Add more config information to the stable diffusion template and update to 2.9 #41987
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Signed-off-by: akshay-anyscale <122416226+akshay-anyscale@users.noreply.github.com>
akshay-anyscale
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justinvyu,
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sofianhnaide
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Signed-off-by: akshay-anyscale <122416226+akshay-anyscale@users.noreply.github.com>
Signed-off-by: akshay-anyscale <122416226+akshay-anyscale@users.noreply.github.com>
Signed-off-by: akshay-anyscale <122416226+akshay-anyscale@users.noreply.github.com>
Signed-off-by: akshay-anyscale <122416226+akshay-anyscale@users.noreply.github.com>
Signed-off-by: akshay-anyscale <122416226+akshay-anyscale@users.noreply.github.com>
Signed-off-by: akshay-anyscale <122416226+akshay-anyscale@users.noreply.github.com>
Signed-off-by: akshay-anyscale <122416226+akshay-anyscale@users.noreply.github.com>
Signed-off-by: akshay-anyscale <122416226+akshay-anyscale@users.noreply.github.com>
akshay-anyscale
changed the title
scottsun94
reviewed
Jan 2, 2024
| @@ -5,7 +5,7 @@ | |||
| | Summary | This app provides users a one click production option for serving a pre-trained Stable Diffusion model from HuggingFace. It leverages [Ray Serve](https://docs.ray.io/en/latest/serve/index.html) to deploy locally and built in IDE integration on an Anyscale Workspace to iterate and add additional logic to the application. You can then use a simple CLI to deploy to production with [Anyscale Services](https://docs.anyscale.com/productionize/services/get-started). | | |||
| | Time to Run | Around 2 minutes to setup the models and generate your first image(s). Less than 10 seconds for every subsequent round of image generation (depending on the image size). | | |||
| | Minimum Compute Requirements | At least 1 GPU node. The default is 4 nodes, each with 1 NVIDIA T4 GPU. | | |||
| | Cluster Environment | This template uses a docker image built on top of the latest Anyscale-provided Ray image using Python 3.9: [`anyscale/ray:latest-py39-cu118`](https://docs.anyscale.com/reference/base-images/overview). See the appendix below for more details. | | |||
| | Cluster Environment | This template uses a docker image built on top of the latest Anyscale-provided Ray 2.9 image using Python 3.9: [`anyscale/ray:latest-py39-cu118`](https://docs.anyscale.com/reference/base-images/overview). See the appendix below for more details. | | |||
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Why do we use the py39-cu118 vs anyscale/ray-ml:2.9.0-py39-gpu?
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I'll try it out, I believe it was first set up this way because ray-ml has some conflicting dependencies? @matthewdeng @justinvyu ?
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I used the anyscale/ray-ml:2.9.0-py39-gpu and installed diffusers, transformers, and accelerate on all nodes. It worked fine when generating images.
matthewdeng
approved these changes
Jan 2, 2024
Signed-off-by: akshay-anyscale <122416226+akshay-anyscale@users.noreply.github.com>
matthewdeng
approved these changes
Jan 2, 2024
vickytsang
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to ROCm/ray
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Jan 12, 2024
…te to 2.9 (ray-project#41987) Signed-off-by: akshay-anyscale <122416226+akshay-anyscale@users.noreply.github.com>
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Why are these changes needed?
Related issue number
Checks
git commit -s) in this PR.scripts/format.shto lint the changes in this PR.method in Tune, I've added it in
doc/source/tune/api/under thecorresponding
.rstfile.