On 2/26/24 19:06, Richard Fontana wrote: <snip>
4. Is it acceptable to package code which downloads pre-trained weights from a non-Fedora source upon first use post-installation by a user if that model and its associated weights are a. For a specific model? b. For a user-defined model which may or may not exist at the time of packaging? I can provide examples of any of these situations if that would be helpful.Can you elaborate on 4a/4b with examples?
There are 2 simple examples for the two cases I mentioned (4a and 4b) at the bottom of this email Tim ----------------------------------------------------------------- 4a - code that downloads pre-trained weights for a specific model ----------------------------------------------------------------- torchvision [1] is a pytorch adjacent library which contains "Datasets, Transforms and Models specific to Computer Vision". torchvision contains code to implement several pre-defined model structures which can be used with or without pre-trained weights [2]. torchvision is distributed under a BSD 3-clause license [3] and is currently packaged in Fedora as python-torchvision but all of the specific model code is removed at package build time and not distributed as a Fedora package. As an example, to instantiate a vision transformer (ViT) base model variant with 16x16 input patch size and download pre-trained weights, the following python code could be used: ``` import torchvision vitb16 = torchvision.models.vit_b_16() ``` The code describing the vit_b_16 model is included in torchvision but the weights are downloaded from an external site when the model is first used. At the time I write this, the weights are downloaded from https://download.pytorch.org/models/vit_b_16-c867db91.pth In this case and for all the other models contained in torchvision, the exact links to the pretrained weights are all contained within the torchvision code. Something worthy of note is that the weights for vit_b_16 are from Facebook's SWAG project [4] which is distributed as CC-BY-NC-4.0 [5] and would not be acceptable for use in a Fedora package. For the other models in torchvision, some of the pre-trained weights have an explicit license (like ViT) but many of them are not distributed under any explicit license (ResNet[6] as an example). [1] https://github.com/pytorch/vision [2] https://github.com/pytorch/vision/tree/main/torchvision/models [3] https://github.com/pytorch/vision/blob/main/LICENSE [4] https://github.com/facebookresearch/SWAG [5] https://github.com/facebookresearch/SWAG/blob/main/LICENSE [6] https://pytorch.org/hub/pytorch_vision_resnet/ ---------------------------------------------------- 4b - code that downloads an somewhat arbitrary model ---------------------------------------------------- One of the newer features of pytorch (which is still considered to be in beta) is the ability to interface with "PyTorch Hub" [7] to use pre-defined and pre-trained models which have been uploaded by other users. At the time of this writing, the pytorch hub appears to be moderated by the pytorch team but the underlying code which supports loading of semi-arbitrary models from user-defined locations at runtime. As an example, this code loads a MiDaS v3 large model with pre-trained weights directly from intel's github repo [8]. ``` model_type = "DPT_Large" midas = torch.hub.load("intel-isl/MiDaS", model_type) ``` Similar to the ViT example above, this model will download weights from a url (https://github.com/isl-org/MiDaS/releases/download/v3/dpt_large_384.pt at the time of this writing) but unlike the ViT example, the definitions of the model and where the weights are located are determined by code contained in the github repository specified by the user [9] and downloaded at runtime to determine the exact link to any code and pre-trained weights. The MiDaS repository is distributed under an MIT license [10]. [7] https://pytorch.org/hub/ [8] https://github.com/isl-org/MiDaS [9] https://github.com/isl-org/MiDaS/blob/master/hubconf.py#L218 [10] https://github.com/isl-org/MiDaS/blob/master/LICENSE -- _______________________________________________ legal mailing list -- legal@xxxxxxxxxxxxxxxxxxxxxxx To unsubscribe send an email to legal-leave@xxxxxxxxxxxxxxxxxxxxxxx Fedora Code of Conduct: https://docs.fedoraproject.org/en-US/project/code-of-conduct/ List Guidelines: https://fedoraproject.org/wiki/Mailing_list_guidelines List Archives: https://lists.fedoraproject.org/archives/list/legal@xxxxxxxxxxxxxxxxxxxxxxx Do not reply to spam, report it: https://pagure.io/fedora-infrastructure/new_issue
