Streaming Video Encoding Guide

1. Overview

Streaming video encoding eliminates the traditional PNG round-trip during video dataset recording. Instead of:

1. Capture frame -> write PNG to disk -> (at episode end) read PNG's -> encode to MP4 -> delete PNG's

Frames can be encoded in real-time during capture:

1. Capture frame -> queue to encoder thread -> encode to MP4 directly

This makes save_episode() near-instant (the video is already encoded by the time the episode ends) and removes the blocking wait that previously occurred between episodes, especially with multiple cameras in long episodes.

2. Tuning Parameters

3. Performance Considerations

Streaming encoding means the CPU is encoding video during the capture loop, not after. This creates a CPU budget that must be shared between:

• Control loop (reading cameras, control the robot, writing non-video data)
• Encoder threads (one pool per camera)
• Rerun visualization (if enabled)
• OS and other processes

Resolution & Number of Cameras Impact

encoder_threads Tuning

This parameter controls how many threads each encoder instance uses internally:

• Higher values (e.g., 4-5): Faster encoding, but uses more CPU cores per camera. Good for high-end systems with many cores.
• Lower values (e.g., 1-2): Less CPU per camera, freeing cores for capture and visualization. Good for low-res images and capable CPUs.
• None (default): Lets the codec decide. Information available in the codec logs.

Backpressure and Frame Dropping

Each camera has a bounded queue (encoder_queue_maxsize, default 60 frames). When the encoder can't keep up:

1. The queue fills up (consuming RAM)
2. New frames are dropped (not blocked) — the capture loop continues uninterrupted
3. A warning is logged: "Encoder queue full for {camera}, dropped N frame(s)"
4. At episode end, total dropped frames per camera are reported

Symptoms of Encoder Falling Behind

• System feels laggy and freezes: all CPUs are at 100%
• Dropped frame warnings in the log or lower frames/FPS than expected in the recorded dataset
• Choppy robot movement: If CPU is severely overloaded, even the capture loop may be affected
• Accumulated rerun lag: Visualization falls behind real-time

4. Hardware-Accelerated Encoding

When to Use

Use HW encoding when:

• CPU is the bottleneck (dropped frames, choppy robot, rerun lag)
• You have compatible hardware (GPU or dedicated encoder)
• You're recording at high throughput (high resolution or with many cameras)

Choosing a Codec

Available HW Encoders

[!NOTE] In order to use the HW accelerated encoders you might need to upgrade your GPU drivers.

[!NOTE] libsvtav1 is the default because it provides the best training performance; other vcodecs can reduce CPU usage and be faster, but they typically produce larger files and may affect training time.

5. Troubleshooting

6. Recommended Configurations

These estimates are conservative; we recommend testing them on your setup—start with a low load and increase it gradually.

High-End Systems: modern 12+ cores (24+ threads)

A throughput between ~250-500M px/sec should be comfortable in CPU. For even better results try HW encoding if available.

# 3camsx 1280x720x3 @30fps: Defaults work well. Optionally increase encoder parallelism.
# 2camsx 1920x1080x3 @30fps: Defaults work well. Optionally increase encoder parallelism.
lerobot-record --dataset.encoder_threads=5 ...

# 3camsx 1920x1080x3 @30fps: Might require some tuning.

Mid-Range Systems: modern 8+ cores (16+ threads) or Apple Silicon

A throughput between ~80-300M px/sec should be possible in CPU.

# 3camsx 640x480x3 @30fps: Defaults work well. Optionally decrease encoder parallelism.
# 2camsx 1280x720x3 @30fps: Defaults work well. Optionally decrease encoder parallelism.
lerobot-record --dataset.encoder_threads=2 ...

# 2camsx 1920x1080x3 @30fps: Might require some tuning.

Low-Resource Systems: modern 4+ cores (8+ threads) or Raspberry Pi 5

On very constrained systems, streaming encoding may compete too heavily with the capture loop. Disabling it falls back to the PNG-based approach where encoding happens between episodes (blocking, but doesn't interfere with capture). Alternatively, record at a lower throughput to reduce both capture and encoding load. Consider also changing codec to h264 and using batch encoding.

# 2camsx 640x480x3 @30fps: Requires some tuning.

# Use H.264, disable streaming, consider batching encoding
lerobot-record --dataset.vcodec=h264 --dataset.streaming_encoding=false ...

7. Closing note

Performance ultimately depends on your exact setup — frames-per-second, resolution, CPU cores and load, available memory, episode length, and the encoder you choose. Always test with your target workload, be mindful about your CPU & system capabilities and tune encoder_threads, encoder_queue_maxsize, and vcodec reasonably. That said, a common practical configuration (for many applications) is three cameras at 640×480x3 @30fps; this usually runs fine with the default streaming video encoding settings in modern systems. Always verify your recorded dataset is healthy by comparing the video duration to the CLI episode duration and confirming the row count equals FPS × CLI duration.