chore: import upstream snapshot with attribution

docs/snippets/all/tutorials/getting_started.py

@@ -0,0 +1,104 @@
+"""Getting Started workflow: Catalog SDK regions (Python only).
+
+The Log step lives in `tutorials/getting_started_log` because it uses only the
+Logging SDK and is therefore available in Python, Rust, and C++.
+"""
+
+import math
+import os
+import tempfile
+from pathlib import Path
+
+import torch.multiprocessing
+
+import rerun as rr
+
+# Rerun's tokio runtime is not fork-safe; DataLoader workers must use `spawn`.
+torch.multiprocessing.set_start_method("spawn", force=True)
+
+# Run from a fresh temp dir so the .rrd files this snippet writes don't
+# collide with other snippets executing in parallel from the same cwd.
+os.chdir(tempfile.mkdtemp())
+
+# Materialize the .rrd that the catalog regions below register against.
+# Same code as the Log step's three-language snippet, repeated here so this
+# file runs end-to-end.
+with rr.RecordingStream(
+    "rerun_example_getting_started", recording_id="run-1"
+) as _rec:
+    _rec.save("run-1.rrd")
+    for _t in range(10):
+        _rec.set_time("step", sequence=_t)
+        _rec.log("/arm/shoulder", rr.Scalars(math.sin(_t * 0.5)))
+        _rec.log("/arm/elbow", rr.Scalars(math.cos(_t * 0.5)))
+
+# Start an in-process catalog server on a random port so this snippet runs
+# end-to-end. In a real workflow you'd run `rerun server` in a separate
+# terminal, which is what the docs show.
+_server = rr.server.Server()
+server_url = _server.url()
+
+
+# region: setup
+# `server_url` is the catalog URL — defaults to "rerun+http://127.0.0.1:51234"
+# when running `rerun server` locally.
+client = rr.catalog.CatalogClient(server_url)
+# endregion: setup
+
+
+# region: ingest
+dataset = client.create_dataset("demo", exist_ok=True)
+dataset.register([Path("run-1.rrd").absolute().as_uri()]).wait()
+# endregion: ingest
+
+
+# region: annotate
+with rr.RecordingStream(
+    "rerun_example_getting_started", recording_id="run-1"
+) as ann:
+    ann.save("run-1-properties.rrd")
+    ann.send_property(
+        "episode", rr.AnyValues(success=True, task="pick_and_place")
+    )
+
+dataset.register(
+    [Path("run-1-properties.rrd").absolute().as_uri()], layer_name="properties"
+).wait()
+# endregion: annotate
+
+
+# region: query
+df = dataset.filter_contents(["/arm/**"]).reader(index="step")
+print(
+    df.select(
+        "rerun_segment_id",
+        "/arm/shoulder:Scalars:scalars",
+        "/arm/elbow:Scalars:scalars",
+    )
+)
+# endregion: query
+
+# region: train
+from torch.utils.data import DataLoader
+
+from rerun.experimental.dataloader import (
+    DataSource,
+    Field,
+    NumericDecoder,
+    RerunIterableDataset,
+)
+
+ds = RerunIterableDataset(
+    source=DataSource(dataset=dataset),
+    index="step",
+    fields={
+        "shoulder": Field(
+            "/arm/shoulder:Scalars:scalars", decode=NumericDecoder()
+        ),
+        "elbow": Field("/arm/elbow:Scalars:scalars", decode=NumericDecoder()),
+    },
+)
+
+for batch in DataLoader(ds, batch_size=4):
+    print(batch)
+# endregion: train