Add integration tests for application notebooks #619
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You can disable this status message by setting the WalkthroughThis pull request introduces a new Conda environment configuration file with predefined channels and dependencies. It adds a new GitHub Actions job to run Jupyter notebook integrations based on the updated environment, thereby extending the CI/CD pipeline. The documentation is updated with a new chapter and subsections for application examples. Furthermore, two new Jupyter notebooks are added to perform computational materials science analyses using GPAW and Quantum Espresso, featuring functions for energy evaluations, structure generation, task submission via Flux executors, and subsequent plotting. Changes
Sequence Diagram(s)sequenceDiagram
participant CI as "CI Job"
participant Repo as "Repository"
participant Env as "Mambaforge Setup"
participant Exec as "Notebook Executor"
participant Flux as "Flux Cluster"
participant Res as "Results"
CI->>Repo: Checkout code & merge notebook config
CI->>Env: Setup Mambaforge (Python 3.12) & install dependencies
Env-->>CI: Environment ready
CI->>Exec: Initiate notebooks for GPAW & QE tests
Exec->>Flux: Submit tasks via FluxClusterExecutor
Flux-->>Exec: Process tasks and return outputs
Exec->>CI: Report notebook test results
CI->>Res: Finalize and log job status
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Actionable comments posted: 4
🧹 Nitpick comments (13)
.ci_support/environment-integration.yml (1)
1-21: A well-structured environment configuration for integration testing.This new Conda environment file correctly defines all the necessary dependencies for running the integration test notebooks. The use of specific versions for most packages (like
cloudpickle =3.1.1,flux-core =0.59.0, etc.) ensures reproducibility across different environments.Add a newline at the end of the file to comply with YAML best practices:
- atomistics =0.2.4 - qe =7.2 - gpaw =24.6.0 +🧰 Tools
🪛 YAMLlint (1.35.1)
[error] 21-21: no new line character at the end of file
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notebooks/5-4-flux-job-qe-bulk-modulus.ipynb (2)
234-240: Invalid escape sequence in string literal.The warning about invalid escape sequence
\Aindicates an issue with the LaTeX formatting in the axis label.Fix the invalid escape sequence by using a raw string:
- plt.xlabel("Volume [$\\AA^3$]") + plt.xlabel(r"Volume [$\AA^3$]")Using a raw string (with the
rprefix) avoids the need to escape backslashes in LaTeX expressions.
187-209: Atomic simulation tasks run sequentially despite using a job executor.The code correctly creates directories for each strain calculation and submits jobs to the Flux executor. However, it then immediately collects results synchronously in the same loop, which does not fully leverage the executor's parallel execution capabilities.
Consider separating job submission from result collection to better leverage parallel execution:
future_dict = {} with FluxJobExecutor(flux_log_files=True, flux_executor_nesting=True) as exe: for k, v in task_loop_dict.items(): os.makedirs(os.path.abspath(("strain_%0.2f" % k).replace(".", "_")), exist_ok=True) future_dict[k] = exe.submit( evaluate_with_quantum_espresso, task_dict=v, pseudopotentials=pseudopotentials, kpts=(3, 3, 3), resource_dict={ "cores": 1, "threads_per_core": 2, "cwd": os.path.abspath(("strain_%0.2f" % k).replace(".", "_")), }, ) sleep(1) pprint.pp(subprocess.check_output(["flux", "jobs", "-a"], universal_newlines=True).split("\n")) + # Wait for all jobs to complete and collect results result_dict = { k: f.result() for k, f in tqdm(future_dict.items()) } sleep(1) pprint.pp(subprocess.check_output(["flux", "jobs", "-a"], universal_newlines=True).split("\n"))This doesn't change functionality but clarifies that you're submitting all jobs first, then collecting results.
notebooks/5-2-flux-job-gpaw-bulk-modulus.ipynb (3)
64-73: Well-defined GPAW evaluation functionThe
evaluate_with_gpawfunction properly configures the GPAW calculator with appropriate parameters. Consider adding docstrings to improve documentation.def evaluate_with_gpaw(task_dict, kpts, encut): + """ + Evaluate energy of a structure using GPAW with plane-wave mode. + + Parameters: + task_dict (dict): Dictionary containing structure in 'calc_energy' + kpts (tuple): k-point sampling as a 3-tuple + encut (float): Energy cutoff in eV + + Returns: + float: Potential energy of the structure + """ from gpaw import GPAW, PW structure = task_dict["calc_energy"].copy() structure.calc = GPAW( xc="PBE", mode=PW(encut), kpts=kpts, ) return structure.get_potential_energy()
170-188: Well-structured job execution loopThe code effectively uses FluxJobExecutor to submit parallel jobs, monitors their status, and collects results. The tqdm progress bar provides good user feedback during execution. Consider adding basic error handling for job failures.
future_dict = {} with FluxJobExecutor() as exe: for k, v in task_loop_dict.items(): future_dict[k] = exe.submit( evaluate_with_gpaw, task_dict=v, kpts=(3, 3, 3), encut=300, resource_dict={"cores": 2}, ) sleep(1) pprint.pp(subprocess.check_output(["flux", "jobs", "-a"], universal_newlines=True).split("\n")) - result_dict = { - k: f.result()[-1] - for k, f in tqdm(future_dict.items()) - } + result_dict = {} + for k, f in tqdm(future_dict.items()): + try: + result_dict[k] = f.result()[-1] + except Exception as e: + print(f"Error in job {k}: {e}") sleep(1) pprint.pp(subprocess.check_output(["flux", "jobs", "-a"], universal_newlines=True).split("\n"))
243-247: Consider enhancing the visualizationWhile the current plot is functional, consider adding data points to show the calculated values alongside the fitted curve, and potentially adding a title to make the plot more informative.
+plt.figure(figsize=(8, 6)) +plt.scatter(fit_dict["volume"], fit_dict["energy"], color='red', label='Calculated points') plt.plot(fit_dict["volume"], fit_dict["energy"], label="$B_0$= %0.2f GPa" % fit_dict["bulkmodul_eq"]) plt.xlabel("Volume [$\\mathrm{\\AA}^3$]") plt.ylabel("Energy [eV]") +plt.title("Al Bulk Modulus Calculation with GPAW") plt.legend() +plt.grid(True, linestyle='--', alpha=0.7)notebooks/5-1-flux-cluster-gpaw-bulk-modulus.ipynb (3)
64-73: The evaluation function is well structured but lacks docstring.The
evaluate_with_gpawfunction is appropriately defined with clear parameters and a logical implementation. However, adding a docstring would improve maintainability and help other developers understand its purpose, parameters, and return value.Consider adding a detailed docstring:
def evaluate_with_gpaw(task_dict, kpts, encut): + """ + Evaluate the potential energy of a structure using GPAW. + + Parameters: + task_dict (dict): Dictionary containing the structure to evaluate + kpts (tuple): k-points mesh (e.g., (3, 3, 3)) + encut (float): Plane-wave energy cutoff in eV + + Returns: + float: Potential energy of the structure in eV + """ from gpaw import GPAW, PW structure = task_dict["calc_energy"].copy() structure.calc = GPAW( xc="PBE", mode=PW(encut), kpts=kpts, ) return structure.get_potential_energy()
38-44: Consider documenting the purpose of key parameters in structure generation.The structure generation parameters are functionally correct but lack context about their significance for this particular calculation.
Add comments to explain the importance of these parameters:
structure_dict = evcurve_generate_structures( structure=bulk("Al", a=4.05, cubic=True), # Aluminum with cubic structure + # Using 7 points gives sufficient data for accurate equation of state fitting num_points=7, + # 5% volume range provides appropriate sampling around equilibrium volume vol_range=0.05, + # Isotropic scaling of all axes preserves the crystal symmetry axes=("x", "y", "z"), )
173-175: Consider adding a comment about the computational parameters.The k-points grid and energy cutoff parameters are critical for accuracy and performance in GPAW calculations, but their values are provided without explanation.
Add comments to explain the choice of parameters:
future_dict[k] = exe.submit( evaluate_with_gpaw, task_dict=v, + # 3x3x3 k-points grid provides good accuracy/performance balance for Al kpts=(3, 3, 3), + # 300 eV cutoff is sufficient for converged results with PW basis encut=300, resource_dict={"cores": 2}, )notebooks/5-3-flux-cluster-qe-bulk-modulus.ipynb (4)
74-90: Hardcoded pseudopotential path may cause portability issues.I noticed the pseudopotential directory is hardcoded to "/home/jan/espresso/pseudo" on line 87, which could fail when running on different environments or CI systems.
Consider making the pseudo_dir configurable through an environment variable or parameter:
def evaluate_with_quantum_espresso(task_dict, pseudopotentials, kpts): from ase.calculators.espresso import Espresso, EspressoProfile from atomistics.calculators import evaluate_with_ase + import os + pseudo_dir = os.environ.get("QE_PSEUDO_DIR", "/home/jan/espresso/pseudo") return evaluate_with_ase( task_dict=task_dict, ase_calculator=Espresso( pseudopotentials=pseudopotentials, tstress=True, tprnfor=True, kpts=kpts, profile=EspressoProfile( command="pw.x", - pseudo_dir="/home/jan/espresso/pseudo", + pseudo_dir=pseudo_dir, ), ), )["energy"]
257-260: Fix the invalid escape sequence warning.There's a syntax warning about invalid escape sequence
\Ain your plot xlabel. This should be fixed for clean execution without warnings.- plt.xlabel("Volume [$\\AA^3$]") + plt.xlabel("Volume [$\\mathrm{\\AA}^3$]")Alternatively, you can use raw strings to avoid escape sequence issues:
plt.xlabel(r"Volume [$\AA^3$]")
211-216: Consider adding error handling for failed calculations.The current implementation collects results from all futures but doesn't handle potential exceptions if any calculations fail. In a production environment, this could cause the entire workflow to fail.
Add exception handling when collecting results:
result_dict = { - k: f.result() + k: f.result() if not f.exception() else None for k, f in tqdm(future_dict.items()) } + # Filter out failed calculations + if None in result_dict.values(): + print("Warning: Some calculations failed. Proceeding with successful ones only.") + result_dict = {k: v for k, v in result_dict.items() if v is not None}
1-293: Add documentation comments to explain calculation parameters.The notebook contains good functional code but lacks explanatory comments for key calculation parameters like k-point mesh (3,3,3) and polynomial fit order (3). These choices impact results and reproducibility.
Add comments to explain key parameters:
- Near line 190:
# Using a 3x3x3 k-point mesh, sufficient for metallic Al ground state convergence- Near line 215:
# Using 3rd order polynomial for E-V fitting - standard approach for bulk modulus calculation
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🔇 Additional comments (13)
.github/workflows/pipeline.yml (1)
176-210: Well-structured CI job for notebook integration tests.This new
notebooks_integrationjob correctly sets up the necessary environment and executes the Quantum Espresso and GPAW bulk modulus notebooks. The job is properly integrated with the existing CI pipeline (depends on theblackjob) and sets appropriate environment variables needed for the tests.A few observations:
- Line 203 uses
5-1-flux-cluster-qe-bulk-modulus.ipynbwhen the actual notebook filename pattern follows5-4-flux-job-qe-bulk-modulus.ipynb- Line 202 uses a similar pattern with
5-2-flux-job-qe-bulk-modulus.ipynbThe filenames should be consistent between the workflow and the actual repository structure.
notebooks/5-4-flux-job-qe-bulk-modulus.ipynb (1)
1-301: Well-structured notebook for Quantum Espresso bulk modulus calculations.This notebook provides a clear implementation of aluminum bulk modulus calculation using Quantum Espresso through the Flux job executor. It follows a logical flow:
- Imports necessary libraries and defines pseudopotentials
- Generates structures for energy vs. volume calculations
- Defines the evaluation function that interfaces with Quantum Espresso
- Uses FluxJobExecutor to submit and manage the calculations
- Analyzes results and plots the energy vs. volume curve
notebooks/5-2-flux-job-gpaw-bulk-modulus.ipynb (6)
1-10: Well-organized notebook structureThe notebook has a clear title and follows a logical structure for calculating the bulk modulus using GPAW. This makes it a good integration test for the workflow.
18-29: Comprehensive imports for the bulk modulus calculationAll necessary libraries are properly imported, including ASE for atomic simulations, atomistics workflows, executorlib for job management, and visualization tools. The code organization follows best practices.
39-44: Appropriate structure generation for aluminumThe code correctly uses ASE's bulk function to create an aluminum structure and generates 7 structures with 5% volume variation to sample the energy-volume curve. This is a suitable approach for bulk modulus calculation.
54-54: Clear task dictionary creationThe dictionary comprehension efficiently transforms the structure dictionary into the task format required for calculations.
197-202: Appropriate use of structure analysisThe notebook correctly uses the
evcurve_analyse_structuresfunction to fit the energy-volume data and extract the bulk modulus. This approach follows standard practice in computational materials science.
1-257: Excellent integration test for GPAW bulk modulus calculationOverall, this notebook provides a well-structured and functional integration test for calculating the bulk modulus of aluminum using GPAW with FluxJobExecutor. It demonstrates the full workflow from structure generation to analysis, making it a valuable addition to the test suite.
The notebook would benefit from additional documentation, error handling, and enhanced visualization, but these are minor improvements to an otherwise solid implementation.
notebooks/5-1-flux-cluster-gpaw-bulk-modulus.ipynb (4)
167-184: Well-implemented parallel execution with proper progress tracking.The FluxClusterExecutor implementation is a good example of parallel task execution with appropriate resource allocation and progress monitoring. The code uses a context manager, submits tasks with specific resource requirements, and properly tracks progress with tqdm.
194-199: The analysis configuration appears appropriate for bulk modulus calculation.The equation of state analysis using polynomial fitting is well configured for extracting the bulk modulus from the energy-volume data.
18-29: Well-organized imports with appropriate dependency selection.The imports are properly organized with standard libraries first, followed by specialized scientific libraries. The specific imports rather than wildcard imports follow best practices.
240-244: The plotting code is concise and effective.The plot effectively visualizes the energy-volume relationship and displays the calculated bulk modulus in the legend.
Once you fix the escape sequence warning, the plot will be complete.
notebooks/5-3-flux-cluster-qe-bulk-modulus.ipynb (1)
184-201: LGTM! Effective use of parallel execution pattern.The FluxClusterExecutor implementation efficiently distributes the computation tasks across available resources. Using a context manager (
with) ensures proper resource cleanup. The progress bar with tqdm provides good user feedback for long-running calculations.
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🧹 Nitpick comments (2)
.github/workflows/pipeline.yml (2)
179-184: Environment Merge Step Verification
The "Merge Notebook environment" step creates a minimal.condarcfile using an echo command. Ensure that this configuration aligns with all integration requirements and that the referenced environment file (.ci_support/environment-integration.yml) is correctly set up with all necessary channels and dependencies.
196-203: Notebook Execution Commands and Timeout Configuration
The "Notebooks" step executes four notebook tests viaflux start papermillwith a 20-minute timeout. Ensure that bothfluxandpapermillare reliably available in the integration environment, and consider capturing log outputs to facilitate debugging in case any of the notebooks fail during execution.
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.github/workflows/pipeline.yml (4)
176-178: Job Initialization:notebooks_integrationAdded
The new integration test job is defined to depend solely on theblackjob. Verify if additional dependencies (e.g., setup or prior notebook test outcomes) are needed to ensure reliable execution of the integration tests.
185-190: Mambaforge Setup with Python 3.12
The job uses Python 3.12 in this integration test, which differs from other jobs that may run on 3.13. Confirm that Python 3.12 is the intended version for these tests, or update accordingly for consistency if needed.
191-195: Installation Step Consistency
The installation commands—installingversioneer[toml]==0.29and the package without dependencies or build isolation—mirror those in other jobs and seem appropriate for ensuring a clean environment.
204-210: Environment Variables Configuration Check
The environment variables (e.g.,ASE_ESPRESSO_COMMAND,ESPRESSO_PSEUDO, and the OpenMPI-related settings) are crucial for the correct execution of the notebooks. Please verify thatESPRESSO_PSEUDOcorrectly points to the intended integration test directory and consider adding inline comments or documentation for these variables to aid future troubleshooting.
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.github/workflows/pipeline.yml (1)
180-184: Checkout and Environment Merge Step Clarity
The step usesactions/checkout@v4followed by a "Merge Notebook environment" step that only writes a simple.condarcfile with the conda-forge channel. Although this meets the minimal requirement for Conda configuration, the step name may be slightly misleading if no merging of multiple environment files is taking place. Consider renaming it (e.g., "Configure Conda Channels") or expanding it to perform an actual merge if intended.
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🔇 Additional comments (4)
.github/workflows/pipeline.yml (4)
176-179: Job Definition: Notebooks Integration Job Added
The newnotebooks_integrationjob is properly defined with a dependency on theblackjob and set to run onubuntu-latest. Ensure that depending solely onblackis sufficient for the integration context, or if additional prerequisites should be chained.
185-190: Setup Mambaforge with Integration Environment
The "Setup Mambaforge" step is correctly configured using Python 3.12 and the latest miniforge version. The step specifies the.ci_support/environment-integration.ymlfile, ensuring the job uses the correct dependencies for notebook execution. Please verify that the integration environment file includes all necessary packages for running your GPAW and Quantum Espresso notebooks.
191-196: Installation Step Consistency
The installation step installsversioneer[toml]==0.29and the current package without dependency or build isolation. This mirrors similar steps in other pipeline parts and should work well for ensuring the package is ready for notebook execution.
197-209: Notebooks Execution and Environment Variables
The "Notebooks" step usesflux start papermillto run four notebook files with a 20-minute timeout. The commands are well structured, and the designated environment variables (such asESPRESSO_PSEUDO,OMPI_MCA_plm, etc.) appear tailored for the integration tests. Confirm that these environment variable settings align with the requirements of your notebooks, particularly for handling simulation inputs and MPI configuration.
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♻️ Duplicate comments (1)
notebooks/5-4-flux-job-qe-bulk-modulus.ipynb (1)
85-87: 🛠️ Refactor suggestionFix the pseudo_dir path to be more CI-friendly.
The path to the pseudopotentials directory is still using a relative path that may not work reliably in CI environments. This was mentioned in a previous review comment, but the updated path "tests/integration" could still cause issues.
profile=EspressoProfile( command="flux run pw.x", - pseudo_dir="tests/integration", + pseudo_dir=os.environ.get("ESPRESSO_PSEUDO", "tests/integration"), ),Using an environment variable with a fallback value makes the code more portable across different environments.
🧹 Nitpick comments (6)
notebooks/5-4-flux-job-qe-bulk-modulus.ipynb (2)
187-201: Potential issue with relative paths in directory creation.The directory creation for each strain calculation uses relative paths, which might lead to unexpected behavior depending on where the notebook is executed from.
- os.makedirs(os.path.abspath((\"strain_%0.2f\" % k).replace(\".\", \"_\")), exist_ok=True) + strain_dir = os.path.abspath((\"strain_%0.2f\" % k).replace(\".\", \"_\")) + os.makedirs(strain_dir, exist_ok=True) future_dict[k] = exe.submit( evaluate_with_quantum_espresso, task_dict=v, pseudopotentials=pseudopotentials, kpts=(3, 3, 3), resource_dict={ "cores": 1, "threads_per_core": 2, - "cwd": os.path.abspath((\"strain_%0.2f\" % k).replace(\".\", \"_\")), + "cwd": strain_dir, }, )This refactoring reduces code duplication and makes the directory management more explicit.
1-302: Code logic is sound but could benefit from error handling.The overall implementation for calculating the bulk modulus of aluminum using Quantum Espresso with Flux job executor works well. The notebook successfully:
- Defines pseudopotentials
- Generates structures with varying volumes
- Sets up and executes the calculations
- Analyzes results to obtain the bulk modulus
- Visualizes the energy-volume curve
However, the notebook lacks error handling for potential failures in job execution or file I/O operations. Consider adding try-except blocks around critical operations.
notebooks/5-3-flux-cluster-qe-bulk-modulus.ipynb (4)
39-40: Consider adding pseudopotential source documentation.The pseudopotential file specification is provided, but it would be helpful to include a comment or documentation reference about where this pseudopotential can be obtained and its properties.
74-90: Path hardcoding in pseudo_dir may cause portability issues.The pseudo_dir parameter is hardcoded to "tests/integration", which might cause issues if the notebook is run from a different directory. Consider using a relative path or environment variable.
- profile=EspressoProfile( - command="pw.x", - pseudo_dir="tests/integration", - ), + profile=EspressoProfile( + command="pw.x", + pseudo_dir=os.path.join(os.path.dirname(os.path.abspath(".")), "tests/integration"), + ),
257-260: Plot formatting could be improved with additional customization.While the plot effectively displays the essential information, consider enhancing it with grid lines, improved font sizes, and a title to make it more publication-ready.
plt.plot(fit_dict["volume"], fit_dict["energy"], label="$B_0$= %0.2f GPa" % fit_dict["bulkmodul_eq"]) plt.xlabel(r"Volume [$\AA^3$]") plt.ylabel("Energy [eV]") +plt.title("Al Bulk Modulus Calculation with Quantum Espresso") +plt.grid(True, linestyle='--', alpha=0.7) +plt.tight_layout() plt.legend()
1-293: Overall a well-structured and functional integration test notebook.This notebook effectively demonstrates the workflow for calculating aluminum bulk modulus using Quantum Espresso with Flux cluster execution. It covers the essential steps: structure generation, task definition, parallel execution, result collection, analysis, and visualization. The code follows a logical flow that would be suitable for integration testing.
For integration testing purposes, consider adding assertion statements to validate the calculated bulk modulus against expected values within acceptable tolerances. This would make the notebook more effective as an automated test.
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🔇 Additional comments (3)
notebooks/5-3-flux-cluster-qe-bulk-modulus.ipynb (3)
1-31: Well-structured imports and introduction.The notebook begins with a clear markdown title and imports all necessary libraries for atomic simulations, subprocess execution, and visualization components. The imports are comprehensive and well-organized.
49-54: Well-defined structure generation parameters.The structure generation is clearly parameterized with appropriate values for aluminum, with a sensible number of points and volume range for accurate bulk modulus calculation.
211-216: Good use of analysis helper function with appropriate fit parameters.The analysis using
evcurve_analyse_structureswith polynomial fit of order 3 is appropriate for bulk modulus calculations. The function parameters are well chosen.
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🧹 Nitpick comments (4)
notebooks/5-3-flux-cluster-qe-bulk-modulus.ipynb (4)
74-90: Well-structured evaluation function for Quantum Espresso.The function is appropriately modular and encapsulates the Quantum Espresso calculation setup. Consider adding docstrings to document the expected format of inputs and outputs.
Consider adding a docstring to explain the parameters and return value:
def evaluate_with_quantum_espresso(task_dict, pseudopotentials, kpts): + """ + Evaluate atomic structures using Quantum Espresso. + + Parameters: + task_dict (dict): Dictionary containing atomic structures to evaluate + pseudopotentials (dict): Dictionary mapping elements to pseudopotential files + kpts (tuple): k-point mesh for the calculation + + Returns: + dict: Dictionary of calculated energies + """ from ase.calculators.espresso import Espresso, EspressoProfile from atomistics.calculators import evaluate_with_ase
113-113: Resource monitoring is properly implemented.The use of
subprocess.check_outputto monitor Flux resources and jobs is appropriate, but consider adding error handling for these calls.Add try-except blocks to handle potential errors in the subprocess calls:
-pprint.pp(subprocess.check_output(["flux", "resource", "list"], universal_newlines=True).split("\n")) +try: + resource_output = subprocess.check_output(["flux", "resource", "list"], universal_newlines=True) + pprint.pp(resource_output.split("\n")) +except subprocess.CalledProcessError as e: + print(f"Error checking resources: {e}")Also applies to: 131-131
257-260: Well-structured visualization code, but needs escape sequence fix.The plotting code is concise and presents the key results effectively. Just fix the escape sequence as noted above.
Additionally, consider adding axis limits and improving the figure size for better visualization:
+plt.figure(figsize=(8, 6)) plt.plot(fit_dict["volume"], fit_dict["energy"], label="$B_0$= %0.2f GPa" % fit_dict["bulkmodul_eq"]) -plt.xlabel("Volume [$\\AA^3$]") +plt.xlabel(r"Volume [$\AA^3$]") plt.ylabel("Energy [eV]") +plt.tight_layout() plt.legend()
211-216: Good use of standard fitting approach.The use of polynomial fitting to extract the bulk modulus is a standard approach. Consider adding some error metrics or visualization of the fit quality.
Add code to visualize and evaluate the fit quality:
# Add after the fitting code residuals = fit_dict["residuals"] if "residuals" in fit_dict else [] if residuals: print(f"Fit quality - Root Mean Square Error: {np.sqrt(np.mean(np.array(residuals)**2)):.6f} eV") # Optional: Plot residuals plt.figure(figsize=(8, 4)) plt.scatter(fit_dict["volume"], residuals, label="Fit Residuals") plt.axhline(y=0, color='r', linestyle='--') plt.xlabel(r"Volume [$\AA^3$]") plt.ylabel("Residuals [eV]") plt.legend() plt.title("Fit Quality") plt.tight_layout()
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🔇 Additional comments (3)
notebooks/5-3-flux-cluster-qe-bulk-modulus.ipynb (3)
18-29: The imports look good, comprehensive set of required libraries.The notebook includes all necessary imports for handling atomic simulations, parallel execution with Flux, and visualization. The organization is clean and follows best practices.
184-201: Consider adding error handling for job submission and monitoring.The job submission and monitoring code lacks error handling. If a job fails or if the Flux cluster is not available, the notebook will crash without helpful error messages.
future_dict = {} with FluxClusterExecutor() as exe: - for k, v in task_loop_dict.items(): - future_dict[k] = exe.submit( - evaluate_with_quantum_espresso, - task_dict=v, - pseudopotentials=pseudopotentials, - kpts=(3, 3, 3), - resource_dict={"cores": 1, "threads_per_core": 2}, - ) + try: + for k, v in task_loop_dict.items(): + future_dict[k] = exe.submit( + evaluate_with_quantum_espresso, + task_dict=v, + pseudopotentials=pseudopotentials, + kpts=(3, 3, 3), + resource_dict={"cores": 1, "threads_per_core": 2}, + ) + except Exception as e: + print(f"Error submitting jobs: {e}") + raise
228-233: Fix invalid escape sequence in the xlabel.The SyntaxWarning about an invalid escape sequence '\A' in the xlabel should be fixed. Use a raw string prefix or double the backslash.
- plt.xlabel("Volume [$\\AA^3$]") + plt.xlabel(r"Volume [$\AA^3$]")
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notebooks/5-3-flux-cluster-qe-bulk-modulus.ipynb (1)
86-88: 🛠️ Refactor suggestionPath configuration still uses hardcoded directory path.
The pseudopotential directory is still hardcoded to a specific path in the CI environment. This could cause issues when the notebook is run in different environments.
- pseudo_dir="/home/runner/work/executorlib/executorlib/tests/integration", + pseudo_dir=os.path.join(os.environ.get("PSEUDO_DIR", "/home/runner/work/executorlib/executorlib/tests/integration")),
🧹 Nitpick comments (4)
.github/workflows/pipeline.yml (1)
400-416: Consider adding the new job to the dependency chain for automation jobs.The new
notebooks_integrationjob is not included in the dependency list for theautobotandumljobs. This means these jobs might run before the integration tests complete, potentially allowing merges even if the integration tests fail.- needs: [unittest_old, unittest_win, unittest_openmpi, unittest_mpich, unittest_flux_openmpi, unittest_flux_mpich, notebooks, benchmark, minimal, pip_check, mypy] + needs: [unittest_old, unittest_win, unittest_openmpi, unittest_mpich, unittest_flux_openmpi, unittest_flux_mpich, notebooks, notebooks_integration, benchmark, minimal, pip_check, mypy]notebooks/5-3-flux-cluster-qe-bulk-modulus.ipynb (3)
74-90: Add a docstring to theevaluate_with_quantum_espressofunction.The function is missing a docstring to describe its purpose, parameters, and return value. This would improve code readability and maintainability.
def evaluate_with_quantum_espresso(task_dict, pseudopotentials, kpts): + """ + Evaluate atomic structures using Quantum Espresso calculator. + + Parameters: + ----------- + task_dict : dict + Dictionary containing the structures to evaluate + pseudopotentials : dict + Dictionary mapping chemical symbols to pseudopotential file names + kpts : tuple + K-point mesh for the calculation + + Returns: + -------- + dict + Dictionary containing calculated energies + """ from ase.calculators.espresso import Espresso, EspressoProfile from atomistics.calculators import evaluate_with_ase return evaluate_with_ase(
67-91: Implement a more robust resource configuration approach.The function doesn't handle resource specification in a flexible way. Consider adding parameters to control computation resources.
-def evaluate_with_quantum_espresso(task_dict, pseudopotentials, kpts): +def evaluate_with_quantum_espresso(task_dict, pseudopotentials, kpts, resource_dict=None): """ Evaluate atomic structures using Quantum Espresso calculator. Parameters: ----------- task_dict : dict Dictionary containing the structures to evaluate pseudopotentials : dict Dictionary mapping chemical symbols to pseudopotential file names kpts : tuple K-point mesh for the calculation + resource_dict : dict, optional + Dictionary with computation resource parameters Returns: -------- dict Dictionary containing calculated energies """ from ase.calculators.espresso import Espresso, EspressoProfile from atomistics.calculators import evaluate_with_ase + # Default QE parameters + params = { + "tstress": True, + "tprnfor": True, + "kpts": kpts, + } + + # Add additional parameters from resource_dict + if resource_dict is not None: + if "conv_thr" in resource_dict: + params["conv_thr"] = resource_dict["conv_thr"] + if "ecutwfc" in resource_dict: + params["ecutwfc"] = resource_dict["ecutwfc"] + return evaluate_with_ase( task_dict=task_dict, ase_calculator=Espresso( pseudopotentials=pseudopotentials, - tstress=True, - tprnfor=True, - kpts=kpts, + **params, profile=EspressoProfile( command="pw.x", pseudo_dir="/home/runner/work/executorlib/executorlib/tests/integration", ), ), )["energy"]
211-216: Add error handling for the fitting process.The analysis of structures is not wrapped in a try-except block, which might cause the notebook to crash if the fitting fails.
-fit_dict = evcurve_analyse_structures( - output_dict={"energy": result_dict}, - structure_dict=structure_dict, - fit_type="polynomial", - fit_order=3, -) +try: + fit_dict = evcurve_analyse_structures( + output_dict={"energy": result_dict}, + structure_dict=structure_dict, + fit_type="polynomial", + fit_order=3, + ) +except Exception as e: + print(f"Error during fitting process: {e}") + raise
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🔇 Additional comments (9)
.github/workflows/pipeline.yml (1)
176-204: Well-structured integration test job added to the workflow.The new
notebooks_integrationjob properly sets up the environment and executes the integration test notebooks for calculating aluminum bulk modulus using GPAW and Quantum Espresso. This aligns with the PR objective to add integration tests for application notebooks.notebooks/5-4-flux-job-qe-bulk-modulus.ipynb (5)
74-90: Theevaluate_with_quantum_espressofunction contains a hardcoded path.The function includes a hardcoded path to the pseudopotentials directory (
pseudo_dir="/home/runner/work/executorlib/executorlib/tests/integration"), which won't work in environments other than the CI runner.Use environment variables or a relative path for portability:
profile=EspressoProfile( command="flux run pw.x", - pseudo_dir="/home/runner/work/executorlib/executorlib/tests/integration", + pseudo_dir=os.environ.get("ESPRESSO_PSEUDO", "pseudo"), ),This will use the
ESPRESSO_PSEUDOenvironment variable if defined, or fall back to a relative "pseudo" directory.
265-266: Fix the invalid escape sequence warning in matplotlib label.The current string format is causing a SyntaxWarning for an invalid escape sequence '\A'. Use a raw string with the correct escape sequence for the Angstrom symbol.
-plt.xlabel("Volume [$\\AA^3$]") +plt.xlabel(r"Volume [$\AA^3$]")This change will properly render the Angstrom symbol without generating warnings.
49-54: LGTM: Appropriate test structure generation parametersThe structure generation for aluminum using ASE's bulk function with reasonable volume range and number of points is well-configured for testing the bulk modulus calculation workflow.
187-209: Robust job execution using FluxJobExecutorGood implementation of the FluxJobExecutor with proper resource specification and directory management. The code correctly submits tasks for evaluation and collects results asynchronously with progress tracking.
219-224: Effective results analysisThe notebook correctly uses the atomistics workflow helper functions to analyze the calculation results and fit the energy vs. volume data to determine the bulk modulus.
notebooks/5-3-flux-cluster-qe-bulk-modulus.ipynb (3)
184-201: Add error handling for job submission and monitoring.The job submission and monitoring code lacks error handling. If a job fails or if the Flux cluster is not available, the notebook will crash without helpful error messages.
future_dict = {} with FluxClusterExecutor() as exe: - for k, v in task_loop_dict.items(): - future_dict[k] = exe.submit( - evaluate_with_quantum_espresso, - task_dict=v, - pseudopotentials=pseudopotentials, - kpts=(3, 3, 3), - resource_dict={"cores": 1, "threads_per_core": 2}, - ) + try: + for k, v in task_loop_dict.items(): + future_dict[k] = exe.submit( + evaluate_with_quantum_espresso, + task_dict=v, + pseudopotentials=pseudopotentials, + kpts=(3, 3, 3), + resource_dict={"cores": 1, "threads_per_core": 2}, + ) + except Exception as e: + print(f"Error submitting jobs: {e}") + raise
196-199: Add error handling for result collection.The result collection process should be wrapped in a try-except block to gracefully handle any failures during job execution.
- result_dict = { - k: f.result() - for k, f in tqdm(future_dict.items()) - } + result_dict = {} + try: + for k, f in tqdm(future_dict.items()): + try: + result_dict[k] = f.result() + except Exception as e: + print(f"Error getting result for job {k}: {e}") + except Exception as e: + print(f"Error collecting results: {e}") + if not result_dict: + raise # Only re-raise if we got no results at all
228-233: Fix invalid escape sequence in the xlabel.The SyntaxWarning about an invalid escape sequence '\A' in the xlabel should be fixed. Use a raw string prefix or double the backslash.
- plt.xlabel("Volume [$\\AA^3$]") + plt.xlabel(r"Volume [$\AA^3$]")
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.github/workflows/pipeline.yml (2)
180-184: Checkout and Environment Merge Step
The steps to check out the repository and create a basic Conda configuration (via echoing the channels) are clear. If additional environment parameters are needed later, consider enhancing this script.
197-204: Notebook Execution Step
The execution phase usesflux start papermillto run the designated notebooks with an extended timeout (20 minutes) suitable for longer-running tasks. Note that the command for the notebook5-3-flux-cluster-qe-bulk-modulus.ipynbis commented out. If this is intentional (e.g., due to pending issues or deprecation), consider adding an inline comment to explain why it’s disabled; otherwise, removing the dead code could tidy up the configuration.
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🔇 Additional comments (3)
.github/workflows/pipeline.yml (3)
176-179: New Integration Job Setup
The newnotebooks_integrationjob is introduced to run integration tests for the notebooks. It correctly declares its dependency on theblackjob and specifies the appropriate runner (ubuntu-latest).
185-190: Mambaforge Setup Step
The configuration for setting up Mambaforge is well defined—using Python 3.12, the latest miniforge, and referencing the integration-specific environment file (.ci_support/environment-integration.yml). This aligns with the integration testing objective.
191-196: Install Dependencies Step
The installation step, which installsversioneer[toml]==0.29and the current package (without dependencies or build isolation), is consistent with other parts of the pipeline. This ensures that the environment is ready to execute the notebooks.
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.github/workflows/pipeline.yml (1)
176-204: Review of the Newly Added "notebooks_integration" Job
Dependencies & Environment Setup:
The job correctly depends on theblackjob and uses the standardubuntu-latestrunner. Ensure that the environment file (.ci_support/environment-integration.yml) referenced in line 190 includes all the necessary dependencies (e.g.,papermill,flux, and any other notebook-specific packages) required for execution.Merge Notebook Environment Step:
The step at lines 180–184 creates a simple.condarcthat specifies theconda-forgechannel. This is consistent with similar steps in other jobs. Just verify that no additional channels or configuration are needed for integration tests.Setup Mambaforge Action:
The configuration of theconda-incubator/setup-miniconda@v3action (lines 184–190) uses Python 3.12 and points to the integration environment file. This appears correct; however, double-check that theminiforge-version: latestwill consistently pick the intended version in your CI environment.Installation Step:
The installation commands at lines 192–195 installversioneerand the package under test without dependencies or build isolation. This matches the patterns in other jobs and should be fine as long as the repository’s metadata is up-to-date.Notebook Execution Step:
The execution block (lines 197–203) runs four notebook commands usingflux start papermill. Please verify that:
- Using
flux start papermillfor all four notebooks is intentional. (Recall the PR objective mentions two different execution modes; if there is a need to differentiate between cluster and job executor modes, adjust the commands accordingly or add clarifying comments.)- The 20-minute timeout (line 198) is adequate for the notebooks’ execution under integration conditions.
Overall Clarity & Consistency:
The newly added job is structured consistently with the existing jobs. Just ensure that any nuances related to notebook execution modes (i.e., cluster versus job executor as described in the PR objectives) are either addressed here or documented elsewhere, to avoid any confusion.
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♻️ Duplicate comments (1)
notebooks/5-3-flux-cluster-qe-bulk-modulus.ipynb (1)
86-88: 🛠️ Refactor suggestionUpdate the pseudopotential directory path
The pseudopotential directory is hardcoded to a specific path related to a GitHub runner, which might cause issues in different environments. This was already flagged in a previous review and reportedly fixed in commit 9e60689, but the path should be made configurable or relative for better portability.
- pseudo_dir="/home/runner/work/executorlib/executorlib/tests/integration", + pseudo_dir=os.path.join(os.path.dirname(os.path.abspath("__file__")), "..", "tests", "integration"),
🧹 Nitpick comments (4)
notebooks/5-3-flux-cluster-qe-bulk-modulus.ipynb (4)
18-29: Add documentation for the importsThe import section lacks documentation explaining the purpose of each imported library. Adding comments would improve maintainability.
Consider adding documentation comments for each import group:
import os import subprocess +# ASE for atomic structure manipulation from ase.build import bulk +# Atomistics helpers for energy-volume curve analysis from atomistics.workflows.evcurve.helper import ( analyse_structures_helper as evcurve_analyse_structures, generate_structures_helper as evcurve_generate_structures, ) +# Parallel execution via Flux from executorlib import FluxClusterExecutor +# Visualization and utilities import matplotlib.pyplot as plt import pprint from tqdm import tqdm from time import sleep
39-39: Consider adding a comment about the pseudopotential definitionThe pseudopotential definition lacks an explanation of what it is and where it comes from. This would help with reproducibility.
Add a comment explaining the pseudopotential:
+# PBE pseudopotential for Aluminum from the pslibrary pseudopotentials = {"Al": "Al.pbe-n-kjpaw_psl.1.0.0.UPF"}
257-260: Consider adding more informative plot labels and titleThe visualization could be improved with a more descriptive title and axis labels that include units.
Enhance the plot with more informative labels:
plt.plot(fit_dict["volume"], fit_dict["energy"], label="$B_0$= %0.2f GPa" % fit_dict["bulkmodul_eq"]) plt.xlabel(r"Volume [$\AA^3$]") plt.ylabel("Energy [eV]") +plt.title("Energy vs. Volume Curve for Aluminum") +plt.grid(True, linestyle='--', alpha=0.7) plt.legend()
74-90: Consider parameterizing calculation settingsThe function hardcodes several calculation parameters that could be made configurable through additional function parameters.
Make the calculation settings more configurable:
-def evaluate_with_quantum_espresso(task_dict, pseudopotentials, kpts): +def evaluate_with_quantum_espresso(task_dict, pseudopotentials, kpts, pseudo_dir=None, + tstress=True, tprnfor=True, command="flux run pw.x"): from ase.calculators.espresso import Espresso, EspressoProfile from atomistics.calculators import evaluate_with_ase + if pseudo_dir is None: + pseudo_dir = os.path.join(os.path.dirname(os.path.abspath("__file__")), "..", "tests", "integration") + return evaluate_with_ase( task_dict=task_dict, ase_calculator=Espresso( pseudopotentials=pseudopotentials, - tstress=True, - tprnfor=True, + tstress=tstress, + tprnfor=tprnfor, kpts=kpts, profile=EspressoProfile( - command="flux run pw.x", - pseudo_dir="/home/runner/work/executorlib/executorlib/tests/integration", + command=command, + pseudo_dir=pseudo_dir, ), ), )["energy"]
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🔇 Additional comments (3)
notebooks/5-3-flux-cluster-qe-bulk-modulus.ipynb (3)
184-193: Add error handling for job submissionThe job submission code lacks error handling. If there are issues with the job submission or the Flux cluster is unavailable, the notebook will fail without a helpful error message.
Apply the previously suggested fix:
future_dict = {} with FluxClusterExecutor() as exe: - for k, v in task_loop_dict.items(): - future_dict[k] = exe.submit( - evaluate_with_quantum_espresso, - task_dict=v, - pseudopotentials=pseudopotentials, - kpts=(3, 3, 3), - resource_dict={"cores": 1, "threads_per_core": 2}, - ) + try: + for k, v in task_loop_dict.items(): + future_dict[k] = exe.submit( + evaluate_with_quantum_espresso, + task_dict=v, + pseudopotentials=pseudopotentials, + kpts=(3, 3, 3), + resource_dict={"cores": 1, "threads_per_core": 2}, + ) + except Exception as e: + print(f"Error submitting jobs: {e}") + raise
196-199: Add error handling for result collectionThe result collection process needs error handling to gracefully handle failures during job execution.
Apply the previously suggested fix:
- result_dict = { - k: f.result() - for k, f in tqdm(future_dict.items()) - } + result_dict = {} + try: + for k, f in tqdm(future_dict.items()): + try: + result_dict[k] = f.result() + except Exception as e: + print(f"Error getting result for job {k}: {e}") + except Exception as e: + print(f"Error collecting results: {e}") + if not result_dict: + raise # Only re-raise if we got no results at all
228-232: Fix invalid escape sequence in the xlabelThe code triggers a SyntaxWarning about an invalid escape sequence '\A' in the xlabel.
Fix the escape sequence by using a raw string:
- plt.xlabel("Volume [$\\AA^3$]") + plt.xlabel(r"Volume [$\AA^3$]")
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♻️ Duplicate comments (2)
notebooks/5-4-flux-job-qe-bulk-modulus.ipynb (2)
74-90:⚠️ Potential issueHardcoded path in
evaluate_with_quantum_espressofunctionThe function includes a hardcoded path to the pseudopotentials directory that won't work in environments other than the CI environment.
Use an environment variable or a relative path for portability:
profile=EspressoProfile( command="flux run -n 2 pw.x", - pseudo_dir="/home/runner/work/executorlib/executorlib/tests/integration", + pseudo_dir=os.environ.get("ESPRESSO_PSEUDO", "pseudo"), ),
265-266:⚠️ Potential issueFix the invalid escape sequence warning in matplotlib label
The current string format is causing a SyntaxWarning for an invalid escape sequence '\A'. Use a raw string with the correct escape sequence for the Angstrom symbol.
-plt.xlabel("Volume [$\\AA^3$]") +plt.xlabel(r"Volume [$\AA^3$]")
🧹 Nitpick comments (3)
.github/workflows/pipeline.yml (2)
400-401: Update autobot dependencies to include the new integration jobThe
autobotjob should depend on the newnotebooks_integrationjob to ensure PRs aren't automatically merged before all tests pass.- needs: [unittest_old, unittest_win, unittest_openmpi, unittest_mpich, unittest_flux_openmpi, unittest_flux_mpich, notebooks, benchmark, minimal, pip_check, mypy] + needs: [unittest_old, unittest_win, unittest_openmpi, unittest_mpich, unittest_flux_openmpi, unittest_flux_mpich, notebooks, notebooks_integration, benchmark, minimal, pip_check, mypy]
198-198: Consider the timeout duration for integration notebooksThe timeout is set to 20 minutes, which is significantly longer than the 5 minutes for regular notebooks. Make sure this is sufficient but not excessive for running these computational notebooks.
notebooks/5-4-flux-job-qe-bulk-modulus.ipynb (1)
187-209: Consider simplifying the flux job execution and monitoringThe notebook has verbose output from flux jobs and resources commands. For better readability, you might want to consolidate the monitoring into a single cell or create a helper function.
Example of a monitoring helper function:
def monitor_flux_jobs(): """Helper function to monitor flux jobs and resources""" print("Flux resources:") print(subprocess.check_output(["flux", "resource", "list"], universal_newlines=True)) print("\nFlux jobs:") print(subprocess.check_output(["flux", "jobs", "-a"], universal_newlines=True))
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🔇 Additional comments (7)
.github/workflows/pipeline.yml (2)
176-204: New integration job for running application notebooks looks well-structuredThe addition of the
notebooks_integrationjob follows the established pattern in the workflow and provides a good way to test the new application notebooks as part of CI.
202-202:❓ Verification inconclusive
Verify if this notebook is intentionally commented out
Line 202 has a commented-out notebook execution for
5-3-flux-cluster-qe-bulk-modulus.ipynb. Is this intentional, or should it be enabled?
🏁 Script executed:
#!/bin/bash # Check if the notebook file exists test -f notebooks/5-3-flux-cluster-qe-bulk-modulus.ipynb && echo "Notebook exists but is commented out in the workflow" || echo "Notebook doesn't exist, so commenting it out is correct"Length of output: 173
Action Required: Confirm the Intent for the Commented Notebook Execution
The shell script confirms that
notebooks/5-3-flux-cluster-qe-bulk-modulus.ipynbexists, yet its execution command remains commented out in.github/workflows/pipeline.yml(line 202). Please verify if this is intentional. If the execution should remain disabled, consider adding a clarifying comment to explain the rationale. Otherwise, if it was commented out by oversight, please enable it.notebooks/5-4-flux-job-qe-bulk-modulus.ipynb (1)
1-301: Well-structured notebook for calculating aluminum bulk modulusThe notebook is well-organized with clear separation between setup, computation, and visualization. It correctly uses the FluxJobExecutor for parallel computation and properly visualizes the results.
notebooks/5-3-flux-cluster-qe-bulk-modulus.ipynb (4)
87-88: Update the pseudopotential directory path.The hardcoded pseudopotential directory path could cause issues in different environments. Consider using a relative path or environment variable to make the notebook more portable.
This issue was identified in a previous review and marked as addressed in commit 9e60689, but the code still contains the hardcoded path. Please verify this is the intended path for the integration environment.
184-193: Add error handling for job submission.The job submission code lacks error handling. If a job fails or if the Flux cluster is not available, the notebook will crash without helpful error messages.
Consider implementing the previously suggested error handling:
future_dict = {} with FluxClusterExecutor() as exe: - for k, v in task_loop_dict.items(): - future_dict[k] = exe.submit( - evaluate_with_quantum_espresso, - task_dict=v, - pseudopotentials=pseudopotentials, - kpts=(3, 3, 3), - resource_dict={"cores": 1, "threads_per_core": 2}, - ) + try: + for k, v in task_loop_dict.items(): + future_dict[k] = exe.submit( + evaluate_with_quantum_espresso, + task_dict=v, + pseudopotentials=pseudopotentials, + kpts=(3, 3, 3), + resource_dict={"cores": 1, "threads_per_core": 2}, + ) + except Exception as e: + print(f"Error submitting jobs: {e}") + raise
196-199: Add error handling for result collection.The result collection process should be wrapped in a try-except block to gracefully handle any failures during job execution.
Implement the previously suggested error handling:
- result_dict = { - k: f.result() - for k, f in tqdm(future_dict.items()) - } + result_dict = {} + try: + for k, f in tqdm(future_dict.items()): + try: + result_dict[k] = f.result() + except Exception as e: + print(f"Error getting result for job {k}: {e}") + except Exception as e: + print(f"Error collecting results: {e}") + if not result_dict: + raise # Only re-raise if we got no results at all
257-260: Fix invalid escape sequence in the xlabel.The warning message in the output (lines 229-233) indicates an invalid escape sequence '\A' in the xlabel. This should be fixed to prevent warnings during execution.
Use a raw string prefix for the xlabel:
- plt.xlabel("Volume [$\\AA^3$]") + plt.xlabel(r"Volume [$\AA^3$]")
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♻️ Duplicate comments (3)
notebooks/5-3-flux-cluster-qe-bulk-modulus.ipynb (3)
184-206:⚠️ Potential issueError handling still needed for job submission and monitoring.
The job submission code still lacks error handling. The previously suggested improvement to wrap the job submission and monitoring code in try-except blocks has not been implemented. If a job fails or the Flux cluster is unavailable, the notebook will crash without helpful error messages.
Apply a similar fix as suggested in the previous review:
future_dict = {} with FluxClusterExecutor(flux_executor_nesting=True) as exe: - for k, v in task_loop_dict.items(): - os.makedirs(os.path.abspath((\"strain_%0.2f\" % k).replace(\".\", \"_\")), exist_ok=True) - future_dict[k] = exe.submit( - evaluate_with_quantum_espresso, - task_dict=v, - pseudopotentials=pseudopotentials, - kpts=(3, 3, 3), - resource_dict={ - \"cores\": 1, - \"threads_per_core\": 2, - \"cwd\": os.path.abspath((\"strain_%0.2f\" % k).replace(\".\", \"_\")), - }, - ) + try: + for k, v in task_loop_dict.items(): + os.makedirs(os.path.abspath((\"strain_%0.2f\" % k).replace(\".\", \"_\")), exist_ok=True) + future_dict[k] = exe.submit( + evaluate_with_quantum_espresso, + task_dict=v, + pseudopotentials=pseudopotentials, + kpts=(3, 3, 3), + resource_dict={ + \"cores\": 1, + \"threads_per_core\": 2, + \"cwd\": os.path.abspath((\"strain_%0.2f\" % k).replace(\".\", \"_\")), + }, + ) + except Exception as e: + print(f"Error submitting jobs: {e}") + raise
201-204:⚠️ Potential issueAdd error handling for result collection as recommended previously.
The result collection process should be wrapped in a try-except block to gracefully handle any failures during job execution, as suggested in the previous review but not implemented.
Apply the previously suggested fix:
- result_dict = { - k: f.result() - for k, f in tqdm(future_dict.items()) - } + result_dict = {} + try: + for k, f in tqdm(future_dict.items()): + try: + result_dict[k] = f.result() + except Exception as e: + print(f"Error getting result for job {k}: {e}") + except Exception as e: + print(f"Error collecting results: {e}") + if not result_dict: + raise # Only re-raise if we got no results at all
263-263:⚠️ Potential issueFix invalid escape sequence in the xlabel.
The SyntaxWarning about an invalid escape sequence '\A' in the xlabel is still present (as shown in lines 233-238 of the output). This should be fixed as suggested in the previous review.
Apply the previously suggested fix:
- plt.xlabel("Volume [$\\AA^3$]") + plt.xlabel(r"Volume [$\AA^3$]")
🧹 Nitpick comments (4)
notebooks/5-3-flux-cluster-qe-bulk-modulus.ipynb (4)
74-90: Add docstring to the evaluation function.The
evaluate_with_quantum_espressofunction lacks a docstring explaining its purpose, parameters, and return values. This would improve the notebook's maintainability and readability.Add a docstring to provide better context:
def evaluate_with_quantum_espresso(task_dict, pseudopotentials, kpts): + """ + Evaluate structures using Quantum Espresso calculator. + + Parameters: + ----------- + task_dict : dict + Dictionary containing atomic structures to evaluate + pseudopotentials : dict + Dictionary mapping element symbols to pseudopotential files + kpts : tuple + K-point mesh for the calculation (e.g., (3, 3, 3)) + + Returns: + -------- + dict + Dictionary of calculated energies + """ from ase.calculators.espresso import Espresso, EspressoProfile from atomistics.calculators import evaluate_with_ase
87-88: Consider using a configurable path for pseudopotentials.The path to pseudopotential files is hardcoded, which could cause problems when running in different environments. Making it configurable would improve portability.
Consider modifying the code to make the pseudopotential directory configurable:
+ # Get the pseudopotential path from environment variable or use default + pseudo_dir = os.environ.get( + "QE_PSEUDO_DIR", + "/home/runner/work/executorlib/executorlib/tests/integration" + ) return evaluate_with_ase( task_dict=task_dict, ase_calculator=Espresso( pseudopotentials=pseudopotentials, tstress=True, tprnfor=True, kpts=kpts, profile=EspressoProfile( command="flux run pw.x", - pseudo_dir="/home/runner/work/executorlib/executorlib/tests/integration", + pseudo_dir=pseudo_dir, ), ), )["energy"]
49-54: Consider adding comments to explain structure generation parameters.The structure generation parameters (number of points, volume range) lack explanation. Adding comments would make it clearer why these specific values were chosen.
Consider adding comments to explain the parameter choices:
structure_dict = evcurve_generate_structures( structure=bulk("Al", a=4.15, cubic=True), - num_points=7, - vol_range=0.05, + num_points=7, # Number of volume points to sample + vol_range=0.05, # ±5% volume variation around equilibrium axes=("x", "y", "z"), )
192-192: Consider making k-point sampling configurable.The k-point mesh is hardcoded to (3, 3, 3), which may not be optimal for all use cases. Making it configurable would improve flexibility.
Consider defining the k-point grid as a variable at the beginning of the notebook to make it easier to adjust:
+# Define computational parameters +kpoint_grid = (3, 3, 3) # k-point grid for QE calculations + +# ... (later in the code) + future_dict[k] = exe.submit( evaluate_with_quantum_espresso, task_dict=v, pseudopotentials=pseudopotentials, - kpts=(3, 3, 3), + kpts=kpoint_grid, resource_dict={ "cores": 1, "threads_per_core": 2,
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🔇 Additional comments (6)
.github/workflows/pipeline.yml (1)
176-204: Well-structured job for integration testing of application notebooksThe added
notebooks_integrationjob follows the project's established patterns and properly sets up the environment to run the new bulk modulus calculation notebooks. The job has appropriate timeouts and dependencies.notebooks/5-4-flux-job-qe-bulk-modulus.ipynb (5)
74-90: Hard-coded path for pseudopotentials may cause issues in different environmentsThe
evaluate_with_quantum_espressofunction contains a hard-coded path to the pseudopotentials directory (pseudo_dir="/home/runner/work/executorlib/executorlib/tests/integration"), which might not work in other environments.Consider using environment variables or a relative path for better portability:
profile=EspressoProfile( command="flux run pw.x", - pseudo_dir="/home/runner/work/executorlib/executorlib/tests/integration", + pseudo_dir=os.environ.get("ESPRESSO_PSEUDO", "pseudo"), ),
265-266: Fix the invalid escape sequence warning in matplotlib labelThe current string format is causing a SyntaxWarning for an invalid escape sequence '\A'. Use a raw string with the correct escape sequence for the Angstrom symbol.
-plt.xlabel("Volume [$\\AA^3$]") +plt.xlabel(r"Volume [$\AA^3$]")
17-30: LGTM: Appropriate imports for the notebook's purposeThe notebook imports necessary libraries for atomic simulations, job execution management, and result visualization.
187-210: Well-structured parallel execution pattern using FluxJobExecutorThe implementation correctly uses FluxJobExecutor to submit multiple strain calculation tasks in parallel, while properly handling directory creation for each task and monitoring job progress.
219-224: Good use of the analysis helper for bulk modulus calculationThe implementation correctly uses the
evcurve_analyse_structuresfunction to process the calculation results and extract the bulk modulus value.
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♻️ Duplicate comments (1)
notebooks/5-4-flux-job-qe-bulk-modulus.ipynb (1)
86-88: 🛠️ Refactor suggestionThe
pseudo_dirpath is still hardcoded.While the path has been updated from the previous personal path to a CI-compatible path, it's still hardcoded which could cause issues in different environments. This approach isn't portable across different systems.
Consider using an environment variable or a relative path:
profile=EspressoProfile( command="flux run pw.x", - pseudo_dir="/home/runner/work/executorlib/executorlib/tests/integration", + pseudo_dir=os.environ.get("ESPRESSO_PSEUDO", "./pseudo"), ),This makes the code more portable and allows for configuration through environment variables.
🧹 Nitpick comments (4)
notebooks/5-4-flux-job-qe-bulk-modulus.ipynb (4)
196-200: Resource allocation is fixed and may not be optimal for all environments.The current implementation uses fixed values for cores and threads which might not be optimal in all execution environments.
Consider making the resource allocation configurable:
resource_dict={ - "cores": 1, - "threads_per_core": 2, + "cores": int(os.environ.get("QE_CORES", 1)), + "threads_per_core": int(os.environ.get("QE_THREADS_PER_CORE", 2)), "cwd": os.path.abspath(("strain_%0.2f" % k).replace(".", "_")), },This allows users to adjust resource allocation through environment variables without modifying the code.
202-203: Use of fixedsleep(1)for synchronization.Using a fixed sleep time for synchronization between job submission and status checking can be unreliable, especially in busy systems.
Consider implementing a more robust synchronization mechanism:
- sleep(1) - pprint.pp(subprocess.check_output(["flux", "jobs", "-a"], universal_newlines=True).split("\n")) + # Wait for job submission to register + max_retries = 5 + for retry in range(max_retries): + try: + job_status = subprocess.check_output(["flux", "jobs", "-a"], universal_newlines=True).split("\n") + if len(job_status) > 1: # More than just the header line + pprint.pp(job_status) + break + except subprocess.SubprocessError: + pass + sleep(0.5)This approach retries the status check with exponential backoff, making it more resilient to timing variations.
74-90: Refactorevaluate_with_quantum_espressoto enhance reusability.The function has limited flexibility because it doesn't allow customizing some important parameters of the Espresso calculator.
Consider making the function more configurable by accepting additional parameters:
-def evaluate_with_quantum_espresso(task_dict, pseudopotentials, kpts): +def evaluate_with_quantum_espresso(task_dict, pseudopotentials, kpts, command="flux run pw.x", + pseudo_dir=None, additional_parameters=None): from ase.calculators.espresso import Espresso, EspressoProfile from atomistics.calculators import evaluate_with_ase + if pseudo_dir is None: + pseudo_dir = os.environ.get("ESPRESSO_PSEUDO", "./pseudo") + + espresso_params = { + "pseudopotentials": pseudopotentials, + "tstress": True, + "tprnfor": True, + "kpts": kpts, + } + + if additional_parameters: + espresso_params.update(additional_parameters) return evaluate_with_ase( task_dict=task_dict, - ase_calculator=Espresso( - pseudopotentials=pseudopotentials, - tstress=True, - tprnfor=True, - kpts=kpts, - profile=EspressoProfile( - command="flux run pw.x", - pseudo_dir="/home/runner/work/executorlib/executorlib/tests/integration", - ), - ), + ase_calculator=Espresso( + **espresso_params, + profile=EspressoProfile( + command=command, + pseudo_dir=pseudo_dir, + ), + ), )["energy"]This refactoring makes the function more flexible, allowing users to customize the behavior without modifying the function itself.
190-201: Consider error handling for job submission and execution.The current implementation doesn't handle potential errors during job submission or execution, which could lead to difficult-to-debug issues.
Add error handling to make the code more robust:
- future_dict[k] = exe.submit( - evaluate_with_quantum_espresso, - task_dict=v, - pseudopotentials=pseudopotentials, - kpts=(3, 3, 3), - resource_dict={ - "cores": 1, - "threads_per_core": 2, - "cwd": os.path.abspath(("strain_%0.2f" % k).replace(".", "_")), - }, - ) + try: + future_dict[k] = exe.submit( + evaluate_with_quantum_espresso, + task_dict=v, + pseudopotentials=pseudopotentials, + kpts=(3, 3, 3), + resource_dict={ + "cores": 1, + "threads_per_core": 2, + "cwd": os.path.abspath(("strain_%0.2f" % k).replace(".", "_")), + }, + ) + except Exception as e: + print(f"Error submitting job for strain {k}: {str(e)}")Similarly, add error handling around the result collection to gracefully handle failed jobs.
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🔇 Additional comments (2)
notebooks/5-4-flux-job-qe-bulk-modulus.ipynb (2)
265-266: Fix the invalid escape sequence warning in matplotlib label.The current string format is causing a SyntaxWarning for an invalid escape sequence '\A'. This issue was identified in a previous review but hasn't been fixed.
Apply this fix to properly render the Angstrom symbol without generating warnings:
-plt.xlabel("Volume [$\\AA^3$]") +plt.xlabel(r"Volume [$\AA^3$]")
1-31: LGTM: Well-structured imports and setupThe imports section is well-organized, importing all necessary libraries for the computation task. The code properly separates different components like structure generation, calculation setup, job execution, and analysis.
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Actionable comments posted: 7
🧹 Nitpick comments (6)
docs/_toc.yml (1)
1-8: Contextual Consistency CheckThe overall structure of the TOC remains consistent with earlier chapters. Ensure that the new chapter’s content in
application.mdincludes sufficient context (e.g., description “Calculate the bulk modulus”) to guide users.Also applies to: 13-15
.github/workflows/pipeline.yml (1)
176-202: Integration Job for Notebooks Added SuccessfullyThe new
notebooks_integrationjob is properly defined:
- It correctly uses
actions/checkout@v4and sets up the environment using Mambaforge with Python 3.12.- The environment file
.ci_support/environment-integration.ymlis specified, which distinguishes it from the other notebook jobs.- The steps for installing dependencies and executing the notebooks with
flux start papermill ...are clear and consistent with the CI/CD pipeline practices.A couple of minor suggestions:
- Logging and Debugging: Consider adding additional logging or enabling verbose output in the notebook execution step to facilitate easier diagnosis if failures occur during integration tests.
- Environment Verification: It might be useful to double-check that the
.ci_support/environment-integration.ymlfile includes all dependencies needed for running the GPAW and Quantum Espresso notebooks.Overall, the changes integrate well into the existing pipeline structure.
notebooks/5-1-gpaw.ipynb (1)
183-201: Well-implemented job submission and management with FluxClusterExecutor.The implementation properly handles job submission, monitoring progress with tqdm, and collecting results. However, there's no error handling for potential job failures.
Consider adding try/except blocks to handle potential job failures:
- result_dict = { - k: f.result()[-1] - for k, f in tqdm(future_dict.items()) - } + result_dict = {} + for k, f in tqdm(future_dict.items()): + try: + result_dict[k] = f.result()[-1] + except Exception as e: + print(f"Job for {k} failed with error: {e}")notebooks/5-2-quantum-espresso.ipynb (3)
38-54: Consider externalizingpseudo_dirpath.
Theevaluate_with_quantum_espressofunction hard-codes thepseudo_dirpath (/home/runner/work/executorlib/executorlib/tests/integration). For improved portability and maintainability, consider using an environment variable or a configuration setting instead of a hard-coded path.
278-279: Avoid repeating plotting code in consecutive cells.
Cells at lines 278-279 and 461-462 produce similarly structured plots. Factor out the common plotting steps into a helper function or a single cell to maintain DRY (Don’t Repeat Yourself) principles.Also applies to: 461-462
32-36: Add docstring for theevaluate_with_quantum_espressofunction.
It's a key method, but it lacks a docstring explaining parameters (liketask_dict,pseudopotentials,kpts) and the return structure. A docstring will improve clarity and maintainability.
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🔇 Additional comments (7)
docs/_toc.yml (1)
9-12: New Application Chapter Added to TOCThe new chapter "application.md" with its sections for
5-1-gpaw.ipynband5-2-quantum-espresso.ipynbis clearly added and follows the established YAML structure. The indentation and syntax appear correct.notebooks/5-1-gpaw.ipynb (5)
28-28: Well-organized imports for this computational notebook.The imports cover all the necessary libraries for this materials science workflow, including system utilities, ASE for atomic structures, plotting tools, and monitoring capabilities.
38-47: Good implementation of the GPAW evaluation function.The function is well-designed with clear parameters and efficiently sets up the GPAW calculator with appropriate settings. This makes it reusable for both executor types later in the notebook.
57-62: Structure generation is concisely implemented.The code efficiently generates aluminum structures with varying volumes for the equation of state calculation using the helper function.
95-114: Useful diagnostic output for Flux resource monitoring.These commands provide important visibility into the cluster resources and job status, which is valuable for understanding the execution environment.
1-471: Well-structured notebook for GPAW materials science calculations.The notebook effectively demonstrates how to:
- Set up computational materials science workflows using GPAW
- Leverage Flux executors for parallel task execution
- Analyze and visualize energy-volume relationships
Some suggestions for further improvement:
- Add more explanatory markdown cells describing the scientific background and expected outcomes
- Parameterize hardcoded values (like kpoints and energy cutoff) for easier experimentation
- Add validation checks for calculation results
Overall, this is a valuable integration test that demonstrates the capability to perform DFT calculations with proper job management.
notebooks/5-2-quantum-espresso.ipynb (1)
64-64: Request verification of pseudopotential coverage.
Currently,pseudopotentialsincludes only "Al" in the dictionary. If additional elements are likely, ensure you dynamically manage or verify coverage to avoid unknown element lookups later.Would you like to run a shell script to search for additional element usage or references to “pseudopotentials” across the codebase?
| "with FluxJobExecutor() as exe:\n", | ||
| " for k, v in task_loop_dict.items():\n", | ||
| " future_dict[k] = exe.submit(\n", | ||
| " evaluate_with_gpaw, \n", | ||
| " task_dict=v, \n", | ||
| " kpts=(3, 3, 3), \n", | ||
| " encut=300,\n", | ||
| " resource_dict={\"cores\": 2},\n", | ||
| " )\n", | ||
| " sleep(1)\n", | ||
| " pprint.pp(subprocess.check_output([\"flux\", \"jobs\", \"-a\"], universal_newlines=True).split(\"\\n\"))\n", | ||
| " result_dict = {\n", | ||
| " k: f.result()[-1] \n", | ||
| " for k, f in tqdm(future_dict.items())\n", | ||
| " }\n", | ||
| " sleep(1)\n", | ||
| " pprint.pp(subprocess.check_output([\"flux\", \"jobs\", \"-a\"], universal_newlines=True).split(\"\\n\"))" |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Code duplication with the FluxClusterExecutor section.
This section repeats the same pattern as the earlier FluxClusterExecutor section with minimal differences.
Consider refactoring the job execution logic into a reusable function:
def run_gpaw_jobs(executor_class, task_dict, kpts=(3, 3, 3), encut=300, cores=2):
"""Run GPAW calculations using the specified executor."""
future_dict = {}
with executor_class() as exe:
for k, v in task_dict.items():
future_dict[k] = exe.submit(
evaluate_with_gpaw,
task_dict=v,
kpts=kpts,
encut=encut,
resource_dict={"cores": cores},
)
sleep(1)
pprint.pp(subprocess.check_output(["flux", "jobs", "-a"], universal_newlines=True).split("\n"))
result_dict = {}
for k, f in tqdm(future_dict.items()):
try:
result_dict[k] = f.result()[-1]
except Exception as e:
print(f"Job for {k} failed with error: {e}")
sleep(1)
pprint.pp(subprocess.check_output(["flux", "jobs", "-a"], universal_newlines=True).split("\n"))
return result_dict
# Then use it like:
# result_dict = run_gpaw_jobs(FluxClusterExecutor, task_loop_dict)
# result_dict = run_gpaw_jobs(FluxJobExecutor, task_loop_dict)| "<>:2: SyntaxWarning: invalid escape sequence '\\A'\n", | ||
| "<>:2: SyntaxWarning: invalid escape sequence '\\A'\n", | ||
| "/tmp/ipykernel_21313/4024930470.py:2: SyntaxWarning: invalid escape sequence '\\A'\n", | ||
| " plt.xlabel(\"Volume [$\\AA^3$]\")\n" |
There was a problem hiding this comment.
Fix the SyntaxWarning for the invalid escape sequence.
The plotting code has an invalid escape sequence \A in the xlabel string.
Apply this fix to correct the escape sequence:
- plt.xlabel("Volume [$\\AA^3$]")
+ plt.xlabel("Volume [$\\mathrm{\\AA}^3$]")📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| "<>:2: SyntaxWarning: invalid escape sequence '\\A'\n", | |
| "<>:2: SyntaxWarning: invalid escape sequence '\\A'\n", | |
| "/tmp/ipykernel_21313/4024930470.py:2: SyntaxWarning: invalid escape sequence '\\A'\n", | |
| " plt.xlabel(\"Volume [$\\AA^3$]\")\n" | |
| "<>:2: SyntaxWarning: invalid escape sequence '\\A'\n", | |
| "<>:2: SyntaxWarning: invalid escape sequence '\\A'\n", | |
| "/tmp/ipykernel_21313/4024930470.py:2: SyntaxWarning: invalid escape sequence '\\A'\n", | |
| " plt.xlabel(\"Volume [$\\mathrm{\\AA}^3$]\")\n" |
| "text": [ | ||
| "<>:2: SyntaxWarning: invalid escape sequence '\\A'\n", | ||
| "<>:2: SyntaxWarning: invalid escape sequence '\\A'\n", | ||
| "/tmp/ipykernel_21313/4024930470.py:2: SyntaxWarning: invalid escape sequence '\\A'\n", | ||
| " plt.xlabel(\"Volume [$\\AA^3$]\")\n" | ||
| ] |
There was a problem hiding this comment.
Same SyntaxWarning for invalid escape sequence appears again.
The same issue with the invalid escape sequence \A appears in the second plotting section.
Apply the same fix:
- plt.xlabel("Volume [$\\AA^3$]")
+ plt.xlabel("Volume [$\\mathrm{\\AA}^3$]")📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| "text": [ | |
| "<>:2: SyntaxWarning: invalid escape sequence '\\A'\n", | |
| "<>:2: SyntaxWarning: invalid escape sequence '\\A'\n", | |
| "/tmp/ipykernel_21313/4024930470.py:2: SyntaxWarning: invalid escape sequence '\\A'\n", | |
| " plt.xlabel(\"Volume [$\\AA^3$]\")\n" | |
| ] | |
| "text": [ | |
| "<>:2: SyntaxWarning: invalid escape sequence '\\A'\n", | |
| "<>:2: SyntaxWarning: invalid escape sequence '\\A'\n", | |
| "/tmp/ipykernel_21313/4024930470.py:2: SyntaxWarning: invalid escape sequence '\\A'\n", | |
| " plt.xlabel(\"Volume [$\\mathrm{\\AA}^3$]\")\n" | |
| ] |
| "pprint.pp(subprocess.check_output([\"flux\", \"resource\", \"list\"], universal_newlines=True).split(\"\\n\"))" | ||
| ] |
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🛠️ Refactor suggestion
Add error handling for subprocess calls and flux commands.
Commands like subprocess.check_output(["flux", "resource", "list"]) lack exception handling, which may cause unhandled failures if the command is unavailable or returns an error. Wrap these calls in try-except blocks or validate return codes for more robust error handling.
Also applies to: 130-131, 216-216, 222-222, 398-398, 404-404
| "text": [ | ||
| "<>:2: SyntaxWarning: invalid escape sequence '\\A'\n", | ||
| "<>:2: SyntaxWarning: invalid escape sequence '\\A'\n", | ||
| "/tmp/ipykernel_31059/4024930470.py:2: SyntaxWarning: invalid escape sequence '\\A'\n", | ||
| " plt.xlabel(\"Volume [$\\AA^3$]\")\n" | ||
| ] |
There was a problem hiding this comment.
Fix invalid escape sequences in plot labels.
You have SyntaxWarning logs referring to invalid escape sequences (e.g., \\A). Update the Volume [$\\AA^3$] string by adding an extra backslash, using raw string notation, or doubling backslashes to avoid these warnings.
Apply this diff to fix one instance of the escape sequence (repeat as needed):
-plt.xlabel("Volume [$\\AA^3$]")
+plt.xlabel("Volume [$\\AA^3$]") # Example fix: double the backslash or use raw stringAlso applies to: 433-437
| " sleep(1)\n", | ||
| " pprint.pp(subprocess.check_output([\"flux\", \"jobs\", \"-a\"], universal_newlines=True).split(\"\\n\"))\n", | ||
| " result_dict = {\n", | ||
| " k: f.result() \n", | ||
| " for k, f in tqdm(future_dict.items())\n", | ||
| " }\n", | ||
| " sleep(1)\n", |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Revisit usage of 'sleep(1)' in the HPC job flow.
Multiple sleeps may not guarantee that flux jobs have initialized or completed. This approach can introduce race conditions or extraneous delays. A more reliable job-polling mechanism or an event-based trigger can lead to a more robust workflow.
Also applies to: 398-404
| "with FluxJobExecutor(flux_executor_nesting=True) as exe:\n", | ||
| " for k, v in task_loop_dict.items():\n", | ||
| " os.makedirs(os.path.abspath((\"strain_%0.2f\" % k).replace(\".\", \"_\")), exist_ok=True)\n", | ||
| " future_dict[k] = exe.submit(\n", | ||
| " evaluate_with_quantum_espresso, \n", | ||
| " task_dict=v, \n", | ||
| " pseudopotentials=pseudopotentials, \n", | ||
| " kpts=(3, 3, 3), \n", | ||
| " resource_dict={\n", | ||
| " \"cores\": 1, \n", | ||
| " \"threads_per_core\": 2, \n", | ||
| " \"cwd\": os.path.abspath((\"strain_%0.2f\" % k).replace(\".\", \"_\")),\n", | ||
| " },\n", | ||
| " )\n", | ||
| " sleep(1)\n", | ||
| " pprint.pp(subprocess.check_output([\"flux\", \"jobs\", \"-a\"], universal_newlines=True).split(\"\\n\"))\n", | ||
| " result_dict = {\n", | ||
| " k: f.result() \n", | ||
| " for k, f in tqdm(future_dict.items())\n", | ||
| " }\n", | ||
| " sleep(1)\n", | ||
| " pprint.pp(subprocess.check_output([\"flux\", \"jobs\", \"-a\"], universal_newlines=True).split(\"\\n\"))" | ||
| ] |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Reduce code duplication between FluxClusterExecutor and FluxJobExecutor usage.
These two code blocks share repetitive logic for job submission and result collection. Consider abstracting these workflows into a common function or a single executor-based approach for easier long-term maintenance.
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Actionable comments posted: 0
🧹 Nitpick comments (1)
docs/application.md (1)
1-7: Documentation Section: Clarity and Consistency
The new "Application" section provides a clear overview of howexecutorlibintegrates with atomistic simulation tools. One minor nitpick: consider replacing “up-scale” with “upscale” for improved clarity and consistency with common usage.Suggested Diff:
-While `executorlib` is designed to up-scale any Python function for high performance computing (HPC), it was initially +While `executorlib` is designed to upscale any Python function for high performance computing (HPC), it was initially🧰 Tools
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[uncategorized] ~2-~2: If this is a compound adjective that modifies the following noun, use a hyphen.
Context: ...ned to up-scale any Python function for high performance computing (HPC), it was initially devel...(EN_COMPOUND_ADJECTIVE_INTERNAL)
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docs/application.md
[uncategorized] ~2-~2: If this is a compound adjective that modifies the following noun, use a hyphen.
Context: ...ned to up-scale any Python function for high performance computing (HPC), it was initially devel...
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🔇 Additional comments (1)
README.md (1)
142-144: New Documentation Links: Application Section
The newly added entries for [Application] with nested links for [GPAW] and [Quantum Espresso] are correctly formatted and align well with the overall documentation structure. These links effectively direct users to the corresponding detailed pages.
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🧹 Nitpick comments (9)
docs/application.md (5)
2-2: Compound Adjective Formatting:
The phrase “up-scale any Python function” might be improved by either removing the hyphen (i.e., “upscale any Python function”) or rephrasing it (e.g., “scale up any Python function”) to adhere to common conventions and enhance clarity.🧰 Tools
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[uncategorized] ~2-~2: If this is a compound adjective that modifies the following noun, use a hyphen.
Context: ...ned to up-scale any Python function for high performance computing (HPC), it was initially devel...(EN_COMPOUND_ADJECTIVE_INTERNAL)
2-6: Technical Context and Consistency:
The description of howexecutorlibaccelerates atomistic simulation is informative. Consider ensuring consistent naming—if “atomistics” is a formal project name, it might be capitalized—and verify that all tool names (e.g., the atomic simulation environment, GPAW, Quantum Espresso) match their official documentation for consistency.🧰 Tools
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[uncategorized] ~2-~2: If this is a compound adjective that modifies the following noun, use a hyphen.
Context: ...ned to up-scale any Python function for high performance computing (HPC), it was initially devel...(EN_COMPOUND_ADJECTIVE_INTERNAL)
7-9: Detail Enhancement for Material Properties:
The explanation of deriving the bulk modulus via energy changes is clear. For added clarity, you might consider linking to additional resources or documentation on the numerical methods (derivatives) used in this context, which could assist users seeking deeper technical insight.
10-11: Extraneous Line Detected:
There appears to be a stray “11” at the end of the document. If this is unintentional, please remove it to maintain a clean format.
1-11: Alignment with PR Objectives:
The PR title and objectives mention “integration tests for application notebooks,” yet this document does not reference the notebooks (e.g., “5-1-gpaw.ipynb” or “5-2-quantum-espresso.ipynb”) or their associated integration tests. Consider adding a section or a note that links to these notebooks or explains how users can run the integration tests. This will help align the documentation with the broader PR objectives.🧰 Tools
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[uncategorized] ~2-~2: If this is a compound adjective that modifies the following noun, use a hyphen.
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notebooks/5-1-gpaw.ipynb (3)
44-53: Add docstring to the evaluate_with_gpaw function.For better code clarity and documentation, add a docstring to explain the function parameters and return value.
def evaluate_with_gpaw(task_dict, kpts, encut): + """ + Calculate potential energy using GPAW. + + Args: + task_dict: Dictionary containing structure to calculate energy for + kpts: k-point sampling as a tuple (e.g., (3, 3, 3)) + encut: Energy cutoff for plane-wave basis in eV + + Returns: + Potential energy of the structure + """ from gpaw import GPAW, PW structure = task_dict["calc_energy"].copy() structure.calc = GPAW( xc="PBE", mode=PW(encut), kpts=kpts, ) return structure.get_potential_energy()
214-231: Add error handling for job results collection.To make the code more robust, add error handling when collecting results from the submitted jobs. This would help identify which specific calculation failed if there's an issue.
result_dict = { - k: f.result()[-1] - for k, f in tqdm(future_dict.items()) + k: f.result()[-1] if f.done() else None + for k, f in tqdm(future_dict.items()) + try: + result_dict[k] = f.result()[-1] + except Exception as e: + print(f"Job for {k} failed with error: {e}") + result_dict[k] = None }
423-428: Add error handling for FluxJobExecutor results collection as well.Similar to the suggestion for the FluxClusterExecutor section, add error handling when collecting results from the jobs in the FluxJobExecutor section.
result_dict = { - k: f.result()[-1] - for k, f in tqdm(future_dict.items()) + k: f.result()[-1] if f.done() else None + for k, f in tqdm(future_dict.items()) + try: + result_dict[k] = f.result()[-1] + except Exception as e: + print(f"Job for {k} failed with error: {e}") + result_dict[k] = None }notebooks/5-2-quantum-espresso.ipynb (1)
39-55: Consider adding a minimal try-except wrapper forevaluate_with_quantum_espresso.
While the function logic is straightforward, you might gracefully handle unexpected errors (e.g., missing pseudopotential files or invalid k-point grids) by logging or re-raising an informative exception.def evaluate_with_quantum_espresso(task_dict, pseudopotentials, kpts): from ase.calculators.espresso import Espresso, EspressoProfile from atomistics.calculators import evaluate_with_ase + try: return evaluate_with_ase( task_dict=task_dict, ase_calculator=Espresso( pseudopotentials=pseudopotentials, tstress=True, tprnfor=True, kpts=kpts, profile=EspressoProfile( command="flux run pw.x", pseudo_dir="/home/runner/work/executorlib/executorlib/tests/integration", ), ), )["energy"] + except Exception as e: + raise RuntimeError(f"Quantum Espresso evaluation failed: {e}")
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🔇 Additional comments (15)
docs/application.md (1)
1-1: Header Clarity:
The "# Application" header is clear and effectively introduces the section.notebooks/5-1-gpaw.ipynb (6)
271-274: Fix the SyntaxWarning for invalid escape sequence (previously noted).The plotting code still has the invalid escape sequence
\Ain the xlabel string. This was noted in a previous review. You should fix this to avoid the warning.- plt.xlabel("Volume [$\\AA^3$]") + plt.xlabel("Volume [$\\mathrm{\\AA}^3$]")
467-472: Same SyntaxWarning for invalid escape sequence appears again.The same invalid escape sequence issue appears in the second plotting section as well, which was previously noted.
- plt.xlabel("Volume [$\\AA^3$]") + plt.xlabel("Volume [$\\mathrm{\\AA}^3$]")
412-428: Eliminate code duplication between executor sections.Both the FluxClusterExecutor and FluxJobExecutor sections contain almost identical code, as noted in a previous review. Consider refactoring this into a reusable function:
def run_gpaw_jobs(executor_class, task_dict, kpts=(3, 3, 3), encut=300, cores=2): """Run GPAW calculations using the specified executor.""" future_dict = {} with executor_class() as exe: for k, v in task_dict.items(): future_dict[k] = exe.submit( evaluate_with_gpaw, task_dict=v, kpts=kpts, encut=encut, resource_dict={"cores": cores}, ) sleep(1) pprint.pp(subprocess.check_output(["flux", "jobs", "-a"], universal_newlines=True).split("\n")) result_dict = {} for k, f in tqdm(future_dict.items()): try: result_dict[k] = f.result()[-1] except Exception as e: print(f"Job for {k} failed with error: {e}") sleep(1) pprint.pp(subprocess.check_output(["flux", "jobs", "-a"], universal_newlines=True).split("\n")) return result_dictThen use it like:
result_dict = run_gpaw_jobs(FluxClusterExecutor, task_loop_dict)
1-11: Well-structured notebook introduction with clear explanation.The notebook starts with a clear introduction of GPAW and how it integrates with executorlib. The explanation of hierarchical workflows and the benefits of using executorlib for parallelization is informative and sets the stage for the rest of the notebook.
69-74: The structure generation implementation is clear and well-parameterized.The code for generating strained aluminum structures is concise and uses appropriate parameters. The use of named parameters improves readability and makes it clear what each value represents.
145-147: Good explanation of the choice of executor.The introduction to the FluxClusterExecutor section provides helpful context about why this executor is used in the demonstration and mentions an alternative (SlurmClusterExecutor) that could be used analogously, which is valuable information for users.
notebooks/5-2-quantum-espresso.ipynb (8)
19-30: Imports appear organized and consistent.
No immediate issues are spotted regarding the import statements, and they provide a clear overview of the required dependencies for the notebook.
113-114: Add error handling for subprocess calls.
The Flux command is invoked directly without try-except. This matches a previously flagged concern regarding unhandled failures if the command is unavailable or returns an error.
131-131: Repeat the earlier request for error handling on Flux command.
This subprocess call is also unguarded in a try-except, mirroring the unhandled scenario from previous lines.
216-224: Revisit usage ofsleep(1)in HPC job flow.
This approach may introduce a race condition or extraneous delay if Flux jobs do not initialize or terminate within the fixed time interval. Implement a more robust polling mechanism or an event-based trigger to confirm job readiness.
280-280: Fix the invalid escape sequence in the plot label.
“\A” can trigger a SyntaxWarning in Python. As noted in earlier reviews, double the backslash or use a raw string.-plt.xlabel("Volume [$\\AA^3$]") +plt.xlabel(r"Volume [$\AA^3$]")
385-407: Reduce code duplication betweenFluxClusterExecutorandFluxJobExecutorusage.
These blocks share mostly identical resource dictionary construction and subsequent polling logic. Consider refactoring out the common logic into a helper function for long-term maintainability.
399-405: Revisitsleep(1)usage once again.
Similar to the earlier comment, relying on an arbitrary delay can produce timing inconsistencies in HPC job flows. A job polling API or callback-based approach is recommended for reliability.
463-463: Address the second invalid escape sequence inplt.xlabel.
The same fix applies here to avoid SyntaxWarnings for “\A.”-plt.xlabel("Volume [$\\AA^3$]") +plt.xlabel(r"Volume [$\AA^3$]")
Summary by CodeRabbit
New Features
Documentation