tensorflow · nallave · Dec 5, 2024 · Dec 5, 2024 · Mar 26, 2025
diff --git a/docs/guide/cli.md b/docs/guide/cli.md
@@ -818,7 +818,7 @@ management.
 -   ${HOME}/tfx/local, beam, airflow, vertex
     -   Pipeline metadata read from the configuration is stored under
         `${HOME}/tfx/${ORCHESTRATION_ENGINE}/${PIPELINE_NAME}`. This location
-        can be customized by setting environment varaible like `AIRFLOW_HOME` or
+        can be customized by setting environment variable like `AIRFLOW_HOME` or
         `KUBEFLOW_HOME`. This behavior might be changed in future releases. This
         directory is used to store pipeline information including pipeline ids
         in the Kubeflow Pipelines cluster which is needed to create runs or

diff --git a/docs/guide/custom_component.md b/docs/guide/custom_component.md
@@ -69,7 +69,7 @@ class HelloComponentSpec(types.ComponentSpec):
 
 Next, write the executor code for the new component. Basically, a new subclass
 of `base_executor.BaseExecutor` needs to be created with its `Do` function
-overriden. In the `Do` function, the arguments `input_dict`, `output_dict` and
+overridden. In the `Do` function, the arguments `input_dict`, `output_dict` and
 `exec_properties` that are passed in map to `INPUTS`, `OUTPUTS` and `PARAMETERS`
 that are defined in ComponentSpec respectively. For `exec_properties`, the value
 can be fetched directly through a dictionary lookup. For artifacts in

diff --git a/docs/tutorials/tfx/cloud-ai-platform-pipelines.md b/docs/tutorials/tfx/cloud-ai-platform-pipelines.md
@@ -769,7 +769,7 @@ gcloud config set project YOUR_PROJECT_ID
 gcloud services list --available | grep Dataflow
 
 # If you don't see dataflow.googleapis.com listed, that means you haven't been
-# granted access to enable the Dataflow API.  See your account adminstrator.
+# granted access to enable the Dataflow API.  See your account administrator.
 
 # Enable the Dataflow service:
 

diff --git a/docs/tutorials/tfx/components.ipynb b/docs/tutorials/tfx/components.ipynb
diff --git a/docs/tutorials/tfx/components_keras.ipynb b/docs/tutorials/tfx/components_keras.ipynb
diff --git a/docs/tutorials/tfx/gcp/vertex_pipelines_vertex_training.ipynb b/docs/tutorials/tfx/gcp/vertex_pipelines_vertex_training.ipynb
diff --git a/docs/tutorials/tfx/gpt2_finetuning_and_conversion.ipynb b/docs/tutorials/tfx/gpt2_finetuning_and_conversion.ipynb
diff --git a/docs/tutorials/tfx/neural_structured_learning.ipynb b/docs/tutorials/tfx/neural_structured_learning.ipynb
diff --git a/docs/tutorials/tfx/penguin_template.ipynb b/docs/tutorials/tfx/penguin_template.ipynb
diff --git a/docs/tutorials/tfx/template.ipynb b/docs/tutorials/tfx/template.ipynb
@@ -283,7 +283,7 @@
         "id": "ozHIomcd0olB"
       },
       "source": [
-        "TFX includes the [`taxi` template](https://github.com/tensorflow/tfx/tree/master/tfx/experimental/templates/taxi) with the TFX python package. If you are planning to solve a point-wise prediction problem, including classification and regresssion, this template could be used as a starting point.\n",
+        "TFX includes the [`taxi` template](https://github.com/tensorflow/tfx/tree/master/tfx/experimental/templates/taxi) with the TFX python package. If you are planning to solve a point-wise prediction problem, including classification and regression, this template could be used as a starting point.\n",
         "\n",
         "The `tfx template copy` CLI command copies predefined template files into your project directory."
       ]