# A-Tune

**Repository Path**: qq514709/A-Tune

## Basic Information

- **Project Name**: A-Tune
- **Description**: A-Tune is an OS tuning engine based on AI.
- **Primary Language**: Go
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 261
- **Created**: 2021-10-23
- **Last Updated**: 2024-04-26

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

<img src="misc/A-Tune-logo.png" width="50%" height="50%"/>

English | [简体中文](./README-zh.md)

## Introduction to A-Tune 

**A-Tune** is an OS tuning engine based on AI. A-Tune uses AI technologies to enable the OS to understand services, simplify IT system optimization, and maximize optimal application performance.


I. A-Tune Installation
----------

Supported OS: openEuler 20.03 LTS or later

### Method 1 (applicable to common users): Use the default A-Tune of openEuler.

```bash
yum install -y atune
```
For openEuler 20.09 or later, atune-engine is needed

```bash
yum install -y atune-engine
```

### Method 2 (applicable to developers): Use the source code of the local repository for installation.

#### 1. Install dependent system software packages.
```bash
yum install -y golang-bin python3 perf sysstat hwloc-gui
```

#### 2. Install Python dependent packages.  

#### 2.1 Install dependency for A-Tune service.
```bash
yum install -y python3-dict2xml python3-flask-restful python3-pandas python3-scikit-optimize python3-xgboost python3-pyyaml
```
Or
```bash
pip3 install dict2xml Flask-RESTful pandas scikit-optimize xgboost scikit-learn pyyaml
```

#### 2.2 (Optional) Install dependency for database.
Once user has already installed database application and wants to store A-Tune collection and tuning data to the database, following packages should also be installed:
```bash
yum install -y python3-sqlalchemy python3-cryptography
```
Or
```bash
pip3 install sqlalchemy cryptography
```
To use database, user should also select either of the following methods to install dependency based on the database applications.
| **Database** | **Install using yum** | **Install using pip** |
| ------------------------------ | ---------- | ------------ |
| PostgreSQL | yum install -y python3-psycopg2 | pip3 install psycopg2 |
#### 3. Download the source code.
```bash
git clone https://gitee.com/openeuler/A-Tune.git
```

#### 4. Compile.
```bash
cd A-Tune
make models
make
```

#### 5. Install.
```bash
make collector-install
make install
```

II. Quick Guide
------------

### 1. Configure the A-Tune service.

#### Modify the network and disk configuration in the atuned.cnf.

You can run the following command to query the NIC that need to be specified for data collecting or optimizing NIC and change the network configuration item in the /etc/atuned/atuned.cnf to the specified NIC.

```bash
ip addr
```

You can run the following command to query the disk that need to be specified for data collection or disk optimization and change the disk configuration item in the /etc/atuned/atuned.cnf to the specified disk.

```bash
fdisk -l | grep dev
```

### 2. Manage the A-Tune service.

#### Load and start the atuned and atune-engine service.

```bash
systemctl daemon-reload
systemctl start atuned
systemctl start atune-engine
```

#### Check the atuned or atune-engine service status.

```bash
systemctl status atuned
systemctl status atune-engine
```

### 3、Generate AI models.

You can save the newly collected data to the A-Tune/analysis/dataset directory and run the model generation tool to update the AI model in the A-Tune/analysis/models directory.

**Format**

python3 generate_models.py <OPTIONS>

**Parameter Description**

- OPTIONS

| Parameter        | Description                                                  |
| ---------------- | ------------------------------------------------------------ |
| --csv_path, -d   | Path for storing CSV files required for model training. The default directory is A-Tune/analysis/dataset. |
| --model_path, -m | Path for storing the new models generated during training. The default path is A-Tune/analysis/models. |
| --select, -s     | Indicates whether to generate feature models. The default value is false. |
| --search, -g     | Indicates whether to enable parameter space search. The default value is false. |

Example：

```
python3 generate_models.py
```

### 4. Run the atune-adm command.

#### The list command.

This command is used to list the supported profiles, and the values of active.

Format:

atune-adm list

Example:

```bash
atune-adm list
```

#### The profile command.

Manually activate the profile to make it in the active state.

Format:

atune-adm profile <PROFILE>

Example: Activate the profile corresponding to the web-nginx-http-long-connection.

```bash
atune-adm profile web-nginx-http-long-connection
```

#### The analysis command. (Online static tuning)

This command is used to collect real-time statistics from the system to identify and automatically optimize workload types.

Format:

atune-adm analysis [OPTIONS]

Example 1: Use the default model to identify applications and perform automatic tuning.

```bash
atune-adm analysis
```

Example 2: Use the user-defined training model for recognition.

```bash
atune-adm analysis --model /usr/libexec/atuned/analysis/models/new-model.m
```

#### The tuning command. (Offline dynamic tuning)

Use the specified project file to search the dynamic space for parameters and find the optimal solution under the current environment configuration.

Format:

atune-adm tuning [OPTIONS] <PROJECT_YAML>

Example: See [the A-Tune offline tuning example](./examples/tuning). Each example has a corresponding README guide.

For details about other commands, see the atune-adm help information or [A-Tune User Guide](./Documentation/UserGuide/A-Tune-User-Guide.md).

III. Web UI
--------

[A-Tune-UI](https://gitee.com/openeuler/A-Tune-UI) is a web project base on A-Tune. Please check A-Tune-UI [README](https://gitee.com/openeuler/A-Tune-UI/blob/master/README.en.md) for details.

IV. How to contribute
--------

We welcome new contributors to participate in the project. And we are happy to provide guidance for new contributors. You need to sign [CLA](https://openeuler.org/en/cla.html) before contribution.

### Mail list
Any question or discussion please contact [A-Tune](https://mailweb.openeuler.org/postorius/lists/a-tune.openeuler.org/).

### Routine Meeting
Holding SIG Meeting at 10:00-12:00 AM on Friday every two weeks. You can apply topic by [A-Tune](https://mailweb.openeuler.org/postorius/lists/a-tune.openeuler.org/) mail list.