Hi @chari, Certainly! It sounds like youโre encountering a common issue when running machine learning jobs in notebooks.
Letโs explore some tips to address this:
Cell Outputs Not Updating:
- When you run a cell in a notebook, the output is typically displayed below the cell. However, if you re-run the cell without clearing the previous output, the new output wonโt replace the old one.
- To ensure that cell outputs update correctly, consider the following:
- Clear Output: Before re-running a cell, manually clear its output. You can do this by right-clicking on the cell and selecting โClear Outputs.โ
- Re-Run All Cells: Sometimes, dependencies between cells can cause issues. Try re-running all cells in the notebook (e.g., by selecting โRun Allโ from the menu) to ensure consistent results.
Keeping Notebook Outputs Updated:
- If you want to keep the notebook outputs updated every time you run a job, consider the following strategies:
- Logging: Explicitly log relevant information (such as model performance metrics, intermediate results, or debugging messages) using a logging library (e.g., Pythonโs logging module). This way, you can review the logs even after re-running the notebook.
- Markdown Cells: Use Markdown cells to document important details about the job, including any relevant outputs. You can add explanatory text, tables, or visualizations directly in Markdown cells.
- Widgets and Interactive Outputs: Consider using interactive widgets (e.g., ipywidgets) to display dynamic outputs. Widgets allow you to create interactive elements (like sliders, dropdowns, or buttons) that update based on user input or changes in variables.
- Save Outputs to Files: If your outputs are large or complex (e.g., model predictions), save them to files (e.g., CSV, JSON, or images). Then, load these files when needed, ensuring that the most recent results are available.
Version Control and Notebooks:
- If youโre working collaboratively or need to track changes over time, consider using version control (e.g., Git) for your notebooks. Committing changes allows you to compare different versions and understand how outputs evolve.
- Note that notebooks can sometimes have large binary outputs (e.g., plots or images). Storing these outputs in version control repositories may not be ideal due to their size.
Automated Pipelines:
- For production workflows, consider moving away from manual notebook execution to automated pipelines. Tools like Apache Airflow, Prefect, or Kubeflow Pipelines allow you to define and schedule data processing and ML workflows.
- These pipelines can handle data ingestion, preprocessing, model training, and deployment, ensuring consistent results and reproducibility.
Feel free to share more details about your specific use case or any attached picture, and Iโll be happy to provide further insights! ๐
