Add clearml-agent build tutorials (#319)

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@ -457,22 +457,27 @@ Build a Docker container that when launched executes a specific experiment, or a
docker run <new-docker-name>
```
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.
Build a Docker container according to the execution environment of a specific task.
```bash
clearml-agent build --id <task-id> --docker --target <new-docker-name>
```
It's possible to add the Docker container as the base Docker image to a Task (experiment), using one of the following methods:
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

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@ -0,0 +1,59 @@
---
title: Executable Experiment Containers
---
This tutorial demonstrates using [`clearml-agent`](../../clearml_agent.md)s [`build`](../../clearml_agent/clearml_agent_build.md)
command to package an experiment into an executable container. In this example, you will build a Docker 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.
## Prerequisites
* [`clearml-agent`](../../clearml_agent.md#installation) installed and configured
* [`clearml`](../../getting_started/ds/ds_first_steps.md#install-clearml) installed and configured
* [clearml](https://github.com/allegroai/clearml) repo cloned (`git clone https://github.com/allegroai/clearml.git`)
## Creating the ClearML Experiment
1. Set up the experiments execution environment:
```console
cd clearml/examples/frameworks/keras
pip install -r requirements.txt
```
1. Run the experiment:
```console
python keras_tensorboard.py
```
This creates a ClearML task called "Keras with TensorBoard example" in the "examples" project.
Note the task ID in the console output when running the script above:
```console
ClearML Task: created new task id=<TASK_ID>
```
This ID will be used in the following section.
## Building and Launching a Containerized Task
1. Execute the following command to build the container. Input the ID of the task created above.
```console
clearml-agent build --id <TASK_ID> --docker --target new-docker --entry-point clone_task
```
:::tip
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 experiment in the
specified `--target` folder. When the Docker 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:
```console
docker run new-docker
```
The task will be executed inside the container. Task details can be viewed in the [ClearML Web UI](../../webapp/webapp_overview.md).
For additional ClearML Agent options, see the [ClearML Agent reference page](../../clearml_agent/clearml_agent_ref.md).

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@ -0,0 +1,83 @@
---
title: Experiment Environment Containers
---
This tutorial demonstrates using [`clearml-agent`](../../clearml_agent.md)s [`build`](../../clearml_agent/clearml_agent_build.md)
command to build a Docker 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
be used when running optimization tasks.
## Prerequisites
* [`clearml-agent`](../../clearml_agent.md#installation) installed and configured
* [`clearml`](../../getting_started/ds/ds_first_steps.md#install-clearml) installed and configured
* [clearml](https://github.com/allegroai/clearml) repo cloned (`git clone https://github.com/allegroai/clearml.git`)
## Creating the ClearML Experiment
1. Set up the experiments execution environment:
```console
cd clearml/examples/frameworks/keras
pip install -r requirements.txt
```
1. Run the experiment:
```console
python keras_tensorboard.py
```
This creates a ClearML task called "Keras with TensorBoard example" in the "examples" project.
Note the task ID in the console output when running the script above:
```console
ClearML Task: created new task id=<TASK_ID>
```
This ID will be used in the following section.
## Building the Docker Container
Execute the following command to build the container. Input the ID of the task created above.
```console
clearml-agent build --id <TASK_ID> --docker --target new_docker
```
:::tip
If the container will not make use of a GPU, add the `--cpu-only` flag
:::
This will create a container with the specified tasks execution environment in the `--target` folder.
When the Docker build completes, the console output shows:
```console
Docker build done
Committing docker container to: new_docker
sha256:460453b93ct1989fd1c6637c236e544031c4d378581433fc0b961103ce206af1
```
## Using the New Docker Container
Make use of the container you've just built by having a ClearML agent make use of it for executing a new experiment:
1. In the [ClearML Web UI](../../webapp/webapp_overview.md), go to the "examples" project, "Keras with TensorBoard
example" task (the one executed [above](#creating-the-clearml-experiment)).
1. [Clone](../../webapp/webapp_exp_reproducing.md) the experiment.
1. In the cloned experiment, go to the **EXECUTION** tab **>** **CONTAINER** section. Under **IMAGE**, insert the name
of the new Docker image, `new_docker`. See [Tuning Experiments](../../webapp/webapp_exp_tuning.md) for more task
modification options.
1. Enqueue the cloned experiment to the `default` queue.
1. Launch a `clearml-agent` in [Docker Mode](../../clearml_agent.md#docker-mode) and assign it to the `default` queue:
```console
clearml-agent daemon --docker --queue default
```
:::tip
If the agent will not make use of a GPU, add the `--cpu-only` flag
:::
This agent will pull the enqueued task and run it using the `new_docker` image to create the execution environment.
In the tasks **CONSOLE** tab, one of the first logs should be:
```console
Executing: ['docker', 'run', ..., 'CLEARML_DOCKER_IMAGE=new_docker', ...].
```

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@ -106,6 +106,7 @@ module.exports = {
{'Advanced': ['guides/advanced/execute_remotely', 'guides/advanced/multiple_tasks_single_process']},
{'Automation': ['guides/automation/manual_random_param_search_example', 'guides/automation/task_piping']},
{'ClearML Task': ['guides/clearml-task/clearml_task_tutorial']},
{'ClearML Agent': ['guides/clearml_agent/executable_exp_containers', 'guides/clearml_agent/exp_environment_containers']},
{'Datasets': ['guides/datasets/data_man_cifar_classification', 'guides/datasets/data_man_python']},
{'Distributed': ['guides/distributed/distributed_pytorch_example', 'guides/distributed/subprocess_example']},
{'Docker': ['guides/docker/extra_docker_shell_script']},