Add MNIST training provenance collection example #3
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thanks @sandlbn ! LGTM, I'll approve once I run the example
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| ## Troubleshooting |
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this is awesome, I wish more docs had such a section :)
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| 1. Extend the Pipeline: Add data preprocessing steps, hyperparameter tuning, or model optimization | ||
| 2. Track Experiments: Create manifests for different training runs with varying parameters | ||
| 3. Build CI/CD Integration: Automatically collect provenance in your ML pipeline | ||
| 4. Create Visualizations: Use the provenance graph to create visual representations of your ML workflow | ||
| 5. Implement Governance: Use provenance data for model approval and deployment decisions |
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Have you tested this example in TDX? If so, we should add a line about running inside of TDX either down here, or higher up in the doc to explain the use of the with-tdx feature
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It would be cool to add it in the second iteration. No, I don’t test it with TDX. I think the best way to enable TDX is to add additional parameters to the script. But I think we can add this as an issue at the moment.
Co-authored-by: Marcela Melara <[email protected]>
This was referenced May 21, 2025
Co-authored-by: Marcela Melara <[email protected]>
marcelamelara
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May 27, 2025
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Thanks for the fixes, LGTM
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This PR adds example demonstrating how to collect provenance data from an ML workflow using Atlas CLI. The example tracks a full MNIST training pipeline including dataset download, model training, and evaluation.