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 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 filesDataset.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) 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 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.
Release files for marin-zephyr 0.2.22.dev202606200836
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| marin_zephyr-0.2.22.dev202606200836.tar.gz | 85.2 kB | With provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| marin_zephyr-0.2.22.dev202606200836-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 180.0 kB
Release files / marin_zephyr-0.2.22.dev202606200836.tar.gz
| Download URL | marin_zephyr-0.2.22.dev202606200836.tar.gz |
|---|---|
| Size | 85.2 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
775feac1e3b8f0fa0a5570dbcd30c870b783928e715ee2821d726523cc35de2d
|
|
BLAKE2b-256 checksum How to use checksums |
6f253083ff972743bf8bbb23be5d345973b89fc123c571a8ddcabb7f3de1a8a8
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.13
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Jun 20, 2026.
Transparency logRelease files / marin_zephyr-0.2.22.dev202606200836-py3-none-any.whl
| Download URL | marin_zephyr-0.2.22.dev202606200836-py3-none-any.whl |
|---|---|
| Size | 94.8 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
16e43fa56a316a732f97584909e14b1d137fc612814d9a36449a67bb275f9b3d
|
|
BLAKE2b-256 checksum How to use checksums |
3f0b15fb5a1664e3413c5a61df85eced10ca9533f20544450d5619e115fd9f9e
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.13
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Jun 20, 2026.
Transparency logSecurity for marin-zephyr 0.2.22.dev202606200836
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
Full provenance
All 2 files in this release have been uploaded with provenance metadata.
Attestation for marin_zephyr-0.2.22.dev202606200836.tar.gz
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Jun 20, 2026.
Transparency logA note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
