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Requirements: Measuring the energy consumption of AI

Hardware/Sofware Requirements

  1. Intel CPU
  2. Linux OS (Tested on Ubuntu) with superuser (i.e., sudo) permissions
  3. NVIDIA GPU (Not used in the session)
  4. TensorFlow (tested on version 2.9.0)
  5. CarbonTracker
  6. Git

Most energy measurement software rely on Intel’s RAPL interface to obtain CPU and energy data. In Linux, one way to access the RAPL interface is via the powercap framework (https://www.kernel.org/doc/html/latest/power/powercap/powercap.html). For the hands-on session, you must the able to access RAPL by the following command

sudo cat /sys/class/powercap/intel-rapl/intel-rapl:0/energy_uj

The sudo became necessary as a patch to avoid a security vulnerability with the RAPL interface (https://www.intel.com/content/www/us/en/developer/articles/technical/software-security-guidance/advisory-guidance/running-average-power-limit-energy-reporting.html)

To avoid additional requirements, the session does not cover monitoring the GPU energy consumption. The way we monitor the energy consumption with CodeCarbon is the same for CPU and GPU. If you want to use GPUs CodeCarbon only supports NVIDIA GPUs, and you should be able to run Tensorflow with GPU support (https://www.tensorflow.org/install/pip)

Installation procedure

This part assumes that you are used to using a terminal. Open a terminal, then run the following commands

1. Create a directory to clone this repository

mkdir idessai-2022-ai-energy-consumption
cd idessai-2022-ai-energy-consumption

2. Clone this repository

git clone https://github.com/danilo-carastan-santos/ai-energy-consumption.git

3. Create and activate a Python virtual environment

cd ai-energy-consumption
python3 -m venv venv/
source venv/bin/activate

4. Install the software requirements

pip install --upgrade pip
pip install -r requirements.txt

5. Run session.py as test

sudo venv/bin/python session.py --sect 1b

Observations

  • Once inside the virtual environment, you can run the deactivate command to

disconnect from the virtual environment.

  • There are four sections (1a, 1b, 1c, 2a), each of which presents new concepts and propose questions (Q1, Q2, Q3, Q4, Q5, Q6). These questions will be explored during the hands-on session. You can run a different section by using the --sect argument in the command at step 5.
  • The questions can be seen at the session.py as comments. Inside a terminal you can use the nano editor (i.e., command nano session.py) to navigate and edit the source code.