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update table and standardize format
Signed-off-by: sirutBuasai <sirutbuasai27@outlook.com>
1 parent d2c0f69 commit 088808f

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docs/mkdocs/stylesheets/extra.css

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color: var(--aws-rind);
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}
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/* TOC active link */
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.md-nav__link--active {
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color: var(--aws-sky) !important;
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}
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/* Table Horizontal Scroll */
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.md-typeset table:not([class]) {
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display: block;
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overflow-x: auto;
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white-space: nowrap;
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}
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/* ==========================================================================
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Light Mode
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}
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/* ==========================================================================
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Dark Mode
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========================================================================== */

docs/reference/available_images.template.md

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@@ -9,7 +9,7 @@ Once you've selected your desired Deep Learning Containers image, continue with
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- [Amazon EC2 Tutorials](https://docs.aws.amazon.com/deep-learning-containers/latest/devguide/deep-learning-containers-ec2.html)
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- [Amazon ECS Tutorials](https://docs.aws.amazon.com/deep-learning-containers/latest/devguide/deep-learning-containers-ecs.html)
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- [Amazon EKS Tutorials](https://docs.aws.amazon.com/deep-learning-containers/latest/devguide/deep-learning-containers-eks.html)
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- [Hugging Face on AWS](https://huggingface.co/docs/sagemaker/en/index)
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- [HuggingFace on AWS](https://huggingface.co/docs/sagemaker/en/index)
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- [Security in AWS Deep Learning Containers](https://docs.aws.amazon.com/deep-learning-containers/latest/devguide/security.html)
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- [Release Notes](https://docs.aws.amazon.com/deep-learning-containers/latest/devguide/dlc-release-notes.html)
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## Image Tag Guide
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Select your framework, job type (`training`, `inference`, or `general`), and Python version (`py38`, `py39`, `py310`, `py311`, or `py312`).
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To form your container image URL, use the following format:
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Pin versions by adding the version tag:
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```
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<account_id>.dkr.ecr.<region>.amazonaws.com/<repository>:<tag>
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```
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Where:
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- `<account_id>`: Find the account ID for your region in the Region Availability table above
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- `<region>`: Your AWS region (e.g., `us-east-1`, `us-west-2`, `eu-west-1`)
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- `<repository>`: The framework repository name (e.g., `pytorch-training`, `base`, `vllm`, `sglang`)
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- `<tag>`: The image tag from the tables below
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Example:
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```
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763104351884.dkr.ecr.us-east-1.amazonaws.com/tensorflow-training:2.4.1-cpu-py37-ubuntu18.04-v1.0
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763104351884.dkr.ecr.us-west-2.amazonaws.com/pytorch-training:2.9.0-gpu-py312-cu130-ubuntu22.04-sagemaker
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```
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---

docs/src/data/images.yml

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@@ -108,62 +108,61 @@ images:
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# Base Containers
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# ===========================================================================
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base:
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- "14.0.0-gpu-py312-cu130-ubuntu22.04-ec2"
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- "13.0.0-gpu-py312-cu130-ubuntu22.04-ec2"
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- "12.9.1-gpu-py312-cu129-ubuntu22.04-ec2"
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- "12.8.0-gpu-py312-cu128-ubuntu22.04-ec2"
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- "12.8.1-gpu-py312-cu128-ubuntu24.04-ec2"
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- "12.8.0-gpu-py312-cu128-ubuntu22.04-ec2"
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# ===========================================================================
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# vLLM Containers
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# ===========================================================================
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vllm:
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- "0.13-gpu-py312"
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- "0.13-gpu-py312-ec2"
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- "0.13.0-gpu-py312-cu129-ubuntu22.04-sagemaker"
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- "0.13.0-gpu-py312-cu129-ubuntu22.04-ec2"
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# ===========================================================================
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# SGLang Containers
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# ===========================================================================
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sglang:
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- "0.5-gpu-py312"
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- "0.5.6-gpu-py312-cu129-ubuntu22.04-sagemaker"
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# ===========================================================================
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# PyTorch Containers
132131
# ===========================================================================
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pytorch-training:
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- "2.9.0-cpu-py312-ubuntu22.04-ec2"
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- "2.9.0-gpu-py312-cu130-ubuntu22.04-ec2"
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- "2.9.0-cpu-py312-ubuntu22.04-sagemaker"
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- "2.9.0-gpu-py312-cu130-ubuntu22.04-sagemaker"
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- "2.8.0-cpu-py312-ubuntu22.04-ec2"
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- "2.8.0-gpu-py312-cu129-ubuntu22.04-ec2"
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- "2.8.0-cpu-py312-ubuntu22.04-sagemaker"
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- "2.9.0-cpu-py312-ubuntu22.04-sagemaker"
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- "2.9.0-gpu-py312-cu130-ubuntu22.04-ec2"
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- "2.9.0-cpu-py312-ubuntu22.04-ec2"
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- "2.8.0-gpu-py312-cu129-ubuntu22.04-sagemaker"
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- "2.7.1-cpu-py312-ubuntu22.04-ec2"
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- "2.7.1-gpu-py312-cu128-ubuntu22.04-ec2"
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- "2.7.1-cpu-py312-ubuntu22.04-sagemaker"
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- "2.8.0-cpu-py312-ubuntu22.04-sagemaker"
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- "2.8.0-gpu-py312-cu129-ubuntu22.04-ec2"
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- "2.8.0-cpu-py312-ubuntu22.04-ec2"
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- "2.7.1-gpu-py312-cu128-ubuntu22.04-sagemaker"
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- "2.6.0-cpu-py312-ubuntu22.04-ec2"
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- "2.6.0-gpu-py312-cu126-ubuntu22.04-ec2"
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- "2.6.0-cpu-py312-ubuntu22.04-sagemaker"
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- "2.7.1-cpu-py312-ubuntu22.04-sagemaker"
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- "2.7.1-gpu-py312-cu128-ubuntu22.04-ec2"
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- "2.7.1-cpu-py312-ubuntu22.04-ec2"
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- "2.6.0-gpu-py312-cu126-ubuntu22.04-sagemaker"
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- "2.6.0-cpu-py312-ubuntu22.04-sagemaker"
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- "2.6.0-gpu-py312-cu126-ubuntu22.04-ec2"
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- "2.6.0-cpu-py312-ubuntu22.04-ec2"
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pytorch-inference:
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- "2.6.0-cpu-py312-ubuntu22.04-ec2"
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- "2.6.0-gpu-py312-cu124-ubuntu22.04-ec2"
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- "2.6.0-cpu-py312-ubuntu22.04-sagemaker"
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- "2.6.0-gpu-py312-cu124-ubuntu22.04-sagemaker"
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- "2.6.0-cpu-py312-ubuntu22.04-sagemaker"
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- "2.6.0-gpu-py312-cu124-ubuntu22.04-ec2"
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- "2.6.0-cpu-py312-ubuntu22.04-ec2"
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# ===========================================================================
158157
# TensorFlow Containers
159158
# ===========================================================================
160159
tensorflow-training:
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- "2.19.0-cpu-py312-ubuntu22.04-sagemaker"
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- "2.19.0-gpu-py312-cu125-ubuntu22.04-sagemaker"
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- "2.19.0-cpu-py312-ubuntu22.04-sagemaker"
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tensorflow-inference:
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- "2.19.0-cpu-py312-ubuntu22.04-sagemaker"
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- "2.19.0-gpu-py312-cu122-ubuntu22.04-sagemaker"
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- "2.19.0-cpu-py312-ubuntu22.04-sagemaker"
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# ===========================================================================
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# ARM64/Graviton Containers
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- "2.7.0-gpu-py312-cu128-ubuntu22.04-ec2"
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pytorch-inference-arm64:
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- "2.6.0-cpu-py312-ubuntu22.04-ec2"
174+
- "2.6.0-gpu-py312-cu124-ubuntu22.04-sagemaker"
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- "2.6.0-gpu-py312-cu124-ubuntu22.04-ec2"
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- "2.6.0-cpu-py312-ubuntu22.04-sagemaker"
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- "2.6.0-cpu-py312-ubuntu22.04-ec2"
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# ===========================================================================
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# Large Model Inference (LMI)
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- "1.13.1-transformers4.26.0-gpu-py39-cu117-ubuntu20.04"
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huggingface-pytorch-inference:
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- "2.6.0-transformers4.49.0-cpu-py312-ubuntu22.04"
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- "2.6.0-transformers4.49.0-gpu-py312-cu124-ubuntu22.04"
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- "2.1.0-transformers4.37.0-cpu-py310-ubuntu22.04"
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- "2.6.0-transformers4.49.0-cpu-py312-ubuntu22.04"
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- "2.1.0-transformers4.37.0-gpu-py310-cu118-ubuntu20.04"
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- "2.1.0-transformers4.37.0-cpu-py310-ubuntu22.04"
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huggingface-tensorflow-training:
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- "2.6.3-transformers4.17.0-gpu-py38-cu112-ubuntu20.04"
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huggingface-tensorflow-inference:
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- "2.11.1-transformers4.26.0-cpu-py39-ubuntu20.04"
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- "2.11.1-transformers4.26.0-gpu-py39-cu112-ubuntu20.04"
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- "2.11.1-transformers4.26.0-cpu-py39-ubuntu20.04"
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# ===========================================================================
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# HuggingFace Neuron Containers

docs/src/tables/autogluon_table.py

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import re
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from constants import TABLE_HEADER
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from utils import render_table
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from utils import build_ecr_url, render_table
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REPO_KEYS = ["autogluon-training", "autogluon-inference"]
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DISPLAY_NAMES = {
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"autogluon-training": "AutoGluon Training",
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"autogluon-inference": "AutoGluon Inference",
2424
}
25-
COLUMNS = ["Framework", "Python", "CUDA", "Accelerator", "Tag"]
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COLUMNS = ["Framework", "Python", "CUDA", "Accelerator", "Platform", "Example URL"]
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def parse_tag(tag: str) -> dict:
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"""Parse AutoGluon tag format: 1.4.0-gpu-py311-cu124-ubuntu22.04"""
30-
result = {"version": "", "accelerator": "", "python": "", "cuda": ""}
30+
result = {"version": "", "accelerator": "", "python": "", "cuda": "-"}
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match = re.match(
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r"^(\d+\.\d+\.\d+)-" # version
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result["version"] = match.group(1)
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result["accelerator"] = match.group(2).upper()
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result["python"] = match.group(3)
43-
result["cuda"] = match.group(4) or ""
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result["cuda"] = match.group(4) or "-"
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4545
return result
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parsed["python"],
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parsed["cuda"],
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parsed["accelerator"],
67-
f"`{tag}`",
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"SageMaker",
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build_ecr_url(repo_key, tag),
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]
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)
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docs/src/tables/base_table.py

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import re
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from constants import TABLE_HEADER
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from utils import render_table
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from utils import build_ecr_url, render_table
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REPO_KEYS = ["base"]
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DISPLAY_NAMES = {"base": "Base"}
22-
COLUMNS = ["Version", "Platform", "Python", "CUDA", "Accelerator", "Tag"]
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COLUMNS = ["Framework", "Python", "CUDA", "Accelerator", "Platform", "Example URL"]
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2525
def parse_tag(tag: str) -> dict:
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3737
result["version"] = match.group(1)
3838
result["accelerator"] = match.group(2).upper()
3939
result["python"] = match.group(3)
40-
result["cuda"] = match.group(4) or ""
40+
result["cuda"] = match.group(4) or "-"
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4242
if tag.endswith("-ec2"):
43-
result["platform"] = "EC2"
43+
result["platform"] = "EC2, ECS, EKS"
4444
elif tag.endswith("-sagemaker"):
4545
result["platform"] = "SageMaker"
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6262
parsed = parse_tag(tag)
6363
rows.append(
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[
65-
parsed["version"],
66-
parsed["platform"],
65+
f"Base {parsed['version']}",
6766
parsed["python"],
6867
parsed["cuda"],
6968
parsed["accelerator"],
70-
f"`{tag}`",
69+
parsed["platform"],
70+
build_ecr_url(repo_key, tag),
7171
]
7272
)
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docs/src/tables/djl_table.py

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import re
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from constants import TABLE_HEADER
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from utils import render_table
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from utils import build_ecr_url, render_table
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2020
REPO_KEYS = ["djl-inference"]
2121
DISPLAY_NAMES = {"djl-inference": "DJL Inference"}
22-
COLUMNS = ["Version", "Engine", "CUDA", "Accelerator", "Tag"]
22+
COLUMNS = ["Framework", "Python", "CUDA", "Engine", "Accelerator", "Platform", "Example URL"]
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2525
def parse_tag(tag: str) -> dict:
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2828
GPU: 0.36.0-lmi18.0.0-cu128 or 0.33.0-tensorrtllm0.21.0-cu128
2929
CPU: 0.36.0-cpu-full
3030
"""
31-
result = {"version": "", "engine": "", "cuda": "", "accelerator": ""}
31+
result = {"version": "", "engine": "", "cuda": "-", "accelerator": "", "python": "-"}
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3333
# GPU with LMI
3434
match = re.match(r"^(\d+\.\d+\.\d+)-lmi([\d.]+)-(cu\d+)$", tag)
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7474
parsed = parse_tag(tag)
7575
rows.append(
7676
[
77-
parsed["version"],
78-
parsed["engine"],
77+
f"DJLServing {parsed['version']}",
78+
parsed["python"],
7979
parsed["cuda"],
80+
parsed["engine"],
8081
parsed["accelerator"],
81-
f"`{tag}`",
82+
"SageMaker",
83+
build_ecr_url(repo_key, tag),
8284
]
8385
)
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