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Delete unnecessary file & Write title in title case (#64)

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docs/guides/services/execute_jupyter_notebook_server.md
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---
title: Jupyter Notebook Server Service
---
The [execute_jupyter_notebook_server.py](https://github.com/allegroai/clearml/blob/master/examples/services/jupyter-service/execute_jupyter_notebook_server.py)
example demonstrates executing a Jupyter Notebook server in **ClearML Agent** services mode. The example script creates
an SSH server, and runs Jupyter Notebook as a subprocess. We integrate **ClearML** into the notebooks running on the Jupyter
Notebook server by passing environment variables to the subprocess, which point to:
* A **ClearML** configuration file
* A **ClearML Server** host configuration (web server, API server, and files server)
* **ClearML** API credentials.
**ClearML** logs the server links and console output, including the notebooks run on the server, in the example script's
Task. When the script runs, it creates an experiment named `Allocate Jupyter Notebook Instance`, which is associated with
the `DevOps` project in the **ClearML Web UI**.
## Running the Jupyter Notebook Server Service
1. The example script must run at least once before it can execute as a **ClearML Agent** service, because the Task must
be stored in **ClearML Server** in order to be enqueued for a **ClearML Agent** to fetch and execute.
python execute_jupyter_notebook_server.py
1. **ClearML Agent** must be running in services mode and listening to the `services` queue.
For example:
clearml-agent daemon --services-mode --detached --queue services --create-queue --cpu-only
1. Enqueue the Jupyter Notebook server Task.
1. In the **ClearML Web (UI)**, Project page **>** **examples** project **>** right click the **Remote Jupyter NoteBook**
experiment.
1. In the context menu, click **Enqueue** **>** select the **services** queue **>** **ENQUEUE**.
The status changes to *Pending* and then to *Running*. Once it is running, the Jupyter Notebook server is ready to
run notebooks.
## Logging the Jupyter Notebook Server
**ClearML** stores the Jupyter Notebook server links in the `Task.comment` property, which appears in the **ClearML Web UI**
**>** the experiment's **INFO** tab **>** **DESCRIPTION** section.
task.comment += "\n" + "".join(
line for line in new_lines if "http://" in line or "https://" in line
)
It shows the server links are:
[I 12:54:48.940 NotebookApp] http://osboxes:8889/?token=3be82d87e83268934dd086e3b136cc408d4bd12e23409f3a
[I 12:54:48.940 NotebookApp] or http://127.0.0.1:8889/?token=3be82d87e83268934dd086e3b136cc408d4bd12e23409f3a
http://osboxes:8889/?token=3be82d87e83268934dd086e3b136cc408d4bd12e23409f3a
or http://127.0.0.1:8889/?token=3be82d87e83268934dd086e3b136cc408d4bd12e23409f3a
![image](../../img/examples_execute_jupyter_notebook_server_02.png)
The Jupyter Note Server console output appears in **RESULTS** **>** **CONSOLE**, including log entries for the notebooks run
on the server.
To test the Jupyter Notebook, we ran a notebook named audio_preprocessing_example.ipynb. The log shows it was saved:
[I 17:26:45.925 NotebookApp] Saving file at /Projects/clearml-internal/examples/frameworks/pytorch/notebooks/table/download_and_preprocessing.ipynb
![image](../../img/examples_execute_jupyter_notebook_server_01.png)

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docs/webapp/webapp_exp_tuning.md
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--- ---
title: Tuning experiments title: Tuning Experiments
--- ---
Tune experiments and edit an experiment's execution details, then execute the tuned experiments on local or remote machines. Tune experiments and edit an experiment's execution details, then execute the tuned experiments on local or remote machines.