CloudAI benchmark framework aims to develop an industry standard benchmark focused on grading Data Center (DC) scale AI systems in the Cloud. The primary motivation is to provide automated benchmarking on various systems.

CloudAI operates on four main schemas:

1. System Schema: Describes the system, including the scheduler type, node list, and global environment variables.
2. Test Template Schema: A template for tests that includes all required command-line arguments and environment variables. This schema allows users to separate test template implementations from systems.
3. Test Schema: An instance of a test template with custom arguments and environment variables.
4. Test Scenario Schema: A set of tests with dependencies and additional descriptions about the test scenario.

These schemas enable CloudAI to be flexible and compatible with different systems and configurations.

First, ensure you have access to the Docker repository. Follow these steps:

1. Sign In: Go to NVIDIA NGC and sign in with your credentials.
2. Generate API Key:
   • On the top right corner, click on the dropdown menu next to your profile.
   • Select "Setup".
   • In the "Setup" section, find "Keys/Secrets".
   • Click "Generate API Key" and confirm when prompted. A new API key will be presented.
   • Important: Save this API key locally as you will not be able to view it again on NGC.

Next, set up your enroot credentials. Ensure you have the correct credentials under ~/.config/enroot/.credentials:

machine nvcr.io login $oauthtoken password <api-key>

• Replace <api-key> with your respective credentials. Keep $oauthtoken as is.

Clone the CloudAI repository to your local machine:

git clone [email protected]:NVIDIA/cloudai.git
cd cloudai

Create a virtual environment:

python -m venv venv
source venv/bin/activate

Next, install the required packages using pip:

pip install -r requirements.txt

After setting up the environment and installing dependencies, install the cloudai package itself:

pip install .

CloudAI supports five modes: install, dry-run, run, generate-report, and uninstall.

• Use the install mode to install all test templates in the specified installation path.
• Use the dry-run mode to simulate running experiments without actually executing them. This is useful for verifying configurations and testing experiment setups.
• Use the run mode to run experiments.
• Use the generate-report mode to generate reports under the test directories alongside the raw data.
• Use the uninstall mode to remove installed test templates.

To install test templates, run CloudAI CLI in install mode. Please make sure to use the correct system configuration file that corresponds to your current setup for installation and experiments.

cloudai\
    --mode install\
    --system-config conf/system/example_slurm_cluster.toml

To simulate running experiments without execution, use the dry-run mode:

cloudai\
    --mode dry-run\
    --system-config conf/system/example_slurm_cluster.toml\
    --test-scenario conf/test_scenario/sleep.toml

To run experiments, execute CloudAI CLI in run mode:

cloudai\
    --mode run\
    --system-config conf/system/example_slurm_cluster.toml\
    --test-scenario conf/test_scenario/sleep.toml

To generate reports, execute CloudAI CLI in generate-report mode:

cloudai\
    --mode generate-report\
    --system-config conf/system/example_slurm_cluster.toml\
    --output-dir /path/to/output_directory

In the generate-report mode, use the --output-dir argument to specify a subdirectory under the result directory. This subdirectory is usually named with a timestamp for unique identification.

To uninstall test templates, run CloudAI CLI in uninstall mode:

cloudai\
    --mode uninstall\
    --system-config conf/system/example_slurm_cluster.toml

Feel free to contribute to the CloudAI project. Your contributions are highly appreciated.

This project is licensed under Apache 2.0. See the LICENSE file for detailed information.