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.ci/docker/requirements.txt

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@@ -69,5 +69,7 @@ iopath
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pygame==2.6.0
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pycocotools
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semilearn==0.3.2
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torchao==0.0.3
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torchao==0.5.0
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segment_anything==1.0
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torchrec==0.8.0
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fbgemm-gpu==0.8.0

.jenkins/metadata.json

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"intermediate_source/model_parallel_tutorial.py": {
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"needs": "linux.16xlarge.nvidia.gpu"
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},
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"intermediate_source/torchrec_intro_tutorial.py": {
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"needs": "linux.g5.4xlarge.nvidia.gpu"
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},
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"recipes_source/torch_export_aoti_python.py": {
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"needs": "linux.g5.4xlarge.nvidia.gpu"
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},

en-wordlist.txt

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webp
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wsi
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wsis
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Meta's
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RecSys
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TorchRec
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sharding
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TBE
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dtype
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EBC
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sharder
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hyperoptimized
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DMP
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unsharded
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lookups
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KJTs
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amongst
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async
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everytime
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prototyped
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GBs
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HBM
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gloo
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nccl
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Localhost
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gpu
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torchmetrics
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url
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colab
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sharders
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Criteo
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torchrec

index.rst

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@@ -852,7 +852,7 @@ Welcome to PyTorch Tutorials
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:header: Introduction to TorchRec
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:card_description: TorchRec is a PyTorch domain library built to provide common sparsity & parallelism primitives needed for large-scale recommender systems.
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:image: _static/img/thumbnails/torchrec.png
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:link: intermediate/torchrec_tutorial.html
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:link: intermediate/torchrec_intro_tutorial.html
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:tags: TorchRec,Recommender
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.. customcarditem::
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:hidden:
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:caption: Recommendation Systems
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intermediate/torchrec_tutorial
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intermediate/torchrec_intro_tutorial
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advanced/sharding
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.. toctree::

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