[Doc] update workspace templates (#34289)

Signed-off-by: Sofian Hnaide <[email protected]>

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

@@ -0,0 +1,28 @@
+# Scaling Batch Inference with Ray Data
+
+This template is a quickstart to using [Ray
+Data](https://docs.ray.io/en/latest/data/dataset.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!
+
+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!
+
+At a high level, this template will:
+
+1.  [Load your dataset using Ray
+    Data.](https://docs.ray.io/en/latest/data/creating-datasets.html)
+2.  [Preprocess your dataset before feeding it to your
+    model.](https://docs.ray.io/en/latest/data/transforming-datasets.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-datasets.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.

doc/source/templates/02_many_model_training/README.md

@@ -0,0 +1,26 @@
+# Scaling Many Model Training with Ray Tune
+
+This template is a quickstart to using [Ray
+Tune](https://docs.ray.io/en/latest/tune/index.html) for batch
+inference. Ray Tune 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/training-one-million-machine-learning-models-in-record-time-with-ray)
+for more information on the benefits of performing many model training
+with Ray!
+
+This template walks through time-series forecasting using
+`statsforecast`, but the framework and data format can be swapped out
+easily \-- they are there just to help you build your own application!
+
+At a high level, this template will:
+
+1.  [Define the training function for a single partition of
+    data.](https://docs.ray.io/en/latest/tune/tutorials/tune-run.html)
+2.  [Define a Tune search space to run training over many partitions of
+    data.](https://docs.ray.io/en/latest/tune/tutorials/tune-search-spaces.html)
+3.  [Extract the best model per dataset partition from the Tune
+    experiment
+    output.](https://docs.ray.io/en/latest/tune/examples/tune_analyze_results.html)
+
+Start coding by clicking on the Jupyter or VSCode icon above.

doc/source/templates/03_serving_stable_diffusion/README.md

@@ -0,0 +1,7 @@
+# Serving a Stable Diffusion Model with Ray Serve
+
+This guide is a quickstart to use [Ray Serve](https://docs.ray.io/en/latest/serve/index.html) for model serving. Ray Serve is one of many libraries under the [Ray AI Runtime](https://docs.ray.io/en/latest/ray-air/getting-started.html).
+
+This template loads a pretrained stable diffusion model from HuggingFace and serves it to a local endpoint as a Ray Serve deployment. 
+
+Start coding by clicking on the Jupyter or VSCode icon above.

doc/source/templates/configs/anyscale_cluster_env.yaml

@@ -1 +1,6 @@
-docker_image: anyscale/ray-ml:latest-py39
+# you can define a custom byod definition e.g.
+# byod:
+#   docker_image: anyscale/ray-ml:latest-py39
+#   ray_version: nightly
+# or define a build_id for existing images, e.g. for "anyscaleray-ml231-py39-gpu"
+build_id: "anyscaleray-mlnightly-py39-gpu"

doc/source/templates/configs/compute/cpu/aws_large.yaml

@@ -1,6 +1,3 @@
-cloud_id: {{ env["ANYSCALE_CLOUD_ID"] }}
-region: us-west-2
-
 # 8 m5.2xlarge nodes --> 64 CPUs
 head_node_type:
   name: head_node_type

doc/source/templates/configs/compute/cpu/gcp_large.yaml

@@ -1,6 +1,3 @@
-cloud_id: {{ env["ANYSCALE_CLOUD_ID"] }}
-region: us-west1
-
 # 8 n2-standard-8 nodes --> 64 CPUs
 head_node_type:
   name: head_node_type

doc/source/templates/configs/compute/gpu/aws_large.yaml

@@ -1,6 +1,3 @@
-cloud_id: {{ env["ANYSCALE_CLOUD_ID"] }}
-region: us-west-2
-
 # 4 g4dn.4xlarge nodes --> 64 CPUs, 4 GPUs
 head_node_type:
   name: head_node_type

doc/source/templates/configs/compute/gpu/gcp_large.yaml

@@ -1,6 +1,3 @@
-cloud_id: {{ env["ANYSCALE_CLOUD_ID"] }}
-region: us-west1
-
 # 4 n1-standard-16-nvidia-tesla-t4-1 nodes --> 64 CPUs, 4 GPUs
 head_node_type:
   name: head_node_type

doc/source/templates/templates.yaml

@@ -1,37 +1,25 @@
-# See README.md for more details.
-
-- name: Scaling Batch Inference with Ray Data
+# Update anyscale/backend/workspace-template.yaml
+batch-inference-ray-data:
+  title: Batch Inference
+  description: Scaling Batch Inference with Ray Data
   path: doc/source/templates/01_batch_inference
   cluster_env: doc/source/templates/configs/anyscale_cluster_env.yaml
-  small:
-    compute_config:
-      gcp: doc/source/templates/configs/compute/gpu/gcp_small.yaml
-      aws: doc/source/templates/configs/compute/gpu/aws_small.yaml
-  large:
-    compute_config:
-      gcp: doc/source/templates/configs/compute/gpu/gcp_large.yaml
-      aws: doc/source/templates/configs/compute/gpu/aws_large.yaml
-
-- name: Scaling Many Model Training with Ray Tune
+  compute_config:
+    GCP: doc/source/templates/configs/compute/gpu/gcp_large.yaml
+    AWS: doc/source/templates/configs/compute/gpu/aws_large.yaml
+many-model-training-ray-tune:
+  title: Many Model Training
+  description: Scaling Many Model Training with Ray Tune
   path: doc/source/templates/02_many_model_training
   cluster_env: doc/source/templates/configs/anyscale_cluster_env.yaml
-  small:
-    compute_config:
-      gcp: doc/source/templates/configs/compute/cpu/gcp_small.yaml
-      aws: doc/source/templates/configs/compute/cpu/aws_small.yaml
-  large:
-    compute_config:
-      gcp: doc/source/templates/configs/compute/cpu/gcp_large.yaml
-      aws: doc/source/templates/configs/compute/cpu/aws_large.yaml
-
-- name: Serving a Stable Diffusion Model with Ray Serve
+  compute_config:
+    GCP: doc/source/templates/configs/compute/cpu/gcp_large.yaml
+    AWS: doc/source/templates/configs/compute/cpu/aws_large.yaml
+serve-stable-diffusion-model-ray-serve:
+  title: Serving Stable Diffusion
+  description: Serving a Stable Diffusion Model with Ray Serve
   path: doc/source/templates/03_serving_stable_diffusion
   cluster_env: doc/source/templates/configs/anyscale_cluster_env.yaml
-  small:
-    compute_config:
-      gcp: doc/source/templates/configs/compute/cpu/gcp_small.yaml
-      aws: doc/source/templates/configs/compute/cpu/aws_small.yaml
-  large:
-    compute_config:
-      gcp: doc/source/templates/configs/compute/cpu/gcp_large.yaml
-      aws: doc/source/templates/configs/compute/cpu/aws_large.yaml
+  compute_config:
+    GCP: doc/source/templates/configs/compute/cpu/gcp_large.yaml
+    AWS: doc/source/templates/configs/compute/cpu/aws_large.yaml