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SLEAP v1.6.0

06 Feb 18:30
@talmo talmo
v1.6.0
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What's New in SLEAP 1.6

SLEAP 1.6 is a major update with new backbone architectures, a redesigned training and inference experience, automated label quality control, ONNX/TensorRT export for faster deployment, a unified CLI, and MANY bug fixes. This release spans 70+ PRs since v1.5.2.

Quick start:

uv tool install --python 3.13 "sleap[nn]==1.6.0" --torch-backend auto

See below for more detailed installation instructions.

Major Changes

New Backbone Architectures in Training Dialog (#2579)

SLEAP 1.6 brings ConvNeXt and Swin Transformer backbone support to the GUI training dialog, alongside UNet:

  • ConvNeXt -- Modern convolutional architecture in tiny/small/base/large variants (28M-198M parameters) with optional ImageNet pretrained weights for faster convergence.
  • Swin Transformer (SwinT) -- Transformer-based backbone in tiny/small/base variants (28M-88M parameters) with optional ImageNet pretrained weights.

Select these from the training dialog's new backbone selector dropdown. See the sleap-nn model documentation for details.

Unified sleap CLI

SLEAP now has a single sleap command as the primary entry point. Running sleap launches the GUI, and subcommands provide access to all tools:

sleap              # Launch the GUI
sleap doctor       # System diagnostics and troubleshooting
sleap export-model # Export models to ONNX/TensorRT

All sleap-io and sleap-nn CLI commands are now integrated as sleap subcommands (#2524, #2541, #2559, #2587, #2595, #2597):

Command Description
sleap doctor System diagnostics and troubleshooting
sleap show Display labels file summary
sleap convert Convert between label formats
sleap split Split labels into train/val/test
sleap unsplit Recombine split labels
sleap merge Merge multiple labels files
sleap render Render pose videos
sleap fix Fix/repair labels files
sleap embed / sleap unembed Manage embedded video data
sleap trim Trim labels to subset
sleap reencode Re-encode embedded videos
sleap transform Coordinate-aware video transformations
sleap filenames List video filenames in labels
sleap train Train models (from sleap-nn)
sleap predict Run inference (from sleap-nn)
sleap export-model Export models to ONNX/TensorRT

ONNX & TensorRT Model Export

Export trained models to optimized formats for 3-6x faster inference (#2573, #2594, #2595, #2597):

sleap export-model model.ckpt -o model.onnx --format onnx
sleap export-model model.ckpt -o model.engine --format tensorrt

Run inference on exported models:

sleap predict model.onnx video.mp4 -o predictions.slp

Benchmark results (NVIDIA RTX A6000, batch size 8):

Model Type PyTorch TensorRT FP16 Speedup
Single Instance 3,111 FPS 11,039 FPS 3.5x
Centroid 453 FPS 1,829 FPS 4.0x
Top-Down 94 FPS 525 FPS 5.6x
Bottom-Up 113 FPS 524 FPS 4.6x

To install export dependencies, reinstall with the appropriate extra: "sleap[nn,export]==1.6.0" (ONNX), "sleap[nn,export-gpu]==1.6.0" (ONNX + GPU), or "sleap[nn,tensorrt]==1.6.0" (TensorRT). See the sleap-nn Export Guide for full benchmarks and details.

Label Quality Control (#2547)

New sleap.qc module with GMM-based anomaly detection to automatically identify annotation errors. Accessible via Analyze > Label QC... in the GUI.

  • Detects 10+ error types: isolated misses, jitter, visibility errors, scale issues, left-right swaps, gross misses, missing instances, and duplicates
  • Dockable GUI widget with score histograms and sensitivity controls
  • Keyboard navigation (Space/Shift+Space) to quickly review flagged instances
  • Export to CSV or add flagged instances to Suggestions for batch review

Redesigned Training & Inference Dialogs (#2506, #2509, #2519, #2556, #2557, #2579)

The training and inference dialogs have been completely redesigned with native Qt for a faster, more polished experience:

  • 55x faster config loading via rapidyaml and lazy loading
  • Smaller dialog that fits on 1280x720 screens
  • Augmentation controls simplified with on/off checkboxes and rotation presets (default: full ±180°)
  • Device and worker settings default from user preferences
  • Random Seed field for reproducible train/validation splits
  • Evaluation metrics can be computed at configurable epoch intervals (mOKS, mAP, mAR, PCK, distance metrics logged to WandB)
  • Prediction handling modes: Choose Keep, Replace, or Clear all predictions during inference
  • "Random sample (current video)" inference target for quick model testing
  • Form state persists after clicking Cancel

Real-Time Inference Progress (#2575)

The inference dialog now provides detailed progress feedback:

  • Threaded inference: UI remains responsive during long-running jobs
  • Live progress display: Predicted: 100/1,410 | FPS: 38.4 | ETA: 34s
  • Log viewer: Dark-themed scrollable log showing subprocess output in real-time
  • Working cancel button: Properly terminates the inference process
  • "Delete All Predictions" now completes in milliseconds (was minutes on large datasets)

Video Rendering Overhaul (#2558)

Rendering is now powered by sleap-io's rendering engine with a live preview before exporting:

  • 12+ new color palettes and 5 marker shapes
  • Color by track, instance, or node
  • Alpha transparency support for overlays
  • Non-blocking video export with progress bar and cancel support

See the rendering documentation for details.

Filter Overlapping Instances (#2574)

New post-inference deduplication to remove duplicate predictions:

  • IOU method: Filter by bounding box overlap
  • OKS method: Filter by keypoint-based similarity
  • Available in both the GUI (checkbox + threshold slider) and CLI (--filter_overlapping flags)

sleap doctor Diagnostics (#2524, #2551, #2553)

The sleap doctor command has been overhauled:

  • Consolidated, copy-paste-friendly diagnostic output
  • Git info display for editable installs (branch, commit hash)
  • Comprehensive UV and conda introspection with conflict warnings
  • System resources display (RAM and disk usage)
  • -o/--output flag to save diagnostics to file
  • Spinner during PyTorch import to show the command is working

How to Install

Step 1: Install uv (skip if already installed)

# Windows
powershell -c "irm https://astral.sh/uv/install.ps1 | iex"

# macOS/Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

Step 2: Install SLEAP v1.6.0

uv tool install --python 3.13 "sleap[nn]==1.6.0" --torch-backend auto

That's it! SLEAP is now available system-wide. The --torch-backend auto flag automatically detects your GPU (NVIDIA, AMD, Intel, or CPU). Run uv self update first if you get an error about this flag.

Step 3: Verify installation and launch

sleap doctor  # Check your setup
sleap          # Launch the GUI

Note: The sleap-label command from v1.5.x still works as an alias for launching the GUI.

Upgrading from v1.5.x?

uv tool install --reinstall --python 3.13 "sleap[nn]==1.6.0" --torch-backend auto

Quick data viewing (no permanent install)

uvx sleap labels.slp

Version compatibility

SLEAP sleap-io sleap-nn
1.6.x 0.6.x 0.1.x
1.5.x 0.5.x 0.0.x

Note: Starting with SLEAP v1.5+, all deep learning functionality is powered by the PyTorch-based sleap-nn backend. TensorFlow models (with UNet backbones) from earlier versions are still supported for inference. Refer to the Migrating to 1.5+ docs for more details!

Dependency Updates

sleap-io v0.6.4 (#2597)

  • ~90x faster SLP loading with lazy loading mode for large prediction files
  • Pose rendering at ~50 FPS for publication-ready videos (sleap render)
  • Data codecs for converting Labels to pandas DataFrames, NumPy arrays, and dictionaries
  • Content-based video matching for reliable cross-platform merges even when file paths differ
  • New Labels.match() API for inspecting matching results without merging
  • Negative frames support: Mark frames as containing no instances (LabeledFrame.is_negative)
  • 8 new CLI commands: merge, unsplit, fix, embed, unembed, trim, reencode, transform
  • CSV format support for MATLAB interoperability
  • Safe video matching prevents silent data corruption during merges
  • Embedded images preserved during CLI operations (sio fix, sio convert, etc.)
  • 23x faster .pkg.slp saves, 2.7x faster embedded video loading
  • Bug fixes for video matching, rendering, embedded videos, skeleton consolidation

sleap-nn v0.1.0 (#2597)

  • ONNX/TensorRT export for 3-6x faster inference
  • Epoch-end evaluation metrics: mOKS, mAP, mAR, PCK, and distance metrics logged to WandB
  • Post-inference filtering: Greedy NMS to remove duplicate predictions
  • 17-51x faster peak refinement (integral refinement now works on Mac)
  • GUI progress mode: JSON output for real-time progress in SLEAP GUI
  • Faster inference via GPU-accelerated normalization (17% typ...
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Contributors

alicup29 and jaw039
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SLEAP v1.6.0a3

21 Jan 00:50
@talmo talmo
v1.6.0a3
f3a6ba1
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SLEAP v1.6.0a3 Pre-release
Pre-release

SLEAP v1.6.0a3

About the v1.6 Pre-release Series

This is a pre-release for SLEAP v1.6.0. It contains many new features and improvements, but is not yet considered stable. For production use, see v1.5.2.

We are releasing a series of pre-releases that incrementally build towards the stable v1.6.0 release. Each pre-release adds new features and bug fixes:

Version Summary
v1.6.0a0 Unified sleap CLI, redesigned training dialog (55x faster loading), bug fixes for adding instances from predictions
v1.6.0a1 Label QC for automated error detection, 8 new CLI commands from sleap-io, video rendering with live preview
v1.6.0a2 Revamped installation docs, epoch-end evaluation metrics, content-based video matching, bug fix for export training package
v1.6.0a3 (current) ConvNeXt/SwinT backbones, ONNX/TensorRT export, real-time inference progress, macOS bug fixes

Note: Starting with SLEAP v1.5+, all deep learning functionality is powered by the PyTorch-based sleap-nn backend. TensorFlow models (with UNet backbones) from earlier versions are still supported for inference. Refer to the Migrating to 1.5+ docs for more details!

How to Install

Step 1: Install uv (skip if already installed)

# Windows
powershell -c "irm https://astral.sh/uv/install.ps1 | iex"

# macOS/Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

Step 2: Install SLEAP v1.6.0a3

uv tool install --python 3.13 "sleap[nn]==1.6.0a3" --with "sleap-io==0.6.3" --with "sleap-nn==0.1.0a4" --prerelease allow --torch-backend auto

That's it! SLEAP is now available system-wide. The --torch-backend auto flag automatically detects your GPU (NVIDIA, AMD, Intel, or CPU). Be sure to do a uv self update if you get an error about this flag.

Step 3: Verify installation

sleap doctor

Upgrading from v1.6.0a2?

uv tool upgrade sleap --upgrade-package sleap-io --upgrade-package sleap-nn

Or for a clean reinstall:

uv tool install --reinstall --python 3.13 "sleap[nn]==1.6.0a3" --with "sleap-io==0.6.3" --with "sleap-nn==0.1.0a4" --prerelease allow --torch-backend auto

Rollback to stable

If you encounter issues, rollback to the latest stable release:

uv tool install --python 3.13 "sleap[nn]==1.5.2" --torch-backend auto

Version compatibility

SLEAP sleap-io sleap-nn
1.6.0a3 0.6.3 0.1.0a4
1.6.0a2 0.6.2 0.1.0a2
1.6.0a1 0.6.1 0.1.0a1

What's New in v1.6.0a3

New Backbone Architectures (#2579)

Train models using modern transformer-based and ConvNeXt architectures as alternatives to UNet:

  • ConvNeXt: Select from tiny/small/base/large variants (28M-198M parameters) with optional ImageNet pretrained weights for faster convergence
  • Swin Transformer (SwinT): Select from tiny/small/base variants (28M-88M parameters) with optional ImageNet pretrained weights

Access these from the training dialog's new backbone selector dropdown.

ONNX/TensorRT Model Export (#2573)

Export trained models to optimized formats for 3-6x faster inference:

sleap-nn-export model.ckpt -o model.onnx --format onnx
sleap-nn-export model.ckpt -o model.engine --format tensorrt

Run inference on exported models:

sleap-nn-predict model.onnx video.mp4 -o predictions.slp

Benchmark results (NVIDIA RTX A6000, batch size 8):

Model Type PyTorch TensorRT FP16 Speedup
Single Instance 3,111 FPS 11,039 FPS 3.5x
Centroid 453 FPS 1,829 FPS 4.0x
Top-Down 94 FPS 525 FPS 5.6x
Bottom-Up 113 FPS 524 FPS 4.6x

See the sleap-nn Export Guide for full benchmarks and usage details.

Real-Time Inference Progress (#2575)

The inference dialog now provides detailed progress feedback:

  • Threaded inference: UI remains responsive during long-running jobs
  • Live progress display: Predicted: 100/1,410 | FPS: 38.4 | ETA: 34s
  • Log viewer: Dark-themed scrollable log showing subprocess output in real-time
  • Working cancel button: Properly terminates inference when clicked

Filter Overlapping Instances (#2574)

New GUI controls to remove duplicate/overlapping predictions after inference:

  • Enable filtering checkbox in the Preprocessing/Postprocessing section
  • Method selection: IOU (bounding box overlap) or OKS (keypoint-based similarity)
  • Threshold control: Lower values = more aggressive filtering (default: 0.8)

Also available via CLI:

sleap-nn-track model.ckpt video.mp4 --filter_overlapping --filter_overlapping_method oks --filter_overlapping_threshold 0.5

Evaluation During Training (#2579)

New evaluation section in the training dialog:

  • Enable evaluation checkbox to compute metrics during training
  • Frequency control to set how often evaluation runs (in epochs)
  • Metrics logged to WandB: mOKS, mAP, mAR, PCK, distance percentiles

Performance Improvements

  • Delete All Predictions (#2575): Now completes in milliseconds instead of minutes on large datasets
  • 17-51x faster peak refinement in sleap-nn (v0.1.0a4): Enables integral refinement on Mac (previously disabled)

Bug Fixes

macOS Fixes

  • Fixed crash when opening training dialog on macOS with Homebrew installed. The crash was caused by a conflict between Homebrew's libpng and macOS's ImageIO framework during Qt font rendering. (#2571)
  • Fixed dialog button ordering on macOS. Training and inference dialog buttons now appear in consistent order across all platforms (Mac, Windows, Linux). (#2576)
  • Fixed default button highlighting: The "Run" button now correctly appears as the default (highlighted) button instead of "Copy to clipboard". (#2576)

UI Fixes

  • Fixed dark mode for training dialog main tab. The background now properly follows the system theme instead of remaining white. (#2572)
  • Fixed loss monitor to recognize sleap-nn's metric naming convention (train/loss, val/loss). (#2579)

Other Fixes

  • Fixed ConvNeXt/SwinT training crash in sleap-nn: Resolved skip connection channel mismatch that caused errors during validation. (sleap-nn v0.1.0a4)
  • Fixed inference progress ending at 99%: Now correctly shows 100% when complete. (sleap-nn v0.1.0a4)
  • Fixed CSV learning rate logging: The learning_rate column in training_log.csv is no longer empty. (sleap-nn v0.1.0a4)

Dependency Updates

sleap-nn v0.1.0a4

Changes since v0.1.0a2 (the previous minimum version):

  • ONNX/TensorRT Export: Export models to optimized formats for 3-6x faster inference
  • Post-Inference Filtering: Greedy NMS to remove duplicate predictions (--filter_overlapping)
  • 17-51x Faster Peak Refinement: Fast tensor indexing replaces kornia's crop_and_resize
  • GUI Progress Mode: New --gui flag enables JSON output for real-time GUI progress
  • Simplified Train CLI: sleap-nn train config.yaml (positional config path)
  • Bug fixes for ConvNeXt/SwinT training, CSV logging, progress display

sleap-io v0.6.3

Changes since v0.6.2 (the previous minimum version):

  • Negative Frames Support: Mark frames as containing no instances (LabeledFrame.is_negative)
  • Embedded Images Preserved: CLI commands (sio fix, sio convert, etc.) no longer strip embedded images
  • Smart Skeleton Consolidation: Compatible skeletons are reassigned instead of deleted

Full Changelog

Enhancements

  • Add ConvNeXt and SwinT backbone options to training dialog by @talmo in #2579
  • Improve inference dialog with real-time progress and UI fixes by @talmo in #2575
  • Add filter_overlapping controls to training/inference dialogs by @talmo in #2574
  • Bump sleap-io to 0.6.3 and sleap-nn to 0.1.0a3, add new CLI commands by @talmo in #2573

Fixes

  • Fix macOS crash caused by Homebrew libpng conflict by @talmo in #2571
  • Fix training dialog main tab background to match config tabs by @talmo in #2572
  • Fix Mac dialog button order and default button styling by @talmo in #2576

Workflows

  • A...
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Contributors

talmo
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SLEAP v1.6.0a2

16 Jan 21:25
@talmo talmo
v1.6.0a2
2f345c6
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GPG key ID: B5690EEEBB952194
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SLEAP v1.6.0a2 Pre-release
Pre-release

SLEAP v1.6.0a2

About the v1.6 Pre-release Series

This is a pre-release for SLEAP v1.6.0. It contains many new features and improvements, but is not yet considered stable. For production use, see v1.5.2.

We are releasing a series of pre-releases that incrementally build towards the stable v1.6.0 release. Each pre-release adds new features and bug fixes:

Version Summary
v1.6.0a0 Unified sleap CLI, redesigned training dialog (55x faster loading), bug fixes for adding instances from predictions
v1.6.0a1 Label QC for automated error detection, 8 new CLI commands from sleap-io, video rendering with live preview
v1.6.0a2 (current) Revamped installation docs, epoch-end evaluation metrics, content-based video matching, bug fix for export training package

Note: Starting with SLEAP v1.5+, all deep learning functionality is powered by the PyTorch-based sleap-nn backend. TensorFlow models (with UNet backbones) from earlier versions are still supported for inference. Refer to the Migrating to 1.5+ docs for more details!

How to Install

Step 1: Install uv (skip if already installed)

# Windows
powershell -c "irm https://astral.sh/uv/install.ps1 | iex"

# macOS/Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

Step 2: Install SLEAP v1.6.0a2

uv tool install --python 3.13 "sleap[nn]==1.6.0a2" --with "sleap-io==0.6.2" --with "sleap-nn==0.1.0a2" --prerelease allow --torch-backend auto

That's it! SLEAP is now available system-wide. The --torch-backend auto flag automatically detects your GPU (NVIDIA, AMD, Intel, or CPU). Be sure to do a uv self update if you get an error about this flag.

Step 3: Verify installation

sleap doctor

Upgrading from v1.6.0a1?

uv tool upgrade sleap --upgrade-package sleap-io --upgrade-package sleap-nn

Or for a clean reinstall:

uv tool install --reinstall --python 3.13 "sleap[nn]==1.6.0a2" --with "sleap-io==0.6.2" --with "sleap-nn==0.1.0a2" --prerelease allow --torch-backend auto

Rollback to stable

If you encounter issues, rollback to the latest stable release:

uv tool install --python 3.13 "sleap[nn]==1.5.2" --torch-backend auto

Version compatibility

SLEAP sleap-io sleap-nn
1.6.0a2 0.6.2 0.1.0a2
1.6.0a1 0.6.1 0.1.0a1
1.6.0aN 0.6.x 0.1.0aN
1.6.x 0.6.x 0.1.x
1.5.x 0.5.x 0.0.x

What's New in v1.6.0a2

  • Revamped Installation Documentation:

    • Complete rewrite of installation docs with simplified workflow (#2567)
    • Single universal install command for all platforms using --torch-backend auto
    • Reduced from 8 installation paths to 2 (tool install + development setup)
    • New uvx sleap labels.slp option for viewing data without permanent installation
    • Streamlined upgrade flow with uv tool upgrade sleap

  • Python 3.13 Default:

    • Python 3.13 is now the default recommended version (#2565)
    • Python 3.12 remains supported

  • Bug Fixes:

    • Fixed "Export Training Job Package" crash with ConfigAttributeError: Missing key zmq that occurred on v1.6.0a0/v1.6.0a1 (#2566, fixes #2562)

  • sleap-io v0.6.2:

    • Content-Based Video Matching: Videos are now automatically matched by pose annotations or pixel content, enabling reliable cross-platform merges even when file paths differ
    • New Labels.match() API: Inspect matching results without merging — ideal for evaluation workflows
    • Video Color Mode Control: New Labels.set_video_color_mode() method and sio fix --video-color CLI option
    • Bug fixes for HDF5 dataset matching and provenance conflict handling

  • sleap-nn v0.1.0a2:

    • Epoch-End Evaluation Metrics: Real-time mOKS, mAP, mAR, PCK, and distance metrics logged to WandB during training
    • Robust Video Matching: Uses sleap-io's Labels.match() API for better cross-platform evaluation
    • Bug fixes for embedded video handling and centroid model ground truth matching

Full Changelog

Enhancements

  • Revamp installation documentation for uv-based workflow by @talmo in #2567

Fixes

  • Fix export training package zmq config error by @talmo in #2566

Workflows

  • Fix docs workflow race condition with concurrency group by @talmo in #2563
  • Housekeeping: Python 3.13 default and sleap-support skill by @talmo in #2565

Dependencies

  • Bump sleap-io min to 0.6.2 and sleap-nn min to 0.1.0a2 by @talmo in #2569

Full Changelog: v1.6.0a1...v1.6.0a2

Contributors

talmo
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SLEAP v1.6.0a1

13 Jan 18:38
@talmo talmo
v1.6.0a1
aa6fb3c
This commit was created on GitHub.com and signed with GitHub’s verified signature.
GPG key ID: B5690EEEBB952194
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SLEAP v1.6.0a1 Pre-release
Pre-release

SLEAP v1.6.0a1

About the v1.6 Pre-release Series

This is a pre-release for SLEAP v1.6.0. It contains many new features and improvements, but is not yet considered stable. For production use, see v1.5.2.

We are releasing a series of pre-releases that incrementally build towards the stable v1.6.0 release. Each pre-release adds new features and bug fixes:

Version Summary
v1.6.0a0 Unified sleap CLI, redesigned training dialog (55x faster loading), bug fixes for adding instances from predictions
v1.6.0a1 (current) Label QC for automated error detection, 8 new CLI commands from sleap-io, video rendering with live preview

Note: Starting with SLEAP v1.5+, all deep learning functionality is powered by the PyTorch-based sleap-nn backend. TensorFlow models (with UNet backbones) from earlier versions are still supported for inference. Refer to the Migrating to 1.5+ docs for more details!

How to Install

Step 1: Install uv (skip if already installed)

# Windows
powershell -c "irm https://astral.sh/uv/install.ps1 | iex"

# macOS/Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

Step 2: Install SLEAP v1.6.0a1

Windows/Linux with NVIDIA GPU (CUDA 12.8)

uv tool install --reinstall --python 3.12 "sleap[nn]==1.6.0a1" --with "sleap-io==0.6.1" --with "sleap-nn==0.1.0a1" --prerelease allow --index https://download.pytorch.org/whl/cu128 --index https://pypi.org/simple

Windows/Linux with NVIDIA GPU (CUDA 13.0)

uv tool install --reinstall --python 3.12 "sleap[nn]==1.6.0a1" --with "sleap-io==0.6.1" --with "sleap-nn==0.1.0a1" --prerelease allow --index https://download.pytorch.org/whl/cu130 --index https://pypi.org/simple

Windows/Linux without GPU (CPU only)

uv tool install --reinstall --python 3.12 "sleap[nn]==1.6.0a1" --with "sleap-io==0.6.1" --with "sleap-nn==0.1.0a1" --prerelease allow --index https://download.pytorch.org/whl/cpu --index https://pypi.org/simple

macOS

uv tool install --reinstall --python 3.12 "sleap[nn]==1.6.0a1" --with "sleap-io==0.6.1" --with "sleap-nn==0.1.0a1" --prerelease allow

Step 3: Verify installation

sleap doctor

Upgrading from v1.6.0a0?

Use the same commands as above. The --reinstall flag will create a clean environment with the new dependencies.

Rollback to stable

If you encounter issues, rollback to the latest stable release:

# Windows/Linux (CUDA 12.8)
uv tool install --reinstall --python 3.12 "sleap[nn]==1.5.2" --index https://download.pytorch.org/whl/cu128 --index https://pypi.org/simple

# Windows/Linux (CPU only)
uv tool install --reinstall --python 3.12 "sleap[nn]==1.5.2" --index https://download.pytorch.org/whl/cpu --index https://pypi.org/simple

# macOS
uv tool install --reinstall --python 3.12 "sleap[nn]==1.5.2"

Version compatibility

SLEAP sleap-io sleap-nn
1.6.0a1 0.6.1 0.1.0a1
1.6.0aN 0.6.x 0.1.0aN
1.6.x 0.6.x 0.1.x
1.5.x 0.5.x 0.0.x

What's New in v1.6.0a1

  • Label Quality Control (QC):

    • New sleap.qc module with GMM-based anomaly detection to automatically identify annotation errors (#2547)
    • Detects 10+ error types: isolated misses, jitter, visibility errors, scale issues, left-right swaps, gross misses, missing instances, and duplicates
    • Dockable GUI widget accessible via Analyze > Label QC... with score histograms and sensitivity controls
    • Keyboard navigation (Space/Shift+Space) to quickly navigate flagged instances
    • Export to CSV or add flagged instances to Suggestions for review

  • Enhanced CLI:

    • 8 new CLI commands from sleap-io: sleap merge, sleap unsplit, sleap fix, sleap embed, sleap unembed, sleap trim, sleap reencode, sleap transform (#2559)
    • See the sleap-io CLI documentation for detailed usage

  • Video Rendering Overhaul:

    • Now powered by sleap-io's rendering engine — see rendering documentation for details (#2558)
    • Live preview of rendered frames with all style options before exporting
    • 12+ new color palettes and 5 marker shapes with options to color by track, instance, or node
    • Alpha transparency support for overlays
    • Non-blocking video export with progress bar and cancel support

  • Training Dialog Improvements:

    • Form state now persists after clicking Cancel (#2557)
    • Device and worker settings default from user preferences instead of being overwritten by profiles (#2557)
    • Updated all baseline profiles to use full ±180° rotation augmentation (#2557)
    • Added Random Seed field for reproducible train/validation splits (#2557)
    • New tooltips for Input Scaling, Batch Size, Predict On, and tracker fields (#2556)

  • Inference Improvements:

    • New "Random sample (current video)" inference target for quick model testing (#2555)
    • Fixed --exclude_user_labeled flag not working when "Skip user labeled frames" is enabled (#2552)

  • sleap doctor Improvements:

    • Consolidated, copy-paste-friendly diagnostic output (#2553)
    • Git info display for editable installs (branch, commit hash) (#2553)
    • Comprehensive UV and conda introspection with conflict warnings (#2553)
    • System resources display (RAM and disk usage) (#2553)
    • New -o/--output flag to save diagnostics to file (#2553)
    • Added spinner during PyTorch import to show command is working (#2551)
    • Fixed path truncation in tables (#2551)

  • Bug Fixes:

    • Fixed terminal spam from "Error processing frame" messages when scrubbing .pkg.slp files (#2554)

  • sleap-io v0.6.1:

    • 8 new CLI commands: merge, unsplit, fix, embed, unembed, trim, reencode, transform
    • CSV format support for MATLAB interoperability
    • Coordinate-aware video transformations
    • 23x faster .pkg.slp saves, 2.7x faster embedded video loading
    • Bug fixes for video matching, rendering, and embedded videos

  • sleap-nn v0.1.0a1:

    • Training progress bar during dataset caching (no more apparent "freeze")
    • Automatic WandB local log cleanup to save disk space
    • Simplified log format for cleaner output

Full Changelog

Enhancements

  • Add sleap.qc module for label quality control by @talmo in #2547
  • Add sleap-io v0.6.1 CLI commands by @talmo in #2559
  • Upgrade video rendering to use sleap-io API with live preview and non-blocking progress by @talmo in #2558
  • Improve training config dialog UX by @talmo in #2557
  • Add missing tooltips to training config and tracker form fields by @talmo in #2556
  • Add "Random sample (current video)" inference target option by @talmo in #2555
  • Improve sleap doctor with consolidated diagnostic output by @talmo in #2553
  • Improve sleap doctor UX: add spinner and fix path truncation by @talmo in #2551

Fixes

  • Suppress frame error spam when scrubbing pkg.slp files by @talmo in #2554
  • Add --exclude_user_labeled flag to sleap-nn-track CLI shim by @talmo in #2552

Workflows

  • Add PR-local docs preview deployment by @talmo in #2550

Dependencies

Full Changelog: v1.6.0a0...v1.6.0a1

Contributors

talmo
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SLEAP v1.6.0a0

11 Jan 20:02
@talmo talmo
v1.6.0a0
c40a547
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SLEAP v1.6.0a0 Pre-release
Pre-release

SLEAP v1.6.0a0

About the v1.6 Pre-release Series

This is a pre-release for SLEAP v1.6.0. It contains many new features and improvements, but is not yet considered stable. For production use, see v1.5.2.

We are releasing a series of pre-releases that incrementally build towards the stable v1.6.0 release. Each pre-release adds new features and bug fixes:

Version Summary
v1.6.0a0 (current) Unified sleap CLI, redesigned training dialog (55x faster loading), bug fixes for adding instances from predictions
v1.6.0a1 Label QC for automated error detection, 8 new CLI commands from sleap-io, video rendering with live preview

Note: Starting with SLEAP v1.5+, all deep learning functionality is powered by the PyTorch-based sleap-nn backend. TensorFlow models (with UNet backbones) from earlier versions are still supported for inference. Refer to the Migrating to 1.5+ docs for more details!

How to Install

Step 1: Install uv (skip if already installed)

# Windows
powershell -c "irm https://astral.sh/uv/install.ps1 | iex"

# macOS/Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

Step 2: Install SLEAP v1.6.0a0

Windows/Linux with NVIDIA GPU (CUDA 12.8)

uv tool install --force --python 3.12 "sleap[nn]==1.6.0a0" --with "sleap-io==0.6.0" --with "sleap-nn==0.1.0a0" --prerelease allow --index https://download.pytorch.org/whl/cu128 --index https://pypi.org/simple

Windows/Linux with NVIDIA GPU (CUDA 13.0 - NEW!)

uv tool install --force --python 3.12 "sleap[nn]==1.6.0a0" --with "sleap-io==0.6.0" --with "sleap-nn==0.1.0a0" --prerelease allow --index https://download.pytorch.org/whl/cu130 --index https://pypi.org/simple

Windows/Linux without GPU (CPU only)

uv tool install --force --python 3.12 "sleap[nn]==1.6.0a0" --with "sleap-io==0.6.0" --with "sleap-nn==0.1.0a0" --prerelease allow --index https://download.pytorch.org/whl/cpu --index https://pypi.org/simple

macOS

uv tool install --force --python 3.12 "sleap[nn]==1.6.0a0" --with "sleap-io==0.6.0" --with "sleap-nn==0.1.0a0" --prerelease allow

Step 3: Verify installation

sleap doctor

Upgrading from v1.5.x?

Use the same commands as above. The --force flag will replace your existing installation.

Rollback to stable

If you encounter issues, rollback to the latest stable release:

# Windows/Linux (CUDA 12.8)
uv tool install --force --python 3.12 "sleap[nn]==1.5.2" --index https://download.pytorch.org/whl/cu128 --index https://pypi.org/simple

# Windows/Linux (CPU only)
uv tool install --force --python 3.12 "sleap[nn]==1.5.2" --index https://download.pytorch.org/whl/cpu --index https://pypi.org/simple

# macOS
uv tool install --force --python 3.12 "sleap[nn]==1.5.2"

Version compatibility

SLEAP sleap-io sleap-nn
1.6.0aN 0.6.x 0.1.0aN
1.6.x 0.6.x 0.1.x
1.5.x 0.5.x 0.0.x

What's New in v1.6.0a0

  • Unified CLI:

    • New sleap command as the primary entry point - just run sleap to launch the GUI (#2524)
    • Integrated sleap-io CLI commands: sleap show, sleap convert, sleap split, sleap filenames, sleap render (#2541)
    • New sleap doctor command for system diagnostics and troubleshooting (#2524)

  • Training GUI Overhaul:

    • 55x faster config loading and much faster dialog startup (#2506, #2516)
    • Completely redesigned training dialog with native Qt, unified frame targeting, and 356 new tests (#2519)
    • New Frame Target Selector for flexible training/inference frame selection (#2519)
    • Prediction handling modes: Keep, Replace, or Clear all predictions during inference (#2519)
    • Smaller dialog that fits on 1280x720 screens (#2509, #2519)
    • Augmentation controls simplified with on/off checkboxes and rotation presets (#2509)
    • WandB integration improvements with run URL display and auto-browser-open (#2525)

  • Crop Size Visualization:

    • Visual crop size preview in training dialog for top-down models (#2483)
    • New Instance Size Distribution widget for analyzing bounding box sizes across your dataset (#2528)
    • Click-to-navigate from size distribution plot to specific instances (#2528)

  • New Features:

    • "Check for Updates" dialog showing versions for sleap, sleap-io, and sleap-nn (#2499)
    • "Delete Predictions on User-Labeled Frames" for cleaning up duplicate instances (#2505)
    • Startup banner with version info and branding when launching sleap-label (#2517)
    • Progress dialog with cancel support for package export (#2522)
    • Support for loading legacy SLEAP metrics from v1.4.1 and earlier (#2480)

  • Critical Bug Fixes:

    • Fixed GUI freeze when editing predictions with NaN coordinates on Linux Qt 6.10+ (#2467)
    • Fixed catastrophic data loss bug where removing a video could delete frames from ALL videos with the same resolution (#2535)
    • Fixed prediction deletion incorrectly removing user-labeled instances (#2478)
    • Fixed predictions not being fully converted to instances when adding from predictions (#2539)

  • sleap-io v0.6.0: (#2540)

    • ~90x faster SLP loading with new lazy loading mode for large prediction files
    • Pose rendering at ~50 FPS for publication-ready videos (sleap render)
    • Data codecs for converting Labels to pandas DataFrames, NumPy arrays, and dictionaries
    • Safe video matching prevents silent data corruption during merges
    • Fixed package export losing videos without labeled frames (#282)
    • Fixed video provenance breaking during merge operations (#302)
    • Breaking: Merge API simplified (video_matcher= → video=, frame_strategy= → frame=)

  • sleap-nn v0.1.0a0: (#2545)

    • Faster inference via GPU-accelerated normalization (17% for typical video, up to 50% for large RGB images)
    • CUDA 13.0 support for latest NVIDIA GPUs
    • Provenance tracking embeds full reproducibility metadata in output files
    • Enhanced WandB with interactive visualizations and per-head loss logging
    • Fixed crash on frames with empty instances (#385)
    • Fixed --exclude_user_labeled being ignored with --video_index (#397)
    • Fixed run folder cleanup when training canceled via GUI (#392)
    • Breaking: Crop size semantics changed - top-down models now crop first, then resize
    • Breaking: Output file naming changed (labels_train_gt_0.slp → labels_gt.train.0.slp)

  • Other dependency changes:

    • Removed 8 unused dependencies for faster installation (#2486)

Full Changelog

Enhancements

  • Add unified CLI with sleap command by @talmo in #2524
  • Add sleap-io CLI command inheritance (show, convert, split, filenames, render) by @talmo in #2541
  • Add "Check for Updates" to Help menu and implement update checker by @jaw039 in #2499
  • Refactor training/inference dialog with native Qt and unified frame targeting by @talmo in #2519
  • Training GUI QOL improvements (augmentation checkboxes, rotation presets, overfit mode) by @talmo in #2509
  • Improve training dialog startup performance (~55x faster config loading) by @talmo in #2506
  • Add crop size visualization for top-down training pipelines by @gitttt-1234 in #2483
  • Add Instance Size Distribution widget for crop size analysis by @talmo in #2528
  • Add Delete Predictions on User-Labeled Frames feature by @talmo in #2505
  • Add support for loading legacy SLEAP metrics (<=v1.4.1) by @gitttt-1234 in #2480
  • Add progress dialog and completion notification for package export by @talmo in #2522
  • Add startup banner with version info and branding by @talmo in #2517
  • Improve baseline config display names in training config selector by @gitttt-1234 in #2471
  • Fix WandB checkbox state and add run URL display by @talmo in #2525

Fixes

  • Fix GUI freeze when editing predictions with NaN coordinates by @gitttt-1234 in #2467
  • Fix remove_video() to use identity comparison instead of matches_content() by @talmo in #2535
  • Fix prediction deletion to prevent removing labeled instances by @gitttt-1234 in #2478
  • Add failing tests for predictions-not-fully-added bugs (and fix) by @talmo in #2539
  • Fix GUI freeze during labeled video export by @gitttt-1234 in #2484
  • Fix skeleton loading returning list instead of single Skeleton by @gitttt-1234 in #2493
  • Fix missing file dialog for ImageVideo backend (list of frame paths) by @alicup29 in #2498
  • Fix delete unused tracks crash with untracked instances by @talmo in #2503
  • Fix plateau detection to use absolute threshold mode by @gitttt-1234 in #2469
  • Update predictions output path for inference (multi-v...
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Contributors

talmo, gitttt-1234, and 2 other contributors
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SLEAP v1.5.2

30 Oct 03:57
@talmo talmo
v1.5.2
ed0635c
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SLEAP v1.5.2

What's Changed

SLEAP v1.5.2 – Bug Fixes & Dependency Updates

This release includes important bug fixes for GUI rendering and Windows compatibility, dependency updates for improved stability, and further documentation improvements.

Note: Starting with SLEAP v1.5+, all deep learning functionality is powered by the PyTorch-based sleap-nn backend. TensorFlow models (with UNet backbones) from earlier versions are still supported for inference. Refer Migrating to 1.5+ docs for more details!

How to install?

You can now install SLEAP quickly using uv

Step 1: Install uv - an ultra-fast Python package manager

# Windows
powershell -c "irm https://astral.sh/uv/install.ps1 | iex"

# macOS/Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

Step 2: Install sleap

# Windows/ Linux (CUDA)
uv tool install --python 3.13 "sleap[nn]==1.5.2" --index https://download.pytorch.org/whl/cu128 --index https://pypi.org/simple

# Windows/ Linux (CPU)
uv tool install --python 3.13 "sleap[nn]==1.5.2" --index https://download.pytorch.org/whl/cpu --index https://pypi.org/simple

# macOS
uv tool install --python 3.13 "sleap[nn]==1.5.2"

Check the full installation guide for platform-specific instructions and advanced options.

Once you've installed SLEAP, run the below command from anywhere in your terminal

sleap-label

The GUI should open up!

Upgrading from v1.5.1?

If you already have SLEAP v1.5.1 installed, you can upgrade to v1.5.2 using the following commands based on your installation method:

If installed with uv tool install:

The simplest upgrade command (preserves your original Python version and index URLs):

uv tool upgrade sleap

Or, if you want to ensure you're using Python 3.13 and refresh your installation:

uv tool uninstall sleap
# Then reinstall with the commands from the installation section above

Note: uv tool upgrade automatically preserves the index URLs (CUDA/CPU) and Python version from your original installation. If you installed with --index https://download.pytorch.org/whl/cu128, the upgrade will continue using the CUDA 12.8 index.

If installed with pip in a conda environment:

conda activate sleap
pip install --upgrade "sleap[nn]"

For platform-specific indexes (CUDA/CPU), add the appropriate --extra-index-url:

# CUDA 12.8
pip install --upgrade "sleap[nn]" --extra-index-url https://download.pytorch.org/whl/cu128 --index-url https://pypi.org/simple

# CPU
pip install --upgrade "sleap[nn]" --extra-index-url https://download.pytorch.org/whl/cpu --index-url https://pypi.org/simple

If installed with uv add (project-based):

# Navigate to your project directory
uv sync --upgrade

If installed from source:

cd sleap
git pull
uv sync --upgrade

After upgrading, verify the installation:

python -c "import sleap; sleap.versions()"

You should see SLEAP: 1.5.2 in the output.

Highlights

  • Dependency updates:

    • Updated minimum sleap-io version to 0.5.7
    • Updated minimum sleap-nn version to 0.0.4
    • Removed cattrs dependency for simplified dependency management
    • Added --python 3.13 flag to installation commands to prevent Python 3.14 compatibility issues

  • Bug fixes:

    • Fixed color rendering in sleap-render: Videos now display correct colors with proper BGR to RGB conversion (#2444)
    • Fixed Windows GUI crash: Resolved Qt widget attribute error when loading .slp files on Windows (#2440)
    • Fixed instance coloring: Multiple instances in older SLEAP projects now display with distinct colors instead of the same color (#2434)

  • Documentation improvements:

    • Consolidated repetitive installation documentation (reduced by 55 lines while preserving all essential information)
    • Improved uv add installation workflow instructions with Windows troubleshooting tips
    • Clearer platform-specific installation guidance

Full Changelog: v1.5.1...v1.5.2

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SLEAP v1.5.1

02 Oct 19:11
@gitttt-1234 gitttt-1234
v1.5.1
daf0d95
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SLEAP v1.5.1

What's Changed

SLEAP v1.5.1 – Bug fixes & Documentation Improvements

This release focuses on a few bug fixes in the training pipeline, improving installation instructions, and updating documentation for a smoother user experience.

Note: Starting with SLEAP v1.5+, all deep learning functionality is powered by the PyTorch-based sleap-nn backend. TensorFlow models (with UNet backbones) from earlier versions are still supported for inference. Refer Migrating to 1.5+ docs for more details!

How to install?

You can now install SLEAP quickly using uv

Step 1: Install uv - an ultra-fast Python package manager

# Windows
powershell -c "irm https://astral.sh/uv/install.ps1 | iex"

# macOS/Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

Step 2: Install sleap

# Windows/ Linux (CUDA)
uv tool install "sleap[nn]" --index https://download.pytorch.org/whl/cu128 --index https://pypi.org/simple

# Windows/ Linux (CPU)
uv tool install "sleap[nn]" --index https://download.pytorch.org/whl/cpu --index https://pypi.org/simple

# macOS
uv tool install "sleap[nn]"

Check the full installation guide for platform-specific instructions and advanced options.

Once you've installed SLEAP, run the below command from anywhere in your terminal

sleap-label

The GUI should open up!

Highlights

  • Improved installation:
    • Platform-specific dependency groups for sleap installation with CUDA support.
    • Fixed CUDA installation issues on Windows.
    • Updated installation instructions and options for clarity.
  • Documentation updates:
    • Fixed typos and broken links.
    • Improved CLI docs with new options and guidance on legacy CLIs.
    • Fixed MkDocs versioning and improved doc site structure.
  • Error handling: sleap-nn import errors are now handled gracefully with clear user guidance.
  • Bug fixes: Minor fixes across CLI and docs to improve stability.

Full Changelog: v1.5.0...v1.5.1

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SLEAP v1.5.0

30 Sep 21:44
@talmo talmo
v1.5.0
aeaba8b
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SLEAP v1.5.0

What's New in SLEAP 1.5

SLEAP 1.5 represents a major milestone with significant architectural improvements, performance enhancements, and new installation methods. Here are the key changes:

Major Changes

Updated dependencies

We have now updated to support Python 3.12+ and support many new versions of the many libraries that SLEAP uses. This should make it much easier to install on modern platforms, support new architectures, and make development much easier.

UV-Based Installation

SLEAP 1.5+ now uses uv for installation, making it much faster than previous methods. Get up and running in seconds with our streamlined installation process.

PyTorch Backend

Neural network backend switched from TensorFlow to PyTorch via sleap-nn, providing:

  • Much faster training and inference speeds: Up to 2.5x faster training and inference times.
  • Modern deep learning capabilities: PyTorch with upcoming integrations with a whole slew of modern deep learning models and packages.
  • Improved developer experience: Check out the dedicated backend repo at https://github.com/talmolab/sleap-nn
  • Multi-GPU training: Full support for using multiple GPUs for accelerated and larger scale training.
  • Backwards compatibility: You are able to use your existing trained SLEAP models from v1.4.1 for the UNet backend with no changes (see notes below).

Refreshed Documentation Websites

Standalone Libraries

SLEAP GUI is now supported by two new packages for modular workflows:

SLEAP-IO

I/O backend for handling labels, processing .slp files, and data manipulation. Essential for any SLEAP workflow and can be used independently for data processing tasks.

SLEAP-NN

PyTorch-based neural network backend for training and inference. Perfect for custom training pipelines, remote processing, and headless server deployments.

Torch Backend Changes

New Backbones

SLEAP 1.5 introduces three powerful new backbone architectures (check here for more details):

  • UNet - Classic encoder-decoder architecture for precise pose estimation
  • SwinT - Swin Transformer for state-of-the-art performance
  • ConvNeXt - Modern convolutional architecture with improved efficiency

Legacy Support

We've maintained full backward compatibility:

  • GUI Support: SLEAP now uses a new YAML-based config file structure, but you can still upload and work with old SLEAP JSON files in the GUI. For details on converting legacy SLEAP 1.4 config/JSON files to the new YAML format, see our conversion guide.
  • TensorFlow Model Inference: Continue to support running inference on old TensorFlow models (UNet backbone only). Check using legacy models for more details.
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SLEAP v1.4.1

19 Dec 17:57
@roomrys roomrys
v1.4.1
369b772

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SLEAP v1.4.1

SLEAP 1.4.1 releases many new changes since the last big release 1.3.3. We hope users enjoy these long awaited new features and fixes!

From 1.3.2+, to install SLEAP through pip use pip install sleap[pypi] to ensure all dependencies are gathered.

As a reminder:

The 1.3.1 dependency update requires Mamba for faster dependency resolution. If you already have anaconda installed, then you can set the solver to libmamba in the base environment:

conda update -n base conda
conda install -n base conda-libmamba-solver
conda config --set solver libmamba

Any subsequent mamba commands in the docs will need to be replaced with conda if you choose to use your existing Anaconda installation.

Otherwise, follow the recommended installation instruction for Mamba.

Quick install

mamba (Windows/Linux/GPU):

mamba create -y -n sleap -c conda-forge -c nvidia -c sleap/label/dev -c sleap -c anaconda sleap=1.4.1

mamba (Mac):

mamba create -y -n sleap -c conda-forge -c anaconda -c sleap sleap=1.4.1

pip (any OS except Apple Silicon):

pip install sleap[pypi]==1.4.1

Highlights

Full Changelog

Enhancements

Fixes

Dependencies

Documentation

Refactors

Read more

Contributors

lambdaloop, talmo, and 17 other contributors
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SLEAP v1.3.4

03 Sep 20:49
@roomrys roomrys
v1.3.4
d4cc425

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SLEAP v1.3.4

SLEAP 1.3.4 has no changes to the SLEAP source code, but adds constraints to the attrs and opencv versions being pulled in.

From 1.3.2+, to install SLEAP through pip use pip install sleap[pypi] to ensure all dependencies are gathered.

As a reminder:

The 1.3.1 dependency update requires Mamba for faster dependency resolution. If you already have anaconda installed, then you can set the solver to libmamba in the base environment:

conda update -n base conda
conda install -n base conda-libmamba-solver
conda config --set solver libmamba

Any subsequent mamba commands in the docs will need to be replaced with conda if you choose to use your existing Anaconda installation.

Otherwise, follow the recommended installation instruction for Mamba.

Quick install

mamba (Windows/Linux/GPU):

mamba create -y -n sleap -c conda-forge -c nvidia -c sleap -c anaconda sleap=1.3.4

mamba (Mac):

mamba create -y -n sleap -c conda-forge -c anaconda -c sleap sleap=1.3.4

pip (any OS except Apple Silicon):

pip install sleap[pypi]==1.3.4

Full Changelog

  • Constrain attrs (mac) and opencv (linux) in 1.3.4 #1927
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