Add k8s code example

.python-version

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+3.13

pyproject.toml

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+[project]
+name = "parallelization-benchmarks"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+authors = [
+    { name = "Egor Marin", email = "me@marinegor.dev" }
+]
+requires-python = ">=3.13"
+dependencies = [
+    "mdanalysis[analysis,parallel]==2.10.0",
+]
+
+[build-system]
+requires = ["uv_build>=0.8.15,<0.9.0"]
+build-backend = "uv_build"
+
+[dependency-groups]
+k8s = [
+    "dask-kubernetes>=2025.7.0",
+]

src/parallelization_benchmarks/__init__.py

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+def hello() -> str:
+    return "Hello from parallelization-benchmarks!"

src/parallelization_benchmarks/k8s.py

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+import MDAnalysis as mda
+from dask.distributed import Client, get_client
+from MDAnalysis.analysis.backends import BackendBase
+from MDAnalysis.analysis.dssp import DSSP
+from dask_kubernetes.operator import KubeCluster
+from dataclasses import dataclass
+from dask.delayed import delayed
+
+from typing import NamedTuple, Literal
+
+
+class Resources(NamedTuple):
+    memory: str
+    cpu: str
+
+    def as_dict(self):
+        return {'memory': self.memory, 'cpu': self.cpu}
+
+@dataclass(frozen=True)
+class Config:
+    """
+    Example:
+    --------
+
+    ```python
+    config = {
+        "name": "dask-cluster-2",
+        "image": "<redacted>",
+        "n_workers": 7,
+        "resources":{"requests": {"memory": "2Gi", "cpu": "800m"},
+                     "limits": {"memory": "4Gi", "cpu": "1"}}
+        }
+    ```
+    """
+
+    name: str
+    image: str
+    n_workers: int
+    resources: dict[Literal['requests', 'limits'], Resources]
+
+    def as_dict(self):
+        resources = {k:v.as_dict() for k, v in self.resources.items()}
+        return {'name': self.name, 'image': self.image, 'n_workers': self.n_workers, 'resources': resources}
+
+
+def start_cluster(config: Config) -> KubeCluster:
+    return KubeCluster(**config.as_dict())
+
+def get_client_for(cluster: KubeCluster) -> Client:
+    return Client(cluster)
+
+
+class DistributedBackend(BackendBase):
+    def __init__(self, n_workers):
+        super().__init__(n_workers)
+
+    def assign_client(self, client):
+        self.client = client
+
+    def apply(self, func, computations):
+        return self.client.compute([delayed(func)(c) for c in computations], sync=True)
+
+
+if __name__ == '__main__':
+    u = mda.Universe("YiiP_lipids.gro.gz", "YiiP_lipids.xtc")
+    backend = DistributedBackend(n_workers=5)
+    config = ...
+    cluster = start_cluster(config)
+    client = get_client(cluster)
+    backend.assign_client(client)
+
+    # test computation
+    tasks = [delayed(lambda x: x + 1)(i) for i in range(11)]
+    print(f"{client.compute(tasks, sync=True)=}")
+
+    # actual MDAnalysis computation
+    print(DSSP(u).run(backend=backend, unsupported_backend=True).results)