docs and examples amended

zenml-io · Jan 22, 2021 · a387ed2 · a387ed2
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diff --git a/docs/book/steps/deployer.md b/docs/book/steps/deployer.md
@@ -17,6 +17,10 @@ pipeline.add_deployment(GCAIPDeployer(
 ))
 ```
 
+```{warning}
+Currently, the GCAIPDeployer only works with Trainers fully implementing the `TFBaseTrainerStep` interface. An example is the standard `tf_ff_trainer.FeedForwardTrainer` step.
+```
+
 #### How to make a request to your served model
 
 Google Cloud AI Platform is using [TFServing](https://www.tensorflow.org/tfx/guide/serving) under-the-hood. TFServing has [defined standards](https://www.tensorflow.org/tfx/serving/api_docs/cc/) 

diff --git a/docs/book/steps/evaluator.md b/docs/book/steps/evaluator.md
@@ -17,6 +17,9 @@ namely `slices` and `metrics`.
   conducted on each slice.
 * `metrics` define which metrics will be computed during the evaluation.
 
+```{warning}
+Currently, the TFMAEvaluator only works with Trainers fully implementing the `TFBaseTrainerStep` interface. An example is the standard `tf_ff_trainer.FeedForwardTrainer` step.
+```
 #### Example
 Let's start with a simple example. Imagine you are dealing with a simple regression task on a single 
 label and you want to compute the `mean_squared_error` of your trained model on the eval dataset. 

diff --git a/examples/README.md b/examples/README.md
@@ -4,13 +4,14 @@ This `example` folder is dedicated to provide our users with some practical exam
 
 Here you can find a list with brief descriptions for each example:
 
-- **experimental**: Quick example on how you can create a custom `Step`, more precisely a `TrainerStep`
-- **gan**: Tutorial on how to create and train a GAN model with a custom `TrainerStep` and `PreprocessorStep` 
-- **gcp_dataflow**: A simple pipeline wth`Google Cloud Platform / Dataflow` as the processing backend
-- **gcp_orchestrator**: A simple pipeline which utilizes a virtual machine on `Google Cloud Platform` 
-  as the orchestration backend
-- **gcp_trained**: A simple pipeline with `Google Cloud AI Platform` as the training backend 
-- **quickstart**: The official quickstart tutorial
+- **experimental**: Experimental examples.
+- **gan**: Tutorial on how to create and train a GAN model with a custom `TrainerStep` and `PreprocessorStep`.
+- **gcp_dataflow**: Showcases distributed preprocessing wth`Google Cloud Platform / Dataflow` as the processing backend.
+- **gcp_orchestrated**: Runs pipeline on a virtual machine on `Google Cloud Platform` as the orchestration backend.
+- **gcp_trained**: A simple pipeline with `Google Cloud AI Platform` as the training backend.
+- **kubernetes**: Launches a kubernetes job on a kubernetes cluster.
+- **pytorch**: Showcases PyTorch training support.
+- **quickstart**: The official quickstart tutorial.
 
 If you have any questions regarding any of these tutorials, feel free to contact us by creating an 
 [issue](https://github.com/maiot-io/zenml/issues) here on our Github or by reaching out to us on our