AI & DL Lab Setup

Setting up an AI (Artificial Intelligence) and DL (Deep Learning) lab can be a complex process, but I can provide you with a general overview of the key components and steps involved in creating such a lab. Keep in mind that the specific requirements and equipment may vary depending on your research or educational needs.

High-Performance Workstations

You'll need powerful computers with multi-core CPUs, ample RAM (16GB or more), and high-end GPUs (NVIDIA GeForce or Tesla) for training deep learning models efficiently.

  • Distributed Computing: For larger-scale projects, consider setting up a cluster of machines or cloud-based resources for distributed computing.

2. Software Requirements:

  • Operating System: Linux is often preferred for deep learning due to its compatibility with many AI libraries and tools.
  • Development Environment: Install Python, Jupyter Notebook, and popular deep learning frameworks like TensorFlow, PyTorch, and Keras.
  • GPU Drivers: Ensure you have the necessary GPU drivers for your hardware.
  • Docker: Containerization can help manage software dependencies efficiently.
  • Version Control: Use Git for code version control.

3. Deep Learning Frameworks and Libraries:

  • Install and configure popular deep learning frameworks such as TensorFlow, PyTorch, Keras, and libraries like OpenCV, scikit-learn, and NumPy.

4. Data Management:

  • Set up a data storage solution, such as a network-attached storage (NAS) or cloud storage, for managing large datasets.
  • Implement data preprocessing and augmentation pipelines.

5. Experiment Management:

  • Use tools like TensorBoard, MLflow, or Neptune.ai to track and manage experiments, hyperparameters, and results.