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An extremely minimal and simple experiment module for machine learning in PyTorch (PyTorch + boilerplate = torchplate).
In addition to abstracting away the training loop, we provide several abstractions to improve the efficiency of machine learning workflows with PyTorch.
$ pip install torchplate
To get started, create a child class of torchplate.experiment.Experiment and provide several key, experiment-unique items: model, optimizer, and a training set dataloader. Then, provide an implementation of the abstract method evaluate. This function takes in a batch from the trainloader and should return the loss (i.e., implement the forward pass + loss calculation). Add whatever custom methods you may want to this class. Then starting training! That's it!
import torchplate
from torchplate import experiment
from torchplate import utils
import torch
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
import requests
import cloudpickle as cp
from urllib.request import urlopen
class Net(nn.Module):
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(3*32*32, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 3)
def forward(self, x):
x = torch.flatten(x, 1)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
class CifarExp(torchplate.experiment.Experiment):
def __init__(self):
self.model = Net()
self.optimizer = optim.Adam(self.model.parameters(), lr=0.001)
self.criterion = nn.CrossEntropyLoss()
dataset = cp.load(urlopen("https://stanford.edu/~rsikand/assets/datasets/mini_cifar.pkl"))
# use various torchplate.utils to improve efficiency of common workflows
self.trainloader, self.testloader = torchplate.utils.get_xy_loaders(dataset)
# inherit from torchplate.experiment.Experiment and pass in
# model, optimizer, and dataloader
super().__init__(
model = self.model,
optimizer = self.optimizer,
trainloader = self.trainloader,
verbose = True
)
# provide this abstract method to calculate loss
def evaluate(self, batch):
x, y = batch
logits = self.model(x)
loss_val = self.criterion(logits, y)
return loss_val
def test(self):
accuracy_count = 0
for x, y in self.testloader:
logits = self.model(x)
pred = torch.argmax(F.softmax(logits, dim=1)).item()
print(f"Prediction: {pred}, True: {y.item()}")
if pred == y:
accuracy_count += 1
print("Accuracy: ", accuracy_count/len(self.testloader))
def on_epoch_end(self):
# to illustrate the concept of callbacks
print("------------------ (Epoch end) --------------------")
exp = CifarExp()
exp.train(num_epochs=100)
exp.test()See examples/cifar for another minimal example. See examples/starter for a full program example. To get started running your own experiments, you can use examples/starter as a base (or use cookiecutter as shown below).
The starter branch holds the source for a cookiecutter project. This allows users to easily create projects from the starter code example by running a simple command. To get started, install cookiecutter and then type
$ cookiecutter https://github.com/rosikand/torchplate.git --checkout starter
which will generate the following structure for you to use as a base for your projects:
torchplate_starter
├── datasets.py
├── experiments.py
├── models.py
└── runner.py