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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.
Supported OS: openEuler 20.03 LTS or later
yum install -y atune
For openEuler 20.09 or later, atune-engine is needed
yum install -y atune-engine
yum install -y golang-bin python3 perf sysstat hwloc-gui
yum install -y python3-dict2xml python3-flask-restful python3-pandas python3-scikit-optimize python3-xgboost python3-pyyaml
pip3 install dict2xml Flask-RESTful pandas scikit-optimize xgboost scikit-learn pyyaml
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:
yum install -y python3-sqlalchemy python3-cryptography
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|
git clone https://gitee.com/openeuler/A-Tune.git
cd A-Tune make models make
make collector-install make install
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.
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.
fdisk -l | grep dev
systemctl daemon-reload systemctl start atuned systemctl start atune-engine
systemctl status atuned systemctl status atune-engine
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.
|--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.|
This command is used to list the supported profiles, and the values of active.
Manually activate the profile to make it in the active state.
Example: Activate the profile corresponding to the web-nginx-http-long-connection.
atune-adm profile web-nginx-http-long-connection
This command is used to collect real-time statistics from the system to identify and automatically optimize workload types.
atune-adm analysis [OPTIONS]
Example 1: Use the default model to identify applications and perform automatic tuning.
Example 2: Use the user-defined training model for recognition.
atune-adm analysis --model /usr/libexec/atuned/analysis/models/new-model.m
Use the specified project file to search the dynamic space for parameters and find the optimal solution under the current environment configuration.
atune-adm tuning [OPTIONS] <PROJECT_YAML>
Example: See the A-Tune offline tuning example. Each example has a corresponding README guide.
For details about other commands, see the atune-adm help information or A-Tune User Guide.
We welcome new contributors to participate in the project. And we are happy to provide guidance for new contributors. You need to sign CLA before contribution.
Any question or discussion please contact A-Tune.
Holding SIG Meeting at 10:00-12:00 AM on Friday every two weeks. You can apply topic by A-Tune mail list.
：Code submit frequency
：React/respond to issue & PR etc.
：Well-balanced team members and collaboration
：Recent popularity of project
：Star counts, download counts etc.