For even easier getting started using fastai by new researchers, would not it be great to have a few clear examples of running in official fastai Docker containers in fastai documentation? In this way the researchers would not need to install anything and yet they would be able to start exploring fastai capabilities right away.
Hello,
Zero Setup Required: Researchers don’t need to worry about dependencies or system configurations. The Docker container would come pre-configured with all the necessary libraries and tools.
Consistency: The environment will be identical for everyone, reducing the “it works on my machine” problem. This ensures that the code behaves consistently across different systems.
Portability: The Docker container can be easily shared and run on any machine that supports Docker, making it accessible to a broader audience.
Reproducibility: Researchers can easily reproduce experiments by sharing the Docker container setup, which is essential for replicating results in the research community.
To make this process even smoother, the official fastai documentation could include step-by-step examples for various use cases, such as:
Basic setup: Instructions to pull the official fastai Docker image and run the container.
Pre-configured notebooks: Sample Jupyter notebooks within the container that demonstrate fastai features like deep learning models, datasets, and transfer learning.
GPU support: Instructions for using Docker with GPU acceleration (using NVIDIA Docker, for example), which is crucial for training larger models.
Integration with cloud services: Demonstrations of how to run fastai containers on cloud platforms like AWS, Google Cloud, or Azure.
Best Regards
Hello,
That’s a great suggestion! Including clear examples of running Fastai in official Docker containers in the documentation would certainly help new researchers get started quickly. It eliminates the need for complex installations and allows them to explore Fastai capabilities right away. Goto NCEdCloud
Best Regards,
Thomas Brown