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🔧 TensorFlow vs PyTorch: The Real Difference Isn't Accuracy


Nachrichtenbereich: 🔧 Programmierung
🔗 Quelle: dev.to

A hands-on comparison of performance, flexibility, and developer experience using CIFAR-10

A few days ago, I set out to build a simple image classification model using Convolutional Neural Networks... [Weiterlesen]

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💾 trunk/46931e0e655d691efc0bf431ab0fe920e7db871a: Remove plain asserts in torch/_dynamo/source.py (#182171)


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🔧 TensorFlow vs PyTorch: The Real Difference Isn't Accuracy


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💾 ciflow/trunk/182326: Update on "Add tearDown checks for torch function state leaks"


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💾 trunk/873eaeb74249197dc7de29303aed4fcf47823949: [BE] Build all CUDA wheels per (arch, cuda) on a single runner (#184045)


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💾 trunk/33004619eb042c0a6b85db3e2e17a6409b89219d: [DTensor] Use explicit hints for unbacked sharding (#183545)


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💾 trunk/419b2d0b867ae2c6481a0f87c1e99e02ea4b3dc0: Remove plain asserts in torch/_dynamo/compiled_autograd.py (#182164)


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💾 ciflow/torchtitan/186020: Reintroduce ptxas bundling for CUDA 13.0 binaries


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💾 trunk/55811c3e7b01509262d4e7862c15eb17b439ceae: [ATen][FakeTensor] Handle unbacked dims in folded matmul (#183397)


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💾 trunk/826ab493be5714d267a00badb3773e0d9b78e00f: [Dynamo] Remove has_unpack_var_sequence (#185153)


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💾 trunk/151e7e0cbb86bb29c72bc5b116670ed3221cb5e5: [inductor] Support InductorBenchmarker for non-CUDA device (#184567)


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💾 trunk/98b772f8ab86176b8e0b572d88f4e09510c1b587: [Inductor] Handle hinted and fallback unbacked symbols (#183840)


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💾 viable/strict/1780616745: [inductor] Fix module exhaustion during exhaustive tuning (#184285)


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💾 trunk/9954ca57923e230e51901bf08265f69c47969702: Remove plain asserts in torch/_dynamo/ (more top level files) (#182162)


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💾 trunk/eaa777900b14e1aa6ffc1959cbcdb055d5de3e75: Update third_party/kineto submodule to 7739225 (#182973)


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💾 viable/strict/1780178955: Use SVE `exp_u20`/`fexp_u20` for SVE128 (#184341)


📈 203.03 Punkte
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