In the world of machine learning, one of the significant challenges is efficiently training models on vast datasets. The technique of Dataset Distillation aims to create a compact synthetic dataset that offers similar performance to a model trained on a real, expansive dataset. This blog post will guide you through the process of implementing the techniques outlined in the paper Dataset Distillation by Matching Training Trajectories, presented at CVPR 2022 by George Cazenavette et al.

Getting Started

To embark on the journey of dataset distillation, follow the steps below:

1. Clone the Repository:
• Open your terminal and run:

git clone https://github.com/GeorgeCazenavette/mtt-distillation.git

• Navigate to the cloned directory:

cd mtt-distillation

2. Set Up the Environment:
• If you have an RTX 30XX GPU (or newer), execute:

conda env create -f requirements_11_3.yaml

• If you have an RTX 20XX GPU (or older), use:

conda env create -f requirements_10_2.yaml

• Activate the conda environment:

conda activate distillation

Generating Expert Trajectories

Before the distillation process, you’ll need to generate expert trajectories. This is akin to training “master chefs” who will provide guidance on how to create the perfect dish – in this case, your synthetic dataset. Here’s how to do it:

1. Run the following command to train 100 ConvNet models on CIFAR-100:

python buffer.py --dataset=CIFAR100 --model=ConvNet --train_epochs=50 --num_experts=100 --zca --buffer_path=path_to_buffer_storage --data_path=path_to_dataset

2. You can adjust the number of experts based on your requirements. Training them only needs to happen once, allowing for re-use in subsequent experiments.

Distillation Process

Now that we have our expert trajectories, it’s time to distill them into a smaller set of synthetic images. Imagine this as the process of distilling oils to get pure and fragrant essences. Here’s how you can achieve this:

1. Run the distillation command:

python distill.py --dataset=CIFAR100 --ipc=1 --syn_steps=20 --expert_epochs=3 --max_start_epoch=20 --zca --lr_img=1000 --lr_lr=1e-05 --lr_teacher=0.01 --buffer_path=path_to_buffer_storage --data_path=path_to_dataset

Troubleshooting

During the process, you may encounter some issues. Below are a few troubleshooting tips:

• Performance Issues: If you experience indefinite hanging during training on Quadro A5000 GPUs, try running with only 1 GPU:

CUDA_VISIBLE_DEVICES=0 python distill.py...

• If encountering memory problems, ensure that your data paths are correctly specified and contain all necessary files.
• For any specific questions or new insights, don’t hesitate to reach out and explore the community at fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

Through this guide, you should now have a clear understanding and the tools necessary to delve into Dataset Distillation. Happy coding!