source-code)
* [Output destination for artifacts](#output-destination)
* [Base Docker image](#base-docker-image)
* [Default container](#default-container)
* [Hyperparameters](#hyperparameters) - Parameters, TensorFlow Definitions, command line options, environment variables, and user-defined properties
:::note
@ -71,23 +71,22 @@ and/or Reset functions.
#### Base Docker Image
Select a pre-configured Docker that **ClearML Agent** will use to remotely execute this task (see [Building Docker containers](../clearml_agent/clearml_agent_docker.md)).
#### Default Container
Select a pre-configured container that the [ClearML Agent](../clearml_agent.md) will use to remotely execute this task (see [Building Docker containers](../clearml_agent/clearml_agent_docker.md)).
**To add, change, or delete a base Docker image:**
**To add, change, or delete a default container:**
* In **EXECUTION** **>** **AGENT CONFIGURATION** **>** **BASE DOCKER IMAGE** **>** hover **>** **EDIT** **>**
Enter the base Docker image.
* In **EXECUTION** **>** **CONTAINER** **>** hover **>** **EDIT** **>**
Enter the default container image.
:::important
For a ClearML Agent to execute the task in a container, the agent must be running in
Docker mode:
[Docker Mode](../clearml_agent/clearml_agent_execution_env.md#docker-mode):
```bash
clearml-agent daemon --queue <execution_queue_to_pull_from> --docker [optional default docker image to use]
clearml-agent daemon --queue <execution_queue_to_pull_from> --docker [optional default container image to use]
```
For more information, see [Docker Mode](../clearml_agent/clearml_agent_execution_env.md#docker-mode).
:::
#### Output Destination