Description:
The analysis of large gene expression datasets can be time-consuming. This issue focuses on optimizing the performance of the pipeline and adding progress tracking to improve the user experience.
Tasks
[ ] Memory Optimization: Profile the pipeline to identify memory bottlenecks and optimize memory usage, especially during data loading and model training.
[ ] Progress Tracking: Add a progress bar (e.g., using tqdm) to the main analysis pipeline to provide feedback to the user on the progress of the analysis.
[ ] Checkpointing: Implement a checkpointing mechanism that allows the pipeline to be resumed from the last completed step in case of an interruption.
Description:
The analysis of large gene expression datasets can be time-consuming. This issue focuses on optimizing the performance of the pipeline and adding progress tracking to improve the user experience.
Tasks
[ ] Memory Optimization: Profile the pipeline to identify memory bottlenecks and optimize memory usage, especially during data loading and model training.
[ ] Progress Tracking: Add a progress bar (e.g., using tqdm) to the main analysis pipeline to provide feedback to the user on the progress of the analysis.
[ ] Checkpointing: Implement a checkpointing mechanism that allows the pipeline to be resumed from the last completed step in case of an interruption.