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Port details
linux-ai-ml-env Linux Python environment for running Stable Diffusion models and PyTorch CUDA examples
1.0.0 sciencenew! on this many watch lists=0 search for ports that depend on this port Find issues related to this port Report an issue related to this port View this port on Repology. pkg-fallout Package not present on quarterly.This port was created during this quarter. It will be in the next quarterly branch but not the current one.
Maintainer: voxnod@gmail.com search for ports maintained by this maintainer
Port Added: 2025-03-28 12:39:07
Last Update: 2025-03-28 12:37:25
Commit Hash: 8f28236
Also Listed In: linux
License: MULTI
WWW:
https://github.com/ai-ml-env/ai-ml-env/
Description:
This port provides a Linux Python environment tailored for running Stable Diffusion models on NVIDIA GPUs. It includes: - A preconfigured Python environment with dependencies for Stable Diffusion and PyTorch examples. - PyTorch examples. - Sample scripts for running Stable Diffusion model and PyTorch examples.
Homepage    cgit ¦ Codeberg ¦ GitHub ¦ GitLab ¦ SVNWeb - no subversion history for this port

Manual pages:
FreshPorts has no man page information for this port.
pkg-plist: as obtained via: make generate-plist
Expand this list (29 items)
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  1. usr/bin/accelerate
  2. usr/bin/accelerate-config
  3. usr/bin/accelerate-estimate-memory
  4. usr/bin/accelerate-launch
  5. usr/bin/accelerate-merge-weights
  6. usr/bin/diffusers-cli
  7. usr/bin/f2py
  8. usr/bin/fonttools
  9. usr/bin/huggingface-cli
  10. usr/bin/isympy
  11. usr/bin/normalizer
  12. usr/bin/numpy-config
  13. usr/bin/proton
  14. usr/bin/proton-viewer
  15. usr/bin/pyftmerge
  16. usr/bin/pyftsubset
  17. usr/bin/torchfrtrace
  18. usr/bin/torchrun
  19. usr/bin/tqdm
  20. usr/bin/transformers-cli
  21. usr/bin/ttx
  22. usr/share/ai-ml-env/dummy-uvm.so
  23. /usr/local/bin/ai-ml-env-bash
  24. /usr/local/bin/ai-ml-env-python
  25. /usr/local/share/ai-ml-env/stable-diffusion-sample.py
  26. @dir /usr/local/bin
  27. @owner
  28. @group
  29. @mode
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Dependency lines:
  • linux-ai-ml-env>0:science/linux-ai-ml-env
To install the port:
cd /usr/ports/science/linux-ai-ml-env/ && make install clean
To add the package, run one of these commands:
  • pkg install science/linux-ai-ml-env
  • pkg install linux-ai-ml-env
NOTE: If this package has multiple flavors (see below), then use one of them instead of the name specified above.
PKGNAME: linux-ai-ml-env
Flavors: there is no flavor information for this port.
ONLY_FOR_ARCHS: amd64
distinfo:
TIMESTAMP = 1742925172 SHA256 (ai-ml-env/miniconda3.tar.gz) = 1d9eb42dd753f462f4ccd82ab7716561b90274eeaa45254fd50b879bb09537b7 SIZE (ai-ml-env/miniconda3.tar.gz) = 3511877484

Expand this list (2 items)

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SHA256 (ai-ml-env/pytorch-examples-5dfeb46902baf444010f2f54bcf4dfbea109ae4d_GH0.tar.gz) = a024b134dfd1edba649289e551a0cd85bd22424dd76df4303b280e20757a602c SIZE (ai-ml-env/pytorch-examples-5dfeb46902baf444010f2f54bcf4dfbea109ae4d_GH0.tar.gz) = 7298483

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Packages (timestamps in pop-ups are UTC):
linux-ai-ml-env
ABIaarch64amd64armv6armv7i386powerpcpowerpc64powerpc64le
FreeBSD:13:latest-1.0.0------
FreeBSD:13:quarterly--------
FreeBSD:14:latest-1.0.0------
FreeBSD:14:quarterly--------
FreeBSD:15:latest-1.0.0n/a-n/a---
Dependencies
NOTE: FreshPorts displays only information on required and default dependencies. Optional dependencies are not covered.
Build dependencies:
  1. linux-rl9-devtools>=0 : devel/linux-rl9-devtools
Runtime dependencies:
  1. linux-nvidia-libs>=0 : x11/linux-nvidia-libs
  2. linux-rl9-python39>=0 : lang/linux-rl9-python3
  3. nvidia-driver>=0 : x11/nvidia-driver
  4. linux_base-rl9>=9.2 : emulators/linux_base-rl9
Fetch dependencies:
  1. linux_base-rl9>=9.2 : emulators/linux_base-rl9
There are no ports dependent upon this port
Configuration Options:
No options to configure
Options name:
science_linux-ai-ml-env
USES:
linux:rl9
pkg-message:
For install:
To utilize CUDA support for PyTorch, both https://github.com/shkhln 's compatibility library preloading and Linux compatibility layer usage are required, as demonstrated in the provided demonstration scripts. To play with Stable Diffusion copy it to a user-writable directory, edit at will and launch it via ai-ml-env-python: cp /usr/local/share/ai-ml-env/stable-diffusion-sample.py ~/ ai-ml-env-python stable-diffusion-sample.py This will download data packages into ~/.cache/huggingface/hub/ during the first-time run. The port also provides Pytorch examples from the official GitHub repository. To run and modify these examples, copy them to a user-writable directory and then launch the entry point script via ai-ml-env-bash: cp -r /usr/local/share/ai-ml-env/pytorch-examples ~/pytorch-examples cd ~/pytorch-examples && ai-ml-env-bash ./run_python_examples.sh You can then run ai-ml-env-bash ./run_python_examples.sh clean to remove downloaded data or just remove the whole directory to clean up everything.
Master Sites:
Expand this list (1 items)
Collapse this list.
  1. https://arrowd.name/
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Number of commits found: 1

Commit History - (may be incomplete: for full details, see links to repositories near top of page)
CommitCreditsLog message
1.0.0
28 Mar 2025 12:37:25
commit hash: 8f2823654667cc746695a83126e15c3172c6261dcommit hash: 8f2823654667cc746695a83126e15c3172c6261dcommit hash: 8f2823654667cc746695a83126e15c3172c6261dcommit hash: 8f2823654667cc746695a83126e15c3172c6261d files touched by this commit		 Gleb Popov (arrowd) search for other commits by this committer
Author: Alexey Donskov		 
science/linux-ai-ml-env: Linux Python environment for running AI and ML stuff

Sponsored by:	Future Crew, LLC
Pull Request:	https://github.com/freebsd/freebsd-ports/pull/361

Number of commits found: 1
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