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Remove deprecated examples/llm_autodeploy (#1797)
Remove the AutoQuant + TensorRT-LLM AutoDeploy example, deprecated in 0.45, after the migration period. Record the removal under the 0.46 Backward Breaking Changes section. Users should use TensorRT-LLM's AutoDeploy directly together with ModelOpt PTQ in examples/llm_ptq. ### What does this PR do? Type of change: ? <!-- Use one of the following: Bug fix, new feature, new example, new tests, documentation. --> <!-- Details about the change. --> ### Usage ```python # Add a code snippet demonstrating how to use this ``` ### Testing <!-- Mention how have you tested your change if applicable. --> ### Before your PR is "*Ready for review*" Make sure you read and follow [Contributor guidelines](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md) and your commits are signed (`git commit -s -S`). Make sure you read and follow the [Security Best Practices](https://github.com/NVIDIA/Model-Optimizer/blob/main/SECURITY.md#security-coding-practices-for-contributors) (e.g. avoiding hardcoded `trust_remote_code=True`, `torch.load(..., weights_only=False)`, `pickle`, etc.). - Is this change backward compatible?: ✅ / ❌ / N/A <!--- If ❌, explain why. --> - If you copied code from any other sources or added a new PIP dependency, did you follow guidance in `CONTRIBUTING.md`: ✅ / ❌ / N/A <!--- Mandatory --> - Did you write any new necessary tests?: ✅ / ❌ / N/A <!--- Mandatory for new features or examples. --> - Did you update [Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?: ✅ / ❌ / N/A <!--- Only for new features, API changes, critical bug fixes or backward incompatible changes. --> - Did you get Claude approval on this PR?: ✅ / ❌ / N/A <!--- Run `/claude review`. NVIDIA org members can self-trigger for complex changes; orthogonal to CodeRabbit. --> ### Additional Information <!-- E.g. related issue. --> <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * **Documentation** * Updated AutoDeploy guidance to a workflow: quantize with ModelOpt PTQ (via `llm_ptq`) to produce a unified Hugging Face checkpoint, then deploy with TensorRT-LLM AutoDeploy. * Revised Hopper notes to recommend using FP8 (and refreshed related optimization guidance). * **Breaking Changes** * Removed the deprecated `examples/llm_autodeploy` example and documented the new recommended approach. * **Chores** * Dropped obsolete example docs, scripts, and coverage; adjusted example test workflow to exclude `llm_autodeploy`; updated ownership mapping for the removed example path. <!-- end of auto-generated comment: release notes by coderabbit.ai --> Signed-off-by: Fridah-nv <201670829+Fridah-nv@users.noreply.github.com> Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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@@ -48,7 +48,7 @@ jobs:
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pip_install_extras: "[hf,dev-test]"
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runner: ${{ startsWith(github.ref, 'refs/heads/pull-request/') && 'linux-amd64-gpu-rtxpro6000-latest-1' || 'linux-amd64-gpu-rtxpro6000-latest-2' }}
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##### TensorRT-LLM Example Tests (pr/non-pr split: non-pr runs extra autodeploy+eval examples) #####
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##### TensorRT-LLM Example Tests (pr/non-pr split: non-pr runs extra eval examples) #####
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trtllm-pr:
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needs: [pr-gate]
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if: startsWith(github.ref, 'refs/heads/pull-request/') && needs.pr-gate.outputs.any_changed == 'true'
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@@ -69,7 +69,7 @@ jobs:
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strategy:
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fail-fast: false
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matrix:
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example: [llm_autodeploy, llm_eval, llm_ptq]
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example: [llm_eval, llm_ptq]
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uses: ./.github/workflows/_example_tests_runner.yml
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secrets: inherit
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with:
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