This release is a pre-release and may not be stable for production use.
Zephyr
Simple data processing library for Marin pipelines. Build lazy dataset pipelines that run on Iris jobs or a local backend.
Quick Start
from zephyr.context import ZephyrContext
from zephyr.dataset import Dataset
from zephyr.readers import load_jsonl
# Read, transform, write
ctx = ZephyrContext(max_workers=100)
pipeline = (
Dataset.from_files("gs://input/", "**/*.jsonl.gz")
.flat_map(load_jsonl)
.filter(lambda x: x["score"] > 0.5)
.map(lambda x: transform_record(x))
.write_jsonl("gs://output/data-{shard:05d}-of-{total:05d}.jsonl.gz")
)
ctx.execute(pipeline)
Key Patterns
Dataset Creation:
Dataset.from_files(path, pattern)- glob filesDataset.from_list(items)- explicit list
Loading Files
.load_{file,parquet,jsonl,vortex}- load rows from a file
Pass include_file_paths=True to add __file_path to each row (or set file_path_column to use another name). Selecting only that column returns the source path for each matching row. Parquet path-only reads skip source data columns unless a filter needs them.
Transformations:
.map(fn)- transform each item.flat_map(fn)- expand items (e.g.,load_jsonl).filter(fn)- filter items by function or expression.select(columna, columnb)- select out the given columns.window(n)- group into batches.reshard(n)- redistribute across n shards
Output:
.write_jsonl(pattern)- write JSONL (gzip if.gz).write_parquet(pattern, schema)- write zstd Parquet with page indexes and at most 256 rows per data page.write_vortex(pattern)- write to a Vortex file
Execution (ZephyrContext):
ZephyrContext(max_workers=N)— auto-detects the backend (Iris inside an Iris job, local otherwise) viafray.current_client()ZephyrContext(client=LocalClient())— explicit local backend (testing)ctx.execute(pipeline)— runs the pipeline; returns aZephyrExecutionResult(results, counters)
Real Usage
Wikipedia Processing:
from zephyr.context import ZephyrContext
from zephyr.dataset import Dataset
from zephyr.readers import load_jsonl
ctx = ZephyrContext(max_workers=100)
pipeline = (
Dataset.from_list(files)
.load_jsonl()
.map(lambda row: process_record(row, config))
.filter(lambda x: x is not None)
.write_jsonl(f"{output}/data-{{shard:05d}}-of-{{total:05d}}.jsonl.gz")
)
ctx.execute(pipeline)
Dataset Sampling:
from zephyr.context import ZephyrContext
from zephyr.dataset import Dataset
ctx = ZephyrContext(max_workers=1000)
pipeline = (
Dataset.from_files(input_path, "**/*.jsonl.gz")
.map(lambda path: sample_file(path, weights))
.write_jsonl(f"{output}/sampled-{{shard:05d}}.jsonl.gz")
)
ctx.execute(pipeline)
Parallel Downloads:
from zephyr.context import ZephyrContext
from zephyr.dataset import Dataset
tasks = [(config, fs, src, dst) for src, dst in file_pairs]
ctx = ZephyrContext(max_workers=32)
pipeline = Dataset.from_list(tasks).map(lambda t: download(*t))
ctx.execute(pipeline)
Installation
# From Marin monorepo
uv sync
# Standalone
cd lib/zephyr
uv pip install -e .
Running Tests
Zephyr tests run against multiple execution backends to ensure correctness across different environments.
All Tests on Both Backends (Default)
uv run pytest lib/zephyr/tests
# Runs all tests on both Local and Iris backends
# Local Iris cluster is started automatically via ClusterManager
Run Specific Backend Only
uv run pytest lib/zephyr/tests -k "local"
uv run pytest lib/zephyr/tests -k "iris"
The Iris cluster is started once per test session and reused across all tests for efficiency.
Design
Zephyr consolidates ad-hoc distributed and Hugging Face dataset processing patterns in Marin into a simple abstraction.
Key Features:
- Lazy evaluation with operation fusion
- Disk-based inter-stage data flow for low memory footprint
- Chunk-by-chunk streaming to minimize memory pressure
- Distributed execution with bounded parallelism (Iris/local backends)
- Automatic chunking to prevent large object overhead
- fsspec integration (GCS, S3, local)
- Type-safe operation chaining
See AGENTS.md for execution internals and source layout.
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