Getting Started with PyTorch on Saturn Cloud

Use PyTorch with a single GPU

This example shows how you can use the power of a GPU to quickly train a neural network in Saturn Cloud. This code runs on a single GPU of a Jupyter server resource.

This is an example of a natural language processing neural network which is trained on Seattle pet license data to then generate new pet names. The model uses LSTM layers which are especially good at discovering patterns in sequences like text. The model takes a partially complete name and determines the probability of each possible next character in the name. Characters are randomly sampled from this distribution and added to the partial name until a stop character is generated and full name has been created.

Setting up model training

This code downloads the already cleaned pet names data, creates functions to process it into a format to feed into an LSTM, and defines the model architecture.

First, import the required modules:

import uuid  # noqa
import datetime
import pickle  # noqa
import json
import torch  # noqa
import torch.nn as nn
import torch.optim as optim
import numpy as np
import urllib.request
import pandas as pd  # noqa
from import Dataset, DataLoader

This chunk downloads the pet names data, then defines functions and classes to help process and manage the data. It also defines the model architecture for the LSTM.

with urllib.request.urlopen(
) as f:
    pet_names = json.loads("utf-8"))

# Our list of characters, where * represents blank and + represents stop
characters = list("*+abcdefghijklmnopqrstuvwxyz-. ")
str_len = 8

def format_training_data(pet_names, device=None):
    def get_substrings(in_str):
        # add the stop character to the end of the name, then generate all the partial names
        in_str = in_str + "+"
        res = [in_str[0:j] for j in range(1, len(in_str) + 1)]
        return res

    pet_names_expanded = [get_substrings(name) for name in pet_names]
    pet_names_expanded = [item for sublist in pet_names_expanded for item in sublist]
    pet_names_characters = [list(name) for name in pet_names_expanded]
    pet_names_padded = [name[-(str_len + 1) :] for name in pet_names_characters]
    pet_names_padded = [
        list((str_len + 1 - len(characters)) * "*") + characters for characters in pet_names_padded
    pet_names_numeric = [[characters.index(char) for char in name] for name in pet_names_padded]

    # the final x and y data to use for training the model. Note that the x data needs to be one-hot encoded
    if device is None:
        y = torch.tensor([name[1:] for name in pet_names_numeric])
        x = torch.tensor([name[:-1] for name in pet_names_numeric])
        y = torch.tensor([name[1:] for name in pet_names_numeric], device=device)
        x = torch.tensor([name[:-1] for name in pet_names_numeric], device=device)
    x = torch.nn.functional.one_hot(x, num_classes=len(characters)).float()
    return x, y

class OurDataset(Dataset):
    def __init__(self, pet_names, device=None):
        self.x, self.y = format_training_data(pet_names, device)

    def __getitem__(self, idx):
        idx = self.permutation[idx]
        return self.x[idx], self.y[idx]

    def __len__(self):
        return len(self.x)

    def permute(self):
        self.permutation = torch.randperm(len(self.x))

class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.lstm_size = 128
        self.lstm = nn.LSTM(
        self.fc = nn.Linear(self.lstm_size, len(characters))

    def forward(self, x):
        output, state = self.lstm(x)
        logits = self.fc(output)
        return logits

Train the model

We define a train() function that will do the work to train the neural network. This function should be called once and will return the trained model. It will use the torch.device(0) command to access the GPU.

def train():
    device = torch.device(0)

    dataset = OurDataset(pet_names, device=device)
    loader = DataLoader(dataset, batch_size=batch_size, shuffle=True, num_workers=0)

    model = Model()
    model =

    criterion = nn.CrossEntropyLoss()
    optimizer = optim.Adam(model.parameters(), lr=0.001)

    for epoch in range(num_epochs):
        for i, (batch_x, batch_y) in enumerate(loader):
            batch_y_pred = model(batch_x)

            loss = criterion(batch_y_pred.transpose(1, 2), batch_y)
            f"{} - epoch {epoch} complete - loss {loss.item()}"
    return model

The next block of code actually runs the training function and creates the trained model.

num_epochs = 8
batch_size = 4096
model = train()

After each epoch you should see a line of output like:

2021-02-23T22:00:36.394824 - epoch 0 complete - loss 1.745424509048462

Generating Names

To generate names, we have a function that takes the model and runs it over and over on a string, generating each new character until a stop character is met.

def generate_name(model, characters, str_len):
    device = torch.device(0)
    in_progress_name = []
    next_letter = ""
    while not next_letter == "+" and len(in_progress_name) < 30:
        # prep the data to run in the model again
        in_progress_name_padded = in_progress_name[-str_len:]
        in_progress_name_padded = (
            list((str_len - len(in_progress_name_padded)) * "*") + in_progress_name_padded
        in_progress_name_numeric = [characters.index(char) for char in in_progress_name_padded]
        in_progress_name_tensor = torch.tensor(in_progress_name_numeric, device=device)
        in_progress_name_tensor = torch.nn.functional.one_hot(
            in_progress_name_tensor, num_classes=len(characters)
        in_progress_name_tensor = torch.unsqueeze(in_progress_name_tensor, 0)

        # get the probabilities of each possible next character by running the model
        with torch.no_grad():
            next_letter_probabilities = model(in_progress_name_tensor)

        next_letter_probabilities = next_letter_probabilities[0, -1, :]
        next_letter_probabilities = (
            torch.nn.functional.softmax(next_letter_probabilities, dim=0).detach().cpu().numpy()
        next_letter_probabilities = next_letter_probabilities[1:]
        next_letter_probabilities = [
            p / sum(next_letter_probabilities) for p in next_letter_probabilities

        # determine what the actual letter is
        next_letter = characters[
            np.random.choice(len(characters) - 1, p=next_letter_probabilities) + 1
        if next_letter != "+":
            # if the next character isn't stop add the latest generated character to the name and continue
    # turn the list of characters into a single string
    pet_name = "".join(in_progress_name).title()
    return pet_name

Finally, let’s generate 50 names! Also let’s remove any names that would have shown up in the training data since those are less fun.

# Generate 50 names then filter out existing ones
generated_names = [generate_name(model, characters, str_len) for i in range(0, 50)]
generated_names = [name for name in generated_names if name not in pet_names]

After running the code above you should see a list of names like:

['Moicu', 'Caspa', 'Penke', 'Lare', 'Otlnys', 'Zexto', 'Toba', 'Siralto', 'Luny', 'Lit',
'Bonhe', 'Mashs', 'Riys Wargen', 'Roli', 'Sape', 'Anhyyhe', 'Lorla', 'Boupir', 'Zicka',
'Muktse', 'Musko', 'Mosdin', 'Yapfe', 'Snevi', 'Zedy', 'Cedi', 'Wivagok Rayten', 'Luzia',
'Teclyn', 'Pibty', 'Cheynet', 'Lazyh', 'Ragopes', 'Bitt', 'Bemmen', 'Duuxy', 'Graggie',
'Rari', 'Kisi', 'Lvanxoeber', 'Bonu', 'Masnen', 'Isphofke', 'Myai', 'Shur', 'Lani', 'Ructli',
'Folsy', 'Icthobewlels', 'Kuet Roter']

We have now trained a neural network using PyTorch on a GPU, and used it for inference! If we wanted to experiment with trying many different hyperparameters for the model we could concurrently train models with different hyperparameters using distributed computing. We could also train a single neural network over many GPUs at once with distributed computed. These scenarios are covered using Dask, a distributed computing framework, in the other PyTorch Saturn Cloud examples.

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