marin-zephyr 0.2.20.dev202606180959

Lightweight dataset library for distributed data processing

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 import Dataset, ZephyrContext, 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 files
• Dataset.from_list(items) - explicit list

Loading Files

• .load_{file,parquet,jsonl,vortex} - load rows from a file

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 to a Parquet file
• .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) via fray.current_client()
• ZephyrContext(client=LocalClient()) — explicit local backend (testing)
• ctx.execute(pipeline) — runs the pipeline; returns a ZephyrExecutionResult(results, counters)

Real Usage

Wikipedia Processing:

from zephyr import Dataset, ZephyrContext, 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 import Dataset, ZephyrContext

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 import Dataset, ZephyrContext

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.