docs: set precision explicitly in the DPA-2 example #4372
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Signed-off-by: Jinzhe Zeng <jinzhe.zeng@rutgers.edu>
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- examples/water/dpa2/input_torch_large.json: Language not supported
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- examples/water/dpa2/input_torch_small.json: Language not supported
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Actionable comments posted: 1
🧹 Outside diff range and nitpick comments (3)
examples/water/dpa2/input_torch_medium.json (1)
64-64: LGTM! Consistent precision setting across components.The matching precision setting in fitting_net ensures computational consistency with the descriptor.
Using the same precision across both descriptor and fitting_net is a good practice to maintain numerical stability and avoid potential precision mismatches during computation.
examples/water/dpa2/input_torch_large.json (1)
64-64: LGTM! Good documentation improvement for fitting_net precision.Explicitly setting the precision for the fitting network is consistent with the descriptor section and provides clear guidance for users.
Consider adding a comment in the documentation to explain the performance implications of using float64 precision, as it might impact training speed compared to float32.
examples/water/dpa2/input_torch_small.json (1)
54-54: LGTM! Good choice of double precision for descriptor.The explicit setting of
"precision": "float64"in the descriptor is appropriate for scientific computing where numerical accuracy is crucial. Double precision helps maintain accuracy in the molecular representation calculations.Double precision is particularly important in the descriptor phase as numerical errors in the molecular representation could propagate through the entire model.
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📒 Files selected for processing (3)
examples/water/dpa2/input_torch_large.json(2 hunks)examples/water/dpa2/input_torch_medium.json(2 hunks)examples/water/dpa2/input_torch_small.json(2 hunks)
🔇 Additional comments (2)
examples/water/dpa2/input_torch_medium.json (1)
54-54: LGTM! Explicit precision setting in descriptor improves clarity.
The addition of explicit precision setting helps users understand that this parameter is configurable.
Let's verify this is a valid precision value in the codebase:
✅ Verification successful
"float64" is a valid and widely used precision value across the codebase
The verification shows that "float64" is consistently used as a precision value across numerous configuration files in the codebase, including test files, examples, and various model configurations. This confirms that the precision setting in the DPA-2 example follows the established pattern and is technically correct.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Search for other precision settings to confirm valid values
rg -g '!*.pyc' -g '!*.so' '"precision":\s*"float64"'
Length of output: 5587
examples/water/dpa2/input_torch_large.json (1)
54-54: LGTM! Good documentation improvement for descriptor precision.
Making the precision setting explicit helps users understand and configure the numerical precision requirements for the descriptor calculations.
✅ Verification successful
Precision setting is consistent across all example configurations
The verification shows that "precision": "float64" is consistently used across all example configurations in the repository, appearing in both descriptor and fitting network sections. This confirms that the added precision setting follows the established standard in the codebase.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Verify if float64 is the standard precision across other examples
# This helps ensure consistency in documentation
# Search for precision settings in other example configurations
rg -g "*.json" '"precision":\s*"float64"' examples/
Length of output: 4122
Codecov ReportAll modified and coverable lines are covered by tests ✅
Additional details and impacted files@@ Coverage Diff @@
## devel #4372 +/- ##
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Files 596 596
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Signed-off-by: Jinzhe Zeng <jinzhe.zeng@rutgers.edu>
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Actionable comments posted: 0
🧹 Outside diff range and nitpick comments (2)
examples/water/dpa2/input_torch_compressible.json (2)
55-55: LGTM! Explicit precision setting in descriptor is valuable.The addition of explicit precision setting in the descriptor component helps users understand they can configure the numerical precision of atomic environment representations.
Note that while float64 provides higher precision, it comes with increased memory usage and computational cost. Users might want to consider float32 for larger systems where memory is a constraint.
65-65: LGTM! Consistent precision setting in fitting_net.The explicit precision setting in the fitting network maintains consistency with the descriptor precision.
Consider adding a comment in the README or documentation to explain:
- The impact of precision settings on training speed and memory usage
- When users might want to adjust these settings (e.g., for production vs. testing)
This reminds users that precision can be changed.
Summary by CodeRabbit
New Features
"precision": "float64", in thedescriptorandfitting_netsections of multiple JSON configuration files to enhance numerical precision specifications for computations.Documentation