Remove pipeline references

src/fondant/dataset/compiler.py

@@ -86,7 +86,7 @@ def _build_entrypoint(image: Image) -> t.List[str]:
 
 
 class DockerCompiler(Compiler):
-    """Compiler that creates a docker-compose spec from a pipeline."""
+    """Compiler that creates a docker-compose spec from a dataset."""
 
     def compile(
         self,
@@ -98,7 +98,7 @@ def compile(
         build_args: t.Optional[t.List[str]] = None,
         auth_provider: t.Optional[CloudCredentialsMount] = None,
     ) -> None:
-        """Compile a pipeline to docker-compose spec and save it to a specified output path.
+        """Compile a dataset workflow to docker-compose spec and save it to a specified output path.
 
         Args:
             dataset: the dataset to compile
@@ -194,7 +194,7 @@ def _generate_spec(
         build_args: t.List[str],
     ) -> dict:
         """Generate a docker-compose spec as a python dictionary,
-        loops over the pipeline graph to create services and their dependencies.
+        loops over the dataset graph to create services and their dependencies.
         """
         path, volume = self._patch_path(base_path=working_directory)
         run_id = dataset.manifest.run_id
@@ -476,7 +476,7 @@ def __repr__(self) -> str:
 
 
 class KubeFlowCompiler(Compiler):
-    """Compiler that creates a Kubeflow pipeline spec from a pipeline."""
+    """Compiler that creates a Kubeflow pipeline spec from a dataset."""
 
     def __init__(self):
         self._resolve_imports()
@@ -503,7 +503,8 @@ def compile(
         working_directory: str,
         output_path: str,
     ) -> None:
-        """Compile a pipeline to Kubeflow pipeline spec and save it to a specified output path.
+        """Compile a dataset workflow to Kubeflow pipeline spec and save it to a specified
+        output path.
 
         Args:
             dataset: the dataset to compile
@@ -874,7 +875,7 @@ def compile(
         *,
         role_arn: t.Optional[str] = None,
     ) -> None:
-        """Compile a fondant pipeline to sagemaker pipeline spec and save it
+        """Compile a fondant dataset workflow to sagemaker pipeline spec and save it
         to a specified output path.
 
         Args:

src/fondant/dataset/dataset.py

@@ -1,4 +1,4 @@
-"""This module defines classes to represent a Fondant Pipeline."""
+"""This module defines classes to represent a Fondant Dataset."""
 
 import copy
 import datetime
@@ -70,15 +70,16 @@ class Resources:
 
     """
     Class representing the resources to assign to a Fondant Component operation in a Fondant
-    Pipeline.
+    Dataset.
 
        Arguments:
            number_of_accelerators: The number of accelerators to assign to the operation (GPU, TPU)
            accelerator_name: The name of the accelerator to assign. If you're using a cluster setup
              on GKE, select "GPU" for GPU or "TPU" for TPU. Make sure
              that you select a nodepool with the available hardware. If you're running the
-             pipeline on Vertex, then select one of the machines specified in the list of
-             accelerators here https://cloud.google.com/vertex-ai/docs/reference/rest/v1/MachineSpec.
+             dataset materilization workflow on Vertex, then select one of the machines specified
+             in the list of accelerators
+             here https://cloud.google.com/vertex-ai/docs/reference/rest/v1/MachineSpec.
            node_pool_label: The label of the node pool to which the operation will be assigned.
            node_pool_name: The name of the node pool to which the operation will be assigned.
            cache: Set to False to disable caching, True by default.
@@ -119,8 +120,8 @@ def to_dict(self) -> t.Dict[str, t.Any]:
 
 class ComponentOp:
     """
-    Class representing an operation for a Fondant Component in a Fondant Pipeline. An operation
-    is a representation of a function that will be executed as part of a pipeline.
+    Class representing an operation for a Fondant Component in a Fondant Dataset. An operation
+    is a representation of a function that will be executed as part of the workflow.
 
     Arguments:
         name_or_path: The name of a reusable component, or the path to the directory containing a
@@ -558,7 +559,7 @@ def create(
 
     def sort_graph(self):
         """Sort the graph topologically based on task dependencies."""
-        logger.info("Sorting pipeline component graph topologically.")
+        logger.info("Sorting workflow graph topologically.")
         sorted_graph = []
         visited = set()
 
@@ -581,7 +582,7 @@ def depth_first_traversal(node: str):
         self._graph = OrderedDict((node, self._graph[node]) for node in sorted_graph)
 
     def validate(self):
-        """Sort and run validation on the pipeline definition.
+        """Sort and run validation on the dataset definition.
 
         Args:
             run_id: run identifier
@@ -603,10 +604,11 @@ def _validate_dataset_definition(self):
         """
         run_id = self.manifest.run_id
         if len(self._graph.keys()) == 0:
-            logger.info("No components defined in the pipeline. Nothing to validate.")
+            logger.info(
+                "No components defined in the dataset workflow. Nothing to validate.",
+            )
             return
 
-        # TODO: change later if we decide to run 2 fondant pipelines after each other
         load_component = True
         load_component_name = list(self._graph.keys())[0]
 
@@ -663,10 +665,10 @@ def _validate_dataset_definition(self):
             )
             load_component = False
 
-        logger.info("All pipeline component specifications match.")
+        logger.info("All workflow component specifications match.")
 
     def __repr__(self) -> str:
-        """Return a string representation of the FondantPipeline object."""
+        """Return a string representation of the Fondant dataset object."""
         return f"{self.__class__.__name__}({self._graph!r}"
 
     @property

src/fondant/dataset/runner.py

@@ -42,14 +42,14 @@ def _run(self, input_spec: str, *args, **kwargs):
             "--remove-orphans",
         ]
 
-        print("Starting pipeline run...")
+        print("Starting workflow run...")
 
         # copy the current environment with the DOCKER_DEFAULT_PLATFORM argument
         subprocess.call(  # nosec
             cmd,
             env=dict(os.environ, DOCKER_DEFAULT_PLATFORM="linux/amd64"),
         )
-        print("Finished pipeline run.")
+        print("Finished workflow run.")
 
     def run(
         self,
@@ -60,7 +60,7 @@ def run(
         build_args: t.Optional[t.List[str]] = None,
         auth_provider: t.Optional[CloudCredentialsMount] = None,
     ) -> None:
-        """Run a pipeline, either from a compiled docker-compose spec or from a fondant pipeline.
+        """Run a workflow, either from a compiled docker-compose spec or from a fondant dataset.
 
         Args:
             dataset: the dataset to compile or a path to an already compiled docker-compose spec
@@ -78,7 +78,7 @@ def run(
             os.makedirs(".fondant", exist_ok=True)
             output_path = ".fondant/compose.yaml"
             logging.info(
-                "Found reference to un-compiled pipeline... compiling",
+                "Found reference to un-compiled workflow... compiling",
             )
             compiler = DockerCompiler()
             compiler.compile(
@@ -190,7 +190,7 @@ def run(
         *,
         experiment_name: str = "Default",
     ):
-        """Run a pipeline, either from a compiled kubeflow spec or from a fondant pipeline.
+        """Run a workflow, either from a compiled kubeflow spec or from a fondant dataset.
 
         Args:
             dataset: the dataset to compile or a path to an already compiled sagemaker spec
@@ -201,7 +201,7 @@ def run(
             os.makedirs(".fondant", exist_ok=True)
             output_path = ".fondant/kubeflow-pipeline.yaml"
             logging.info(
-                "Found reference to un-compiled pipeline... compiling",
+                "Found reference to un-compiled workflow... compiling",
             )
             compiler = KubeFlowCompiler()
             compiler.compile(
@@ -237,8 +237,8 @@ def _run(
             pipeline_package_path=input_spec,
         )
 
-        pipeline_url = f"{self.host}/#/runs/details/{runner.run_id}"
-        logger.info(f"Pipeline is running at: {pipeline_url}")
+        workflow_url = f"{self.host}/#/runs/details/{runner.run_id}"
+        logger.info(f"Pipeline is running at: {workflow_url}")
 
     def get_name_from_spec(self, input_spec: str):
         """Get the name of the pipeline from the spec."""
@@ -274,7 +274,7 @@ def run(
         dataset: t.Union[Dataset, str],
         working_directory: str,
     ):
-        """Run a pipeline, either from a compiled vertex spec or from a fondant pipeline.
+        """Run a workflow, either from a compiled vertex spec or from a fondant dataset.
 
         Args:
             dataset: the dataset to compile or a path to an already compiled sagemaker spec
@@ -284,7 +284,7 @@ def run(
             os.makedirs(".fondant", exist_ok=True)
             output_path = ".fondant/vertex-pipeline.yaml"
             logging.info(
-                "Found reference to un-compiled pipeline... compiling",
+                "Found reference to un-compiled workflow... compiling",
             )
             compiler = VertexCompiler()
             compiler.compile(
@@ -344,15 +344,15 @@ def run(
         Args:
             dataset: the dataset to compile or a path to a already compiled sagemaker spec
             working_directory: path of the working directory
-            pipeline_name: the name of the pipeline to create
+            pipeline_name: the name of the workflow to create
             role_arn: the Amazon Resource Name role to use for the processing steps,
             if none provided the `sagemaker.get_execution_role()` role will be used.
         """
         if isinstance(dataset, Dataset):
             os.makedirs(".fondant", exist_ok=True)
             output_path = ".fondant/sagemaker-pipeline.yaml"
             logging.info(
-                "Found reference to un-compiled pipeline... compiling",
+                "Found reference to un-compiled workflow... compiling",
             )
             compiler = SagemakerCompiler()
             compiler.compile(