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- # Update anyscale/backend/workspace-template.yaml
- # <unique-template-id>:
- # emoji: 📊
- # title: Batch Inference
- # description: Description
- # path: Relative path to the template directory, from the Ray root directory
- # labels:
- # - ...
- # cluster_env:
- # ## Some sample `build_id`'s to choose from:
- # ## - anyscaleray-ml240-py39-gpu -> anyscale/ray-ml:2.4.0-py39-gpu
- # ## - anyscale240-py39 -> anyscale/ray:2.4.0-py39
- # build_id: anyscaleray-ml250-py39-gpu
- # ## OR, use a publicly hosted image
- # # byod:
- # # docker_image: url of docker image
- # # ray_version: 2.4.0
- # ## Make sure these compute configs don't contain region/cloud ID
- # compute_config:
- # GCP: doc/source/templates/configs/compute/gpu/gce.yaml
- # AWS: doc/source/templates/configs/compute/gpu/aws.yaml
- batch-inference-ray-data:
- emoji: 📊
- title: Batch Inference
- description: Parallelize batch inference of a dataset on a distributed Ray cluster with the Ray Data library. This template runs GPU batch inference on an image dataset using a PyTorch model.
- path: doc/source/templates/01_batch_inference
- labels:
- - Ray Data
- cluster_env:
- build_id: anyscaleray-ml250-py39-gpu
- compute_config:
- GCP: doc/source/templates/configs/compute/gpu/gce.yaml
- AWS: doc/source/templates/configs/compute/gpu/aws.yaml
- many-model-training-ray-tune:
- emoji: ⚡
- title: Many Model Training
- description: Train thousands of models in parallel on a distributed Ray cluster using the Ray Tune library. This template trains multiple forecasting models for different partitions of a time-series dataset and selects the best-performing model for each partition.
- path: doc/source/templates/02_many_model_training
- labels:
- - Ray Tune
- cluster_env:
- build_id: anyscaleray-ml250-py39-gpu
- compute_config:
- GCP: doc/source/templates/configs/compute/cpu/gce.yaml
- AWS: doc/source/templates/configs/compute/cpu/aws.yaml
- serve-stable-diffusion-model-ray-serve:
- emoji: 📡
- title: Serving a Stable Diffusion Model
- description: Deploy a stable diffusion model using the Ray Serve library and showcase its capabilities by generating images from text prompts! This template loads a pre-trained stable diffusion model from HuggingFace and serves it to a local endpoint.
- path: doc/source/templates/03_serving_stable_diffusion
- labels:
- - Ray Serve
- cluster_env:
- byod:
- docker_image: us-docker.pkg.dev/anyscale-workspace-templates/workspace-templates/serve-stable-diffusion-model-ray-serve:2.5.0
- ray_version: 2.5.0
- compute_config:
- GCP: doc/source/templates/configs/compute/gpu/gce.yaml
- AWS: doc/source/templates/configs/compute/gpu/aws.yaml
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