# Research workflow

This guide contains good practices for a machine learning researcher to structure day-to-day work. It’s broken down into four sections, that can each be read independently depending on your needs.

### Content

The most important section is 1. Code structure as it covers how to design machine learning code, with a detailed example. Anyone who want to get started, or better organise their codebase, should find this section useful.

The remaining sections (2. Run experiments, 3. Python environment, 4. Remote work) should benefit those who work day-to-day on machine learning research (e.g. run and organise experiments on multiple machines, while working remotely).

## 1. Code structure

Quality code is essential for good research. We should aim at building a codebase that becomes increasingly richer while remaining easy to use. We’re going to discuss the structure of a machine learning codebase in PyTorch, but it’s also applicable to other frameworks (TensorFlow, Keras, Caffe..).

In machine learning, we first prepare the data, then define the model architecture + loss, and finally train the model. This is where we should focus our research efforts on.

Everything else (monitoring metrics on tensorboard, saving/restoring model weights, printing log outputs etc.) should only be implemented once, and reused across projects.

We’re going to implement a general Trainer class that contains all the training logic (i.e. everything else). Whenever we want to start a new machine learning project, we simply need to inherit from Trainer and implement the data and model creation. To illustrate how straightforward it is, we’ll go through a detailed example shortly.

### 1.1. Trainer initialisation

First, let’s go step by step in the init function of the general Trainer class. The only argument of the trainer is the path to a config file (more details on the config file in Section 2), that contains all the training settings (batch size, number of workers, learning rate etc.) and the hyperparameters of the model.

class Trainer:
def __init__(self, config):
self.config = config
self.session_name = None
# Initialise the training session by creating a new folder
self.initialise_session()

# Monitor training with tensorboard
self.tensorboard = SummaryWriter(self.session_name)
# Use the gpu if available
self.device = torch.device('cuda') if self.config.gpu else torch.device('cpu')


A new folder will be created each time an experiment is run. The name of this folder follows the format session_{machine_name}_{time}_{tag}, with the tag (contained in the config file) specifying the name of the experiment (e.g. baseline).

This folder will contain: a copy of the config file used to create the training session (to easily reproduce experiments), checkpoints of the model/optimiser (to restore weights), a tensorboard file (to monitor metrics) and a .txt log file saving all the terminal outputs.

        # Data
self.train_dataset, self.val_dataset = None, None
# Initialise the PyTorch Dataset and DataLoader classes
self.create_data()

# Model
self.model = None
# Build the neural network and move it to the desired device
self.create_model()
self.model.to(self.device)

# Loss
self.loss_fn = None
# Instantiate the loss function
self.create_loss()

# Optimiser
self.optimiser = None
# Initialise the optimiser
self.create_optimiser()

# Metrics
self.train_metrics = None
self.val_metrics = None
# What we monitor during training on both the train and validation sets
self.create_metrics()


For each new project, we simply need to implement the abstract methods self.create_data, self.create_model, self.create_loss, self.create_optimiser and self.create_metrics. The general Trainer class will handle everything else. We will shortly show (in subsection 1.4) an example implementation on CIFAR10, a classification dataset of tiny 32x32 images.

### 1.2. Train step

Now let’s see how the trainer computes one training step. This is the method Trainer.train_step:

def train_step(self):
# Fetch a training batch. batch is a dictionary containing all the inputs and labels.
batch = self._get_next_batch()
# Cast the batch to the correct device
self.cast_to_device(batch)

# Forward pass
output = self.forward_model(batch)
loss = self.forward_loss(batch, output)

# Backward pass
loss.backward()
self.optimiser.step()

# Print a log output to the terminal, and save loss on tensorboard.
self.print_log(loss)

# Visualisation
self.visualise(batch, output, 'train')


The log output printed in the terminal looks like:

Iteration  100/10000 | examples/s: 7785.4 | loss: 1.3832 | time elapsed: 00h00m02s
| time left: 00h04m46s
Fetch data time: 2ms, model update time: 7ms


We monitor how long fetching one data batch takes (2ms) – if it’s too slow, we might need more workers – and how long a single model update takes (7ms). Optimising these two values will result in an overall lower training time (indicated by ‘time left’).

The train_step method is very general and operates with any input batch: a python dictionary, created by the data loader self.train_dataloader, containing the inputs and labels of the model.

### 1.3 Training the model

The main method of the trainer is Trainer.train: it optimises and evaluates the model, outputs the metrics and visualisation, and saves checkpoints regularly.

def train(self):
while self.global_step < self.config.n_iterations:
self.global_step += 1
self.train_step()

if self.global_step % self.config.val_iterations == 0:
# Evaluate the model on the validation set
score = self.validate()

if score > self.best_score:
self.best_score = score
self.save_checkpoint()


### 1.4. Example

In practice, simply fork my repository and implement the abstract methods of the trainer. For illustration, the repository contains an example that trains a CIFAR10 model with only a few lines of code:

# Inherit from the general Trainer class
class CifarTrainer(Trainer):
# Implement all the abstract classes.
def create_data(self):
# Load dataset containing input 32x32 images and corresponding labels.
self.train_dataset = CifarDataset(mode='train')
self.val_dataset = CifarDataset(mode='val')

num_workers=self.config.n_workers, shuffle=True)
num_workers=self.config.n_workers, shuffle=False)

def create_model(self):
# A simple convolutional net.
self.model = CifarModel()

def create_loss(self):
self.loss_fn = nn.CrossEntropyLoss()

def create_optimiser(self):
# Parameters of the model that are optimisable.
# Use an Adam optimiser with L2 regularisation.
weight_decay=self.config.weight_decay)

def create_metrics(self):
# Monitor the accuracy of our model (percentage of correctly classified images).
self.train_metrics = AccuracyMetrics()
self.val_metrics = AccuracyMetrics()

def forward_model(self, batch):
return self.model(batch['image'])

def forward_loss(self, batch, output):
return self.loss_fn(output, batch['label'])

def visualise(self, batch, output, mode):
# Visualise the input images to our model.
self.global_step)


Running python run_training.py --config experiments/cifar.yml then produces the following output:

Iteration  100/10000 | examples/s: 7785.4 | loss: 1.3832 | time elapsed: 00h00m02s
| time left: 00h04m46s
Fetch data time: 2ms, model update time: 7ms

Iteration  200/10000 | examples/s: 7326.7 | loss: 1.3379 | time elapsed: 00h00m04s
| time left: 00h03m58s
Fetch data time: 2ms, model update time: 9ms

----------------------------------------------------------------------------------
Validation
----------------------------------------------------------------------------------
100%|█████████████████████████████████████████████| 79/79 [00:01<00:00, 47.14it/s]
Val loss: 1.1156
----------------------------------------------------------------------------------
Metrics
----------------------------------------------------------------------------------
Train score: 0.648
Val score: 0.596
New best score: -inf -> 0.596
Model saved to: /path/to/experiment/checkpoint

-----------------------------------------------------------------------------------
Iteration  300/10000 | examples/s: 5816.1 | loss: 1.0277 | time elapsed: 00h00m09s
| time left: 00h03m39s
Fetch data time: 2ms, model update time: 11ms

...


If the training is interrupted, it can be resumed by pointing to the path of the experiment (the folder whose name is self.session_name that was created in the init function of the Trainer). Running python run_training.py --restore /path/to/experiment/ will restore the weights of the model and optimiser, and continue training where we left it.

Next we will cover how to run reproducible experiments, how to setup a reliable python environment, and how to productively work remotely.

Big thanks to the Wayve team, who taught me how to effectively structure my code.