PythonFunctionContainerJob and ExecutableContainerJob: Use to_dict() and from_dict() #1378
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…and from_dict() Replace to_hdf() and from_hdf() with to_dict() and from_dict()
pmrv
reviewed
Mar 26, 2024
Comment on lines
+101
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110
| def from_dict(self, job_dict): | ||
| super().from_dict(job_dict=job_dict) | ||
| if "write_input_function" in job_dict.keys(): | ||
| self._write_input_funct = cloudpickle.loads( | ||
| self.project_hdf5["write_input_function"] | ||
| job_dict["write_input_function"] | ||
| ) | ||
| if "write_input_function" in self.project_hdf5.list_nodes(): | ||
| if "write_input_function" in job_dict.keys(): | ||
| self._collect_output_funct = cloudpickle.loads( | ||
| self.project_hdf5["collect_output_function"] | ||
| job_dict["collect_output_function"] | ||
| ) |
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If I understand correctly GenericJob.from_hdf calls hdf.read_dict_from_hdf with recursive=False, hence "write_input_function" would never be in job_dict. Same below for the python function container.
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There are two parts in the from_hdf() method on GenericJob:
def from_hdf(self, hdf=None, group_name=None):
"""
Restore the GenericJob from an HDF5 file
Args:
hdf (ProjectHDFio): HDF5 group object - optional
group_name (str): HDF5 subgroup name - optional
"""
self._set_hdf(hdf=hdf, group_name=group_name)
job_dict = self._hdf5.read_dict_from_hdf()
with self._hdf5.open("input") as hdf5_input:
job_dict["input"] = hdf5_input.read_dict_from_hdf(recursive=True)
self.from_dict(job_dict=job_dict)The first read_dict_from_hdf() call reads the nodes at the root level of the job group in the HDF5 file and then the second read_dict_from_hdf(recursive=True) recursively reads the nodes in the group input.
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Replace to_hdf() and from_hdf() with to_dict() and from_dict()