Test Speed by Version

test_batchsize_first_speed-1-1.0.40dev0.ipynb

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   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2"
   ]
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   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
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       "'1.0.40.dev0'"
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     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import fastai\n",
    "fastai.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "300000\n"
     ]
    }
   ],
   "source": [
    "from fastai import *\n",
    "from fastai.text import *\n",
    "\n",
    "imdb = untar_data(URLs.IMDB_SAMPLE)\n",
    "df = pd.read_csv(imdb/'texts.csv')\n",
    "replicated_data = pd.concat([df.copy() for _ in range(300)])\n",
    "print(len(replicated_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_lm = TextLMDataBunch.from_df('./',replicated_data,df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SequentialRNN(\n",
       "  (0): RNNCore(\n",
       "    (encoder): Embedding(19159, 400, padding_idx=1)\n",
       "    (encoder_dp): EmbeddingDropout(\n",
       "      (emb): Embedding(19159, 400, padding_idx=1)\n",
       "    )\n",
       "    (rnns): ModuleList(\n",
       "      (0): QRNNLayer(\n",
       "        (linear): WeightDropout(\n",
       "          (module): Linear(in_features=800, out_features=3333, bias=True)\n",
       "        )\n",
       "      )\n",
       "      (1): QRNNLayer(\n",
       "        (linear): WeightDropout(\n",
       "          (module): Linear(in_features=1111, out_features=1200, bias=True)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (input_dp): RNNDropout()\n",
       "    (hidden_dps): ModuleList(\n",
       "      (0): RNNDropout()\n",
       "      (1): RNNDropout()\n",
       "    )\n",
       "  )\n",
       "  (1): LinearDecoder(\n",
       "    (decoder): Linear(in_features=400, out_features=19159, bias=True)\n",
       "    (output_dp): RNNDropout()\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bptt = 100\n",
    "emb_sz,nh,nl = 400,1111,2\n",
    "\n",
    "learn = language_model_learner(data_lm,bptt,emb_sz,nh,nl,drop_mult=0.5,qrnn=True)\n",
    "learn.unfreeze()\n",
    "learn.model"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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   "outputs": [
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       "      0.00% [0/1 00:00<00:00]\n",
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       "    \n",
       "<table style='width:300px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "    <th>accuracy</th>\n",
       "  </tr>\n",
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   "source": [
    "learn.fit_one_cycle(1, 3.e-2, moms=(0.9,0.8), wd=0.01, pct_start=0.25)\n",
    "learn.recorder.plot_losses()"
   ]
  }
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