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

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

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

• The new landing page is now live at: https://sleap.ai
• The new documentation is now live at: https://docs.sleap.ai
• The old docs (v1.4.1) are will remain available at: https://legacy.sleap.ai

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.

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

• Add options to set background color when exporting video by @scott-yj-yang in #1328
• Add resize/scroll to training GUI by @KevinZ0217 in #1565
• Highlight instance box on hover by @talmo in #2055
• Enable touchpad pinch to zoom by @talmo in #2058
• Do not always color skeletons table black by @roomrys in #1952
• Make status bar dependent on UI mode by @7174Andy in #2063
• Graceful failing with seeking errors by @talmo in #1712
• Import DLC with uniquebodyparts, add Tracks by @getzze in #1562
• Fix GUI crash on scroll by @roomrys in #1883

Full Changelog

Enhancements

• Add options to set background color when exporting video by @scott-yj-yang in #1328
• Increase range on batch size by @roomrys in #1513
• Add resize/scroll to training GUI by @KevinZ0217 in #1565
• support loading slp files with non-compound types and str in metadata by @lambdaloop in #1566
• change inference pipeline option to tracking-only by @shrivaths16 in #1666
• Only propagate Transpose Tracks when propagate is checked by @vaibhavtrip29 in #1748
• Add batch size to GUI for inference by @shrivaths16 in #1771
• Add ZMQ support via GUI and CLI by @shrivaths16 in #1780
• Change menu name to match deleting predictions beyond max instance by @shrivaths16 in #1790
• Adding ragged metadata to info.json by @shrivaths16 in #1765
• Add option to export to CSV via sleap-convert and API by @eberrigan in #1730
• Add normalized_instance_similarity method by @gitttt-1234 in #1939
• Update installation docs 1.4.1 by @roomrys in #1810
• Option for Max Stride to be 128 by @MweinbergUmass in #1941
• Allow csv and text file support on sleap track by @emdavis02 in #1875
• Added Three Different Cases for Adding a New Instance by @7174Andy in #1859
• Generate suggestions using max point displacement threshold by @gqcpm in #1862
• Add object keypoint similarity method by @getzze in #1003
• Allowing inference on multiple videos via sleap-track by @emdavis02 in #1784
• Add Keep visualizations checkbox to training GUI by @hajin-park in #1824
• Menu option to open preferences directory and update to util functions to pathlib by @shrivaths16 in #1843
• Add tracking score as seekbar header options by @talmo in #2047
• Don't mark complete on instance scaling by @talmo in #2049
• Add check for instances with track assigned before training ID models by @talmo in #2053
• Add menu item for deleting instances beyond frame limit by @shrivaths16 in #1797
• Highlight instance box on hover by @talmo in #2055
• Make node marker and label sizes configurable via preferences by @talmo in #2057
• Enable touchpad pinch to zoom by @talmo in #2058
• Separate the video name and its filepath columns in VideoTablesModel by @7174Andy in #2052
• Make status bar dependent on UI mode by @7174Andy in #2063

Fixes

• Graceful failing with seeking errors by @talmo in #1712
• Fix IndexError for hdf5 file import for single instance analysis files by @shrivaths16 in #1695
• Import DLC with uniquebodyparts, add Tracks by @getzze in #1562
• Make the hdf5 videos store as int8 format by @lambdaloop in #1559
• Scale new instances to new frame size by @ssrinath22 in #1568
• Fix package export by @talmo in #1619
• View Hyperparameter nonetype fix by @shrivaths16 in #1766
• Set selected instance to None after removal by @roomrys in #1808
• Fix zmq inference by @roomrys in #1800
• Remove no module named work error by @roomrys in #1956
• Use tf.math.mod instead of % by @roomrys in #1931
• Do not always color skeletons table black by @roomrys in #1952
• Do not apply offset when double clicking a PredictedInstance by @roomrys in #1888
• Fix typo to allow rendering videos with mp4 (Mac) by @roomrys in #1892
• Fix GUI crash on scroll by @roomrys in #1883
• Handle case when no frame selection for trail overlay by @roomrys in #1832
• Fix COCO Dataset Loading for Invisible Keypoints by @felipe-parodi in #2035
• Fix import PySide2 -> qtpy by @talmo in #2065

Dependencies

• Replace imgaug with albumentations by @talmo in #1623
• Fix out of bounds albumentations issues and update dependencies by @eberrigan in #1724
• Update to new TensorFlow conda package by @eberrigan in #1726
• Fix conda builds by @eberrigan in #1776
• Handle skeleton encoding internally by @eberrigan in #1970
• Handle skeleton decoding internally by @roomrys in #1961
• Add imageio dependencies for pypi wheel by @roomrys in #1950
• Add missing imageio-ffmpeg to meta.ymls by @roomrys in #1943
• Manually handle Instance.from_predicted structuring when not None by @roomrys in #1930
• Refactor LossViewer to use matplotlib by @eberrigan in #1899
• Replace all Video structuring with Video.cattr() by @roomrys in #1911
• Use positional argument for exception type by @roomrys in #1912
• Remove unsupported |= operand to prepare for PySide6 by @roomrys in #1910
• Replace QtDesktop widget in preparation for PySide6 by @roomrys in #1908
• Use | instead of + in key commands by @roomrys in #1907
• Use Video.from_filename when structuring videos by @roomrys in #1905
• Refactor video writer to use imageio instead of skvideo by @eberrigan in #1900
• Pin ndx-pose<0.2.0 by @talmo in #1978

Documentation

• Add bonsai guide for sleap docs by @croblesMed in #2050
• Add channels for pip conda env by @roomrys in #2067

Refactors

• Set default callable for match_lists_function by @roomrys in #1520
• Allow passing in Labels to app.main by @roomrys in #1524
• Replace (broken) --unrag with --ragged by @roomrys in #1539
• Add function to create app by @roomrys in https://github.com/talmolab/s...

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

SLEAP v1.4.1a2

SLEAP v1.4.1a2 is a pre-release. See 1.3.3 for the latest stable release. The crucial change here is Fix zmq inference by @roomrys in #1800 since inference was not working in the pre-release v1.4.1a1 due to the addition of zmq port options for training in #1780 that were not being used for inference.

Quick install

mamba (Windows/Linux/GPU):

mamba create -y -n sleap_v1.4.1a2 -c conda-forge -c nvidia -c sleap/label/dev -c anaconda sleap=1.4.1a2

mamba (Mac):

mamba create -y -n sleap_v1.4.1a2 -c conda-forge -c anaconda -c sleap/label/dev sleap=1.4.1a2

pip (any OS except Apple Silicon):

pip install sleap[pypi]==1.4.1a2

What's Changed

Fixes

• Fix zmq inference by @roomrys in #1800

Workflow Changes

• Fix windows conda package upload and build ci by @eberrigan in #1792
• Bump to v1.4.1a2 by @eberrigan in #1835

Enhancements and Refactors

• Set selected instance to None after removal by @roomrys in #1808
• Add InstancesList class to handle backref to LabeledFrame by @roomrys in #1807

Full Changelog: v1.4.1a1...v1.4.1a2

SLEAP v1.4.1a1

SLEAP v1.4.1a1 is a pre-release. See 1.3.3 for the latest stable release. There are many changes to dependencies in this pre-release: if you are having installation issues with v1.3.3, you should try this version instead.

Quick install

mamba (Windows/Linux/GPU):

mamba create -y -n sleap_v1.4.1a1 -c conda-forge -c nvidia -c sleap/label/dev -c anaconda sleap=1.4.1a1

mamba (Mac):

mamba create -y -n sleap_v1.4.1a1 -c conda-forge -c anaconda -c sleap/label/dev sleap=1.4.1a1

pip (any OS except Apple Silicon):

pip install sleap[pypi]==1.4.1a1

What's Changed

Enhancements

• Add options to set background color when exporting video by @scott-yj-yang in #1328
• Increase range on batch size by @roomrys in #1513
• Add resize/scroll to training GUI by @KevinZ0217 in #1565
• support loading slp files with non-compound types and str in metadata by @lambdaloop in #1566
• change inference pipeline option to tracking-only by @shrivaths16 in #1666
• Only propagate Transpose Tracks when propagate is checked by @vaibhavtrip29 in #1748
• Add batch size to GUI for inference by @shrivaths16 in #1771
• Add ZMQ support via GUI and CLI by @shrivaths16 in #1780
• Change menu name to match deleting predictions beyond max instance by @shrivaths16 in #1790
• Adding ragged metadata to info.json by @shrivaths16 in #1765
• Add option to export to CSV via sleap-convert and API by @eberrigan in #1730

Refactors

• Set default callable for match_lists_function by @roomrys in #1520
• Allow passing in Labels to app.main by @roomrys in #1524
• Replace (broken) --unrag with --ragged by @roomrys in #1539
• Add function to create app by @roomrys in #1546
• Refactor AddInstance command by @roomrys in #1561

Fixes

• Graceful failing with seeking errors by @talmo in #1712
• Fix IndexError for hdf5 file import for single instance analysis files by @shrivaths16 in #1695
• Import DLC with uniquebodyparts, add Tracks by @getzze in #1562
• Make the hdf5 videos store as int8 format by @lambdaloop in #1559
• Scale new instances to new frame size by @ssrinath22 in #1568
• Fix package export by @talmo in #1619
• View Hyperparameter nonetype fix by @shrivaths16 in #1766

Dependency Changes

• Replace imgaug with albumentations by @talmo in #1623
• Fix out of bounds albumentations issues and update dependencies by @eberrigan in #1724
• Update to new TensorFlow conda package by @eberrigan in #1726
• Fix conda builds by @eberrigan in #1776

Workflow Changes

• Fix CI on macosx-arm64 by @talmo in #1734
• Upgrade build actions for release by @eberrigan in #1779
• Fix website build and remove build cache across workflows by @eberrigan in #1786
• Bump to 1.4.1a1 by @eberrigan in #1791

Website Changes

• Add ABL:AOC 2023 Workshop link by @roomrys in #1673

New Contributors

• @scott-yj-yang made their first contribution in #1328
• @lambdaloop made their first contribution in #1559
• @ssrinath22 made their first contribution in #1568

Full Changelog: v1.3.3...v1.4.1a1

SLEAP v1.4.1a0

What's Changed

• Add options to set background color when exporting video by @scott-yj-yang in #1328
• Increase range on batch size by @roomrys in #1513
• Set default callable for match_lists_function by @roomrys in #1520
• Allow passing in Labels to app.main by @roomrys in #1524
• Replace (broken) --unrag with --ragged by @roomrys in #1539
• Add function to create app by @roomrys in #1546
• Refactor AddInstance command by @roomrys in #1561
• Import DLC with uniquebodyparts, add Tracks by @getzze in #1562
• Make the hdf5 videos store as int8 format by @lambdaloop in #1559
• Scale new instances to new frame size by @ssrinath22 in #1568
• Fix package export by @talmo in #1619
• Add resize/scroll to training GUI by @KevinZ0217 in #1565
• support loading slp files with non-compound types and str in metadata by @lambdaloop in #1566
• change inference pipeline option to tracking-only by @shrivaths16 in #1666
• Add ABL:AOC 2023 Workshop link by @roomrys in #1673
• Graceful failing with seeking errors by @talmo in #1712
• Fix IndexError for hdf5 file import for single instance analysis files by @shrivaths16 in #1695
• Replace imgaug with albumentations by @talmo in #1623
• Fix out of bounds albumentations issues and update dependencies by @eberrigan in #1724
• Update to new TensorFlow conda package by @eberrigan in #1726
• Fix CI on macosx-arm64 by @talmo in #1734
• Add option to export to CSV via sleap-convert and API by @eberrigan in #1730
• Only propagate Transpose Tracks when propagate is checked by @vaibhavtrip29 in #1748
• View Hyperparameter nonetype fix by @shrivaths16 in #1766
• Adding ragged metadata to info.json by @shrivaths16 in #1765
• Add batch size to GUI for inference by @shrivaths16 in #1771
• Fix conda builds by @eberrigan in #1776
• Upgrade build actions for release by @eberrigan in #1779

New Contributors

• @scott-yj-yang made their first contribution in #1328
• @lambdaloop made their first contribution in #1559
• @ssrinath22 made their first contribution in #1568

Full Changelog: v1.3.3...v1.4.1a0

SLEAP v1.4.0a0

What's Changed

• Add options to set background color when exporting video by @scott-yj-yang in #1328
• Increase range on batch size by @roomrys in #1513
• Set default callable for match_lists_function by @roomrys in #1520
• Allow passing in Labels to app.main by @roomrys in #1524
• Replace (broken) --unrag with --ragged by @roomrys in #1539
• Add function to create app by @roomrys in #1546
• Refactor AddInstance command by @roomrys in #1561
• Import DLC with uniquebodyparts, add Tracks by @getzze in #1562
• Make the hdf5 videos store as int8 format by @lambdaloop in #1559
• Scale new instances to new frame size by @ssrinath22 in #1568
• Fix package export by @talmo in #1619
• Add resize/scroll to training GUI by @KevinZ0217 in #1565
• support loading slp files with non-compound types and str in metadata by @lambdaloop in #1566
• change inference pipeline option to tracking-only by @shrivaths16 in #1666
• Add ABL:AOC 2023 Workshop link by @roomrys in #1673
• Graceful failing with seeking errors by @talmo in #1712
• Fix IndexError for hdf5 file import for single instance analysis files by @shrivaths16 in #1695
• Replace imgaug with albumentations by @talmo in #1623
• Fix out of bounds albumentations issues and update dependencies by @eberrigan in #1724
• Update to new TensorFlow conda package by @eberrigan in #1726
• Fix CI on macosx-arm64 by @talmo in #1734
• Add option to export to CSV via sleap-convert and API by @eberrigan in #1730
• Only propagate Transpose Tracks when propagate is checked by @vaibhavtrip29 in #1748
• View Hyperparameter nonetype fix by @shrivaths16 in #1766
• Adding ragged metadata to info.json by @shrivaths16 in #1765
• Add batch size to GUI for inference by @shrivaths16 in #1771
• Fix conda builds by @eberrigan in #1776

New Contributors

• @scott-yj-yang made their first contribution in #1328
• @lambdaloop made their first contribution in #1559
• @ssrinath22 made their first contribution in #1568

Full Changelog: v1.3.3...v1.4.0a0

SLEAP v1.3.3

This is a brown-bag release following insufficient restrictions on allowable tensorflow versions for the "pypi" extra sleap[pypi] in 1.3.2. While the conda packages for 1.3.2 were not affected (since tensorflow is pulled in from anaconda), the PyPI only package installed via pip install sleap[pypi] had conflicts between the version of tensorflow and the version of keras. See 1.3.0, 1.3.1, and 1.3.2 for previous notable changes.

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=1.3.3

mamba (Mac):

mamba create -y -n sleap -c conda-forge -c anaconda -c sleap sleap=1.3.3

pip (any OS except Apple Silicon):

pip install sleap[pypi]==1.3.3

Full Changelog

Fixes

• Do not try to remove item if already deleted by @roomrys in #1498
• Set LD_LIBRARY_PATH on mamba activate by @roomrys in #1496
• Reset LD_LIBRARY_PATH on deactivate by @roomrys in #1502

Dependencies

• Add version restirctions to tensorflow for pypi by @roomrys in #1485
• Remove imageio pin by @roomrys in #1501

Full Changelog: v1.3.2...v1.3.3