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tyoc213/goemotions-blurr.ipynb Secret

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goemotions-blurr.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip install swifter"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# less than 100MB total\n",
"!wget -P data/full_dataset/ https://storage.googleapis.com/gresearch/goemotions/data/full_dataset/goemotions_1.csv\n",
"!wget -P data/full_dataset/ https://storage.googleapis.com/gresearch/goemotions/data/full_dataset/goemotions_2.csv\n",
"!wget -P data/full_dataset/ https://storage.googleapis.com/gresearch/goemotions/data/full_dataset/goemotions_3.csv"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"!cat data/full_dataset/goemotions_1.csv data/full_dataset/goemotions_2.csv data/full_dataset/goemotions_3.csv > data/full_dataset/goemotions.csv"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"from fastai.text.all import *"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(#4) [Path('data/full_dataset/goemotions_3.csv'),Path('data/full_dataset/goemotions_1.csv'),Path('data/full_dataset/goemotions.csv'),Path('data/full_dataset/goemotions_2.csv')]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"path = Path(\"data/full_dataset/\")\n",
"path.ls()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"text,id,author,subreddit,link_id,parent_id,created_utc,rater_id,example_very_unclear,admiration,amusement,anger,annoyance,approval,caring,confusion,curiosity,desire,disappointment,disapproval,disgust,embarrassment,excitement,fear,gratitude,grief,joy,love,nervousness,optimism,pride,realization,relief,remorse,sadness,surprise,neutral\r\n",
"That game hurt.,eew5j0j,Brdd9,nrl,t3_ajis4z,t1_eew18eq,1548381039.0,1,False,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0\r\n",
"\" >sexuality shouldn’t be a grouping category It makes you different from othet ppl so imo it fits the definition of \"\"grouping\"\" \",eemcysk,TheGreen888,unpopularopinion,t3_ai4q37,t3_ai4q37,1548084169.0,37,True,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\r\n",
"\"You do right, if you don't care then fuck 'em!\",ed2mah1,Labalool,confessions,t3_abru74,t1_ed2m7g7,1546427744.0,37,False,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\r\n",
"Man I love reddit.,eeibobj,MrsRobertshaw,facepalm,t3_ahulml,t3_ahulml,1547965054.0,18,False,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0\r\n",
"\"[NAME] was nowhere near them, he was by the Falcon. \",eda6yn6,American_Fascist713,starwarsspeculation,t3_ackt2f,t1_eda65q2,1546668601.0,2,False,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1\r\n",
"\"Right? Considering it’s such an important document, I should know the damned thing backwards and forwards... thanks again for the help!\",eespn2i,ImperialBoss,TrueReddit,t3_aizyuz,t1_eesoak0,1548280208.0,61,False,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0\r\n",
"\"He isn't as big, but he's still quite popular. I've heard the same thing about his content. Never watched him much.\",eczuekb,Rallings,cringe,t3_abeksv,t1_eczsmp8,1546320076.0,3,False,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\r\n",
"That's crazy; I went to a super [RELIGION] high school and I think I can remember 2 girls the entire 4 years that became teen moms.,ed5tx8y,Beachy5313,TeenMomOGandTeenMom2,t3_ac10u9,t1_ed5tfyc,1546536158.0,23,False,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\r\n",
"that's adorable asf,ef961hv,RedRails1917,traaaaaaannnnnnnnnns,t3_akz2ia,t3_akz2ia,1548763543.0,73,False,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\r\n"
]
}
],
"source": [
"!head data/full_dataset/goemotions.csv"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/tyoc213/miniconda3/envs/fastai/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3145: DtypeWarning: Columns (6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36) have mixed types.Specify dtype option on import or set low_memory=False.\n",
" has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
]
},
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" <td>Man I love reddit.</td>\n",
" <td>eeibobj</td>\n",
" <td>MrsRobertshaw</td>\n",
" <td>facepalm</td>\n",
" <td>t3_ahulml</td>\n",
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" <td>shittyfoodporn</td>\n",
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" <td>The FDA has plenty to criticize. But like here, it's usually criticized horribly off base. It needs to grow some balls and actually enforce things.</td>\n",
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"text/plain": [
" text \\\n",
"0 That game hurt. \n",
"1 >sexuality shouldn’t be a grouping category It makes you different from othet ppl so imo it fits the definition of \"grouping\" \n",
"2 You do right, if you don't care then fuck 'em! \n",
"3 Man I love reddit. \n",
"4 [NAME] was nowhere near them, he was by the Falcon. \n",
"... ... \n",
"211222 Everyone likes [NAME]. \n",
"211223 Well when you’ve imported about a gazillion of them I or your country it’s gets serious. \n",
"211224 That looks amazing \n",
"211225 The FDA has plenty to criticize. But like here, it's usually criticized horribly off base. It needs to grow some balls and actually enforce things. \n",
"211226 Desktop link: ^^/r/HelperBot_ ^^Downvote ^^to ^^remove. ^^Counter: ^^231558 \n",
"\n",
" id author subreddit link_id \\\n",
"0 eew5j0j Brdd9 nrl t3_ajis4z \n",
"1 eemcysk TheGreen888 unpopularopinion t3_ai4q37 \n",
"2 ed2mah1 Labalool confessions t3_abru74 \n",
"3 eeibobj MrsRobertshaw facepalm t3_ahulml \n",
"4 eda6yn6 American_Fascist713 starwarsspeculation t3_ackt2f \n",
"... ... ... ... ... \n",
"211222 ee6pagw Senshado heroesofthestorm t3_agjf24 \n",
"211223 ef28nod 5inchloser nottheonion t3_ak26t3 \n",
"211224 ee8hse1 springt1me shittyfoodporn t3_agrnqb \n",
"211225 edrhoxh enamedata medicine t3_aejqzd \n",
"211226 edze9g4 HelperBot_ MorbidReality t3_afhw30 \n",
"\n",
" parent_id created_utc rater_id example_very_unclear admiration ... \\\n",
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"3 t3_ahulml 1.54797e+09 18 False 0 ... \n",
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"... ... ... ... ... ... ... \n",
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"211225 t1_edrgdtx 1.54717e+09 4 False 0 ... \n",
"211226 t1_edze91s 1.5474e+09 61 True 0 ... \n",
"\n",
" love nervousness optimism pride realization relief remorse sadness \\\n",
"0 0 0 0 0 0 0 0 1 \n",
"1 0 0 0 0 0 0 0 0 \n",
"2 0 0 0 0 0 0 0 0 \n",
"3 1 0 0 0 0 0 0 0 \n",
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"... ... ... ... ... ... ... ... ... \n",
"211222 1 0 0 0 0 0 0 0 \n",
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"211224 0 0 0 0 0 0 0 0 \n",
"211225 0 0 0 0 0 0 0 0 \n",
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"\n",
" surprise neutral \n",
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"... ... ... \n",
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"\n",
"[211227 rows x 37 columns]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv(path/'goemotions.csv') #, header=0, names=['label', 'text'])\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
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" <th>remorse</th>\n",
" <th>sadness</th>\n",
" <th>surprise</th>\n",
" <th>neutral</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>211227</td>\n",
" <td>211227</td>\n",
" <td>211227</td>\n",
" <td>211227</td>\n",
" <td>211227</td>\n",
" <td>211227</td>\n",
" <td>2.112270e+05</td>\n",
" <td>211227</td>\n",
" <td>211227</td>\n",
" <td>211227</td>\n",
" <td>...</td>\n",
" <td>211227</td>\n",
" <td>211227</td>\n",
" <td>211227</td>\n",
" <td>211227</td>\n",
" <td>211227</td>\n",
" <td>211227</td>\n",
" <td>211227</td>\n",
" <td>211227</td>\n",
" <td>211227</td>\n",
" <td>211227</td>\n",
" </tr>\n",
" <tr>\n",
" <th>unique</th>\n",
" <td>57733</td>\n",
" <td>58012</td>\n",
" <td>49179</td>\n",
" <td>484</td>\n",
" <td>44898</td>\n",
" <td>54925</td>\n",
" <td>8.320000e+04</td>\n",
" <td>164</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>top</th>\n",
" <td>Thank you.</td>\n",
" <td>ef3qaua</td>\n",
" <td>[deleted]</td>\n",
" <td>cringe</td>\n",
" <td>t3_ae6ejj</td>\n",
" <td>t3_ae6ejj</td>\n",
" <td>1.548447e+09</td>\n",
" <td>4</td>\n",
" <td>False</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>freq</th>\n",
" <td>45</td>\n",
" <td>5</td>\n",
" <td>3882</td>\n",
" <td>895</td>\n",
" <td>92</td>\n",
" <td>64</td>\n",
" <td>1.000000e+01</td>\n",
" <td>8873</td>\n",
" <td>175547</td>\n",
" <td>163989</td>\n",
" <td>...</td>\n",
" <td>171529</td>\n",
" <td>176907</td>\n",
" <td>171078</td>\n",
" <td>177367</td>\n",
" <td>171068</td>\n",
" <td>177373</td>\n",
" <td>176322</td>\n",
" <td>172719</td>\n",
" <td>173789</td>\n",
" <td>131747</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>4 rows × 37 columns</p>\n",
"</div>"
],
"text/plain": [
" text id author subreddit link_id parent_id \\\n",
"count 211227 211227 211227 211227 211227 211227 \n",
"unique 57733 58012 49179 484 44898 54925 \n",
"top Thank you. ef3qaua [deleted] cringe t3_ae6ejj t3_ae6ejj \n",
"freq 45 5 3882 895 92 64 \n",
"\n",
" created_utc rater_id example_very_unclear admiration ... love \\\n",
"count 2.112270e+05 211227 211227 211227 ... 211227 \n",
"unique 8.320000e+04 164 5 5 ... 5 \n",
"top 1.548447e+09 4 False 0 ... 0 \n",
"freq 1.000000e+01 8873 175547 163989 ... 171529 \n",
"\n",
" nervousness optimism pride realization relief remorse sadness \\\n",
"count 211227 211227 211227 211227 211227 211227 211227 \n",
"unique 5 5 5 5 5 5 5 \n",
"top 0 0 0 0 0 0 0 \n",
"freq 176907 171078 177367 171068 177373 176322 172719 \n",
"\n",
" surprise neutral \n",
"count 211227 211227 \n",
"unique 5 5 \n",
"top 0 0 \n",
"freq 173789 131747 \n",
"\n",
"[4 rows x 37 columns]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.describe()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# TextDataLoaders.from_df??"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"text That game hurt.\n",
"id eew5j0j\n",
"author Brdd9\n",
"subreddit nrl\n",
"link_id t3_ajis4z\n",
"parent_id t1_eew18eq\n",
"created_utc 1.54838e+09\n",
"rater_id 1\n",
"example_very_unclear False\n",
"admiration 0\n",
"amusement 0\n",
"anger 0\n",
"annoyance 0\n",
"approval 0\n",
"caring 0\n",
"confusion 0\n",
"curiosity 0\n",
"desire 0\n",
"disappointment 0\n",
"disapproval 0\n",
"disgust 0\n",
"embarrassment 0\n",
"excitement 0\n",
"fear 0\n",
"gratitude 0\n",
"grief 0\n",
"joy 0\n",
"love 0\n",
"nervousness 0\n",
"optimism 0\n",
"pride 0\n",
"realization 0\n",
"relief 0\n",
"remorse 0\n",
"sadness 1\n",
"surprise 0\n",
"neutral 0\n",
"Name: 0, dtype: object"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.iloc[0]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"categories = 'admiration,amusement,anger,annoyance,approval,caring,confusion,curiosity,desire,disappointment,disapproval,disgust,embarrassment,excitement,fear,gratitude,grief,joy,love,nervousness,optimism,pride,realization,relief,remorse,sadness,surprise,neutral'\n",
"categories = categories.split(',')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.iloc[0][list(df.iloc[0][categories]==1).index(True)+9]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'sadness'"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"categories[list(df.iloc[0][categories]==1).index(True)]"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"def get_emotion(row):\n",
" l = list(row[categories]==1)\n",
" if True in l: return categories[l.index(True)]\n",
" else: return 'unknown'\n",
"\n",
"assert get_emotion(df.iloc[0]) == 'sadness'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create a new column based on `get_emotion` https://stackoverflow.com/a/26887820"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"use all cores with swifter https://stackoverflow.com/a/51669468"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 2.56 s, sys: 346 ms, total: 2.91 s\n",
"Wall time: 28.4 s\n"
]
},
{
"data": {
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" <td>0</td>\n",
" <td>sadness</td>\n",
" </tr>\n",
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" <th>1</th>\n",
" <td>&gt;sexuality shouldn’t be a grouping category It makes you different from othet ppl so imo it fits the definition of \"grouping\"</td>\n",
" <td>eemcysk</td>\n",
" <td>TheGreen888</td>\n",
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" <td>t3_ai4q37</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>unknown</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>You do right, if you don't care then fuck 'em!</td>\n",
" <td>ed2mah1</td>\n",
" <td>Labalool</td>\n",
" <td>confessions</td>\n",
" <td>t3_abru74</td>\n",
" <td>t1_ed2m7g7</td>\n",
" <td>1.54643e+09</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Man I love reddit.</td>\n",
" <td>eeibobj</td>\n",
" <td>MrsRobertshaw</td>\n",
" <td>facepalm</td>\n",
" <td>t3_ahulml</td>\n",
" <td>t3_ahulml</td>\n",
" <td>1.54797e+09</td>\n",
" <td>18</td>\n",
" <td>False</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
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" <td>love</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>[NAME] was nowhere near them, he was by the Falcon.</td>\n",
" <td>eda6yn6</td>\n",
" <td>American_Fascist713</td>\n",
" <td>starwarsspeculation</td>\n",
" <td>t3_ackt2f</td>\n",
" <td>t1_eda65q2</td>\n",
" <td>1.54667e+09</td>\n",
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" <tr>\n",
" <th>211222</th>\n",
" <td>Everyone likes [NAME].</td>\n",
" <td>ee6pagw</td>\n",
" <td>Senshado</td>\n",
" <td>heroesofthestorm</td>\n",
" <td>t3_agjf24</td>\n",
" <td>t3_agjf24</td>\n",
" <td>1.54763e+09</td>\n",
" <td>16</td>\n",
" <td>False</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>love</td>\n",
" </tr>\n",
" <tr>\n",
" <th>211223</th>\n",
" <td>Well when you’ve imported about a gazillion of them I or your country it’s gets serious.</td>\n",
" <td>ef28nod</td>\n",
" <td>5inchloser</td>\n",
" <td>nottheonion</td>\n",
" <td>t3_ak26t3</td>\n",
" <td>t3_ak26t3</td>\n",
" <td>1.54855e+09</td>\n",
" <td>15</td>\n",
" <td>False</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
" <td>caring</td>\n",
" </tr>\n",
" <tr>\n",
" <th>211224</th>\n",
" <td>That looks amazing</td>\n",
" <td>ee8hse1</td>\n",
" <td>springt1me</td>\n",
" <td>shittyfoodporn</td>\n",
" <td>t3_agrnqb</td>\n",
" <td>t3_agrnqb</td>\n",
" <td>1.54768e+09</td>\n",
" <td>70</td>\n",
" <td>False</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>admiration</td>\n",
" </tr>\n",
" <tr>\n",
" <th>211225</th>\n",
" <td>The FDA has plenty to criticize. But like here, it's usually criticized horribly off base. It needs to grow some balls and actually enforce things.</td>\n",
" <td>edrhoxh</td>\n",
" <td>enamedata</td>\n",
" <td>medicine</td>\n",
" <td>t3_aejqzd</td>\n",
" <td>t1_edrgdtx</td>\n",
" <td>1.54717e+09</td>\n",
" <td>4</td>\n",
" <td>False</td>\n",
" <td>0</td>\n",
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" <td>anger</td>\n",
" </tr>\n",
" <tr>\n",
" <th>211226</th>\n",
" <td>Desktop link: ^^/r/HelperBot_ ^^Downvote ^^to ^^remove. ^^Counter: ^^231558</td>\n",
" <td>edze9g4</td>\n",
" <td>HelperBot_</td>\n",
" <td>MorbidReality</td>\n",
" <td>t3_afhw30</td>\n",
" <td>t1_edze91s</td>\n",
" <td>1.5474e+09</td>\n",
" <td>61</td>\n",
" <td>True</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>unknown</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>211227 rows × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" text \\\n",
"0 That game hurt. \n",
"1 >sexuality shouldn’t be a grouping category It makes you different from othet ppl so imo it fits the definition of \"grouping\" \n",
"2 You do right, if you don't care then fuck 'em! \n",
"3 Man I love reddit. \n",
"4 [NAME] was nowhere near them, he was by the Falcon. \n",
"... ... \n",
"211222 Everyone likes [NAME]. \n",
"211223 Well when you’ve imported about a gazillion of them I or your country it’s gets serious. \n",
"211224 That looks amazing \n",
"211225 The FDA has plenty to criticize. But like here, it's usually criticized horribly off base. It needs to grow some balls and actually enforce things. \n",
"211226 Desktop link: ^^/r/HelperBot_ ^^Downvote ^^to ^^remove. ^^Counter: ^^231558 \n",
"\n",
" id author subreddit link_id \\\n",
"0 eew5j0j Brdd9 nrl t3_ajis4z \n",
"1 eemcysk TheGreen888 unpopularopinion t3_ai4q37 \n",
"2 ed2mah1 Labalool confessions t3_abru74 \n",
"3 eeibobj MrsRobertshaw facepalm t3_ahulml \n",
"4 eda6yn6 American_Fascist713 starwarsspeculation t3_ackt2f \n",
"... ... ... ... ... \n",
"211222 ee6pagw Senshado heroesofthestorm t3_agjf24 \n",
"211223 ef28nod 5inchloser nottheonion t3_ak26t3 \n",
"211224 ee8hse1 springt1me shittyfoodporn t3_agrnqb \n",
"211225 edrhoxh enamedata medicine t3_aejqzd \n",
"211226 edze9g4 HelperBot_ MorbidReality t3_afhw30 \n",
"\n",
" parent_id created_utc rater_id example_very_unclear admiration ... \\\n",
"0 t1_eew18eq 1.54838e+09 1 False 0 ... \n",
"1 t3_ai4q37 1.54808e+09 37 True 0 ... \n",
"2 t1_ed2m7g7 1.54643e+09 37 False 0 ... \n",
"3 t3_ahulml 1.54797e+09 18 False 0 ... \n",
"4 t1_eda65q2 1.54667e+09 2 False 0 ... \n",
"... ... ... ... ... ... ... \n",
"211222 t3_agjf24 1.54763e+09 16 False 0 ... \n",
"211223 t3_ak26t3 1.54855e+09 15 False 0 ... \n",
"211224 t3_agrnqb 1.54768e+09 70 False 1 ... \n",
"211225 t1_edrgdtx 1.54717e+09 4 False 0 ... \n",
"211226 t1_edze91s 1.5474e+09 61 True 0 ... \n",
"\n",
" nervousness optimism pride realization relief remorse sadness surprise \\\n",
"0 0 0 0 0 0 0 1 0 \n",
"1 0 0 0 0 0 0 0 0 \n",
"2 0 0 0 0 0 0 0 0 \n",
"3 0 0 0 0 0 0 0 0 \n",
"4 0 0 0 0 0 0 0 0 \n",
"... ... ... ... ... ... ... ... ... \n",
"211222 0 0 0 0 0 0 0 0 \n",
"211223 0 0 0 0 0 0 0 0 \n",
"211224 0 0 0 0 0 0 0 0 \n",
"211225 0 0 0 0 0 0 0 0 \n",
"211226 0 0 0 0 0 0 0 0 \n",
"\n",
" neutral emotion \n",
"0 0 sadness \n",
"1 0 unknown \n",
"2 1 neutral \n",
"3 0 love \n",
"4 1 neutral \n",
"... ... ... \n",
"211222 0 love \n",
"211223 0 caring \n",
"211224 0 admiration \n",
"211225 0 anger \n",
"211226 0 unknown \n",
"\n",
"[211227 rows x 38 columns]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%time\n",
"\n",
"import swifter\n",
"\n",
"df['emotion'] = df.swifter.apply (lambda row: get_emotion(row), axis=1)\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[nltk_data] Downloading package wordnet to /home/tyoc213/nltk_data...\n",
"[nltk_data] Package wordnet is already up-to-date!\n"
]
}
],
"source": [
"import torch\n",
"from transformers import *\n",
"from fastai.text.all import *\n",
"\n",
"from blurr.data.all import *\n",
"from blurr.modeling.all import *"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Get your hugs objects"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"task = HF_TASKS_AUTO.SequenceClassification\n",
"\n",
"pretrained_mode_name = 'bert-base-uncased'\n",
"hf_arch, hf_config, hf_tokenizer, hf_model = BLURR_MODEL_HELPER.get_hf_objects(pretrained_mode_name, task=task)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Build your data blocks and your DataLoaders"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 1min 42s, sys: 2.04 s, total: 1min 44s\n",
"Wall time: 1min 43s\n"
]
}
],
"source": [
"%%time\n",
"# single input\n",
"\n",
"blocks = (HF_TextBlock(hf_arch, hf_config, hf_tokenizer, hf_model), CategoryBlock)\n",
"dblocks = DataBlock(blocks=blocks, get_x=ColReader('text'), get_y=ColReader('emotion'), splitter=RandomSplitter())\n",
"\n",
"dls = dblocks.dataloaders(df, bs=16)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>text</th>\n",
" <th>category</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>this person is the smartest person to play town of salem literally iq</td>\n",
" <td>admiration</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>funny they decided to show a bunch of t2 images demonstrating the chronic microvascular ischemic disease and omitted the diffusion which had the more interesting findings pertinent to tga.</td>\n",
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"text/plain": [
"<IPython.core.display.HTML object>"
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},
"metadata": {},
"output_type": "display_data"
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],
"source": [
"dls.show_batch(dataloaders=dls, max_n=2)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <div>\n",
" <style>\n",
" /* Turns off some styling */\n",
" progress {\n",
" /* gets rid of default border in Firefox and Opera. */\n",
" border: none;\n",
" /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
" background-size: auto;\n",
" }\n",
" .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
" background: #F44336;\n",
" }\n",
" </style>\n",
" <progress value='0' class='' max='3' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" 0.00% [0/3 00:00<00:00]\n",
" </div>\n",
" \n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>epoch</th>\n",
" <th>train_loss</th>\n",
" <th>valid_loss</th>\n",
" <th>accuracy</th>\n",
" <th>time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" </tbody>\n",
"</table><p>\n",
"\n",
" <div>\n",
" <style>\n",
" /* Turns off some styling */\n",
" progress {\n",
" /* gets rid of default border in Firefox and Opera. */\n",
" border: none;\n",
" /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
" background-size: auto;\n",
" }\n",
" .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
" background: #F44336;\n",
" }\n",
" </style>\n",
" <progress value='0' class='' max='10561' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" 0.00% [0/10561 00:00<00:00]\n",
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],
"text/plain": [
"<IPython.core.display.HTML object>"
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"metadata": {},
"output_type": "display_data"
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{
"ename": "RuntimeError",
"evalue": "CUDA error: device-side assert triggered",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/fastai/lib/python3.8/site-packages/fastai/callback/schedule.py\u001b[0m in \u001b[0;36mfit_one_cycle\u001b[0;34m(self, n_epoch, lr_max, div, div_final, pct_start, wd, moms, cbs, reset_opt)\u001b[0m\n\u001b[1;32m 110\u001b[0m scheds = {'lr': combined_cos(pct_start, lr_max/div, lr_max, lr_max/div_final),\n\u001b[1;32m 111\u001b[0m 'mom': combined_cos(pct_start, *(self.moms if moms is None else moms))}\n\u001b[0;32m--> 112\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_epoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcbs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mParamScheduler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscheds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mL\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcbs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreset_opt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreset_opt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwd\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 113\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0;31m# Cell\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/fastai/lib/python3.8/site-packages/fastai/learner.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, n_epoch, lr, wd, cbs, reset_opt)\u001b[0m\n\u001b[1;32m 209\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_hypers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlr\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlr\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 210\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_epoch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mn_epoch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 211\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_with_events\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_fit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'fit'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCancelFitException\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_end_cleanup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 212\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 213\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_end_cleanup\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdl\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpred\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/fastai/lib/python3.8/site-packages/fastai/learner.py\u001b[0m in \u001b[0;36m_with_events\u001b[0;34m(self, f, event_type, ex, final)\u001b[0m\n\u001b[1;32m 158\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 159\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_with_events\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevent_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfinal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnoop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 160\u001b[0;31m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'before_{event_type}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 161\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mex\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'after_cancel_{event_type}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 162\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'after_{event_type}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mfinal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/fastai/lib/python3.8/site-packages/fastai/learner.py\u001b[0m in \u001b[0;36m_do_fit\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 200\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mepoch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_epoch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 201\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 202\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_with_events\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_epoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'epoch'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCancelEpochException\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 203\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 204\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_epoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwd\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcbs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreset_opt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/fastai/lib/python3.8/site-packages/fastai/learner.py\u001b[0m in \u001b[0;36m_with_events\u001b[0;34m(self, f, event_type, ex, final)\u001b[0m\n\u001b[1;32m 158\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 159\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_with_events\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevent_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfinal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnoop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 160\u001b[0;31m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'before_{event_type}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 161\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mex\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'after_cancel_{event_type}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 162\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'after_{event_type}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mfinal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/fastai/lib/python3.8/site-packages/fastai/learner.py\u001b[0m in \u001b[0;36m_do_epoch\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 194\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 195\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_do_epoch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 196\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_epoch_train\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 197\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_epoch_validate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 198\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/fastai/lib/python3.8/site-packages/fastai/learner.py\u001b[0m in \u001b[0;36m_do_epoch_train\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 186\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_do_epoch_train\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 187\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 188\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_with_events\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall_batches\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'train'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCancelTrainException\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 189\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 190\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_do_epoch_validate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mds_idx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/fastai/lib/python3.8/site-packages/fastai/learner.py\u001b[0m in \u001b[0;36m_with_events\u001b[0;34m(self, f, event_type, ex, final)\u001b[0m\n\u001b[1;32m 158\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 159\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_with_events\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevent_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfinal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnoop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 160\u001b[0;31m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'before_{event_type}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 161\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mex\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'after_cancel_{event_type}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 162\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'after_{event_type}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mfinal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/fastai/lib/python3.8/site-packages/fastai/learner.py\u001b[0m in \u001b[0;36mall_batches\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 164\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mall_batches\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 165\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_iter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 166\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mo\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mone_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 167\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 168\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_do_one_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/fastai/lib/python3.8/site-packages/fastai/learner.py\u001b[0m in \u001b[0;36mone_batch\u001b[0;34m(self, i, b)\u001b[0m\n\u001b[1;32m 182\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 183\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 184\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_with_events\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_one_batch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'batch'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCancelBatchException\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 185\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 186\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_do_epoch_train\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/fastai/lib/python3.8/site-packages/fastai/learner.py\u001b[0m in \u001b[0;36m_with_events\u001b[0;34m(self, f, event_type, ex, final)\u001b[0m\n\u001b[1;32m 158\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 159\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_with_events\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevent_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfinal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnoop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 160\u001b[0;31m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'before_{event_type}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 161\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mex\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'after_cancel_{event_type}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 162\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'after_{event_type}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mfinal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m~/miniconda3/envs/fastai/lib/python3.8/site-packages/torch/tensor.py\u001b[0m in \u001b[0;36m__torch_function__\u001b[0;34m(cls, func, types, args, kwargs)\u001b[0m\n\u001b[1;32m 993\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 994\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0m_C\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDisableTorchFunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 995\u001b[0;31m \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 996\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0m_convert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mret\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 997\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/fastai/lib/python3.8/site-packages/torch/tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph)\u001b[0m\n\u001b[1;32m 219\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 220\u001b[0m create_graph=create_graph)\n\u001b[0;32m--> 221\u001b[0;31m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautograd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 222\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 223\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mregister_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/fastai/lib/python3.8/site-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables)\u001b[0m\n\u001b[1;32m 124\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 125\u001b[0m \u001b[0mgrad_tensors_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_tensor_or_tensors_to_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrad_tensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 126\u001b[0;31m \u001b[0mgrad_tensors_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_make_grads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_tensors_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 127\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mretain_graph\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 128\u001b[0m \u001b[0mretain_graph\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/fastai/lib/python3.8/site-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36m_make_grads\u001b[0;34m(outputs, grads)\u001b[0m\n\u001b[1;32m 49\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"grad can be implicitly created only for scalar outputs\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m \u001b[0mnew_grads\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mones_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemory_format\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpreserve_format\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 52\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 53\u001b[0m \u001b[0mnew_grads\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mRuntimeError\u001b[0m: CUDA error: device-side assert triggered"
]
}
],
"source": [
"%%time\n",
"model = HF_BaseModelWrapper(hf_model)\n",
"\n",
"learn = Learner(dls, model, opt_func=partial(Adam, decouple_wd=True),\n",
" loss_func=CrossEntropyLossFlat(),\n",
" metrics=[accuracy],\n",
" cbs=[HF_BaseModelCallback],\n",
" splitter=hf_splitter)\n",
"\n",
"\n",
"learn.model = learn.model.cuda()\n",
"learn.dls = learn.dls.cuda()\n",
"#learn.create_opt()\n",
"#learn.freeze()\n",
"\n",
"learn.fit_one_cycle(3, lr_max=1e-3)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"```text\n",
"=== Software === \n",
"python : 3.8.5\n",
"fastai : 2.2.5\n",
"fastprogress : 0.2.7\n",
"torch : 1.7.0\n",
"nvidia driver : 460.32\n",
"torch cuda : 11.0 / is available\n",
"torch cudnn : 8003 / is enabled\n",
"\n",
"=== Hardware === \n",
"nvidia gpus : 1\n",
"torch devices : 1\n",
" - gpu0 : GeForce RTX 3090\n",
"\n",
"=== Environment === \n",
"platform : Linux-5.8.0-38-generic-x86_64-with-glibc2.10\n",
"distro : #43~20.04.1-Ubuntu SMP Tue Jan 12 16:39:47 UTC 2021\n",
"conda env : fastai\n",
"python : /home/tyoc213/miniconda3/envs/fastai/bin/python\n",
"sys.path : /home/tyoc213/miniconda3/envs/fastai/lib/python3.8/site-packages/ray/thirdparty_files\n",
"/home/tyoc213/Documents/github/blog/__explorations__/2021_03\n",
"/home/tyoc213/miniconda3/envs/fastai/lib/python3.8\n",
"/home/tyoc213/miniconda3/envs/fastai/lib/python3.8/site-packages\n",
"/home/tyoc213/Documents/github/blog/__explorations__/2021_03\n",
"/home/tyoc213/miniconda3/envs/fastai/lib/python3.8\n",
"/home/tyoc213/miniconda3/envs/fastai/lib/python3.8\n",
"/home/tyoc213/miniconda3/envs/fastai/lib/python3.8/site-packages\n",
"/home/tyoc213/miniconda3/envs/fastai/lib/python3.8/site-packages/ray/pickle5_files\n",
"/home/tyoc213/Documents/github/blog/__explorations__/2021_03\n",
"/home/tyoc213/miniconda3/envs/fastai/lib/python38.zip\n",
"/home/tyoc213/miniconda3/envs/fastai/lib/python3.8\n",
"/home/tyoc213/miniconda3/envs/fastai/lib/python3.8/lib-dynload\n",
"\n",
"/home/tyoc213/miniconda3/envs/fastai/lib/python3.8/site-packages\n",
"/home/tyoc213/Documents/github/fastai_xla_extensions\n",
"/home/tyoc213/Documents/github/reformer_fastai\n",
"/home/tyoc213/Documents/github/walkwithfastai.github.io\n",
"/home/tyoc213/Documents/github/fastcore\n",
"/home/tyoc213/Documents/github/nbdev\n",
"/home/tyoc213/Documents/github/fast-reinforcement-learning-2\n",
"/home/tyoc213/Documents/github/blurr\n",
"/home/tyoc213/miniconda3/envs/fastai/lib/python3.8/site-packages/IPython/extensions\n",
"/home/tyoc213/.ipython\n",
"/home/tyoc213/.cache/huggingface/modules\n",
"```\n",
"\n",
"Please make sure to include opening/closing ``` when you paste into forums/github to make the reports appear formatted as code sections.\n",
"\n",
"Optional package(s) to enhance the diagnostics can be installed with:\n",
"pip install distro\n",
"Once installed, re-run this utility to get the additional information\n"
]
}
],
"source": [
"from fastai.test_utils import *\n",
"show_install()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'/home/tyoc213/.nvm/versions/node/v15.6.0/bin:/home/tyoc213/.deta/bin:/home/tyoc213/miniconda3/envs/fastai/bin:/home/tyoc213/miniconda3/condabin:/home/tyoc213/.rvm/gems/ruby-2.7.0/bin:/home/tyoc213/.rvm/gems/ruby-2.7.0@global/bin:/home/tyoc213/.rvm/rubies/ruby-2.7.0/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin:/home/tyoc213/.rvm/bin:/home/tyoc213/.rvm/bin:/usr/local/go/bin:/home/tyoc213/go/bin'"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"get_env('PATH')"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['24267']"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nvidia_mem()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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