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Mention small discrepancy due to Dropout non-reproducibility in PyTorch (#519)
* Mention small discrepancy due to Dropout non-reproducibility in PyTorch * bump pytorch version
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@ -1,4 +1,4 @@
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name: Test PyTorch 2.0 and 2.5
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name: Test PyTorch 2.0 and 2.6
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on:
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push:
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@ -23,7 +23,7 @@ jobs:
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runs-on: ubuntu-latest
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strategy:
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matrix:
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pytorch-version: [ 2.0.1, 2.5.0 ]
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pytorch-version: [ 2.0.1, 2.6.0 ]
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steps:
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- uses: actions/checkout@v4
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@ -317,6 +317,7 @@
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"```\n",
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"\n",
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"- Since these are just random numbers, this is not a reason for concern, and you can proceed with the remainder of the chapter without issues\n",
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"- One possible reason for this discrepancy is the differing behavior of `nn.Dropout` across operating systems, depending on how PyTorch was compiled, as discussed [here on the PyTorch issue tracker](https://github.com/pytorch/pytorch/issues/121595)\n",
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"\n",
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"---"
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]
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@ -1348,6 +1348,16 @@
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"# print(f\"Training completed in {execution_time_minutes:.2f} minutes.\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2e8b86f0-b07d-40d7-b9d3-a9218917f204",
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"metadata": {},
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"source": [
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"- Note that you might get slightly different loss values on your computer, which is not a reason for concern if they are roughly similar (a training loss below 1 and a validation loss below 7)\n",
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"- Small differences can often be due to different GPU hardware and CUDA versions or small changes in newer PyTorch versions\n",
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"- Even if you are running the example on a CPU, you may observe slight differences; a possible reason for a discrepancy is the differing behavior of `nn.Dropout` across operating systems, depending on how PyTorch was compiled, as discussed [here on the PyTorch issue tracker](https://github.com/pytorch/pytorch/issues/121595)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 28,
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