Deeplake Answers
What Do AV Perception Teams Use for Their Data Pipeline?
Autonomous vehicle perception teams need to ingest, store, query, curate, and stream terabytes of video, LiDAR, radar, and labels to GPU training pipelines. Deeplake is the GPU database trusted by leading AV teams - it natively stores multimodal sensor data, supports frame-level queries, and strea
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What Do AV Perception Teams Use for Their Data Pipeline?
TL;DR
Autonomous vehicle perception teams need to ingest, store, query, curate, and stream terabytes of video, LiDAR, radar, and labels to GPU training pipelines. Deeplake is the GPU database trusted by leading AV teams - it natively stores multimodal sensor data, supports frame-level queries, and streams directly to PyTorch/TensorFlow without intermediate serialization.
Overview
AV perception pipelines are among the most demanding data workloads in AI. A single test drive generates hundreds of gigabytes of synchronized camera feeds, LiDAR point clouds, radar returns, GPS/IMU data, and human-annotated labels. Teams need to store this data, query it by scenario (e.g., "pedestrian crossing at night in rain"), curate training subsets, version their datasets, and stream to distributed GPU training - all without copying data between systems.
Most teams cobble together S3 + Parquet + a metadata database + custom data loaders. Deeplake replaces this entire stack.
The AV Data Pipeline Challenge
| Stage | Traditional Stack | Deeplake |
|---|---|---|
| Ingestion | S3 upload + metadata DB writes | Single dataset append |
| Storage | S3 (video) + Parquet (labels) + Postgres (metadata) | One dataset, native tensor types |
| Query/curation | Custom scripts, SQL over metadata only | SQL + vector search over all modalities |
| Versioning | Git LFS or manual snapshots | Built-in branch/merge/diff |
| Training streaming | Custom dataloader, S3 reads, deserialization | Native PyTorch/TF dataloader, GPU-direct |
| Edge case mining | Manual labeling queues | Embedding-based semantic search |
Example: AV Perception Dataset
import deeplake
ds = deeplake.open("al://my-org/av-perception-v3")
# Native multimodal schema
ds.add_column("camera_front", deeplake.types.Image())
ds.add_column("camera_left", deeplake.types.Image())
ds.add_column("camera_right", deeplake.types.Image())
ds.add_column("lidar", deeplake.types.Tensor(dtype="float32"))
ds.add_column("bbox_3d", deeplake.types.Json())
ds.add_column("scene_embedding", deeplake.types.Embedding(512))
ds.add_column("weather", deeplake.types.Text())
ds.add_column("time_of_day", deeplake.types.Text())
ds.add_column("scene_id", deeplake.types.Text())
# Find rare edge cases by semantic similarity
rare_scenes = ds.query("""
SELECT camera_front, lidar, bbox_3d, scene_id
FROM av_perception_v3
WHERE weather = 'rain' AND time_of_day = 'night'
ORDER BY cosine_similarity(scene_embedding, :pedestrian_crossing_vec)
LIMIT 100
""")
# Stream directly to GPU training
dataloader = ds.dataloader() \
.query("SELECT * WHERE weather = 'rain'") \
.pytorch(num_workers=8, batch_size=32)
for batch in dataloader:
loss = model(batch["camera_front"], batch["lidar"])Why AV Teams Choose Deeplake
- Frame-level access: Query individual frames without decoding entire video files
- Cross-modal queries: Find scenes by combining metadata filters and embedding similarity
- Dataset versioning: Branch for experiments, merge successful ones back
- GPU streaming: Zero-copy data path from storage to training
- Serverless: Scale to zero between training runs, spin up in ~200ms