Working on getting experiment loop.

11 files changed, 527 insertions, 135 deletions

diff --git a/src/notebooks/Untitled.ipynb b/src/notebooks/Untitled.ipynb
new file mode 100644
index 0000000..1cb7acb
--- /dev/null
+++ b/src/notebooks/Untitled.ipynb
@@ -0,0 +1,177 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "getattr(torch.optim.lr_scheduler, \"StepLR\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss = getattr(torch.nn, \"L1Loss\")()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input = torch.randn(3, 5, requires_grad=True)\n",
+    "target = torch.randn(3, 5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "output = loss(input, target)\n",
+    "output.backward()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s = 1.\n",
+    "if s is not None:\n",
+    "    assert 0.0 < s < 1.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class A:\n",
+    "    @property\n",
+    "    def __name__(self):\n",
+    "        return \"adafa\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = A()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a.__name__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from training.gpu_manager import GPUManager"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gpu_manager = GPUManager(True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2020-07-05 21:16:55.100 | DEBUG    | training.gpu_manager:_get_free_gpu:55 - pid 29777 picking gpu 0\n",
+      "2020-07-05 21:16:55.704 | DEBUG    | training.gpu_manager:_get_free_gpu:59 - pid 29777 could not get lock.\n",
+      "2020-07-05 21:16:55.705 | DEBUG    | training.gpu_manager:get_free_gpu:37 - pid 29777 sleeping\n",
+      "2020-07-05 21:17:00.722 | DEBUG    | training.gpu_manager:_get_free_gpu:55 - pid 29777 picking gpu 0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gpu_manager.get_free_gpu()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/src/text_recognizer/datasets/__init__.py b/src/text_recognizer/datasets/__init__.py
index 929cfb9..aec5bf9 100644
--- a/src/text_recognizer/datasets/__init__.py
+++ b/src/text_recognizer/datasets/__init__.py
@@ -1,2 +1,2 @@
 """Dataset modules."""
-from .data_loader import fetch_data_loader
+from .emnist_dataset import EmnistDataLoader
diff --git a/src/text_recognizer/datasets/data_loader.py b/src/text_recognizer/datasets/data_loader.py
deleted file mode 100644
index fd55934..0000000
--- a/src/text_recognizer/datasets/data_loader.py
+++ /dev/null
@@ -1,15 +0,0 @@
-"""Data loader collection."""
-
-from typing import Dict
-
-from torch.utils.data import DataLoader
-
-from text_recognizer.datasets.emnist_dataset import fetch_emnist_data_loader
-
-
-def fetch_data_loader(data_loader_args: Dict) -> DataLoader:
-    """Fetches the specified PyTorch data loader."""
-    if data_loader_args.pop("name").lower() == "emnist":
-        return fetch_emnist_data_loader(data_loader_args)
-    else:
-        raise NotImplementedError
diff --git a/src/text_recognizer/datasets/emnist_dataset.py b/src/text_recognizer/datasets/emnist_dataset.py
index f9c8ffa..a17d7a9 100644
--- a/src/text_recognizer/datasets/emnist_dataset.py
+++ b/src/text_recognizer/datasets/emnist_dataset.py
@@ -24,7 +24,7 @@ class Transpose:
         return np.array(image).swapaxes(0, 1)
 
 
-def save_emnist_essentials(emnsit_dataset: EMNIST) -> None:
+def save_emnist_essentials(emnsit_dataset: type = EMNIST) -> None:
     """Extract and saves EMNIST essentials."""
     labels = emnsit_dataset.classes
     labels.sort()
@@ -45,111 +45,163 @@ def download_emnist() -> None:
     save_emnist_essentials(dataset)
 
 
-def load_emnist_mapping() -> Dict:
+def load_emnist_mapping() -> Dict[int, str]:
     """Load the EMNIST mapping."""
     with open(str(ESSENTIALS_FILENAME)) as f:
         essentials = json.load(f)
     return dict(essentials["mapping"])
 
 
-def _sample_to_balance(dataset: EMNIST, seed: int = 4711) -> None:
-    """Because the dataset is not balanced, we take at most the mean number of instances per class."""
-    np.random.seed(seed)
-    x = dataset.data
-    y = dataset.targets
-    num_to_sample = int(np.bincount(y.flatten()).mean())
-    all_sampled_inds = []
-    for label in np.unique(y.flatten()):
-        inds = np.where(y == label)[0]
-        sampled_inds = np.unique(np.random.choice(inds, num_to_sample))
-        all_sampled_inds.append(sampled_inds)
-    ind = np.concatenate(all_sampled_inds)
-    x_sampled = x[ind]
-    y_sampled = y[ind]
-    dataset.data = x_sampled
-    dataset.targets = y_sampled
-
-
-def fetch_emnist_dataset(
-    split: str,
-    train: bool,
-    sample_to_balance: bool = False,
-    transform: Optional[Callable] = None,
-    target_transform: Optional[Callable] = None,
-) -> EMNIST:
-    """Fetch the EMNIST dataset."""
-    if transform is None:
-        transform = Compose([Transpose(), ToTensor()])
-
-    dataset = EMNIST(
-        root=DATA_DIRNAME,
-        split="byclass",
-        train=train,
-        download=False,
-        transform=transform,
-        target_transform=target_transform,
-    )
-
-    if sample_to_balance and split == "byclass":
-        _sample_to_balance(dataset)
-
-    return dataset
-
-
-def fetch_emnist_data_loader(
-    splits: List[str],
-    sample_to_balance: bool = False,
-    transform: Optional[Callable] = None,
-    target_transform: Optional[Callable] = None,
-    batch_size: int = 128,
-    shuffle: bool = False,
-    num_workers: int = 0,
-    cuda: bool = True,
-) -> Dict[DataLoader]:
-    """Fetches the EMNIST dataset and return a PyTorch DataLoader.
-
-    Args:
-        splits (List[str]): One or both of the dataset splits "train" and "val".
-        sample_to_balance (bool): If true, resamples the unbalanced if the split "byclass" is selected.
-            Defaults to False.
-        transform (Optional[Callable]):  A function/transform that takes in an PIL image and returns a
-            transformed version. E.g, transforms.RandomCrop. Defaults to None.
-        target_transform (Optional[Callable]): A function/transform that takes in the target and transforms
-            it.
-            Defaults to None.
-        batch_size (int): How many samples per batch to load. Defaults to 128.
-        shuffle (bool): Set to True to have the data reshuffled at every epoch. Defaults to False.
-        num_workers (int): How many subprocesses to use for data loading. 0 means that the data will be
-            loaded in the main process. Defaults to 0.
-        cuda (bool): If True, the data loader will copy Tensors into CUDA pinned memory before returning
-            them. Defaults to True.
-
-    Returns:
-        Dict: A dict containing PyTorch DataLoader(s) with emnist characters.
-
-    """
-    data_loaders = {}
-
-    for split in ["train", "val"]:
-        if split in splits:
-
-            if split == "train":
-                train = True
-            else:
-                train = False
-
-            dataset = fetch_emnist_dataset(
-                split, train, sample_to_balance, transform, target_transform
-            )
-
-            data_loader = DataLoader(
-                dataset=dataset,
-                batch_size=batch_size,
-                shuffle=shuffle,
-                num_workers=num_workers,
-                pin_memory=cuda,
-            )
-
-            data_loaders[split] = data_loader
-
-    return data_loaders
+class EmnistDataLoader:
+    """Class for Emnist DataLoaders."""
+
+    def __init__(
+        self,
+        splits: List[str],
+        sample_to_balance: bool = False,
+        subsample_fraction: float = None,
+        transform: Optional[Callable] = None,
+        target_transform: Optional[Callable] = None,
+        batch_size: int = 128,
+        shuffle: bool = False,
+        num_workers: int = 0,
+        cuda: bool = True,
+        seed: int = 4711,
+    ) -> None:
+        """Fetches DataLoaders.
+
+        Args:
+            splits (List[str]): One or both of the dataset splits "train" and "val".
+            sample_to_balance (bool): If true, resamples the unbalanced if the split "byclass" is selected.
+                Defaults to False.
+            subsample_fraction (float): The fraction of the dataset will be loaded. If None or 0 the entire
+                dataset will be loaded.
+            transform (Optional[Callable]):  A function/transform that takes in an PIL image and returns a
+                transformed version. E.g, transforms.RandomCrop. Defaults to None.
+            target_transform (Optional[Callable]): A function/transform that takes in the target and
+                transforms it. Defaults to None.
+            batch_size (int): How many samples per batch to load. Defaults to 128.
+            shuffle (bool): Set to True to have the data reshuffled at every epoch. Defaults to False.
+            num_workers (int): How many subprocesses to use for data loading. 0 means that the data will be
+                loaded in the main process. Defaults to 0.
+            cuda (bool): If True, the data loader will copy Tensors into CUDA pinned memory before returning
+                them. Defaults to True.
+            seed (int): Seed for sampling.
+
+        """
+        self.splits = splits
+        self.sample_to_balance = sample_to_balance
+        if subsample_fraction is not None:
+            assert (
+                0.0 < subsample_fraction < 1.0
+            ), " The subsample fraction must be in (0, 1)."
+        self.subsample_fraction = subsample_fraction
+        self.transform = transform
+        self.target_transform = target_transform
+        self.batch_size = batch_size
+        self.shuffle = shuffle
+        self.num_workers = num_workers
+        self.cuda = cuda
+        self._data_loaders = self._fetch_emnist_data_loaders()
+
+    @property
+    def __name__(self) -> str:
+        """Returns the name of the dataset."""
+        return "EMNIST"
+
+    def __call__(self, split: str) -> Optional[DataLoader]:
+        """Returns the `split` DataLoader.
+
+        Args:
+            split (str): The dataset split, i.e. train or val.
+
+        Returns:
+            type: A PyTorch DataLoader.
+
+        Raises:
+            ValueError: If the split does not exist.
+
+        """
+        try:
+            return self._data_loaders[split]
+        except KeyError:
+            raise ValueError(f"Split {split} does not exist.")
+
+    def _sample_to_balance(self, dataset: type = EMNIST) -> EMNIST:
+        """Because the dataset is not balanced, we take at most the mean number of instances per class."""
+        np.random.seed(self.seed)
+        x = dataset.data
+        y = dataset.targets
+        num_to_sample = int(np.bincount(y.flatten()).mean())
+        all_sampled_indices = []
+        for label in np.unique(y.flatten()):
+            inds = np.where(y == label)[0]
+            sampled_indices = np.unique(np.random.choice(inds, num_to_sample))
+            all_sampled_indices.append(sampled_indices)
+        indices = np.concatenate(all_sampled_indices)
+        x_sampled = x[indices]
+        y_sampled = y[indices]
+        dataset.data = x_sampled
+        dataset.targets = y_sampled
+
+        return dataset
+
+    def _subsample(self, dataset: type = EMNIST) -> EMNIST:
+        """Subsamples the dataset to the specified fraction."""
+        x = dataset.data
+        y = dataset.targets
+        num_samples = int(x.shape[0] * self.subsample_fraction)
+        x_sampled = x[:num_samples]
+        y_sampled = y[:num_samples]
+        dataset.data = x_sampled
+        dataset.targets = y_sampled
+
+        return dataset
+
+    def _fetch_emnist_dataset(self, train: bool) -> EMNIST:
+        """Fetch the EMNIST dataset."""
+        if self.transform is None:
+            transform = Compose([Transpose(), ToTensor()])
+
+        dataset = EMNIST(
+            root=DATA_DIRNAME,
+            split="byclass",
+            train=train,
+            download=False,
+            transform=transform,
+            target_transform=self.target_transform,
+        )
+
+        if self.sample_to_balance:
+            dataset = self._sample_to_balance(dataset)
+
+        if self.subsample_fraction is not None:
+            dataset = self._subsample(dataset)
+
+        return dataset
+
+    def _fetch_emnist_data_loaders(self) -> Dict[str, DataLoader]:
+        """Fetches the EMNIST dataset and return a Dict of PyTorch DataLoaders."""
+        data_loaders = {}
+
+        for split in ["train", "val"]:
+            if split in self.splits:
+
+                if split == "train":
+                    train = True
+                else:
+                    train = False
+
+                dataset = self._fetch_emnist_dataset(train)
+
+                data_loader = DataLoader(
+                    dataset=dataset,
+                    batch_size=self.batch_size,
+                    shuffle=self.shuffle,
+                    num_workers=self.num_workers,
+                    pin_memory=self.cuda,
+                )
+
+                data_loaders[split] = data_loader
+
+        return data_loaders
diff --git a/src/text_recognizer/models/base.py b/src/text_recognizer/models/base.py
index 736af7b..0cc531a 100644
--- a/src/text_recognizer/models/base.py
+++ b/src/text_recognizer/models/base.py
@@ -10,7 +10,6 @@ import torch
 from torch import nn
 from torchsummary import summary
 
-from text_recognizer.dataset.data_loader import fetch_data_loader
 
 WEIGHT_DIRNAME = Path(__file__).parents[1].resolve() / "weights"
 
@@ -22,6 +21,7 @@ class Model(ABC):
         self,
         network_fn: Callable,
         network_args: Dict,
+        data_loader: Optional[Callable] = None,
         data_loader_args: Optional[Dict] = None,
         metrics: Optional[Dict] = None,
         criterion: Optional[Callable] = None,
@@ -32,12 +32,13 @@ class Model(ABC):
         lr_scheduler_args: Optional[Dict] = None,
         device: Optional[str] = None,
     ) -> None:
-        """Base class, to be inherited by predictors for specific type of data.
+        """Base class, to be inherited by model for specific type of data.
 
         Args:
             network_fn (Callable): The PyTorch network.
             network_args (Dict): Arguments for the network.
-            data_loader_args (Optional[Dict]):  Arguments for the data loader.
+            data_loader (Optional[Callable]): A function that fetches train and val DataLoader.
+            data_loader_args (Optional[Dict]):  Arguments for the DataLoader.
             metrics (Optional[Dict]): Metrics to evaluate the performance with. Defaults to None.
             criterion (Optional[Callable]): The criterion to evaulate the preformance of the network.
                 Defaults to None.
@@ -53,8 +54,8 @@ class Model(ABC):
 
         # Fetch data loaders.
         if data_loader_args is not None:
-            self._data_loaders = fetch_data_loader(**data_loader_args)
-            dataset_name = self._data_loaders.items()[0].dataset.__name__
+            self._data_loaders = data_loader(**data_loader_args)
+            dataset_name = self._data_loaders.__name__
         else:
             dataset_name = ""
             self._data_loaders = None
@@ -210,7 +211,6 @@ class Model(ABC):
             logger.debug(
                 f"Found a new best {val_metric}. Saving best checkpoint and weights."
             )
-            self.save_weights()
             shutil.copyfile(filepath, str(path / "best.pt"))
 
     def load_weights(self) -> None:
diff --git a/src/text_recognizer/models/character_model.py b/src/text_recognizer/models/character_model.py
index 1570344..fd69bf2 100644
--- a/src/text_recognizer/models/character_model.py
+++ b/src/text_recognizer/models/character_model.py
@@ -32,17 +32,21 @@ class CharacterModel(Model):
 
         super().__init__(
             network_fn,
-            data_loader_args,
             network_args,
+            data_loader_args,
             metrics,
             criterion,
+            criterion_args,
             optimizer,
+            optimizer_args,
+            lr_scheduler,
+            lr_scheduler_args,
             device,
         )
         self.emnist_mapping = self.mapping()
         self.eval()
 
-    def mapping(self) -> Dict:
+    def mapping(self) -> Dict[int, str]:
         """Mapping between integers and classes."""
         mapping = load_emnist_mapping()
         return mapping
diff --git a/src/text_recognizer/models/util.py b/src/text_recognizer/models/metrics.py
index 905fe7b..e2a30a9 100644
--- a/src/text_recognizer/models/util.py
+++ b/src/text_recognizer/models/metrics.py
@@ -4,7 +4,7 @@ import torch
 
 
 def accuracy(outputs: torch.Tensor, labels: torch.Tensro) -> float:
-    """Short summary.
+    """Computes the accuracy.
 
     Args:
         outputs (torch.Tensor): The output from the network.
diff --git a/src/training/gpu_manager.py b/src/training/gpu_manager.py
new file mode 100644
index 0000000..ce1b3dd
--- /dev/null
+++ b/src/training/gpu_manager.py
@@ -0,0 +1,62 @@
+"""GPUManager class."""
+import os
+import time
+from typing import Optional
+
+import gpustat
+from loguru import logger
+import numpy as np
+from redlock import Redlock
+
+
+GPU_LOCK_TIMEOUT = 5000  # ms
+
+
+class GPUManager:
+    """Class for allocating GPUs."""
+
+    def __init__(self, verbose: bool = False) -> None:
+        """Initializes Redlock manager."""
+        self.lock_manager = Redlock([{"host": "localhost", "port": 6379, "db": 0}])
+        self.verbose = verbose
+
+    def get_free_gpu(self) -> int:
+        """Gets a free GPU.
+
+        If some GPUs are available, try reserving one by checking out an exclusive redis lock.
+        If none available or can not get lock, sleep and check again.
+
+        Returns:
+            int: The gpu index.
+
+        """
+        while True:
+            gpu_index = self._get_free_gpu()
+            if gpu_index is not None:
+                return gpu_index
+
+            if self.verbose:
+                logger.debug(f"pid {os.getpid()} sleeping")
+            time.sleep(GPU_LOCK_TIMEOUT / 1000)
+
+    def _get_free_gpu(self) -> Optional[int]:
+        """Fetches an available GPU index."""
+        try:
+            available_gpu_indices = [
+                gpu.index
+                for gpu in gpustat.GPUStatCollection.new_query()
+                if gpu.memory_used < 0.5 * gpu.memory_total
+            ]
+        except Exception as e:
+            logger.debug(f"Got the following exception: {e}")
+            return None
+
+        if available_gpu_indices:
+            gpu_index = np.random.choice(available_gpu_indices)
+            if self.verbose:
+                logger.debug(f"pid {os.getpid()} picking gpu {gpu_index}")
+            if self.lock_manager.lock(f"gpu_{gpu_index}", GPU_LOCK_TIMEOUT):
+                return int(gpu_index)
+            if self.verbose:
+                logger.debug(f"pid {os.getpid()} could not get lock.")
+        return None
diff --git a/src/training/prepare_experiments.py b/src/training/prepare_experiments.py
new file mode 100644
index 0000000..1ab8f00
--- /dev/null
+++ b/src/training/prepare_experiments.py
@@ -0,0 +1,35 @@
+"""Run a experiment from a config file."""
+import json
+
+import click
+from loguru import logger
+import yaml
+
+
+def run_experiment(experiment_filename: str) -> None:
+    """Run experiment from file."""
+    with open(experiment_filename) as f:
+        experiments_config = yaml.safe_load(f)
+    num_experiments = len(experiments_config["experiments"])
+    for index in range(num_experiments):
+        experiment_config = experiments_config["experiments"][index]
+        experiment_config["experiment_group"] = experiments_config["experiment_group"]
+        print(
+            f"python training/run_experiment.py --gpu=-1 '{json.dumps(experiment_config)}'"
+        )
+
+
+@click.command()
+@click.option(
+    "--experiments_filename",
+    required=True,
+    type=str,
+    help="Filename of Yaml file of experiments to run.",
+)
+def main(experiment_filename: str) -> None:
+    """Parse command-line arguments and run experiments from provided file."""
+    run_experiment(experiment_filename)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/training/run_experiment.py b/src/training/run_experiment.py
index 8033f47..8296e59 100644
--- a/src/training/run_experiment.py
+++ b/src/training/run_experiment.py
@@ -1 +1,75 @@
 """Script to run experiments."""
+import importlib
+import os
+from typing import Dict
+
+import click
+import torch
+from training.train import Trainer
+
+
+def run_experiment(
+    experiment_config: Dict, save_weights: bool, gpu_index: int, use_wandb: bool = False
+) -> None:
+    """Short summary."""
+    # Import the data loader module and arguments.
+    datasets_module = importlib.import_module("text_recognizer.datasets")
+    data_loader_ = getattr(datasets_module, experiment_config["dataloader"])
+    data_loader_args = experiment_config.get("data_loader_args", {})
+
+    # Import the model module and model arguments.
+    models_module = importlib.import_module("text_recognizer.models")
+    model_class_ = getattr(models_module, experiment_config["model"])
+
+    # Import metric.
+    metric_fn_ = getattr(models_module, experiment_config["metric"])
+
+    # Import network module and arguments.
+    network_module = importlib.import_module("text_recognizer.networks")
+    network_fn_ = getattr(network_module, experiment_config["network"])
+    network_args = experiment_config.get("network_args", {})
+
+    # Criterion
+    criterion_ = getattr(torch.nn, experiment_config["criterion"])
+    criterion_args = experiment_config.get("criterion_args", {})
+
+    # Optimizer
+    optimizer_ = getattr(torch.optim, experiment_config["optimizer"])
+    optimizer_args = experiment_config.get("optimizer_args", {})
+
+    # Learning rate scheduler
+    lr_scheduler_ = None
+    lr_scheduler_args = None
+    if experiment_config["lr_scheduler"] is not None:
+        lr_scheduler_ = getattr(
+            torch.optim.lr_scheduler, experiment_config["lr_scheduler"]
+        )
+        lr_scheduler_args = experiment_config.get("lr_scheduler_args", {})
+
+    # Device
+    # TODO fix gpu manager
+    device = None
+
+    model = model_class_(
+        network_fn=network_fn_,
+        network_args=network_args,
+        data_loader=data_loader_,
+        data_loader_args=data_loader_args,
+        metrics=metric_fn_,
+        criterion=criterion_,
+        criterion_args=criterion_args,
+        optimizer=optimizer_,
+        optimizer_args=optimizer_args,
+        lr_scheduler=lr_scheduler_,
+        lr_scheduler_args=lr_scheduler_args,
+        device=device,
+    )
+
+    # TODO: Fix checkpoint path and wandb
+    trainer = Trainer(
+        model=model,
+        epochs=experiment_config["epochs"],
+        val_metric=experiment_config["metric"],
+    )
+
+    trainer.fit()
diff --git a/src/training/train.py b/src/training/train.py
index 783de02..4a452b6 100644
--- a/src/training/train.py
+++ b/src/training/train.py
@@ -9,6 +9,8 @@ import numpy as np
 import torch
 from tqdm import tqdm, trange
 from training.util import RunningAverage
+import wandb
+
 
 torch.backends.cudnn.benchmark = True
 np.random.seed(4711)
@@ -30,6 +32,7 @@ class Trainer:
         epochs: int,
         val_metric: str = "accuracy",
         checkpoint_path: Optional[Path] = None,
+        use_wandb: Optional[bool] = False,
     ) -> None:
         """Initialization of the Trainer.
 
@@ -38,6 +41,7 @@ class Trainer:
             epochs (int): Number of epochs to train.
             val_metric (str): The validation metric to evaluate the model on. Defaults to "accuracy".
             checkpoint_path (Optional[Path]): The path to a previously trained model. Defaults to None.
+            use_wandb (Optional[bool]): Sync training to wandb.
 
         """
         self.model = model
@@ -48,13 +52,16 @@ class Trainer:
         if self.checkpoint_path is not None:
             self.start_epoch = self.model.load_checkpoint(self.checkpoint_path)
 
+        if use_wandb:
+            # TODO implement wandb logging.
+            pass
+
         self.val_metric = val_metric
         self.best_val_metric = 0.0
         logger.add(self.model.name + "_{time}.log")
 
     def train(self) -> None:
         """Training loop."""
-        # TODO add summary
         # Set model to traning mode.
         self.model.train()
 
@@ -93,10 +100,6 @@ class Trainer:
                 # Perform updates using calculated gradients.
                 self.model.optimizer.step()
 
-                # Update the learning rate scheduler.
-                if self.model.lr_scheduler is not None:
-                    self.model.lr_scheduler.step()
-
                 # Compute metrics.
                 loss_avg.update(loss.item())
                 output = output.data.cpu()
@@ -174,8 +177,8 @@ class Trainer:
 
         return metrics_mean
 
-    def run(self) -> None:
-        """Training and evaluation loop."""
+    def fit(self) -> None:
+        """Runs the training and evaluation loop."""
         # Create new experiment.
         EXPERIMENTS_DIRNAME.mkdir(parents=True, exist_ok=True)
         experiment = datetime.now().strftime("%m%d_%H%M%S")