Amazing! In my graduate research I use PyTorch due to its flexibility and its more close to my thoughts when writing code in order to validate some idea. All of this came from it being a tool fist-based in scientific research and programming experience. PyTorch became the new numpy, but comes with all of this ecosystem that now I can compile my research ideas to deploy it into production ready server. Awesome!
I know im asking the wrong place but does anyone know a tool to log back into an instagram account..? I was dumb lost my login password. I would appreciate any tricks you can offer me
Thanks for the great video. I was only wondering how the last part about PyTorch JIT compilation for more dynamic setups, differs from the scripting (or tracing) part explained earlier in the video. Is this an extra step we can take alongside scripting? I am kind of confused since both scripting and tracing are part of the jit module, and since scripting (I believe) already allows for more dynamic models as explained in the video, so I am trying to figure out how this differs. Thanks in advance for any clarification!
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Wish we had more people involved on the C++ endpoint deployment for Libtorch #drogon #rustBinding #golangBinding
Amazing! In my graduate research I use PyTorch due to its flexibility and its more close to my thoughts when writing code in order to validate some idea. All of this came from it being a tool fist-based in scientific research and programming experience. PyTorch became the new numpy, but comes with all of this ecosystem that now I can compile my research ideas to deploy it into production ready server. Awesome!
I know im asking the wrong place but does anyone know a tool to log back into an instagram account..?
I was dumb lost my login password. I would appreciate any tricks you can offer me
I really love PyTorch. It's flexible and has great supports for production.
Great video! Plenty of useful insights. I would like to see more of these videos :-)
Cheers
PyTorch is amazing!
Very informative, Thank you!
Great presentation.
Is James a machine? :D (Like Commander Data from Starship Enterprise?)
Thanks for the great video. I was only wondering how the last part about PyTorch JIT compilation for more dynamic setups, differs from the scripting (or tracing) part explained earlier in the video. Is this an extra step we can take alongside scripting? I am kind of confused since both scripting and tracing are part of the jit module, and since scripting (I believe) already allows for more dynamic models as explained in the video, so I am trying to figure out how this differs. Thanks in advance for any clarification!
Why there is not fuse group present in the IR representation? How can we visualize the optimization made by torchscript over the model?
Will torchscript allow run models on opencl backend at runtime
Hi can anyone suggest a place or book where I can learn more about these optimizations
Hi Clive, for more information on TorchScript, we recommend visiting: pytorch.org/docs/stable/jit.html. We hope this helps!
How about Torchscript into Julia instead of C++?
Hi C, please join and post this question to the PyTorch Forums: discuss.pytorch.org/c/jit/
So I can write Bert, transformer, and more advance model in Pytorch and run them in Java and C++?