Assign tasks to workers

azimuth/modules/base_classes/dask_module.py

@@ -7,6 +7,7 @@
 import threading
 import time
 import uuid
+from enum import IntEnum
 from functools import partial
 from os.path import join as pjoin
 from typing import Any, Callable, Dict, Generic, List, Optional, TypeVar, cast
@@ -25,6 +26,11 @@
 ConfigScope = TypeVar("ConfigScope", bound=CommonFieldsConfig)
 
 
+class Worker(IntEnum):
+    model = 0
+    encoder = 0
+
+
 class DaskModule(HDF5CacheMixin, Generic[ConfigScope]):
     """Abstract class that define an item of work to be computed on the cluster.
 
@@ -37,6 +43,7 @@ class DaskModule(HDF5CacheMixin, Generic[ConfigScope]):
     """
 
     allowed_splits = {DatasetSplitName.train, DatasetSplitName.eval}
+    worker: Optional[Worker] = None
 
     def __init__(
         self,
@@ -118,6 +125,7 @@ def start_task_on_dataset_split(
             pure=False,
             dependencies=deps,
             key=f"{self.task_id}_{uuid.uuid4()}",  # Unique identifier
+            workers=self.worker,
         )
         # Tell that this future is used on which indices.
         self.future.indices = self.get_caching_indices()
@@ -148,7 +156,11 @@ def start_task(self, client: Client, custom_query: Dict[str, Any]) -> "DaskModul
         log.info(f"Starting custom query {self.name}")
         # pure=false to be sure that everything is rerun.
         self.future = client.submit(
-            self.compute, custom_query, key=self.custom_query_task_id(custom_query), pure=False
+            self.compute,
+            custom_query,
+            key=self.custom_query_task_id(custom_query),
+            pure=False,
+            workers=self.worker,
         )
         # Tell that this future is for custom use only.
         self.future.is_custom = True

azimuth/modules/base_classes/indexable_module.py

@@ -12,6 +12,7 @@
 from azimuth.config import ModelContractConfig
 from azimuth.dataset_split_manager import DatasetSplitManager
 from azimuth.modules.base_classes import ConfigScope, Module
+from azimuth.modules.base_classes.dask_module import Worker
 from azimuth.types import (
     DatasetColumn,
     DatasetSplitName,
@@ -76,6 +77,7 @@ def save_result(self, res: List[ModuleResponse], dm: DatasetSplitManager):
 class ModelContractModule(DatasetResultModule[ModelContractConfig], abc.ABC):
     required_mod_options: Set[str] = {"pipeline_index", "model_contract_method_name"}
     optional_mod_options: Set[str] = DatasetResultModule.optional_mod_options | {"threshold"}
+    worker = Worker.model
 
     def compute(self, batch: Dataset) -> List[ModuleResponse]:
         my_func = self.route_request(assert_not_none(self.model_contract_method_name))

azimuth/modules/base_classes/module.py

@@ -10,6 +10,7 @@
 from azimuth.config import ModelContractConfig, PipelineDefinition
 from azimuth.dataset_split_manager import DatasetSplitManager, PredictionTableKey
 from azimuth.modules.base_classes import ArtifactManager, ConfigScope, DaskModule
+from azimuth.modules.base_classes.dask_module import Worker
 from azimuth.types import DatasetColumn, DatasetSplitName, ModuleOptions, ModuleResponse
 from azimuth.types.general.module_arguments import ModuleEffectiveArguments
 from azimuth.utils.conversion import md5_hash
@@ -164,6 +165,8 @@ def get_model(self):
         Raises:
             ValueError if no valid pipeline exists.
         """
+        if self.worker != Worker.model:
+            raise RuntimeError("This module cannot load the model. Modify self.worker.")
         _ = self.get_pipeline_definition()  # Validate current pipeline exists
         return self.artifact_manager.get_model(self.config, self.mod_options.pipeline_index)
 

azimuth/modules/dataset_analysis/similarity_analysis.py

@@ -16,6 +16,7 @@
 from azimuth.config import SimilarityConfig, SimilarityOptions
 from azimuth.dataset_split_manager import FEATURE_FAISS, DatasetSplitManager
 from azimuth.modules.base_classes import DatasetResultModule, IndexableModule
+from azimuth.modules.base_classes.dask_module import Worker
 from azimuth.modules.task_execution import get_task_result
 from azimuth.types import Array, DatasetColumn, DatasetSplitName, ModuleOptions
 from azimuth.types.similarity_analysis import FAISSResponse
@@ -28,6 +29,8 @@
 class FAISSModule(IndexableModule[SimilarityConfig]):
     """Compute the FAISS features for a dataset split."""
 
+    worker = Worker.encoder
+
     def __init__(
         self,
         dataset_split_name: DatasetSplitName,
@@ -45,6 +48,8 @@ def get_encoder_name_or_path(self):
         return model_name_or_path
 
     def get_encoder(self):
+        if self.worker != Worker.encoder:
+            raise RuntimeError("This module cannot load the encoder. Modify self.worker.")
         if self.encoder is None:
             with FileLock(os.path.join(self.cache_dir, "st.lock")):
                 self.encoder = SentenceTransformer(self.get_encoder_name_or_path())

azimuth/modules/perturbation_testing/perturbation_testing.py

@@ -10,6 +10,7 @@
 from azimuth.config import PerturbationTestingConfig
 from azimuth.dataset_split_manager import DatasetSplitManager
 from azimuth.modules.base_classes import DatasetResultModule
+from azimuth.modules.base_classes.dask_module import Worker
 from azimuth.modules.model_contract_task_mapping import model_contract_task_mapping
 from azimuth.types import (
     DatasetColumn,
@@ -58,6 +59,9 @@ class PerturbationTestingModule(DatasetResultModule[PerturbationTestingConfig]):
     """
 
     required_mod_options = {"pipeline_index"}
+    # This module doesn't call self.get_model() but requires the model (predict_task.compute(batch))
+    # TODO Find a more robust way to determine when modules require models.
+    worker = Worker.model
 
     def __init__(
         self,

azimuth/modules/validation/validation.py

@@ -9,6 +9,7 @@
 
 from azimuth.config import ModelContractConfig
 from azimuth.modules.base_classes import AggregationModule
+from azimuth.modules.base_classes.dask_module import Worker
 from azimuth.modules.model_contract_task_mapping import model_contract_task_mapping
 from azimuth.types import ModuleOptions, SupportedMethod, SupportedModelContract
 from azimuth.types.validation import ValidationResponse
@@ -36,6 +37,7 @@ def try_calling_function(self, fn, *args, **kwargs) -> Optional[Any]:
 
 class ValidationModule(AggregationModule[ModelContractConfig]):
     optional_mod_options = {"pipeline_index"}
+    worker = Worker.model
 
     def compute_on_dataset_split(self) -> List[ValidationResponse]:  # type: ignore
         cuda_available = torch.cuda.is_available()

azimuth/modules/word_analysis/top_words.py

@@ -9,6 +9,7 @@
 
 from azimuth.config import TopWordsConfig
 from azimuth.modules.base_classes import FilterableModule
+from azimuth.modules.base_classes.dask_module import Worker
 from azimuth.modules.task_execution import get_task_result
 from azimuth.modules.word_analysis.tokens_to_words import TokensToWordsModule
 from azimuth.types import ModuleOptions
@@ -33,6 +34,7 @@ class TopWordsModule(FilterableModule[TopWordsConfig]):
         "th_importance",
         "force_no_saliency",
     }
+    worker = Worker.model
 
     @staticmethod
     def count_words(list_of_words: List[str], top_x: int) -> List[TopWordsResult]:

pyproject.toml

@@ -88,6 +88,7 @@ bokeh = "<3"
 # Documentation
 mkdocs = "^1.2.3"
 mkdocs-material = "^8.1.7"
+memory-profiler = "^0.61.0"
 
 [build-system]
 requires = ["poetry-core>=1.0.0"]