Refactor pipeline interface

docs/architecture.md

@@ -37,10 +37,9 @@ At a high level, Fondant consists of three main parts:
       component type.
 
 
-* The `/pipeline` directory which contains the modules for implementing a Fondant pipeline.
-    * `pipeline.py`: Defines the `Pipeline` class which is used to define the pipeline graph and the
-      pipeline run. The
-      implemented class is then consumed by the compiler to compile to a specific pipeline runner.
+* The `/dataset` directory which contains the modules for implementing a Fondant pipeline.
+    * `dataset.py`: Defines the `Dataset` class which is used to define the graph. The
+      implemented class is then consumed by the compiler to compile to a specific runner.
       This module also implements the
       `ComponentOp` class which is used to define the component operation in the pipeline graph.
     * `compiler.py`: Defines the `Compiler` class which is used to define the compiler that

docs/components/component_spec.md

@@ -102,7 +102,7 @@ If your dataset has a field called `custom_text` with type `string`, you can map
 
 ```python 
 
-dataset = pipeline.read(...)
+dataset = Dataset.read(...)
 dataset = dataset.apply(
     "example_component",
     consumes={
@@ -127,7 +127,7 @@ so as follows:
 
 ```python 
 
-dataset = pipeline.read(...)
+dataset = Dataset.read(...)
 dataset = dataset.apply(
     "example_component",
     produces={
@@ -165,7 +165,7 @@ in the component specification, so we will need to specify the schema of the
 fields when defining the components
 
 ```python
-dataset = pipeline.read(
+dataset = Dataset.read(
     "load_from_csv",
     arguments={
         "dataset_uri": "path/to/dataset.csv",
@@ -196,7 +196,7 @@ by the next component. We can either load the `image` field:
 
 ```python 
 
-dataset = pipeline.read(
+dataset = Dataset.read(
     "load_from_csv",
     arguments={
         "dataset_uri": "path/to/dataset.csv",
@@ -219,7 +219,7 @@ or the `embedding` field:
 
 ```python 
 
-dataset = pipeline.read(
+dataset = Dataset.read(
     "load_from_csv",
     arguments={
         "dataset_uri": "path/to/dataset.csv",
@@ -268,7 +268,7 @@ These arguments are passed in when the component is instantiated.
 If an argument is not explicitly provided, the default value will be used instead if available.
 
 ```python
-dataset = pipeline.read(
+dataset = Dataset.read(
     "custom_component",
     arguments={
         "custom_argument": "foo"

docs/components/lightweight_components.md

@@ -53,7 +53,7 @@ pipeline = Pipeline(
     base_path="./data",
 )
 
-dataset = pipeline.read(
+dataset = Dataset.read(
     ref=CreateData,
 )
 

docs/guides/build_a_simple_pipeline.md

@@ -44,7 +44,7 @@ pipeline = Pipeline(
 
 ??? "View a detailed reference of the options accepted by the `Pipeline` class"
 
-    ::: fondant.pipeline.Pipeline.__init__
+    ::: fondant.dataset.Dataset.__init__
         handler: python
         options:
             show_source: false
@@ -69,13 +69,13 @@ As a first step, we want to read data into our pipeline. In this case, we will l
 from the HuggingFace Hub. For this, we can use the reusable 
 [load_from_hf_hub](../components/hub.md#load_from_hugging_face_hub#description) component.
 
-We can read data into our pipeline using the `Pipeline.read()` method, which returns a (lazy) 
+We can read data into our pipeline using the `Dataset.read()` method, which returns a (lazy) 
 `Dataset`.
 
 ```python
 import pyarrow as pa
 
-dataset = pipeline.read(
+dataset = Dataset.read(
     "load_from_hf_hub",
     arguments={
         "dataset_name": "fondant-ai/fondant-cc-25m",
@@ -101,9 +101,9 @@ We provide three arguments to the `.read()` method:
   defined in the component [documentation](../components/hub.md#load_from_hugging_face_hub#inputs_outputs) with 
   `additionalProperties: true` under the produces section.
 
-??? "View a detailed reference of the `Pipeline.read()` method"
+??? "View a detailed reference of the `Dataset.read()` method"
 
-    ::: fondant.pipeline.Pipeline.read
+    ::: fondant.dataset.Dataset.read
         handler: python
         options:
             show_source: false
@@ -171,7 +171,7 @@ english_images = images.apply(
 
 ??? "View a detailed reference of the `Dataset.apply()` method"
 
-    ::: fondant.pipeline.pipeline.Dataset.apply
+    ::: fondant.dataset.Dataset.apply
         handler: python
         options:
             show_source: false

docs/guides/implement_custom_components.md

@@ -33,7 +33,7 @@ pipeline = Pipeline(
     base_path="./data"
 )
 
-dataset = pipeline.read(
+dataset = Dataset.read(
     "load_from_hf_hub",
     arguments={
         "dataset_name": "fondant-ai/fondant-cc-25m",

docs/pipeline.md

@@ -1,20 +1,21 @@
-# Pipeline
+# Dataset
 
-A Fondant pipeline is a tool for building complex workflows by creating a Directed Acyclic Graph 
-(DAG) of different components that need to be executed. With Fondant, you can use both reusable
-components and custom components, and chain them into a pipeline.
+A Fondant Dataset is a checkpoint in a Directed Acyclic Graph 
+(DAG) of one or more different components that need to be executed. With Fondant, you can use both reusable
+components and custom components, and chain them together.
 
-## Composing a pipeline
 
-Start by creating a `pipeline.py` file and adding the following code.
+[//]: # (TODO update this section once we have the workspace)
+## Composing a Pipeline
 
+Start by creating a `pipeline.py` file and adding the following code.
 ```python
-from fondant.pipeline import Pipeline
+from fondant.dataset import Dataset
+
+#dataset = Dataset.read(
+#    ..
+#)
 
-pipeline = Pipeline(
-    name="my-pipeline",
-    base_path="./data",
-)
 ```
 
 We identify our pipeline with a name and provide a base path where the pipeline will store its 
@@ -30,34 +31,25 @@ The base path can be:
 * **A local directory**: only valid for the local runner, points to a local directory. This is
   useful for local development.
 
-!!! note "IMPORTANT"
-
-    Make sure the provided base_path already exists.
-
-??? "View a detailed reference of the options accepted by the `Pipeline` class"
-
-    ::: fondant.pipeline.Pipeline.__init__
-        handler: python
-        options:
-            show_source: false
 
 ### Adding a load component
 
-You can read data into your pipeline by using the `Pipeline.read()` method with a load component.
+You can read data into your pipeline by using the `Dataset.read()` method with a load component.
 
 ```python
-dataset = pipeline.read(
+dataset = Dataset.read(
     "load_from_parquet",
     arguments={
         "dataset_uri": "path/to/dataset",
         "n_rows_to_load": 100,
     },
 )
 ```
+[//]: # (TODO: Add example of init from manifest)
 
-??? "View a detailed reference of the `Pipeline.read()` method"
+??? "View a detailed reference of the `Dataset.read()` method"
 
-    ::: fondant.pipeline.Pipeline.read
+    ::: fondant.dataset.Dataset.read
         handler: python
         options:
             show_source: false
@@ -68,7 +60,7 @@ graph. It returns a lazy `Dataset` instance which you can use to chain transform
 ### Adding transform components
 
 ```python
-from fondant.pipeline import Resources
+from fondant.dataset import Resources
 
 dataset = dataset.apply(
     "embed_text",
@@ -90,7 +82,7 @@ can choose the type of GPU as well.
 
 ??? "View a detailed reference of the `Dataset.apply()` method"
 
-    ::: fondant.pipeline.pipeline.Dataset.apply
+    ::: fondant.dataset.Dataset.apply
         handler: python
         options:
             show_source: false
@@ -112,7 +104,7 @@ dataset = dataset.write(
 
 ??? "View a detailed reference of the `Dataset.write()` method"
 
-    ::: fondant.pipeline.pipeline.Dataset.write
+    ::: fondant.dataset.Dataset.write
         handler: python
         options:
             show_source: false

docs/runners/kfp.md

@@ -16,39 +16,40 @@ Make sure to install Fondant with the Kubeflow runner extra.
 pip install fondant[kfp]
 ```
 
-### Running a pipeline with Kubeflow
+### Materialize a dataset with Kubeflow
 
-You will need a Kubeflow cluster to run your pipeline on and specify the host of that cluster. More
+You will need a Kubeflow cluster to run your workflow on and specify the host of that cluster. More
 info on setting up a Kubeflow pipelines deployment and the host path can be found in
 the [kubeflow infrastructure documentation](kfp_infrastructure.md).
 
 === "Console"
 
     ```bash 
-    fondant run kubeflow <pipeline_ref> \
+    fondant run kubeflow <dataset_ref> \
+     --working-dir $WORKING_DIR \
      --host $KUBEFLOW_HOST
     ```
 
-    The pipeline ref is reference to a fondant pipeline (e.g. `pipeline.py`) where a pipeline instance
+    The dataset ref is reference to a fondant dataset (e.g. `pipeline.py`) where a dataset instance
     exists.
 
 
 === "Python"
 
     ```python
-    from fondant.pipeline.compiler import KubeFlowCompiler
-    from fondant.pipeline.runner import KubeFlowRunner
+    from fondant.dataset.compiler import KubeFlowCompiler
+    from fondant.dataset.runner import KubeFlowRunner
 
     compiler= KubeFlowCompiler()
-    compiler.compile(pipeline=<pipeline_object>)
+    compiler.compile(dataset=<dataset_object>)
 
     runner = KubeFlowRunner(host=<kubeflow_host>)
-    runner.run(input_spec=<path_to_compiled_spec>)
+    runner.run(input_spec=<path_to_compiled_spec>, working_dir=<working_dir>)
     ```
 
-Once your pipeline is running you can monitor it using the Kubeflow UI.
+Once your workflow is running you can monitor it using the Kubeflow UI.
 
-#### Assigning custom resources to the pipeline
+#### Assigning custom resources to your run
 
 Each component can optionally be constrained to run on particular node(s) using `node_pool_label`
 and `node_pool_name`. You can find these under the Kubernetes labels of your cluster.
@@ -58,7 +59,7 @@ needs to
 have an available GPU.
 
 ```python
-from fondant.pipeline import Resources
+from fondant.dataset import Resources
 
 dataset = dataset.apply(
     "...",