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Terminology update (#1035)

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pollfly
2025-02-10 10:17:24 +02:00
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6
docs/guides/clearml_agent/executable_exp_containers.md
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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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docs/guides/clearml_agent/exp_environment_containers.md
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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

2
docs/guides/docker/extra_docker_shell_script.md
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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.

4
docs/guides/ide/remote_jupyter_tutorial.md
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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: