fix: documentation inconsistencies

docs/components/components.md

@@ -1,5 +1,3 @@
-from distributed import Client
-
 # Components
 
 Fondant makes it easy to build dataset collaborative leveraging reusable components. Fondant
@@ -70,17 +68,18 @@ schema defined by the `produces` section of the component specification.
 ### Configuring Dask
 
 You can configure the [Dask client](https://docs.dask.org/en/stable/scheduling.html) based on the 
-needs of your component by overriding the `dask_client` method:
+needs of your component by overriding the `setup` method:
 
 ```python
 import os
+import typing as t
 
 from dask.distributed import Client, LocalCluster
 from fondant.component import PandasTransformComponent
 
 class Component(PandasTransformComponent):
 
-    def dask_client(self) -> Client:
+    def setup(self) -> t.Any:
         """Initialize the dask client to use for this component."""
         cluster = LocalCluster(
             processes=True,

docs/dataset.md

@@ -13,7 +13,7 @@ You can initialise a dataset from a previous run by using the `read` method.
 ```python
 from fondant.dataset import Dataset
 
-dataset = Dataset.read("path/to/manfiest.json")
+dataset = Dataset.read("path/to/manifest.json")
 
 ```
 

docs/guides/build_a_simple_dataset.md

@@ -99,7 +99,7 @@ We provide three arguments to the `.create()` method:
 To materialize your dataset, you can execute the following command within the directory:
 
 ```bash
-fondant run local dataset.py --working_direcotry ./data
+fondant run local dataset.py --working-directory ./data
 ```
 
 The workflow execution will start, initiating the download of the dataset from HuggingFace.

docs/runners/local.md

@@ -17,7 +17,7 @@ about this in the [installation](../guides/installation.md) guide.
 
 ### Running a dataset with the local runner
 
-Fondant will create a default working directory (for intermediate artifacts) for the dataset in the current working directory called '/.artifacts'. You can override this by passing the `--working-dir` argument to the `run` command. Or by setting the `working_dir` argument in the `run` method of the `DockerRunner` class.
+Fondant will create a default working directory (for intermediate artifacts) for the dataset in the current working directory called '/.artifacts'. You can override this by passing the `--working-directory` argument to the `run` command. Or by setting the `working_directory` argument in the `run` method of the `DockerRunner` class.
 
 === "Console"
 

docs/runners/sagemaker.md

@@ -49,7 +49,7 @@ AWS with a role that has all the required permissions to launch a SageMaker pipe
 
     ```bash 
     fondant run sagemaker <dataset_ref> \
-     --working-dir $S3_BUCKET \
+     --working-directory $S3_BUCKET \
      --role-arn $SAGEMAKER_ROLE_ARN 
     ```
 
@@ -61,8 +61,8 @@ AWS with a role that has all the required permissions to launch a SageMaker pipe
 
     runner = SageMakerRunner()
     runner.run(
-        input=<path_to_dataset>,
-        working_dir=<s3_bucket>,
+        dataset=<path_to_dataset>,
+        working_directory=<s3_bucket>,
         role_arn=<role_arn>,
         pipeline_name=<sagemaker_pipeline_name>
     )

docs/runners/vertex.md

@@ -56,7 +56,7 @@ info [here](https://codelabs.developers.google.com/vertex-pipelines-intro#2)
         region=project_region,
         service_account=service_account)
     )
-    runner.run(input_spec=<path_to_compiled_spec>, working_dir=<working_dir>)
+    runner.run(dataset=<path_to_compiled_spec>, working_directory=<working_directory>)
     ```