[Templates] Remove runtime_env usage and use a custom docker image …

…when needed (ray-project#35475)

Signed-off-by: Justin Yu <[email protected]>

doc/BUILD

@@ -252,57 +252,20 @@ py_test_run_all_subdirectory(
 )
 
 # --------------------------------------------------------------------
-# Test all Workspace template notebooks in doc/source/templates
-# as smoke tests.
+# Workspace templates tests
 # --------------------------------------------------------------------
 
-filegroup(
-    name = "workspace_templates",
-    srcs = glob([
-        "source/templates/tests/**/*.ipynb",
-        "source/templates/tests/**/requirements.txt"
-    ]),
-    visibility = ["//doc:__subpackages__"]
-)
-
 # Validate that all the paths and yamls within the templates.yaml file are valid.
 
 py_test(
     name = "templates_directory_validation",
     size = "small",
-    main = "source/templates/validate.py",
-    srcs = ["source/templates/validate.py"],
+    main = "source/templates/testing/validate.py",
+    srcs = ["source/templates/testing/validate.py"],
     data = glob(["source/templates/**/*"]),
     tags = ["exclusive", "team:ml"]
 )
 
-# Templates that only require CPU
-
-py_test_run_all_notebooks(
-    size = "large",
-    # TODO(justinvyu): Merge tests/ with the regular versions of the templates.
-    include = ["source/templates/tests/02_many_model_training/many_model_training.ipynb"],
-    exclude = [],
-    data = ["//doc:workspace_templates"],
-    tags = ["exclusive", "team:ml", "ray_air"],
-    env = {"SMOKE_TEST": "1"},
-)
-
-# Templates that require GPU
-
-py_test_run_all_notebooks(
-    size = "large",
-    include = [
-        # TODO(justinvyu): Merge tests/ with the regular versions of the templates.
-        "source/templates/tests/01_batch_inference/batch_inference.ipynb",
-        "source/templates/tests/03_serving_stable_diffusion/serving_stable_diffusion.ipynb"
-    ],
-    exclude = [],
-    data = ["//doc:workspace_templates"],
-    tags = ["exclusive", "team:ml", "ray_air", "gpu"],
-    env = {"SMOKE_TEST": "1"},
-)
-
 # --------------
 # Run GPU tests
 # --------------

doc/source/templates/01_batch_inference/README.md

@@ -1,28 +1,14 @@
 # Scaling Batch Inference with Ray Data
 
-This template is a quickstart to using [Ray
-Data](https://docs.ray.io/en/latest/data/data.html) for batch
-inference. Ray Data is one of many libraries under the [Ray AI
-Runtime](https://docs.ray.io/en/latest/ray-air/getting-started.html).
-See [this blog
-post](https://www.anyscale.com/blog/model-batch-inference-in-ray-actors-actorpool-and-datasets)
-for more information on why and how you should perform batch inference
-with Ray!
+| Template Specification | Description |
+| ---------------------- | ----------- |
+| Summary | This template walks through GPU batch inference on an image dataset using a PyTorch ResNet model. |
+| Time to Run | Less than 2 minutes to compute predictions on the dataset. |
+| Minimum Compute Requirements | No hard requirements. The default is 4 nodes, each with 1 NVIDIA T4 GPU. |
+| Cluster Environment | This template uses the latest Anyscale-provided Ray ML image using Python 3.9: [`anyscale/ray-ml:2.4.0-py39-gpu`](https://docs.anyscale.com/reference/base-images/ray-240/py39#ray-ml-2-4-0-py39). If you want to change to a different cluster environment, make sure that it is based off of this image! |
 
-This template walks through GPU batch prediction on an image dataset
-using a PyTorch model, but the framework and data format are there just
-to help you build your own application!
+## Getting Started
 
-At a high level, this template will:
+**When the workspace is up and running, start coding by clicking on the Jupyter or VSCode icon above. Open the `start.ipynb` file and follow the instructions there.**
 
-1.  [Load your dataset using Ray
-    Data.](https://docs.ray.io/en/latest/data/loading-data.html)
-2.  [Preprocess your dataset before feeding it to your
-    model.](https://docs.ray.io/en/latest/data/transforming-data.html)
-3.  [Initialize your model and perform inference on a shard of your
-    dataset with a remote
-    actor.](https://docs.ray.io/en/latest/data/transforming-data.html#callable-class-udfs)
-4.  [Save your prediction
-    results.](https://docs.ray.io/en/latest/data/api/input_output.html)
-
-Start coding by clicking on the Jupyter or VSCode icon above.
+By the end, we will have classified around 4000 images using the pre-trained ResNet model and saved these predictions to a local directory.