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July 15, 2022 17:17
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BenchMark-Collab.ipynb
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{ | |
"nbformat": 4, | |
"nbformat_minor": 0, | |
"metadata": { | |
"colab": { | |
"name": "BenchMark-Collab.ipynb", | |
"provenance": [], | |
"machine_shape": "hm", | |
"authorship_tag": "ABX9TyMJ1nUmZHe73kXDjU8XXiX7", | |
"include_colab_link": true | |
}, | |
"kernelspec": { | |
"name": "python3", | |
"display_name": "Python 3" | |
}, | |
"language_info": { | |
"name": "python" | |
}, | |
"accelerator": "GPU", | |
"gpuClass": "standard" | |
}, | |
"cells": [ | |
{ | |
"cell_type": "markdown", | |
"metadata": { | |
"id": "view-in-github", | |
"colab_type": "text" | |
}, | |
"source": [ | |
"<a href=\"https://colab.research.google.com/gist/Arnav-Ladkat/e6c2da956bcad617ea6313aa0c918aed/benchmark-collab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 4, | |
"metadata": { | |
"colab": { | |
"base_uri": "https://localhost:8080/" | |
}, | |
"id": "HdDiruiy22-n", | |
"outputId": "f67d7d53-d62d-42d2-937d-2296ffa5aaf3" | |
}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"name": "stdout", | |
"text": [ | |
"Fri Jul 15 17:17:19 2022 \n", | |
"+-----------------------------------------------------------------------------+\n", | |
"| NVIDIA-SMI 460.32.03 Driver Version: 460.32.03 CUDA Version: 11.2 |\n", | |
"|-------------------------------+----------------------+----------------------+\n", | |
"| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n", | |
"| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n", | |
"| | | MIG M. |\n", | |
"|===============================+======================+======================|\n", | |
"| 0 Tesla P100-PCIE... Off | 00000000:00:04.0 Off | 0 |\n", | |
"| N/A 36C P0 26W / 250W | 0MiB / 16280MiB | 0% Default |\n", | |
"| | | N/A |\n", | |
"+-------------------------------+----------------------+----------------------+\n", | |
" \n", | |
"+-----------------------------------------------------------------------------+\n", | |
"| Processes: |\n", | |
"| GPU GI CI PID Type Process name GPU Memory |\n", | |
"| ID ID Usage |\n", | |
"|=============================================================================|\n", | |
"| No running processes found |\n", | |
"+-----------------------------------------------------------------------------+\n" | |
] | |
} | |
], | |
"source": [ | |
"gpu_info = !nvidia-smi\n", | |
"gpu_info = '\\n'.join(gpu_info)\n", | |
"if gpu_info.find('failed') >= 0:\n", | |
" print('Select the Runtime > \"Change runtime type\" menu to enable a GPU accelerator, ')\n", | |
" print('and then re-execute this cell.')\n", | |
"else:\n", | |
" print(gpu_info)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"source": [ | |
"from psutil import virtual_memory\n", | |
"ram_gb = virtual_memory().total / 1e9\n", | |
"print('Your runtime has {:.1f} gigabytes of available RAM\\n'.format(ram_gb))\n", | |
"\n", | |
"if ram_gb < 20:\n", | |
" print('To enable a high-RAM runtime, select the Runtime > \"Change runtime type\"')\n", | |
" print('menu, and then select High-RAM in the Runtime shape dropdown. Then, ')\n", | |
" print('re-execute this cell.')\n", | |
"else:\n", | |
" print('You are using a high-RAM runtime!')" | |
], | |
"metadata": { | |
"colab": { | |
"base_uri": "https://localhost:8080/" | |
}, | |
"id": "kNVrMvSn2_lP", | |
"outputId": "ab287b3b-0b54-4ef2-97c9-d22e07939a54" | |
}, | |
"execution_count": 5, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"name": "stdout", | |
"text": [ | |
"Your runtime has 27.3 gigabytes of available RAM\n", | |
"\n", | |
"You are using a high-RAM runtime!\n" | |
] | |
} | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"source": [ | |
"!lscpu" | |
], | |
"metadata": { | |
"colab": { | |
"base_uri": "https://localhost:8080/" | |
}, | |
"id": "dVREiUJg3EZt", | |
"outputId": "b5bdfcdc-4f69-4a08-8610-d9a91fbbc293" | |
}, | |
"execution_count": 6, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"name": "stdout", | |
"text": [ | |
"Architecture: x86_64\n", | |
"CPU op-mode(s): 32-bit, 64-bit\n", | |
"Byte Order: Little Endian\n", | |
"CPU(s): 4\n", | |
"On-line CPU(s) list: 0-3\n", | |
"Thread(s) per core: 2\n", | |
"Core(s) per socket: 2\n", | |
"Socket(s): 1\n", | |
"NUMA node(s): 1\n", | |
"Vendor ID: GenuineIntel\n", | |
"CPU family: 6\n", | |
"Model: 79\n", | |
"Model name: Intel(R) Xeon(R) CPU @ 2.20GHz\n", | |
"Stepping: 0\n", | |
"CPU MHz: 2199.998\n", | |
"BogoMIPS: 4399.99\n", | |
"Hypervisor vendor: KVM\n", | |
"Virtualization type: full\n", | |
"L1d cache: 32K\n", | |
"L1i cache: 32K\n", | |
"L2 cache: 256K\n", | |
"L3 cache: 56320K\n", | |
"NUMA node0 CPU(s): 0-3\n", | |
"Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm rdseed adx smap xsaveopt arat md_clear arch_capabilities\n" | |
] | |
} | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"source": [ | |
"" | |
], | |
"metadata": { | |
"id": "OL5OWB3Q3H3h" | |
}, | |
"execution_count": null, | |
"outputs": [] | |
} | |
] | |
} |
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