An issue regarding the use of LAMMPS with the deep potential trained using MACE #44
Comments
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Setting the environment variable |
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Dear Dr. Zeng, Thank you for your reply. Enclosed is the output file generated with the environment variable LD_DEBUG=libs. I am sorry that I cannot find the solution to this issue. Could you kindly take a look? It should be noted that this simulation was performed on a single core to avoid redundant output. Thank you for your time! Sincerely, |
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It seems that |
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Dear Dr. Zeng, Thank you for your reply. I am confused about the requirement for the libcuda.so.1 file, as my job submission script indicates that I am running LAMMPS simulations using only the CPU, not the GPU. Besides, I can only find the libcuda.so file in the /gpfs/softs/cuda/12.6.2/targets/x86_64-linux/lib/stubs/ dictionary of my cluster, but the libcuda.so.1 file is not present in the corresponding CUDA-12.6.2 directory. Could you kindly clarify if it is still possible to perform the LAMMPS simulations (with the deep potential trained using MACE implemented in DeePMD-kit 3.0.0) under these conditions? Thank you for your time! Sincerely, |
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Does your pytorch link to |
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xref: deepmodeling/deepmd-gnn#44 <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **New Features** - Enhanced error messages for library loading failures on non-Windows platforms. - Updated thread management environment variable checks for improved compatibility. - Added support for mixed types in tensor input handling, allowing for more flexible configurations. - **Bug Fixes** - Improved error reporting for dynamic library loading issues. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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xref: deepmodeling/deepmd-gnn#44 <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **New Features** - Enhanced error messages for library loading failures on non-Windows platforms. - Updated thread management environment variable checks for improved compatibility. - Added support for mixed types in tensor input handling, allowing for more flexible configurations. - **Bug Fixes** - Improved error reporting for dynamic library loading issues. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> (cherry picked from commit cfe17a3)
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xref: njzjz/deepmd-gnn#44 <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **New Features** - Enhanced error messages for library loading failures on non-Windows platforms. - Updated thread management environment variable checks for improved compatibility. - Added support for mixed types in tensor input handling, allowing for more flexible configurations. - **Bug Fixes** - Improved error reporting for dynamic library loading issues. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> (cherry picked from commit cfe17a3)
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* change property.npy to any name * Init branch * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * change | to Union * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * change sub_var_name default to [] * Solve pre-commit * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * solve scanning github * fix UT * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * delete useless file * Solve some UT * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Solve precommit * slove pre * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Solve dptest UT, dpatomicmodel UT, code scannisang * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * delete param and * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Solve UT fail caused by task_dim and property_name * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix UT * Fix UT * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix UT * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix permutation error * Add property bias UT * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * recover rcond doc * recover blank * Change code according according to coderabbitai * solve pre-commit * Fix UT * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * change apply_bias doc * update the version compatibility * feat (tf/pt): add atomic weights to tensor loss (deepmodeling#4466) Interfaces are of particular interest in many studies. However, the configurations in the training set to represent the interface normally also include large parts of the bulk material. As a result, the final model would prefer the bulk information while the interfacial information is less learnt. It is difficult to simply improve the proportion of interfaces in the configurations since the electronic structures of the interface might only be reasonable with a certain thickness of bulk materials. Therefore, I wonder whether it is possible to define weights for atomic quantities in loss functions. This allows us to add higher weights for the atomic information for the regions of interest and probably makes the model "more focused" on the region of interest. In this PR, I add the keyword `enable_atomic_weight` to the loss function of the tensor model. In principle, it could be generalised to any atomic quantity, e.g., atomic forces. I would like to know the developers' comments/suggestions about this feature. I can add support for other loss functions and finish unit tests once we agree on this feature. Best. <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **New Features** - Introduced an optional parameter for atomic weights in loss calculations, enhancing flexibility in the `TensorLoss` class. - Added a suite of unit tests for the `TensorLoss` functionality, ensuring consistency between TensorFlow and PyTorch implementations. - **Bug Fixes** - Updated logic for local loss calculations to ensure correct application of atomic weights based on user input. - **Documentation** - Improved clarity of documentation for several function arguments, including the addition of a new argument related to atomic weights. <!-- end of auto-generated comment: release notes by coderabbit.ai --> * delete sub_var_name * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * recover to property key * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix conflict * Fix UT * Add document of property fitting * Delete checkpoint * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Add get_property_name to DeepEvalBackend * pd: fix learning rate setting when resume (deepmodeling#4480) "When resuming training, there is no need to add `self.start_step` to the step count because Paddle uses `lr_sche.last_epoch` as the input for `step`, which already records the `start_step` steps." learning rate are correct after fixing  <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **New Features** - Enhanced training process with improved optimizer configuration and learning rate adjustments. - Refined logging of training and validation results for clarity. - Improved model saving logic to preserve the latest state during interruptions. - Enhanced tensorboard logging for detailed tracking of training metrics. - **Bug Fixes** - Corrected lambda function for learning rate scheduler to reference warmup steps accurately. - **Chores** - Streamlined data loading and handling for efficient training across different tasks. <!-- end of auto-generated comment: release notes by coderabbit.ai --> * docs: update deepmd-gnn URL (deepmodeling#4482) <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **Documentation** - Updated guidelines for creating and integrating new models in the DeePMD-kit framework. - Added new sections on descriptors, fitting networks, and model requirements. - Enhanced unit testing section with instructions for regression tests. - Updated URL for the DeePMD-GNN plugin to reflect new repository location. <!-- end of auto-generated comment: release notes by coderabbit.ai --> Signed-off-by: Jinzhe Zeng <[email protected]> * docs: update DPA-2 citation (deepmodeling#4483) <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **New Features** - Updated references in the bibliography for the DPA-2 model to include a new article entry for 2024. - Added a new reference for an attention-based descriptor. - **Bug Fixes** - Corrected reference links in documentation to point to updated DOI links instead of arXiv. - **Documentation** - Revised entries in the credits and model documentation to reflect the latest citations and details. - Enhanced clarity and detail in fine-tuning documentation for TensorFlow and PyTorch implementations. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: Jinzhe Zeng <[email protected]> * docs: fix a minor typo on the title of `install-from-c-library.md` (deepmodeling#4484) <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **Documentation** - Updated formatting of the installation guide for the pre-compiled C library. - Icons for TensorFlow and JAX are now displayed together in the header. - Retained all installation instructions and compatibility notes. <!-- end of auto-generated comment: release notes by coderabbit.ai --> Signed-off-by: Jinzhe Zeng <[email protected]> * fix: print dlerror if dlopen fails (deepmodeling#4485) xref: deepmodeling/deepmd-gnn#44 <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **New Features** - Enhanced error messages for library loading failures on non-Windows platforms. - Updated thread management environment variable checks for improved compatibility. - Added support for mixed types in tensor input handling, allowing for more flexible configurations. - **Bug Fixes** - Improved error reporting for dynamic library loading issues. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * change doc to py * Add out_bias out_std doc * change bias method to compute_stats_do_not_distinguish_types * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * change var_name to property_name * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * change logic of extensive bias * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * add doc for neww added parameter * change doc for compute_stats_do_not_distinguish_types * try to fix dptest * change all property to property_name * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix UT * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Delete key 'property' completely * Fix UT * Fix dptest UT * pd: fix oom error (deepmodeling#4493) Paddle use `MemoryError` rather than `RuntimeError` used in pytorch, now I can test DPA-1 and DPA-2 in 16G V100...  <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **Bug Fixes** - Improved detection of out-of-memory (OOM) errors to enhance application stability. - Ensured cached memory is cleared upon OOM errors, preventing potential memory leaks. <!-- end of auto-generated comment: release notes by coderabbit.ai --> * pd: add missing `dp.eval()` in pd backend (deepmodeling#4488) Switch to eval mode when evaluating model, otherwise `self.training` will be `True`, backward graph will be created and cause OOM <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **New Features** - Enhanced model evaluation state management to ensure correct behavior during evaluation. - **Bug Fixes** - Improved type consistency in the `normalize_coord` function for better computational accuracy. <!-- end of auto-generated comment: release notes by coderabbit.ai --> * [pre-commit.ci] pre-commit autoupdate (deepmodeling#4497) <!--pre-commit.ci start--> updates: - [github.com/astral-sh/ruff-pre-commit: v0.8.3 → v0.8.4](astral-sh/ruff-pre-commit@v0.8.3...v0.8.4) <!--pre-commit.ci end--> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * Delete attribute * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Solve comment * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Solve error * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * delete property_name in serialize --------- Signed-off-by: Jinzhe Zeng <[email protected]> Co-authored-by: root <[email protected]> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Chenqqian Zhang <[email protected]> Co-authored-by: Jia-Xin Zhu <[email protected]> Co-authored-by: HydrogenSulfate <[email protected]> Co-authored-by: Jinzhe Zeng <[email protected]>
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* Refactor property (#37) * change property.npy to any name * Init branch * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * change | to Union * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * change sub_var_name default to [] * Solve pre-commit * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * solve scanning github * fix UT * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * delete useless file * Solve some UT * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Solve precommit * slove pre * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Solve dptest UT, dpatomicmodel UT, code scannisang * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * delete param and * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Solve UT fail caused by task_dim and property_name * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix UT * Fix UT * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix UT * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix permutation error * Add property bias UT * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * recover rcond doc * recover blank * Change code according according to coderabbitai * solve pre-commit * Fix UT * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * change apply_bias doc * update the version compatibility * feat (tf/pt): add atomic weights to tensor loss (deepmodeling#4466) Interfaces are of particular interest in many studies. However, the configurations in the training set to represent the interface normally also include large parts of the bulk material. As a result, the final model would prefer the bulk information while the interfacial information is less learnt. It is difficult to simply improve the proportion of interfaces in the configurations since the electronic structures of the interface might only be reasonable with a certain thickness of bulk materials. Therefore, I wonder whether it is possible to define weights for atomic quantities in loss functions. This allows us to add higher weights for the atomic information for the regions of interest and probably makes the model "more focused" on the region of interest. In this PR, I add the keyword `enable_atomic_weight` to the loss function of the tensor model. In principle, it could be generalised to any atomic quantity, e.g., atomic forces. I would like to know the developers' comments/suggestions about this feature. I can add support for other loss functions and finish unit tests once we agree on this feature. Best. <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **New Features** - Introduced an optional parameter for atomic weights in loss calculations, enhancing flexibility in the `TensorLoss` class. - Added a suite of unit tests for the `TensorLoss` functionality, ensuring consistency between TensorFlow and PyTorch implementations. - **Bug Fixes** - Updated logic for local loss calculations to ensure correct application of atomic weights based on user input. - **Documentation** - Improved clarity of documentation for several function arguments, including the addition of a new argument related to atomic weights. <!-- end of auto-generated comment: release notes by coderabbit.ai --> * delete sub_var_name * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * recover to property key * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix conflict * Fix UT * Add document of property fitting * Delete checkpoint * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Add get_property_name to DeepEvalBackend * pd: fix learning rate setting when resume (deepmodeling#4480) "When resuming training, there is no need to add `self.start_step` to the step count because Paddle uses `lr_sche.last_epoch` as the input for `step`, which already records the `start_step` steps." learning rate are correct after fixing  <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **New Features** - Enhanced training process with improved optimizer configuration and learning rate adjustments. - Refined logging of training and validation results for clarity. - Improved model saving logic to preserve the latest state during interruptions. - Enhanced tensorboard logging for detailed tracking of training metrics. - **Bug Fixes** - Corrected lambda function for learning rate scheduler to reference warmup steps accurately. - **Chores** - Streamlined data loading and handling for efficient training across different tasks. <!-- end of auto-generated comment: release notes by coderabbit.ai --> * docs: update deepmd-gnn URL (deepmodeling#4482) <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **Documentation** - Updated guidelines for creating and integrating new models in the DeePMD-kit framework. - Added new sections on descriptors, fitting networks, and model requirements. - Enhanced unit testing section with instructions for regression tests. - Updated URL for the DeePMD-GNN plugin to reflect new repository location. <!-- end of auto-generated comment: release notes by coderabbit.ai --> Signed-off-by: Jinzhe Zeng <[email protected]> * docs: update DPA-2 citation (deepmodeling#4483) <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **New Features** - Updated references in the bibliography for the DPA-2 model to include a new article entry for 2024. - Added a new reference for an attention-based descriptor. - **Bug Fixes** - Corrected reference links in documentation to point to updated DOI links instead of arXiv. - **Documentation** - Revised entries in the credits and model documentation to reflect the latest citations and details. - Enhanced clarity and detail in fine-tuning documentation for TensorFlow and PyTorch implementations. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: Jinzhe Zeng <[email protected]> * docs: fix a minor typo on the title of `install-from-c-library.md` (deepmodeling#4484) <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **Documentation** - Updated formatting of the installation guide for the pre-compiled C library. - Icons for TensorFlow and JAX are now displayed together in the header. - Retained all installation instructions and compatibility notes. <!-- end of auto-generated comment: release notes by coderabbit.ai --> Signed-off-by: Jinzhe Zeng <[email protected]> * fix: print dlerror if dlopen fails (deepmodeling#4485) xref: deepmodeling/deepmd-gnn#44 <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **New Features** - Enhanced error messages for library loading failures on non-Windows platforms. - Updated thread management environment variable checks for improved compatibility. - Added support for mixed types in tensor input handling, allowing for more flexible configurations. - **Bug Fixes** - Improved error reporting for dynamic library loading issues. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * change doc to py * Add out_bias out_std doc * change bias method to compute_stats_do_not_distinguish_types * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * change var_name to property_name * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * change logic of extensive bias * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * add doc for neww added parameter * change doc for compute_stats_do_not_distinguish_types * try to fix dptest * change all property to property_name * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix UT * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Delete key 'property' completely * Fix UT * Fix dptest UT * pd: fix oom error (deepmodeling#4493) Paddle use `MemoryError` rather than `RuntimeError` used in pytorch, now I can test DPA-1 and DPA-2 in 16G V100...  <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **Bug Fixes** - Improved detection of out-of-memory (OOM) errors to enhance application stability. - Ensured cached memory is cleared upon OOM errors, preventing potential memory leaks. <!-- end of auto-generated comment: release notes by coderabbit.ai --> * pd: add missing `dp.eval()` in pd backend (deepmodeling#4488) Switch to eval mode when evaluating model, otherwise `self.training` will be `True`, backward graph will be created and cause OOM <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **New Features** - Enhanced model evaluation state management to ensure correct behavior during evaluation. - **Bug Fixes** - Improved type consistency in the `normalize_coord` function for better computational accuracy. <!-- end of auto-generated comment: release notes by coderabbit.ai --> * [pre-commit.ci] pre-commit autoupdate (deepmodeling#4497) <!--pre-commit.ci start--> updates: - [github.com/astral-sh/ruff-pre-commit: v0.8.3 → v0.8.4](astral-sh/ruff-pre-commit@v0.8.3...v0.8.4) <!--pre-commit.ci end--> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * Delete attribute * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Solve comment * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Solve error * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * delete property_name in serialize --------- Signed-off-by: Jinzhe Zeng <[email protected]> Co-authored-by: root <[email protected]> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Chenqqian Zhang <[email protected]> Co-authored-by: Jia-Xin Zhu <[email protected]> Co-authored-by: HydrogenSulfate <[email protected]> Co-authored-by: Jinzhe Zeng <[email protected]> * add multig1 mess --------- Signed-off-by: Jinzhe Zeng <[email protected]> Signed-off-by: Duo <[email protected]> Co-authored-by: root <[email protected]> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Chenqqian Zhang <[email protected]> Co-authored-by: Jia-Xin Zhu <[email protected]> Co-authored-by: HydrogenSulfate <[email protected]> Co-authored-by: Jinzhe Zeng <[email protected]>
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Dear Dr. Zeng,
I am sorry to bother you for an issue regarding the use of LAMMPS (internally installed within DeePMD-kit 3.0.0) with the deep potential trained using MACE implemented in DeePMD-kit 3.0.0.
Following the detailed guidance provided by Bohrium and the WeChat post by DeepModeling, I added the following command in my job submission script for LAMMPS:
The file libdeepmd_gnn.so does exist in the dictionary /gpfs/software/deepmd-kit-3.0.0-cuda12.6_gpu/lib/python3.12/site-packages/deepmd_gnn/lib/. Additionally, I successfully trained a deep potential using MACE in DeePMD-kit 3.0.0, indicating that there should not be any issues with the DeePMD-kit itself.
However, an error occurs, stating that the libdeepmd_gnn.so file cannot be found:
I requested someone else to submit the job on another cluster, but they encountered the same error. My job submission script for LAMMPS is attached for reference. Could you kindly provide any expert suggestions to resolve this issue?
Thank you for your time, and I am looking forward to your reply.
Sincerely,
Xu
myjob.sh.txt
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