hyperai/tvm-cn: a Chinese translation of the TVM docs, pinned to v0.10.0

The gap this repository fills, and the version it fills it for

TVM is an open source deep learning compiler that targets CPUs, GPUs, ARM and other backends. Its documentation is written in English. The README states the motivation plainly: Chinese-language material about TVM is scattered, which makes systematic study hard, so the project exists to produce a coherent Chinese translation of the docs rather than a collection of blog posts. The audience is therefore narrow and specific. It is for a Chinese-speaking engineer who is trying to learn TVM's concepts in their first language, and for a translator who wants to fix an inaccurate or ambiguous passage. It is not for someone looking for a packaged TVM build, a Python wheel, or a fork of the compiler. Nothing in the repository description or README suggests it ships compiler code. The README also pins the scope: the current Chinese documentation is based on TVM v0.10.0, and the text says the Chinese docs will keep being adjusted as the official version and documentation change. That sentence is the whole risk profile of the project in one line, because the base is a fixed release and the adjustment is a promise about future work, not a mechanism.

Docusaurus as the delivery layer, and TypeScript as the only real code

The repository's primary language is listed as TypeScript, which can read as surprising for a translation project until you look at the structure. The README says the project is built on Docusaurus, and the contribution instructions reference docusaurus.config.ts, so the TypeScript is site configuration and tooling rather than application logic. The content itself is Markdown. That division matters when you evaluate the project: there is no translation engine, no glossary enforcement, no automated diff against upstream, and no test that fails when a page drifts from the English source. The data flow is manual. A translator reads an upstream page, writes or edits a Markdown file, adjusts image references, and opens a pull request. The site then renders that Markdown. If you are expecting tooling that keeps the Chinese text synchronised with the English text, this repository does not describe any. The version label lives in docusaurus.config.ts, and the README treats editing that label as a deliberate human step, which confirms the pipeline is human-driven end to end.

Getting the site running locally: pnpm, then pnpm start

The README is short on prerequisites and precise where it matters. Local development requires Node.js and pnpm, with a link to the pnpm installation page. After that the commands are two lines: pnpm install, then pnpm start. That is the whole local loop for viewing the documentation site, and it is the same loop a translator uses to preview edits before opening a pull request. The README points contributors at the Docusaurus documentation for Markdown formatting requirements, which is where you go if you need to know how admonitions, code blocks or sidebars are expressed. There is also a deprecated path: sphinx-build -b html docs build. The README labels it Deprecated in the heading itself, so treat it as historical rather than as a supported build. If you only want to read the docs, you do not need any of this. The homepage at tvm.hyper.ai serves the rendered site, and the README's first link points there.

The image migration convention is the most opinionated rule in the repo

Translation projects usually break on assets, and this one has a concrete rule for them. The README gives an example where a Markdown image points at a raw GitHub URL on the tvmai.github.io host, with the full path including the relay/dataflow.png segment. The instruction is to download that image and store it under static/img/docs/ followed by the original host and full path, so the example lands at static/img/docs/tvmai/tvmai.github.io/main/images/relay/dataflow.png. The Markdown reference is then rewritten to an absolute site path beginning with /img/docs/ and the same tail. The effect is that external images stop being hotlinked and become part of the repository. Two consequences follow. First, the mirror path encodes the source host and directory structure, so if upstream reorganises its image tree, the local path no longer tells you where the image came from. Second, images are now versioned alongside the prose, which means a translator updating a diagram has to touch both the file and the reference. This is a reasonable trade for a documentation site that should not depend on a third-party host staying up, but it is manual work that a contributor can get wrong, and the README does not describe any check that catches a missed image.

Versioning is a manual snapshot, not a tracking mechanism

The README documents the upgrade procedure with a worked example. If the current version is 0.12.0 and you want to move to 0.13.0, you first preserve the current version by running pnpm run docusaurus docs:version 0.12.0. Then you edit docusaurus.config.ts and set versions.current.label to the new version, 0.13.0. Read that sequence carefully, because it describes how to cut a versioned snapshot of the site, not how to translate a new upstream release. Nothing in the instructions pulls the English source, compares it to the Chinese text, or lists pages that changed between releases. The actual translation work for a new TVM release is invisible in the README, and the version bump is a label change plus a snapshot command. For a reader, the practical implication is that the version number on the site tells you which snapshot you are browsing, while the accuracy of any given page depends on when a human last compared it to upstream. Those are two different dates, and only one of them is displayed.

Where this is the wrong tool

The clearest failure mode is version mismatch between what you read and what you run. The README states the Chinese documentation is based on TVM v0.10.0. If you have installed a later TVM, API names, function signatures and tutorial code in the Chinese pages may no longer match your environment, and the repository does not claim otherwise. A second limitation is coverage. The README describes the project as a translation effort and invites issues and pull requests for inaccurate or ambiguous passages, which implies the corpus is a work in progress rather than a complete mirror of the English documentation. You should not assume a given English page has a Chinese counterpart. A third issue is that this is documentation only. If your problem is a compiler bug, a missing operator, or a build failure on your target hardware, nothing here will help; you need the upstream TVM repository. Finally, a translated error message or a translated API description can be harder to search for than the English original, because community answers and issue threads are overwhelmingly in English. Reading the Chinese page to understand a concept and then searching in English for the error is a normal workflow, and it is worth knowing that going in.

The alternative: upstream TVM docs, and what actually differs

The direct alternative is the official Apache TVM documentation in English. The difference is not just language. Upstream tracks the current release, so the page you read matches the code you installed, and the surrounding ecosystem of issues, pull requests and forum threads uses the same vocabulary. What hyperai/tvm-cn offers instead is a Chinese-language entry point with a settled base version, which is genuinely useful when you are learning concepts such as Relay dataflow or the compiler's scheduling model and you want to read long explanations without translating in your head. The trade is explicit: you get comprehension speed in Chinese, you give up the guarantee that the text matches your installed version. A second alternative is to read the upstream English docs directly and treat the Chinese site as a glossary when terminology gets dense. That combination costs you nothing and avoids the version trap entirely. The choice is between a fixed snapshot in a more comfortable language and a moving target in English.

Licence, maintenance and what to check before you commit

The repository is Apache-2.0, the same licence family as Apache TVM itself. That is permissive and matches the upstream project, which reduces friction if you want to reuse translated passages inside your own internal documentation. It is not legal advice, and the licence covers the repository's contents; the underlying TVM documentation may carry its own notices, so check the upstream repository if you plan to redistribute text at scale. On maintenance, the material supports only a limited reading. The last push is dated 2026-05-20 and no releases were retrieved, so there is no published version history to reason about. The README's contribution section describes an ongoing process with issue and PR links, and the versioning instructions show the maintainers know how to snapshot a release, but neither tells you how quickly a new upstream release gets translated. Before you depend on this site, do two things. Open the page you need next to the upstream English page for the same topic and compare the API names and code samples. Then confirm the TVM version you actually have installed, because the README's stated base of v0.10.0 is the boundary that decides whether the Chinese text is a shortcut or a source of confusion.

Editorial conclusion

Adopt this if you read Chinese faster than English and you are working against TVM v0.10.0 or nearby, or if you want to contribute translations through the issue and PR links in the README. Do not adopt it as your reference for a newer TVM release, because the README states the localisation is based on v0.10.0 and the versioning step it documents is a manual rename of a label in docusaurus.config.ts, not a tracking mechanism. Before relying on any page, open the corresponding upstream English page and diff the API names, then check whether the claim you are about to build on still exists in the release you actually installed.

Official sources

1. hyperai/tvm-cn on GitHub
2. Issues
3. License: Apache-2.0
4. Project website
5. README