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v0.0.69
1 parent 959dfbf commit 325f3b6

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‎notebooks/11_spot_hpt_torch_fashion_mnist.ipynb‎

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‎notebooks/12_spot_hpt_torch_cifar10.ipynb‎

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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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{
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"cell_type": "code",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'12-torch_p040025_1min_5init_2023-05-12_11-11-36'"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pickle\n",
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"import socket\n",
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},
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{
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"cell_type": "code",
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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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"spotPython 0.0.68\n",
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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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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"pip list | grep \"spot[RiverPython]\""
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]
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"cell_type": "code",
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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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"2.0.1\n",
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"MPS device: mps\n"
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]
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}
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],
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"print(torch.__version__)\n",
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"# Check that MPS is available\n",
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},
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"cell_type": "code",
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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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"Files already downloaded and verified\n",
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"Files already downloaded and verified\n"
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]
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},
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{
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"data": {
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"((50000, 32, 32, 3), (10000, 32, 32, 3))"
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}
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],
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"train, test = load_data()\n",
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"train.data.shape, test.data.shape"
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]
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"shuffle = True\n",
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"eval = \"train_hold_out\"\n",
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"device = DEVICE\n",
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"show_batch_interval = 100_000\n",
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"\n",
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"fun_control.update({\n",
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" \"data_dir\": None,\n",
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" \"shuffle\": shuffle,\n",
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" \"eval\": eval,\n",
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" \"device\": device,\n",
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" \"show_batch_interval\": show_batch_interval,\n",
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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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},
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"cell_type": "code",
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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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"| name | type | default | lower | upper | transform |\n",
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"|------------|--------|-----------|---------|---------|-----------------------|\n",
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"| l1 | int | 5 | 2 | 9 | transform_power_2_int |\n",
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"| l2 | int | 5 | 2 | 9 | transform_power_2_int |\n",
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"| lr | float | 0.001 | 1e-05 | 0.01 | None |\n",
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"| batch_size | int | 4 | 1 | 4 | transform_power_2_int |\n",
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"| epochs | int | 3 | 3 | 4 | transform_power_2_int |\n",
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"| k_folds | int | 2 | 2 | 2 | None |\n"
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]
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}
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],
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"print(gen_design_table(fun_control))"
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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": 18,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[5.e+00, 5.e+00, 1.e-03, 4.e+00, 3.e+00, 2.e+00]])"
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]
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},
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"execution_count": 18,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from spotPython.hyperparameters.values import get_default_hyperparameters_as_array\n",
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"hyper_dict=TorchHyperDict().load()\n",
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},
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"cell_type": "code",
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"execution_count": 19,
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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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"Additional attributes: {'lr', 'batch_size', 'k_folds', 'epochs'}\n",
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"Removed attributes: {'lr', 'batch_size', 'k_folds', 'epochs'}\n",
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"Epoch: 1\n",
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"Batch: 1000. Batch Size: 8. Training Loss (running): 2.126\n",
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"Batch: 2000. Batch Size: 8. Training Loss (running): 0.898\n",
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"Batch: 3000. Batch Size: 8. Training Loss (running): 0.552\n",
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"Loss on hold-out set: 1.590382700252533\n",
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"Accuracy on hold-out set: 0.4229\n",
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"Epoch: 2\n",
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"Batch: 1000. Batch Size: 8. Training Loss (running): 1.524\n",
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"Batch: 2000. Batch Size: 8. Training Loss (running): 0.733\n",
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"Batch: 3000. Batch Size: 8. Training Loss (running): 0.476\n",
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"Loss on hold-out set: 1.4275218252420425\n",
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"Accuracy on hold-out set: 0.4857\n",
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"Epoch: 3\n",
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"Batch: 1000. Batch Size: 8. Training Loss (running): 1.353\n",
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"Batch: 2000. Batch Size: 8. Training Loss (running): 0.664\n",
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"Batch: 3000. Batch Size: 8. Training Loss (running): 0.436\n",
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"Loss on hold-out set: 1.3183471252799035\n",
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"Accuracy on hold-out set: 0.5349\n",
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"Epoch: 4\n",
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"Batch: 1000. Batch Size: 8. Training Loss (running): 1.227\n",
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"Batch: 2000. Batch Size: 8. Training Loss (running): 0.623\n",
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"Batch: 3000. Batch Size: 8. Training Loss (running): 0.414\n",
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"Loss on hold-out set: 1.3052782666921616\n",
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"Accuracy on hold-out set: 0.5461\n",
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"Epoch: 5\n",
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"Batch: 1000. Batch Size: 8. Training Loss (running): 1.155\n",
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"Batch: 2000. Batch Size: 8. Training Loss (running): 0.591\n",
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"Batch: 3000. Batch Size: 8. Training Loss (running): 0.378\n",
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"Loss on hold-out set: 1.2335647928357125\n",
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"Accuracy on hold-out set: 0.57405\n",
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"Epoch: 6\n"
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]
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}
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],
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"spot_tuner = spot.Spot(fun=fun,\n",
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" lower = lower,\n",
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},
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 35,
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"metadata": {},
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"outputs": [],
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"source": [

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