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Merge pull request #149 from Exabyte-io/feature/SOF-7632
chore: remove exabyteml
2 parents 05e052c + 574a4f2 commit 5ded4f0

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‎express/parsers/exabyteml.py

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This file was deleted.

‎express/parsers/mixins/exabyteml.py

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This file was deleted.

‎express/properties/workflow.py

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@@ -51,7 +51,7 @@ def _serialize(self) -> dict:
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class PyMLTrainAndPredictWorkflow(WorkflowProperty):
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"""
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Next generation of ExabyteML. We expect workflows to have a format as follows:
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We expect workflows to have a format as follows:
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Workflow_Head_Subworkflow - Contains various units which prepare an ML job. For example, we may have the following
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units present.
@@ -203,7 +203,7 @@ def is_using_dataset(self):
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@property
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def workflow_specific_config(self) -> dict:
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"""
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Generates the specific config for the new implementation of ExabyteML. The remainder of the config is
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Generates the specific config for the workflow. The remainder of the config is
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generated inside of the parent Workflow class.
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Returns:
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}
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return specific_config
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class ExabyteMLPredictWorkflow(WorkflowProperty):
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"""
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Legacy implementation of Exabyte ML's predict Workflow property class.
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"""
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def __init__(self, name, parser, *args, **kwargs):
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super().__init__(name, parser, *args, **kwargs)
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self.model = self.parser.model
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self.targets = self.parser.targets
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self.features = self.parser.features
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self.scaling_params_per_feature = self.parser.scaling_params_per_feature
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@property
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def workflow_specific_config(self) -> dict:
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"""
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Generates the specific config for a legacy ExabyteML workflow. The remainder of the config is generated
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inside of the parent Worfklow class.
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Returns:
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dict
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"""
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specific_config = {
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"units": [
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{
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"_id": "LCthJ6E2QabYCZqf4",
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"name": "ml_predict_subworkflow",
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"type": "subworkflow",
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"flowchartId": "subworkflow",
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"head": True,
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}
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],
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"subworkflows": [
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{
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"name": "ml_predict_subworkflow",
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"isDraft": True,
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"application": {
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"version": "0.2.0",
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"summary": "Exabyte Machine Learning Engine",
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"name": "exabyteml",
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"shortName": "ml",
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"build": "Default",
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},
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"units": [
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{
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"status": "idle",
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"statusTrack": [],
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"head": True,
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"flowchartId": "io",
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"name": "input",
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"application": {
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"version": "0.2.0",
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"summary": "Exabyte Machine Learning Engine",
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"name": "exabyteml",
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"shortName": "ml",
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"build": "Default",
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},
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"results": [],
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"next": "data_transformation_manipulation",
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"source": "api",
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"postProcessors": [],
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"preProcessors": [],
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"subtype": "dataFrame",
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"input": [
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{
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"endpoint": "dataframe",
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"endpoint_options": {
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"headers": {},
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"data": {"features": self.features, "ids": [], "targets": self.targets},
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"method": "POST",
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"params": {},
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"jobId": "",
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},
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}
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],
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"type": "io",
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"monitors": [],
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},
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{
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"status": "idle",
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"statusTrack": [],
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"head": False,
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"flowchartId": "data_transformation_manipulation",
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"name": "clean data",
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"monitors": [],
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"results": [],
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"next": "data_transformation_scale_and_reduce",
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"application": {
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"version": "0.2.0",
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"summary": "Exabyte Machine Learning Engine",
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"name": "exabyteml",
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"shortName": "ml",
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"build": "Default",
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},
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"postProcessors": [],
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"preProcessors": [],
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"operationType": "manipulation",
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"operation": "data_transformation",
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"type": "processing",
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"inputData": {
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"cleanMissingData": True,
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"replaceNoneValuesWith": 0,
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"removeDuplicateRows": True,
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},
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},
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{
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"status": "idle",
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"statusTrack": [],
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"head": False,
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"flowchartId": "data_transformation_scale_and_reduce",
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"name": "scale and reduce",
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"monitors": [],
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"results": [],
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"next": "score",
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"application": {
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"version": "0.2.0",
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"build": "Default",
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"name": "exabyteml",
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"shortName": "ml",
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"summary": "Exabyte Machine Learning Engine",
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},
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"postProcessors": [],
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"preProcessors": [],
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"operationType": "scale_and_reduce",
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"operation": "data_transformation",
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"type": "processing",
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"inputData": {
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"scaler": "standard_scaler",
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"perFeature": self.scaling_params_per_feature,
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},
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},
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{
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"status": "idle",
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"statusTrack": [],
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"executable": {"name": "score"},
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"flowchartId": "score",
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"name": "score",
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"head": False,
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"results": [{"name": "predicted_properties"}],
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"application": {
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"version": "0.2.0",
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"build": "Default",
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"name": "exabyteml",
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"shortName": "ml",
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"summary": "Exabyte Machine Learning Engine",
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},
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"postProcessors": [],
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"preProcessors": [],
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"context": {},
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"input": [],
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"flavor": {"name": "score"},
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"type": "execution",
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"monitors": [{"name": "standard_output"}],
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},
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],
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"model": self.model,
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"_id": "LCthJ6E2QabYCZqf4",
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"properties": self.targets,
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}
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],
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"properties": self.targets,
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}
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return specific_config

‎express/settings.py

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@@ -29,7 +29,6 @@
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},
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"material": {"reference": "express.properties.material.Material"},
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"symmetry": {"reference": "express.properties.non_scalar.symmetry.Symmetry"},
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"workflow:ml_predict": {"reference": "express.properties.workflow.ExabyteMLPredictWorkflow"},
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"workflow:pyml_predict": {"reference": "express.properties.workflow.PyMLTrainAndPredictWorkflow"},
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"phonon_dos": {"reference": "express.properties.non_scalar.two_dimensional_plot.phonon_dos.PhononDOS"},
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"phonon_dispersions": {
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"vasp": "express.parsers.apps.vasp.parser.VaspParser",
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"nwchem": "express.parsers.apps.nwchem.parser.NwchemParser",
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"structure": "express.parsers.structure.StructureParser",
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"exabyteml": "express.parsers.exabyteml.ExabyteMLParser",
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}
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# Used to round to zero by default

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