mosaicml-streaming
Streaming lets users create PyTorch compatible datasets that can be streamed from cloud-based object stores
Decision gist · record as of 2026-08-14
Yes, if you train on large datasets in cloud storage and need deterministic, distributed data loading. The active maintenance, low install friction, and PyTorch integration make it a solid choice. However, verify the license terms first (currently marked unclear in metadata), and confirm that the 18 runtime dependencies align with your environment. Not necessary if your data fits locally or if you use a different data pipeline framework.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >=3.10 and torch installed.
- Cloud credentials (AWS, GCS, Azure, OCI) must be configured in your environment for remote access.
- Low friction: pure Python wheel with no compiled dependencies.
License · maintenance · safety
(unclear) — License treatment is unclear—no SPDX identifier or raw license text in metadata. Verify the actual license terms in the repository before adopting in proprietary or restricted-license contexts.
last release 2025-07-15 (395 days) · last repo commit 2026-06-25 · 1,545 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 928,673 downloads/mo, #4,707 on PyPI
Alternatives
Verify before relying
pip install mosaicml-streaming
from streaming import StreamingDataset
from torch.utils.data import DataLoader
dataset = StreamingDataset(local='/tmp/cache', remote='s3://bucket/data', shuffle=True)
loader = DataLoader(dataset, batch_size=32)
for batch in loader:
pass # train- Actual license terms and restrictions (metadata shows 'unclear' treatment)
- Whether all 18 runtime dependencies are truly required or optional/conditional
- Performance characteristics and throughput guarantees for different cloud providers
- Specific dataset mixing proportions and sampling behavior in production scenarios
What it is and what it does
StreamingDataset is a PyTorch data loader designed to efficiently fetch training data from cloud object storage on-demand, rather than downloading entire datasets upfront. It acts as a drop-in replacement for PyTorch's IterableDataset, integrating with AWS S3, Google Cloud Storage, Azure Blob Storage, OCI Object Storage, and S3-compatible services. The package handles data serialization in MDS (Mosaic Data Shard) format, CSV, TSV, or JSONL, with optional compression via zstd, Brotli, or snappy.
The core value proposition is enabling distributed multi-node training without requiring all data to be co-located with compute. It guarantees deterministic sample ordering across any number of GPUs, nodes, or workers—a feature that aids reproducibility and debugging. The package supports seamless mixing of multiple datasets via the Stream abstraction, allowing proportional or absolute sampling strategies. It is built for large-scale model training workflows where data volume and cloud infrastructure costs are primary concerns.
Use it for
- Train vision models on large image datasets stored in S3 or GCS without local disk staging.
- Stream multimodal data (text + images, video + captions) from cloud storage for foundation model pretraining.
- Mix multiple datasets on-the-fly during training with deterministic reproducibility across training runs.
- Distribute training across multiple nodes where each worker streams only the samples it needs, reducing network and storage bottlenecks.
- Convert and persist raw datasets into sharded MDS format for efficient repeated training runs.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you train on large datasets in cloud storage and need deterministic, distributed data loading.
The active maintenance, low install friction, and PyTorch integration make it a solid choice. However, verify the license terms first (currently marked unclear in metadata), and confirm that the 18 runtime dependencies align with your environment. Not necessary if your data fits locally or if you use a different data pipeline framework.
Install
mosaicml-streaming on PyPI
Before you install
Low friction: pure Python wheel with no compiled dependencies. Active maintenance (last commit 2026-06-25, 1545 stars). However, 18 runtime dependencies including torch, torchvision, and transformers mean a substantial environment footprint if not already present.
Requires Python >=3.10 and torch installed. Cloud credentials (AWS, GCS, Azure, OCI) must be configured in your environment for remote access.
License in practice
License treatment is unclear—no SPDX identifier or raw license text in metadata. Verify the actual license terms in the repository before adopting in proprietary or restricted-license contexts.
Quickstart
pip install mosaicml-streaming
from streaming import StreamingDataset
from torch.utils.data import DataLoader
dataset = StreamingDataset(local='/tmp/cache', remote='s3://bucket/data', shuffle=True)
loader = DataLoader(dataset, batch_size=32)
for batch in loader:
pass # trainVerify before relying
- Actual license terms and restrictions (metadata shows 'unclear' treatment)
- Whether all 18 runtime dependencies are truly required or optional/conditional
- Performance characteristics and throughput guarantees for different cloud providers
- Specific dataset mixing proportions and sampling behavior in production scenarios
Package facts
| License | Not declared unclear |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 18 packagesboto3Brotligoogle-cloud-storagematplotlibnumpyparamikopython-snappytorchtorchvisiontqdmtransformersxxhashzstdociazure-storage-blobazure-storage-file-datalakeazure-identitycatalogue |
| Maintenance | Actively maintained 395 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 928,673 / month, #4,707 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Programming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12 |
Evidence: mosaicml_streaming-0.13.0-py3-none-any.whl
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