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+# Kernel requirements
+
+Kernels on the Hub must fulfill the requirements outlined on this page.
+You can use [kernel-builder](https://github.com/huggingface/kernel-builder/)
+to build conforming kernels.
+
+## Directory layout
+
+A kernel repository on the Hub must contain a `build` directory. This
+directory contains build variants of a kernel in the form of directories
+following the template
+`<framework><version>-cxx<abiver>-<cu><cudaver>-<arch>-<os>`.
+For example `build/torch26-cxx98-cu118-x86_64-linux`. The currently
+recommended build variants are:
+
+- `torch25-cxx11-cu118-x86_64-linux`
+- `torch25-cxx11-cu121-x86_64-linux`
+- `torch25-cxx11-cu124-x86_64-linux`
+- `torch25-cxx98-cu118-x86_64-linux`
+- `torch25-cxx98-cu121-x86_64-linux`
+- `torch25-cxx98-cu124-x86_64-linux`
+- `torch26-cxx11-cu118-x86_64-linux`
+- `torch26-cxx11-cu124-x86_64-linux`
+- `torch26-cxx11-cu126-x86_64-linux`
+- `torch26-cxx98-cu118-x86_64-linux`
+- `torch26-cxx98-cu124-x86_64-linux`
+- `torch26-cxx98-cu126-x86_64-linux`
+
+This list will be updated as new PyTorch versions are released. Each
+variant directory should contain a single directory with the same name
+as the repository (replacing `-` by `_`). For instance, kernels in the
+`kernels-community/activation` repository have a directories like
+`build/<variant>/activation`. This directory
+must be a Python package with an `__init__.py` file.
+
+## Native Python module
+
+Kernels will typically contain a native Python module with precompiled
+compute kernels and bindings. This module must fulfill the following
+requirements:
+
+- Use [ABI3/Limited API](https://docs.python.org/3/c-api/stable.html#stable-application-binary-interface)
+  for compatibility with Python 3.9 and later.
+- Compatible with glibc 2.27 or later. This means that no symbols
+  from later versions must be used. To archive this, the module should
+  be built against this glibc version. **Warning:** libgcc must also be
+  built against glibc 2.27 to avoid leaking symbols.
+- No dynamic linkage against libstdc++/libc++. Linkage for C++ symbols
+  must be static.
+- No dynamic library dependencies outside Torch or CUDA libraries
+  installed as dependencies of Torch.
+
+(These requirements will be updated as new PyTorch versions are released.)
+
+## Torch extension
+
+Torch native extension functions must be [registered](https://pytorch.org/tutorials/advanced/cpp_custom_ops.html#cpp-custom-ops-tutorial)
+in `torch.ops.<namespace>`. Since we allow loading of multiple versions of
+a module in the same Python process, `namespace` must be unique for each
+version of a kernel. Failing to do so will create clashes when different
+versions of the same kernel are loaded. Two suggested ways of doing this
+are:
+
+- Appending a truncated SHA-1 hash of the git commit that the kernel was
+  built from to the name of the extension.
+- Appending random material to the name of the extension.
+
+**Note:** we recommend against appending a version number or git tag.
+Version numbers are typically not bumped on each commit, so users
+might use two different commits that happen to have the same version
+number. Git tags are not stable, so they do not provide a good way
+of guaranteeing uniqueness of the namespace.
+
+## Python requirements
+
+- Python code must be compatible with Python 3.9 and later.
+- All Python code imports from the kernel itself must be relative. So,
+  for instance if in the example kernel `example`,
+  `module_b` needs a function from `module_a`, import as:
+
+  ```python
+  from .module_a import foo
+  ```
+
+  **Never use:**
+
+  ```python
+  # DO NOT DO THIS!
+
+  from example.module_a import foo
+  ```
+
+  The latter would import from the module `example` that is in Python's
+  global module dict. However, since we allow loading multiple versions
+  of a module, we uniquely name the module.
+
+- Only modules from the Python standard library, Torch, or the kernel itself
+  can be imported.