Release

Release slime

Prepare a release without publishing, tagging, pushing images, or changing the dependency baseline unless the user explicitly requests those external or scope-expanding actions.

Establish the release baseline

• Inspect the worktree and preserve unrelated user changes.
• Compare the previous release tag and release commit to identify the current repository conventions.
• Confirm the requested slime version and the current stable SGLang version from docker/Dockerfile and docker/README.md.
• Do not pull in an unmerged SGLang/Docker upgrade merely because a newer branch exists. Treat that as a separate decision.

Update release versions

• Set the package version in setup.py.
• Set the documentation version in docs/conf.py.
• Bump docker/version.txt to a unique image version following its existing dated naming convention.
• Search the repository for the old slime version and review every remaining occurrence instead of replacing unrelated dependency versions.

Freeze the stable Docker patches

• Treat docker/patch/latest/ as the patch stack for the current Docker base.
• Snapshot it exactly into docker/patch/<stable-sglang-version>/. At release time, the two directories must contain the same patch filenames and bytes.
• Preserve older SGLang patch directories. Remove an obsolete file from the current stable snapshot only after confirming it is absent from latest.
• Verify the stable patch stack applies in Dockerfile order to clean checkouts of the pinned SGLang and Megatron commits. Do not validate against a dirty developer checkout.

Audit Docker and conda together

Compare build_conda.sh with docker/Dockerfile, docker/justfile, and the stable patch snapshot. Check at least:

• SGLang version, commit, CUDA variant, sglang-kernel, and sgl-deep-gemm;
• Megatron, torch-memory-saver, FlashQLA, and other shared source pins;
• PATCH_VERSION, patch filenames, application order, optional patches, and failure-on-conflict behavior;
• PyTorch, torchvision, torchaudio, CUDA Python, Transformer Engine, router, NumPy, and SciPy pins;
• whether dependency resolution can undo a compatibility pin later in the script; reassert and validate such pins after the resolving install;
• intentional differences such as conda being CUDA-12-only, omitting FA3, or not rebuilding feature-specific DeepGEMM/DeepEP forks. Keep a difference only when the release CI scope makes it intentional.

Prefer direct loops over duplicated patch-application blocks while preserving required-versus-optional semantics and useful failure messages.

Validate before handoff

Run the checks that are available locally:

• python .claude/skills/release/scripts/check_release.py --repo . --expected-version <version>;
• python setup.py --version;
• bash -n build_conda.sh;
• byte-for-byte comparison of docker/patch/latest/ and the stable snapshot;
• patch parsing plus clean-checkout application against the pinned upstream commits;
• git diff --check and a final review of the complete release diff.

Run the release conda CI and relevant Docker builds when the environment and requested scope permit. The conda workflow is selected by a PR title containing [release]. Explicitly report any full build or GPU validation that was not run.