Welcome to the official PyTorch RU-vid Channel. Learn about the latest PyTorch tutorials, new, and more.
PyTorch is an open source machine learning framework that is used by both researchers and developers to build, train, and deploy ML systems that solve many different complex challenges. PyTorch is an open source project at the Linux Foundation.
Responses to questions we didn't have time to address live: Q: SVE backend patch was merged in PyTorch main branch before PyTorch 2.5 release , but not include in release/pytorch 2.5 . Any specific reason ? A: The branch cut for a given release usually happens quite in advance of the release itself as we run a lot of validation. See dev-discuss.pytorch.org/c/release-announcements/27 for all the details on the process for each release. Q: The binary release on PyTorch site has dependency on CUDA / NCCL libraries from pypi. It is not using system installed CUDA. Is building from source the only option to use system installed libraries? A: If you want to do this because you want to use a CUDA version without pre-built binaries, then yes. Unfortunately the PyTorch binary has a hard dependency on each CUDA version and so can only run with that exact version. If you want to not have to install all the nvidia package and you already have the same cuda installed locally. You can use --no-deps when you do pip install to not grab these packages. And the PyTorch install will use any available cuda install (it is just that the one coming from pypi is prioritized to avoid mismatched cuda versions).
Can anyone help me with this?? RuntimeError Traceback (most recent call last) <ipython-input-13-336ac0521b7a> in <cell line: 10>() 8 9 # Load the quantized model weights ---> 10 quantized_model.load_state_dict(torch.load('/content/drive/MyDrive/Aditya Pandey/global_wheat_detection-2-20241004T044942Z-001/global_wheat_detection-2/Copy of faster_rcnn_40MB.pth', map_location=torch.device('cpu'))) 11 12 # Set the model to evaluation mode /usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py in load_state_dict(self, state_dict, strict, assign) 2213 2214 if len(error_msgs) > 0: -> 2215 raise RuntimeError('Error(s) in loading state_dict for {}: \t{}'.format( 2216 self.__class__.__name__, " \t".join(error_msgs))) 2217 return _IncompatibleKeys(missing_keys, unexpected_keys) RuntimeError: Error(s) in loading state_dict for QuantizedFasterRCNN: Missing key(s) in state_dict: "model.backbone.body.conv1.weight", "model.backbone.body.bn1.weight", "model.backbone.body.bn1.bias", "model.backbone.body.bn1.running_mean", "model.backbone.body.bn1.running_var", "model.backbone.body.layer1.0.conv1.weight", "model.backbone.body.layer1.0.bn1.weight", "model.backbone.body.layer1.0.bn1.bias", "model.backbone.body.layer1.0.bn1.running_mean", "model.backbone.body.layer1.0.bn1.running_var"........
Have been running 2.5 nightly on Arc on windows and GPU Max on IDC and it's been fine so far. Excited it's finally available directly via pip with static mkl
Everything should be open source. Innovation slows down as soon as the guys in the suits come in with their whips and start the endless pursuit of returns stressing everyone out.
on a related topic - check out my series "Bone Hunt" using AI to find dinosaur bone beds (11 part series) ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-rEsRFiu-I90.html
If you still think you don't understand Autograd, this video (ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-hjnVLfvhN0Q.htmlsi=Y78dMifRIL_hJwyZ) walks through examples to calculate simple grads by hand and verify them using PyTorch. I understood from it more than any other video.
there is a small error in the content : Linear Layers The most basic type of neural network layer is a linear or fully connected layer. This is a layer where every input influences every output of the layer to a degree specified by the layer's weights. If a model has m inputs and n outputs, the weights will be an m * n matrix. here it should be n*m matrix.
