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updated notebooks:
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‎colab_starter.ipynb‎

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@@ -31,7 +31,7 @@
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"outputs": [],
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"source": [
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"%%bash\n",
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"git clone https://github.com/npatta01/pytorch-serving-workshop.git -b main"
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"git clone https://github.com/npatta01/pytorch-serving-workshop.git -b notebooks"
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]
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},
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{
@@ -79,6 +79,63 @@
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"source": [
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"!pip install -r pytorch-serving-workshop/requirements.txt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "neither-shipping",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "checked-priest",
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"metadata": {},
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"outputs": [],
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"source": [
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"!sudo apt-get install htop tmux -y"
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]
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},
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{
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"cell_type": "markdown",
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"id": "immune-institution",
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"metadata": {},
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"source": [
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"## Colabcode\n",
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"Colab doesn't support multiple notebooks, so we use colabcode to setup vscode + ngrok"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "insured-navigator",
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"metadata": {},
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"outputs": [],
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"source": [
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"!pip install colabcode==0.3.0"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "capital-major",
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"metadata": {},
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"outputs": [],
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"source": [
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"from colabcode import ColabCode\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "operational-spotlight",
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"metadata": {},
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"outputs": [],
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"source": [
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"ColabCode(port=10000, mount_drive=False)"
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]
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}
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],
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"metadata": {

‎notebooks/03_optimizing_model.ipynb‎

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"source": [
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"def time_model_evaluation(model, dataset=test_dataset,device:str='cpu'):\n",
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"\n",
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" if device ==\"cuda\" and not torch.cuda.is_available():\n",
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" return \n",
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"\n",
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" eval_start_time = time.time()\n",
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" \n",
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" result = utils.prediction_batch(model , dataset, device=device )\n",
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" print(\"Evaluate total time (ms): {0:.1f}\".format(eval_duration_time_ms))\n",
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" \n",
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"def time_model_evaluation_single(model,dataset=test_dataset, device:str='cpu'):\n",
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"\n",
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" if device ==\"cuda\" and not torch.cuda.is_available():\n",
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" return \n",
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" \n",
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" model = model.to(device)\n",
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"\n",
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],
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"source": [
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"%%timeit -r 3 -n 5\n",
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"\n",
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"time_model_evaluation_single(model, device='cuda')"
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]
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},

‎notebooks/04_packaging.ipynb‎

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"source": [
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"%%bash\n",
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"cd .. \n",
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"\n",
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"rm -rf serving/model_store\n",
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"mkdir -p serving/model_store\n",
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"\n",
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"cp artifacts/model/distilbert-base-uncased__trace/model_store/* serving/model_store\n",
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"cp artifacts/model/distilbert-base-uncased__trace/setup_config.json serving/model_store/"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 1,
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"id": "pretty-graphic",
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"metadata": {},
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"outputs": [],
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": 2,
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"id": "missing-champagne",
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"metadata": {},
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"outputs": [
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"execution_count": 3,
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"id": "endangered-responsibility",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"2021-10-25 16:47:59,303 [INFO ] W-9001-pt_classifier_1.0-stdout MODEL_LOG - Torch worker started.\r\n",
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"2021-10-25 16:47:59,303 [INFO ] W-9001-pt_classifier_1.0-stdout MODEL_LOG - Python runtime: 3.7.10\r\n",
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"2021-10-25 16:47:59,306 [INFO ] W-9001-pt_classifier_1.0-stdout MODEL_LOG - Connection accepted: /tmp/.ts.sock.9001.\r\n",
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"2021-10-25 16:47:59,311 [INFO ] W-9001-pt_classifier_1.0-stdout MODEL_LOG - model_name: pt_classifier, batchSize: 1\r\n",
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"2021-10-25 16:47:59,357 [INFO ] W-9003-pt_classifier_1.0-stdout MODEL_LOG - Listening on port: /tmp/.ts.sock.9003\r\n",
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"2021-10-25 16:47:59,358 [INFO ] W-9003-pt_classifier_1.0-stdout MODEL_LOG - [PID]6980\r\n",
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"2021-10-25 16:47:59,358 [INFO ] W-9003-pt_classifier_1.0-stdout MODEL_LOG - Torch worker started.\r\n",
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"2021-10-25 16:47:59,358 [INFO ] W-9003-pt_classifier_1.0-stdout MODEL_LOG - Python runtime: 3.7.10\r\n",
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"2021-10-25 16:47:59,359 [INFO ] W-9003-pt_classifier_1.0-stdout MODEL_LOG - Connection accepted: /tmp/.ts.sock.9003.\r\n",
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"2021-10-25 16:47:59,364 [INFO ] W-9003-pt_classifier_1.0-stdout MODEL_LOG - model_name: pt_classifier, batchSize: 1\r\n"
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"2021-10-25 17:52:13,208 [INFO ] W-9000-pt_classifier_1.0-stdout MODEL_LOG - Connection accepted: /tmp/.ts.sock.9000.\r\n",
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"2021-10-25 17:52:13,233 [INFO ] W-9000-pt_classifier_1.0-stdout MODEL_LOG - model_name: pt_classifier, batchSize: 1\r\n",
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"2021-10-25 17:52:13,676 [INFO ] W-9000-pt_classifier_1.0-stdout MODEL_LOG - Transformers version 4.11.1\r\n",
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"2021-10-25 17:52:35,354 [INFO ] W-9000-pt_classifier_1.0-stdout MODEL_LOG - Transformer model from path /tmp/models/84448fbb0cf64f8fa122a52b62531894 loaded successfully\r\n",
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"2021-10-25 17:53:10,874 [INFO ] W-9000-pt_classifier_1.0-stdout MODEL_LOG - Received text: 'herbal tea'\r\n",
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"2021-10-25 17:53:10,874 [WARN ] W-9000-pt_classifier_1.0-stderr MODEL_LOG - Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.\r\n",
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"2021-10-25 17:53:19,419 [INFO ] W-9000-pt_classifier_1.0-stdout MODEL_LOG - Received text: 'herbal tea'\r\n",
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"2021-10-25 17:53:23,218 [INFO ] W-9000-pt_classifier_1.0-stdout MODEL_LOG - Received text: 'herbal tea'\r\n",
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"2021-10-25 17:53:25,753 [INFO ] W-9000-pt_classifier_1.0-stdout MODEL_LOG - Received text: 'herbal tea'\r\n",
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"2021-10-25 17:53:27,878 [INFO ] W-9000-pt_classifier_1.0-stdout MODEL_LOG - Received text: 'Herbal Tea'\r\n"
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]
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}
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],
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": 4,
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"id": "forbidden-marriage",
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"metadata": {},
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"outputs": [
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 5,
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"id": "sixth-gardening",
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"metadata": {},
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"outputs": [],
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"execution_count": 6,
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"id": "false-council",
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"metadata": {},
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"outputs": [
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},
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"cell_type": "code",
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"execution_count": 13,
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"execution_count": 7,
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"id": "fancy-rings",
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"metadata": {},
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"outputs": [
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" \"id\": \"9000\",\r\n",
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" \"startTime\": \"2021-10-25T17:52:12.170Z\",\r\n",
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" \"status\": \"READY\",\r\n",
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" \"memoryUsage\": 0,\r\n",
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" \"memoryUsage\": 3148378112,\r\n",
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" \"pid\": 6903,\r\n",
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" \"gpu\": true,\r\n",
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" \"gpuUsage\": \"gpuId::0 utilization.gpu [%]::0 % utilization.memory [%]::0 % memory.used [MiB]::1362 MiB\"\r\n",
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" \"gpuUsage\": \"gpuId::0 utilization.gpu [%]::0 % utilization.memory [%]::0 % memory.used [MiB]::1640 MiB\"\r\n",
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" }\r\n",
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" ]\r\n",
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" }\r\n",
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"execution_count": 8,
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"id": "norman-trader",
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"metadata": {},
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"outputs": [
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" \"SHOES\": 3.6940335121471435e-05\r\n",
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" }\r\n",
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"]\r\n",
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"elasped time (sec):0.035052\r\n"
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"elasped time (sec):0.039702\r\n"
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]
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}
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],
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"execution_count": 9,
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"id": "solid-internship",
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"metadata": {},
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"outputs": [
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" \"SHOES\": 3.6940335121471435e-05\n",
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" }\n",
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"]\n",
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"elasped time (sec):0.036023\n"
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"elasped time (sec):0.035042\n"
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]
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},
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{
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" Dload Upload Total Spent Left Speed\n",
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"\r",
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" 0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0\r",
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"100 265 100 214 100 51 5940 1415 --:--:-- --:--:-- --:--:-- 7571\n"
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"100 265 100 214 100 51 6106 1455 --:--:-- --:--:-- --:--:-- 7794\n"
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"metadata": {},
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"id": "sitting-attack",
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"metadata": {},
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"outputs": [],
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"cell_type": "code",
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"execution_count": null,
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"id": "positive-geography",
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"metadata": {},
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"source": []
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"id": "smart-tennis",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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"cell_type": "code",
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"execution_count": null,
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"id": "deluxe-nebraska",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {

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