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[DLMED] update notebooks
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‎multi_gpu_test.ipynb

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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": "Note: you may need to restart the kernel to use updated packages.\n"
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"output_type": "stream",
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"text": [
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"Note: you may need to restart the kernel to use updated packages.\n"
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]
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}
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],
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"source": [
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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": "MONAI version: 0.2.0\nPython version: 3.7.5 (default, Nov 7 2019, 10:50:52) [GCC 8.3.0]\nNumpy version: 1.19.1\nPytorch version: 1.6.0\n\nOptional dependencies:\nPytorch Ignite version: 0.3.0\nNibabel version: NOT INSTALLED or UNKNOWN VERSION.\nscikit-image version: NOT INSTALLED or UNKNOWN VERSION.\nPillow version: NOT INSTALLED or UNKNOWN VERSION.\nTensorboard version: NOT INSTALLED or UNKNOWN VERSION.\n\nFor details about installing the optional dependencies, please visit:\n https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n\n"
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"output_type": "stream",
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"text": [
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"MONAI version: 0.2.0\n",
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"Python version: 3.7.5 (default, Nov 7 2019, 10:50:52) [GCC 8.3.0]\n",
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"Numpy version: 1.19.1\n",
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"Pytorch version: 1.6.0\n",
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"\n",
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"Optional dependencies:\n",
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"Pytorch Ignite version: 0.3.0\n",
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"Nibabel version: NOT INSTALLED or UNKNOWN VERSION.\n",
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"scikit-image version: NOT INSTALLED or UNKNOWN VERSION.\n",
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"Pillow version: NOT INSTALLED or UNKNOWN VERSION.\n",
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"Tensorboard version: NOT INSTALLED or UNKNOWN VERSION.\n",
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"\n",
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"For details about installing the optional dependencies, please visit:\n",
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" https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
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"\n"
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]
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}
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],
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"source": [
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 1 GPU"
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],
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"metadata": {
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"collapsed": false
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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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"source": [
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"opt = torch.optim.Adam(net.parameters(), lr)\n",
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"trainer = create_multigpu_supervised_trainer(net, opt, fake_loss, [torch.device(\"cuda:0\")])\n",
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"trainer.run(fake_data_stream(), 2, 2)\n"
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],
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"execution_count": 4,
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"execution_count": 4,
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": "State:\n\titeration: 4\n\tepoch: 2\n\tepoch_length: 2\n\tmax_epochs: 2\n\toutput: 40707.8984375\n\tbatch: <class 'tuple'>\n\tmetrics: <class 'dict'>\n\tdataloader: <class 'generator'>\n\tseed: 12"
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"text/plain": [
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"State:\n",
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"\titeration: 4\n",
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"\tepoch: 2\n",
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"\tepoch_length: 2\n",
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"\tmax_epochs: 2\n",
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"\toutput: 40707.8984375\n",
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"\tbatch: <class 'tuple'>\n",
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"\tmetrics: <class 'dict'>\n",
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"\tdataloader: <class 'generator'>\n",
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"\tseed: 12"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"execution_count": 4
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"output_type": "execute_result"
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}
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],
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"source": [
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"opt = torch.optim.Adam(net.parameters(), lr)\n",
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"trainer = create_multigpu_supervised_trainer(net, opt, fake_loss, [torch.device(\"cuda:0\")])\n",
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"trainer.run(fake_data_stream(), 2, 2)\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### all GPUs"
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],
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"metadata": {
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"collapsed": false
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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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"source": [
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"opt = torch.optim.Adam(net.parameters(), lr)\n",
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"trainer = create_multigpu_supervised_trainer(net, opt, fake_loss, None)\n",
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"trainer.run(fake_data_stream(), 2, 2)\n"
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],
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"execution_count": 5,
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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},
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"tags": []
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},
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"execution_count": 5,
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": "State:\n\titeration: 4\n\tepoch: 2\n\tepoch_length: 2\n\tmax_epochs: 2\n\toutput: 35669.37109375\n\tbatch: <class 'tuple'>\n\tmetrics: <class 'dict'>\n\tdataloader: <class 'generator'>\n\tseed: 12"
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"text/plain": [
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"State:\n",
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"\titeration: 4\n",
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"\tepoch: 2\n",
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"\tepoch_length: 2\n",
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"\tmax_epochs: 2\n",
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"\toutput: 35669.37109375\n",
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"\tbatch: <class 'tuple'>\n",
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"\tmetrics: <class 'dict'>\n",
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"\tdataloader: <class 'generator'>\n",
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"\tseed: 12"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"execution_count": 5
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"output_type": "execute_result"
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}
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],
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"source": [
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"opt = torch.optim.Adam(net.parameters(), lr)\n",
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"trainer = create_multigpu_supervised_trainer(net, opt, fake_loss, None)\n",
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"trainer.run(fake_data_stream(), 2, 2)\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### CPU"
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],
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"metadata": {
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"collapsed": false
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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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"source": [
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"opt = torch.optim.Adam(net.parameters(), lr)\n",
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"trainer = create_multigpu_supervised_trainer(net, opt, fake_loss, [])\n",
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"trainer.run(fake_data_stream(), 2, 2)"
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],
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"execution_count": 6,
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"execution_count": 6,
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": "State:\n\titeration: 4\n\tepoch: 2\n\tepoch_length: 2\n\tmax_epochs: 2\n\toutput: 29662.359375\n\tbatch: <class 'tuple'>\n\tmetrics: <class 'dict'>\n\tdataloader: <class 'generator'>\n\tseed: 12"
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"text/plain": [
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"State:\n",
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"\titeration: 4\n",
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"\tepoch: 2\n",
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"\tepoch_length: 2\n",
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"\tmax_epochs: 2\n",
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"\toutput: 29662.359375\n",
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"\tbatch: <class 'tuple'>\n",
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"\tmetrics: <class 'dict'>\n",
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"\tdataloader: <class 'generator'>\n",
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"\tseed: 12"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"execution_count": 6
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"output_type": "execute_result"
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}
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],
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"source": [
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"opt = torch.optim.Adam(net.parameters(), lr)\n",
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"trainer = create_multigpu_supervised_trainer(net, opt, fake_loss, [])\n",
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"trainer.run(fake_data_stream(), 2, 2)"
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]
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}
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],

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