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Oleg Sheynin
2024-06-07 00:47:34 +00:00
parent da781060e0
commit 275bdcada6
8 changed files with 1010 additions and 67 deletions
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pytorch/notebooks/Testing GPU.ipynb
+73 -65
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@@ -1,8 +1,16 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "53fb9dcb-843b-4633-b031-22236bfeb815",
"metadata": {},
"source": [
"### Check CUDA device"
]
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 8,
"id": "6b269e64-be58-43b5-ad60-0fbd1d37861a",
"metadata": {},
"outputs": [
@@ -27,90 +35,95 @@
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "97906ea2-b284-4966-9c11-b8629f053815",
"cell_type": "markdown",
"id": "e6b4bc89-4cbd-4d4a-99a8-2d5bfb5d095d",
"metadata": {},
"outputs": [],
"source": [
"import plotly"
"### Environment"
]
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 10,
"id": "7313a620-a0eb-4207-a12a-90aeee3cd980",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'3.10.13 (main, Sep 11 2023, 13:44:35) [GCC 11.2.0]'"
"('3.10.13 (main, Sep 11 2023, 13:44:35) [GCC 11.2.0]',\n",
" environ{'PATH': '/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin',\n",
" 'HOSTNAME': '6c24ffc52409',\n",
" 'JUPYTER_ENABLE_LAB': 'yes',\n",
" 'PYTHONPATH': '/cvtt/prod',\n",
" 'NVIDIA_VISIBLE_DEVICES': 'all',\n",
" 'NVIDIA_DRIVER_CAPABILITIES': 'compute,utility',\n",
" 'LD_LIBRARY_PATH': '/usr/local/nvidia/lib:/usr/local/nvidia/lib64',\n",
" 'PYTORCH_VERSION': '2.2.1',\n",
" 'HOME': '/root',\n",
" 'LC_CTYPE': 'C.UTF-8',\n",
" 'JPY_SESSION_NAME': '/workspace/Testing GPU.ipynb',\n",
" 'JPY_PARENT_PID': '1',\n",
" 'PYDEVD_USE_FRAME_EVAL': 'NO',\n",
" 'TERM': 'xterm-color',\n",
" 'CLICOLOR': '1',\n",
" 'FORCE_COLOR': '1',\n",
" 'CLICOLOR_FORCE': '1',\n",
" 'PAGER': 'cat',\n",
" 'GIT_PAGER': 'cat',\n",
" 'MPLBACKEND': 'module://matplotlib_inline.backend_inline',\n",
" 'CUDA_MODULE_LOADING': 'LAZY'})"
]
},
"execution_count": 3,
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import sys\n",
"sys.version"
"sys.version,os.environ"
]
},
{
"cell_type": "markdown",
"id": "943ac637-42c7-4a69-a6c4-94c382e22653",
"metadata": {},
"source": [
"### Test packages avai"
]
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 11,
"id": "95d9a2e6-3464-4dbe-9a97-0c2d5eb34193",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"environ{'PATH': '/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin',\n",
" 'HOSTNAME': '1fc69c311e22',\n",
" 'JUPYTER_ENABLE_LAB': 'yes',\n",
" 'NVIDIA_VISIBLE_DEVICES': 'all',\n",
" 'NVIDIA_DRIVER_CAPABILITIES': 'compute,utility',\n",
" 'LD_LIBRARY_PATH': '/usr/local/nvidia/lib:/usr/local/nvidia/lib64',\n",
" 'PYTORCH_VERSION': '2.2.1',\n",
" 'HOME': '/root',\n",
" 'LC_CTYPE': 'C.UTF-8',\n",
" 'JPY_SESSION_NAME': '/workspace/oleg/Testing GPU.ipynb',\n",
" 'JPY_PARENT_PID': '1',\n",
" 'PYDEVD_USE_FRAME_EVAL': 'NO',\n",
" 'TERM': 'xterm-color',\n",
" 'CLICOLOR': '1',\n",
" 'FORCE_COLOR': '1',\n",
" 'CLICOLOR_FORCE': '1',\n",
" 'PAGER': 'cat',\n",
" 'GIT_PAGER': 'cat',\n",
" 'MPLBACKEND': 'module://matplotlib_inline.backend_inline',\n",
" 'CUDA_MODULE_LOADING': 'LAZY'}"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"outputs": [],
"source": [
"import os\n",
"\n",
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"import matplotlib\n",
"os.environ"
"import plotly\n",
"\n",
"import cvttpy.tools.timeutils as tu\n",
"\n",
"import autogluon\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 12,
"id": "eb38de31-fc19-4515-b08d-9cd7607ea958",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "75642bda0eed47598e340b8c1766949a",
"model_id": "2b2a98cdd61d477b811279a6753630a2",
"version_major": 2,
"version_minor": 0
},
@@ -127,42 +140,37 @@
"text": [
"Done\n"
]
},
{
"data": {
"text/plain": [
"1717721060059418080"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import ipywidgets\n",
"from ipywidgets import interact, IntSlider\n",
"import cvttpy.tools.timeutils as tu\n",
"\n",
"def f(x):\n",
" return x\n",
"\n",
"interact(f, x=IntSlider(min=0, max=10, step=1, value=5))\n",
"print(\"Done\")\n"
"print(\"Done\")\n",
"tu.current_nanoseconds()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": null,
"id": "f46e46a7-9b57-44aa-9bc9-dcbcf643bc88",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"jupyter-events==0.10.0\n",
"jupyter-lsp==2.2.5\n",
"jupyter_client==8.6.2\n",
"jupyter_core==5.7.2\n",
"jupyter_server==2.14.1\n",
"jupyter_server_terminals==0.5.3\n",
"jupyterlab==4.2.1\n",
"jupyterlab_pygments==0.3.0\n",
"jupyterlab_server==2.27.2\n",
"jupyterlab_widgets==3.0.11\n"
]
}
],
"outputs": [],
"source": [
"!pip freeze | grep jupyter"
]
pytorch/notebooks/autogluon_chronos.ipynb
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