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@@ -134,7 +134,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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- "# verify profiling capacility \n",
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+ "# verify profiling capability \n",
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"!nsys status -e"
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]
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},
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@@ -142,8 +142,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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- "---\n",
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- "Verify Profiling Capability, the expected output should look something simialr to the below\n",
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+ "Verify profiling capability, the expected output should look something simialr to the below\n",
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"\n",
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" Sampling Environment Check\n",
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" Linux Kernel Paranoid Level = 2: OK\n",
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@@ -160,7 +159,7 @@
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"metadata": {},
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"source": [
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"\n",
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- "To start with, we need to create placeholder for dataset. We are going to populate these folders later."
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+ "To start with, we need to create folders as placeholders for dataset. We are going to populate these folders later."
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]
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},
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{
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@@ -225,7 +224,7 @@
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"\n",
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"Basic understanding on Deep learning and Pytorch is required, if you are new to Deep learning and or new to Pytorch, it is recommended to go through the [Distributed_Deep_Learning bootcamp](https://github.com/gpuhackathons-org/gpubootcamp/tree/master/ai/Distributed_Deep_Learning/English/python) and [Pytorch tutorials](https://pytorch.org/tutorials/) as prior.\n",
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" \n",
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- "**Disclaimer** : All the results mentioned in the notebooks were tested on a *DGX-2 machine equipped with 2 or 4 or 8 x A100 connected via NVLink*. The results would vary when using different hardware and would also depend on the Interconnect bandwidth and the thermal conditions of the machine."
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+ "**Disclaimer** : All the results mentioned in the notebooks were tested on a *DGX-2 machine equipped with 2 x A100 GPUs connected via NVLink*. The results would vary when using different hardware and would also depend on the Interconnect bandwidth and the thermal conditions of the machine."
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]
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},
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{
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zenodia