diff --git a/development/.buildinfo b/development/.buildinfo
index 5ae96061..c2f4dccf 100644
--- a/development/.buildinfo
+++ b/development/.buildinfo
@@ -1,4 +1,4 @@
# Sphinx build info version 1
# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 5eac73f91509e54461eda47c063a78e4
+config: 1a3e2d12d88382c2eaa1d36d686cb541
tags: 645f666f9bcd5a90fca523b33c5a78b7
diff --git a/development/.doctrees/environment.pickle b/development/.doctrees/environment.pickle
index 378078f4..352f9496 100644
Binary files a/development/.doctrees/environment.pickle and b/development/.doctrees/environment.pickle differ
diff --git a/development/.doctrees/examples/record/mnist_pytorch.doctree b/development/.doctrees/examples/record/mnist_pytorch.doctree
index ec8c0378..6fc4e117 100644
Binary files a/development/.doctrees/examples/record/mnist_pytorch.doctree and b/development/.doctrees/examples/record/mnist_pytorch.doctree differ
diff --git a/development/_downloads/2769ed1bb81a7c36ce249476914fbf92/mnist_pytorch.py b/development/_downloads/2769ed1bb81a7c36ce249476914fbf92/mnist_pytorch.py
index b50704cf..ea1772cb 100644
--- a/development/_downloads/2769ed1bb81a7c36ce249476914fbf92/mnist_pytorch.py
+++ b/development/_downloads/2769ed1bb81a7c36ce249476914fbf92/mnist_pytorch.py
@@ -63,7 +63,7 @@ def __init__(self, activation="relu", learning_rate=1e-4, dropout_rate=0.1, batc
]
)
- self.accuracy = Accuracy()
+ self.accuracy = Accuracy(task="multiclass", num_classes=self.num_classes)
def prepare_data(self):
# download
@@ -71,7 +71,6 @@ def prepare_data(self):
MNIST(self.data_dir, train=False, download=True)
def setup(self, stage=None):
-
# Assign train/val datasets for use in dataloaders
if stage == "fit" or stage is None:
mnist_full = MNIST(self.data_dir, train=True, transform=self.transform)
@@ -257,10 +256,9 @@ def get_configspace(seed):
# The model weights are trained
trainer = pl.Trainer(
- accelerator="gpu",
+ accelerator="cpu",
devices=1,
num_sanity_val_steps=0, # No validation sanity
- auto_scale_batch_size="power",
deterministic=True,
min_epochs=epochs,
max_epochs=epochs,
diff --git a/development/_downloads/bc82bea3a5dd7bdba60b65220891d9e5/examples_python.zip b/development/_downloads/bc82bea3a5dd7bdba60b65220891d9e5/examples_python.zip
index a837d5c1..59e0c502 100644
Binary files a/development/_downloads/bc82bea3a5dd7bdba60b65220891d9e5/examples_python.zip and b/development/_downloads/bc82bea3a5dd7bdba60b65220891d9e5/examples_python.zip differ
diff --git a/development/_downloads/bdc2709d62468d73e63248ef71ebc760/mnist_pytorch.ipynb b/development/_downloads/bdc2709d62468d73e63248ef71ebc760/mnist_pytorch.ipynb
index c6588335..d3bf38ba 100644
--- a/development/_downloads/bdc2709d62468d73e63248ef71ebc760/mnist_pytorch.ipynb
+++ b/development/_downloads/bdc2709d62468d73e63248ef71ebc760/mnist_pytorch.ipynb
@@ -15,7 +15,7 @@
},
"outputs": [],
"source": [
- "from inspect import BoundArguments\nimport os\nfrom re import T\nimport time as t\nimport random\nimport ConfigSpace as CS\nfrom ConfigSpace import ConfigurationSpace\nfrom ConfigSpace.hyperparameters import (\n UniformFloatHyperparameter,\n UniformIntegerHyperparameter,\n CategoricalHyperparameter,\n)\nfrom deepcave import Recorder, Objective\nfrom deepcave.runs import Status\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.utils.data import DataLoader, random_split\nimport torchvision.transforms as transforms\nfrom torchmetrics import Accuracy\nfrom torchvision.datasets import MNIST\nimport pytorch_lightning as pl\n\n\nNUM_WORKERS = 16\n\n\nclass MNISTModel(pl.LightningModule):\n def __init__(self, activation=\"relu\", learning_rate=1e-4, dropout_rate=0.1, batch_size=64):\n super().__init__()\n\n if activation == \"relu\":\n self.activation = nn.ReLU\n elif activation == \"tanh\":\n self.activation = nn.Tanh\n elif activation == \"sigmoid\":\n self.activation = nn.Sigmoid\n else:\n raise RuntimeError(\"Activation not found.\")\n\n self.learning_rate = learning_rate\n self.dropout_rate = dropout_rate\n self.batch_size = batch_size\n\n self.data_dir = os.path.join(os.getcwd(), \"datasets\")\n self.num_classes = 10\n self.dims = (1, 28, 28)\n self.channels, self.width, self.height = self.dims\n self.transform = transforms.Compose(\n [\n transforms.ToTensor(),\n transforms.Normalize((0.1307,), (0.3081,)),\n ]\n )\n\n self.accuracy = Accuracy()\n\n def prepare_data(self):\n # download\n MNIST(self.data_dir, train=True, download=True)\n MNIST(self.data_dir, train=False, download=True)\n\n def setup(self, stage=None):\n\n # Assign train/val datasets for use in dataloaders\n if stage == \"fit\" or stage is None:\n mnist_full = MNIST(self.data_dir, train=True, transform=self.transform)\n self.mnist_train, self.mnist_val = random_split(mnist_full, [20000, 40000])\n\n # Assign test dataset for use in dataloader(s)\n if stage == \"test\" or stage is None:\n self.mnist_test = MNIST(self.data_dir, train=False, transform=self.transform)\n\n def train_dataloader(self):\n return DataLoader(self.mnist_train, batch_size=self.batch_size, num_workers=NUM_WORKERS)\n\n def val_dataloader(self):\n return DataLoader(self.mnist_val, batch_size=self.batch_size, num_workers=NUM_WORKERS)\n\n def test_dataloader(self):\n return DataLoader(self.mnist_test, batch_size=self.batch_size, num_workers=NUM_WORKERS)\n\n def training_step(self, batch, batch_idx):\n x, y = batch\n logits = self(x)\n loss = F.nll_loss(logits, y)\n\n return loss\n\n def validation_step(self, batch, batch_idx):\n x, y = batch\n logits = self(x)\n loss = F.nll_loss(logits, y)\n preds = torch.argmax(logits, dim=1)\n self.accuracy(preds, y)\n\n self.log(\"val_loss\", loss, prog_bar=True)\n self.log(\"val_acc\", self.accuracy, prog_bar=True)\n\n return loss\n\n def test_step(self, batch, batch_idx):\n return self.validation_step(batch, batch_idx)\n\n def configure_optimizers(self):\n optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\n return optimizer\n\n\nclass MLP(MNISTModel):\n def __init__(self, activation, learning_rate, dropout_rate, batch_size, num_neurons=(64, 32)):\n super().__init__(activation, learning_rate, dropout_rate, batch_size)\n\n self.layers = nn.Sequential(\n nn.Flatten(),\n nn.Linear(self.channels * self.width * self.height, num_neurons[0]),\n self.activation(),\n nn.Dropout(dropout_rate),\n nn.Linear(num_neurons[0], num_neurons[1]),\n self.activation(),\n nn.Dropout(dropout_rate),\n nn.Linear(num_neurons[1], self.num_classes),\n )\n\n def forward(self, x):\n x = self.layers(x)\n return F.log_softmax(x, dim=1)\n\n\nclass CNN(MNISTModel):\n def __init__(self, activation, learning_rate, dropout_rate, batch_size):\n super().__init__(activation, learning_rate, dropout_rate, batch_size)\n\n self.conv1 = nn.Sequential(\n nn.Conv2d(\n in_channels=self.channels,\n out_channels=16,\n kernel_size=5,\n stride=1,\n padding=2,\n ),\n self.activation(),\n nn.Dropout(dropout_rate),\n nn.MaxPool2d(kernel_size=2),\n )\n self.conv2 = nn.Sequential(\n nn.Conv2d(16, 32, 5, 1, 2),\n self.activation(),\n nn.Dropout(dropout_rate),\n nn.MaxPool2d(2),\n nn.Flatten(),\n )\n # fully connected layer, output 10 classes\n self.out = nn.Linear(32 * 7 * 7, self.num_classes)\n\n def forward(self, x):\n x = self.conv1(x)\n x = self.conv2(x)\n x = self.out(x)\n\n return F.log_softmax(x, dim=1)\n\n\ndef get_configspace(seed):\n configspace = ConfigurationSpace(seed=seed)\n\n model = CategoricalHyperparameter(name=\"model\", choices=[\"mlp\", \"cnn\"])\n activation = CategoricalHyperparameter(name=\"activation\", choices=[\"sigmoid\", \"tanh\", \"relu\"])\n learning_rate = UniformFloatHyperparameter(\n name=\"learning_rate\", lower=0.0001, upper=0.1, log=True\n )\n dropout_rate = UniformFloatHyperparameter(name=\"dropout_rate\", lower=0.1, upper=0.9)\n batch_size = UniformIntegerHyperparameter(name=\"batch_size\", lower=16, upper=256)\n\n # MLP specific\n num_neurons_layer1 = UniformIntegerHyperparameter(name=\"num_neurons_layer1\", lower=5, upper=100)\n num_neurons_layer2 = UniformIntegerHyperparameter(name=\"num_neurons_layer2\", lower=5, upper=100)\n\n configspace.add_hyperparameters(\n [\n model,\n activation,\n learning_rate,\n dropout_rate,\n batch_size,\n num_neurons_layer1,\n num_neurons_layer2,\n ]\n )\n\n # Now add sub configspace\n configspace.add_condition(CS.EqualsCondition(num_neurons_layer1, model, \"mlp\"))\n configspace.add_condition(CS.EqualsCondition(num_neurons_layer2, model, \"mlp\"))\n\n return configspace\n\n\nif __name__ == \"__main__\":\n # Define objectives\n accuracy = Objective(\"accuracy\", lower=0, upper=1, optimize=\"upper\")\n loss = Objective(\"loss\", lower=0, optimize=\"lower\")\n time = Objective(\"time\", lower=0, optimize=\"lower\")\n\n # Define budgets\n max_epochs = 8\n n_epochs = 4\n budgets = np.linspace(0, max_epochs, num=n_epochs)\n\n # Others\n num_configs = 1000\n num_runs = 3\n save_path = \"logs/DeepCAVE/mnist_pytorch\"\n\n for run_id in range(num_runs):\n random.seed(run_id)\n configspace = get_configspace(run_id)\n\n with Recorder(configspace, objectives=[accuracy, loss, time], save_path=save_path) as r:\n for config in configspace.sample_configuration(num_configs):\n pl.seed_everything(run_id)\n kwargs = dict(\n activation=config[\"activation\"],\n learning_rate=config[\"learning_rate\"],\n dropout_rate=config[\"dropout_rate\"],\n batch_size=config[\"batch_size\"],\n )\n\n if config[\"model\"] == \"mlp\":\n model = MLP(\n **kwargs,\n num_neurons=(\n config[\"num_neurons_layer1\"],\n config[\"num_neurons_layer2\"],\n ),\n )\n elif config[\"model\"] == \"cnn\":\n model = CNN(**kwargs) # type: ignore\n\n start_time = t.time()\n for i in range(1, n_epochs):\n budget = budgets[i]\n # How many epochs has to be run in this round\n epochs = int(budgets[i]) - int(budgets[i - 1])\n\n pl.seed_everything(run_id)\n r.start(config, budget, model=model)\n\n # The model weights are trained\n trainer = pl.Trainer(\n accelerator=\"gpu\",\n devices=1,\n num_sanity_val_steps=0, # No validation sanity\n auto_scale_batch_size=\"power\",\n deterministic=True,\n min_epochs=epochs,\n max_epochs=epochs,\n )\n trainer.fit(model)\n result = trainer.test(model)\n accuracy_ = result[0][\"val_acc\"]\n loss_ = result[0][\"val_loss\"]\n\n # We just add some random stati to show the potential later in DeepCAVE\n if accuracy_ < 0.5:\n status = Status.CRASHED\n accuracy_, loss_ = None, None\n elif random.uniform(0, 1) < 0.05: # 5% chance\n statusses = [Status.MEMORYOUT, Status.TIMEOUT]\n status = random.choice(statusses)\n accuracy_, loss_ = None, None\n else:\n status = Status.SUCCESS\n\n end_time = t.time()\n elapsed_time = end_time - start_time\n\n r.end(costs=[accuracy_, loss_, elapsed_time], status=status)"
+ "from inspect import BoundArguments\nimport os\nfrom re import T\nimport time as t\nimport random\nimport ConfigSpace as CS\nfrom ConfigSpace import ConfigurationSpace\nfrom ConfigSpace.hyperparameters import (\n UniformFloatHyperparameter,\n UniformIntegerHyperparameter,\n CategoricalHyperparameter,\n)\nfrom deepcave import Recorder, Objective\nfrom deepcave.runs import Status\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.utils.data import DataLoader, random_split\nimport torchvision.transforms as transforms\nfrom torchmetrics import Accuracy\nfrom torchvision.datasets import MNIST\nimport pytorch_lightning as pl\n\n\nNUM_WORKERS = 16\n\n\nclass MNISTModel(pl.LightningModule):\n def __init__(self, activation=\"relu\", learning_rate=1e-4, dropout_rate=0.1, batch_size=64):\n super().__init__()\n\n if activation == \"relu\":\n self.activation = nn.ReLU\n elif activation == \"tanh\":\n self.activation = nn.Tanh\n elif activation == \"sigmoid\":\n self.activation = nn.Sigmoid\n else:\n raise RuntimeError(\"Activation not found.\")\n\n self.learning_rate = learning_rate\n self.dropout_rate = dropout_rate\n self.batch_size = batch_size\n\n self.data_dir = os.path.join(os.getcwd(), \"datasets\")\n self.num_classes = 10\n self.dims = (1, 28, 28)\n self.channels, self.width, self.height = self.dims\n self.transform = transforms.Compose(\n [\n transforms.ToTensor(),\n transforms.Normalize((0.1307,), (0.3081,)),\n ]\n )\n\n self.accuracy = Accuracy(task=\"multiclass\", num_classes=self.num_classes)\n\n def prepare_data(self):\n # download\n MNIST(self.data_dir, train=True, download=True)\n MNIST(self.data_dir, train=False, download=True)\n\n def setup(self, stage=None):\n # Assign train/val datasets for use in dataloaders\n if stage == \"fit\" or stage is None:\n mnist_full = MNIST(self.data_dir, train=True, transform=self.transform)\n self.mnist_train, self.mnist_val = random_split(mnist_full, [20000, 40000])\n\n # Assign test dataset for use in dataloader(s)\n if stage == \"test\" or stage is None:\n self.mnist_test = MNIST(self.data_dir, train=False, transform=self.transform)\n\n def train_dataloader(self):\n return DataLoader(self.mnist_train, batch_size=self.batch_size, num_workers=NUM_WORKERS)\n\n def val_dataloader(self):\n return DataLoader(self.mnist_val, batch_size=self.batch_size, num_workers=NUM_WORKERS)\n\n def test_dataloader(self):\n return DataLoader(self.mnist_test, batch_size=self.batch_size, num_workers=NUM_WORKERS)\n\n def training_step(self, batch, batch_idx):\n x, y = batch\n logits = self(x)\n loss = F.nll_loss(logits, y)\n\n return loss\n\n def validation_step(self, batch, batch_idx):\n x, y = batch\n logits = self(x)\n loss = F.nll_loss(logits, y)\n preds = torch.argmax(logits, dim=1)\n self.accuracy(preds, y)\n\n self.log(\"val_loss\", loss, prog_bar=True)\n self.log(\"val_acc\", self.accuracy, prog_bar=True)\n\n return loss\n\n def test_step(self, batch, batch_idx):\n return self.validation_step(batch, batch_idx)\n\n def configure_optimizers(self):\n optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\n return optimizer\n\n\nclass MLP(MNISTModel):\n def __init__(self, activation, learning_rate, dropout_rate, batch_size, num_neurons=(64, 32)):\n super().__init__(activation, learning_rate, dropout_rate, batch_size)\n\n self.layers = nn.Sequential(\n nn.Flatten(),\n nn.Linear(self.channels * self.width * self.height, num_neurons[0]),\n self.activation(),\n nn.Dropout(dropout_rate),\n nn.Linear(num_neurons[0], num_neurons[1]),\n self.activation(),\n nn.Dropout(dropout_rate),\n nn.Linear(num_neurons[1], self.num_classes),\n )\n\n def forward(self, x):\n x = self.layers(x)\n return F.log_softmax(x, dim=1)\n\n\nclass CNN(MNISTModel):\n def __init__(self, activation, learning_rate, dropout_rate, batch_size):\n super().__init__(activation, learning_rate, dropout_rate, batch_size)\n\n self.conv1 = nn.Sequential(\n nn.Conv2d(\n in_channels=self.channels,\n out_channels=16,\n kernel_size=5,\n stride=1,\n padding=2,\n ),\n self.activation(),\n nn.Dropout(dropout_rate),\n nn.MaxPool2d(kernel_size=2),\n )\n self.conv2 = nn.Sequential(\n nn.Conv2d(16, 32, 5, 1, 2),\n self.activation(),\n nn.Dropout(dropout_rate),\n nn.MaxPool2d(2),\n nn.Flatten(),\n )\n # fully connected layer, output 10 classes\n self.out = nn.Linear(32 * 7 * 7, self.num_classes)\n\n def forward(self, x):\n x = self.conv1(x)\n x = self.conv2(x)\n x = self.out(x)\n\n return F.log_softmax(x, dim=1)\n\n\ndef get_configspace(seed):\n configspace = ConfigurationSpace(seed=seed)\n\n model = CategoricalHyperparameter(name=\"model\", choices=[\"mlp\", \"cnn\"])\n activation = CategoricalHyperparameter(name=\"activation\", choices=[\"sigmoid\", \"tanh\", \"relu\"])\n learning_rate = UniformFloatHyperparameter(\n name=\"learning_rate\", lower=0.0001, upper=0.1, log=True\n )\n dropout_rate = UniformFloatHyperparameter(name=\"dropout_rate\", lower=0.1, upper=0.9)\n batch_size = UniformIntegerHyperparameter(name=\"batch_size\", lower=16, upper=256)\n\n # MLP specific\n num_neurons_layer1 = UniformIntegerHyperparameter(name=\"num_neurons_layer1\", lower=5, upper=100)\n num_neurons_layer2 = UniformIntegerHyperparameter(name=\"num_neurons_layer2\", lower=5, upper=100)\n\n configspace.add_hyperparameters(\n [\n model,\n activation,\n learning_rate,\n dropout_rate,\n batch_size,\n num_neurons_layer1,\n num_neurons_layer2,\n ]\n )\n\n # Now add sub configspace\n configspace.add_condition(CS.EqualsCondition(num_neurons_layer1, model, \"mlp\"))\n configspace.add_condition(CS.EqualsCondition(num_neurons_layer2, model, \"mlp\"))\n\n return configspace\n\n\nif __name__ == \"__main__\":\n # Define objectives\n accuracy = Objective(\"accuracy\", lower=0, upper=1, optimize=\"upper\")\n loss = Objective(\"loss\", lower=0, optimize=\"lower\")\n time = Objective(\"time\", lower=0, optimize=\"lower\")\n\n # Define budgets\n max_epochs = 8\n n_epochs = 4\n budgets = np.linspace(0, max_epochs, num=n_epochs)\n\n # Others\n num_configs = 1000\n num_runs = 3\n save_path = \"logs/DeepCAVE/mnist_pytorch\"\n\n for run_id in range(num_runs):\n random.seed(run_id)\n configspace = get_configspace(run_id)\n\n with Recorder(configspace, objectives=[accuracy, loss, time], save_path=save_path) as r:\n for config in configspace.sample_configuration(num_configs):\n pl.seed_everything(run_id)\n kwargs = dict(\n activation=config[\"activation\"],\n learning_rate=config[\"learning_rate\"],\n dropout_rate=config[\"dropout_rate\"],\n batch_size=config[\"batch_size\"],\n )\n\n if config[\"model\"] == \"mlp\":\n model = MLP(\n **kwargs,\n num_neurons=(\n config[\"num_neurons_layer1\"],\n config[\"num_neurons_layer2\"],\n ),\n )\n elif config[\"model\"] == \"cnn\":\n model = CNN(**kwargs) # type: ignore\n\n start_time = t.time()\n for i in range(1, n_epochs):\n budget = budgets[i]\n # How many epochs has to be run in this round\n epochs = int(budgets[i]) - int(budgets[i - 1])\n\n pl.seed_everything(run_id)\n r.start(config, budget, model=model)\n\n # The model weights are trained\n trainer = pl.Trainer(\n accelerator=\"cpu\",\n devices=1,\n num_sanity_val_steps=0, # No validation sanity\n deterministic=True,\n min_epochs=epochs,\n max_epochs=epochs,\n )\n trainer.fit(model)\n result = trainer.test(model)\n accuracy_ = result[0][\"val_acc\"]\n loss_ = result[0][\"val_loss\"]\n\n # We just add some random stati to show the potential later in DeepCAVE\n if accuracy_ < 0.5:\n status = Status.CRASHED\n accuracy_, loss_ = None, None\n elif random.uniform(0, 1) < 0.05: # 5% chance\n statusses = [Status.MEMORYOUT, Status.TIMEOUT]\n status = random.choice(statusses)\n accuracy_, loss_ = None, None\n else:\n status = Status.SUCCESS\n\n end_time = t.time()\n elapsed_time = end_time - start_time\n\n r.end(costs=[accuracy_, loss_, elapsed_time], status=status)"
]
}
],
diff --git a/development/_downloads/fb625db3c50d423b1b7881136ffdeec8/examples_jupyter.zip b/development/_downloads/fb625db3c50d423b1b7881136ffdeec8/examples_jupyter.zip
index 1467182b..49b45068 100644
Binary files a/development/_downloads/fb625db3c50d423b1b7881136ffdeec8/examples_jupyter.zip and b/development/_downloads/fb625db3c50d423b1b7881136ffdeec8/examples_jupyter.zip differ
diff --git a/development/_sources/examples/record/mnist_pytorch.rst.txt b/development/_sources/examples/record/mnist_pytorch.rst.txt
index 6ab1c2b2..f337ca5b 100644
--- a/development/_sources/examples/record/mnist_pytorch.rst.txt
+++ b/development/_sources/examples/record/mnist_pytorch.rst.txt
@@ -24,7 +24,7 @@ Multi-Layer Perceptron via PyTorch
This more advanced example incorporates multiple objectives, budgets and statusses to
show the strenghts of DeepCAVE's recorder.
-.. GENERATED FROM PYTHON SOURCE LINES 8-288
+.. GENERATED FROM PYTHON SOURCE LINES 8-286
.. code-block:: Python
@@ -86,7 +86,7 @@ show the strenghts of DeepCAVE's recorder.
]
)
- self.accuracy = Accuracy()
+ self.accuracy = Accuracy(task="multiclass", num_classes=self.num_classes)
def prepare_data(self):
# download
@@ -94,7 +94,6 @@ show the strenghts of DeepCAVE's recorder.
MNIST(self.data_dir, train=False, download=True)
def setup(self, stage=None):
-
# Assign train/val datasets for use in dataloaders
if stage == "fit" or stage is None:
mnist_full = MNIST(self.data_dir, train=True, transform=self.transform)
@@ -280,10 +279,9 @@ show the strenghts of DeepCAVE's recorder.
# The model weights are trained
trainer = pl.Trainer(
- accelerator="gpu",
+ accelerator="cpu",
devices=1,
num_sanity_val_steps=0, # No validation sanity
- auto_scale_batch_size="power",
deterministic=True,
min_epochs=epochs,
max_epochs=epochs,
diff --git a/development/examples/record/mnist_pytorch.html b/development/examples/record/mnist_pytorch.html
index 18f31fc3..aa2164e8 100644
--- a/development/examples/record/mnist_pytorch.html
+++ b/development/examples/record/mnist_pytorch.html
@@ -758,7 +758,7 @@
]
)
- self.accuracy = Accuracy()
+ self.accuracy = Accuracy(task="multiclass", num_classes=self.num_classes)
def prepare_data(self):
# download
@@ -766,7 +766,6 @@
MNIST(self.data_dir, train=False, download=True)
def setup(self, stage=None):
-
# Assign train/val datasets for use in dataloaders
if stage == "fit" or stage is None:
mnist_full = MNIST(self.data_dir, train=True, transform=self.transform)
@@ -952,10 +951,9 @@
# The model weights are trained
trainer = pl.Trainer(
- accelerator="gpu",
+ accelerator="cpu",
devices=1,
num_sanity_val_steps=0, # No validation sanity
- auto_scale_batch_size="power",
deterministic=True,
min_epochs=epochs,
max_epochs=epochs,
diff --git a/development/searchindex.js b/development/searchindex.js
index e648758f..d4d7c9eb 100644
--- a/development/searchindex.js
+++ b/development/searchindex.js
@@ -1 +1 @@
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