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Training and Managing MNIST Predictions with SuperDuperDB

note

This tutorial guides you through the implementation of a classic machine learning task: MNIST handwritten digit recognition. The twist? We perform the task directly on data hosted in a database using SuperDuperDB.

This example makes it easy to connect any of your image recognition models directly to your database in real-time.

!pip install torch torchvision
Outputs

First, we need to establish a connection to a MongoDB datastore via SuperDuperDB.

from superduperdb import superduper
    
db = superduper('mongomock://')
Outputs
2024-May-24 16:42:04.28| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.base.build:69   | Data Client is ready. mongomock.MongoClient('localhost', 27017)
2024-May-24 16:42:04.30| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.base.build:42   | Connecting to Metadata Client with engine:  mongomock.MongoClient('localhost', 27017)
2024-May-24 16:42:04.30| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.base.build:155  | Connecting to compute client: None
2024-May-24 16:42:04.30| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.base.datalayer:85   | Building Data Layer
2024-May-24 16:42:04.30| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.base.build:220  | Configuration:
+---------------+--------------+
| Configuration |    Value     |
+---------------+--------------+
|  Data Backend | mongomock:// |
+---------------+--------------+

After establishing a connection to MongoDB, the next step is to load the MNIST dataset. SuperDuperDB's strength lies in handling diverse data types, especially those that are not supported by standard databases. To achieve this, we use an Encoder in conjunction with Document wrappers. These components allow Python dictionaries containing non-JSONable or bytes objects to be seamlessly inserted into the underlying data infrastructure.

import torchvision
from superduperdb import Document

import random

# Load MNIST images as Python objects using the Python Imaging Library.
# Each MNIST item is a tuple (image, label)
mnist_data = list(torchvision.datasets.MNIST(root='./data', download=True))

document_list = [Document({'img': x[0], 'class': x[1]}) for x in mnist_data]

# Shuffle the data and select a subset of 1000 documents
random.shuffle(document_list)
data = document_list[:1000]

# Insert the selected data into the mnist_collection which we mentioned before 
db['mnist'].insert_many(data[:-100]).execute()
Outputs
2024-May-24 16:42:05.79| WARNING  | Duncans-MacBook-Pro.fritz.box| superduperdb.misc.annotations:117  | add is deprecated and will be removed in a future release.
2024-May-24 16:42:05.79| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.components.component:386  | Initializing DataType : dill
2024-May-24 16:42:05.79| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.components.component:389  | Initialized  DataType : dill successfully
2024-May-24 16:42:05.79| WARNING  | Duncans-MacBook-Pro.fritz.box| superduperdb.backends.local.artifacts:82   | File /tmp/e6eb888f0b0fbbab905029cb309537b9383919a6 already exists
2024-May-24 16:42:05.79| WARNING  | Duncans-MacBook-Pro.fritz.box| superduperdb.backends.local.artifacts:82   | File /tmp/ee1a946181f065af29a3c8637b2858b153d8fc8e already exists
2024-May-24 16:42:05.79| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.components.component:386  | Initializing DataType : pil_image
2024-May-24 16:42:05.79| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.components.component:389  | Initialized  DataType : pil_image successfully
2024-May-24 16:42:05.94| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.backends.local.compute:37   | Submitting job. function:<function callable_job at 0x110261c60>
2024-May-24 16:42:06.06| SUCCESS  | Duncans-MacBook-Pro.fritz.box| superduperdb.backends.local.compute:43   | Job submitted on <superduperdb.backends.local.compute.LocalComputeBackend object at 0x151f9e8d0>.  function:<function callable_job at 0x110261c60> future:77cca682-2f87-4374-8f3a-097430cfc3b5
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TaskWorkflow(database=<superduperdb.base.datalayer.Datalayer object at 0x1520664d0>, G=<networkx.classes.digraph.DiGraph object at 0x155c58350>))

Now that the images and their classes are inserted into the database, we can query the data in its original format. Particularly, we can use the PIL.Image instances to inspect the data.

# Get and display one of the images
r = db['mnist'].find_one().execute()
r.unpack()['img'].resize((300, 300))
Outputs

Following that, we build our machine learning model. SuperDuperDB conveniently supports various frameworks, and for this example, we opt for PyTorch, a suitable choice for computer vision tasks. In this instance, we combine torch with torchvision.

To facilitate communication with the SuperDuperDB Datalayer, we design postprocess and preprocess functions. These functions are then wrapped with the TorchModel wrapper to create a native SuperDuperDB object.

from superduperdb.ext.torch import TorchModel

import torch

# Define the LeNet-5 architecture for image classification
class LeNet5(torch.nn.Module):
    def __init__(self, num_classes):
        super().__init__()
        # Layer 1
        self.layer1 = torch.nn.Sequential(
            torch.nn.Conv2d(1, 6, kernel_size=5, stride=1, padding=0),
            torch.nn.BatchNorm2d(6),
            torch.nn.ReLU(),
            torch.nn.MaxPool2d(kernel_size=2, stride=2))
        # Layer 2
        self.layer2 = torch.nn.Sequential(
            torch.nn.Conv2d(6, 16, kernel_size=5, stride=1, padding=0),
            torch.nn.BatchNorm2d(16),
            torch.nn.ReLU(),
            torch.nn.MaxPool2d(kernel_size=2, stride=2))
        # Fully connected layers
        self.fc = torch.nn.Linear(400, 120)
        self.relu = torch.nn.ReLU()
        self.fc1 = torch.nn.Linear(120, 84)
        self.relu1 = torch.nn.ReLU()
        self.fc2 = torch.nn.Linear(84, num_classes)

    def forward(self, x):
        out = self.layer1(x)
        out = self.layer2(out)
        out = out.reshape(out.size(0), -1)
        out = self.fc(out)
        out = self.relu(out)
        out = self.fc1(out)
        out = self.relu1(out)
        out = self.fc2(out)
        return out

# Postprocess function for the model output    
def postprocess(x):
    return int(x.topk(1)[1].item())

# Preprocess function for input data
def preprocess(x):
    return torchvision.transforms.Compose([
        torchvision.transforms.Resize((32, 32)),
        torchvision.transforms.ToTensor(),
        torchvision.transforms.Normalize(mean=(0.1307,), std=(0.3081,))]
    )(x)

# Create an instance of the LeNet-5 model
lenet_model = LeNet5(10)


model = TorchModel(
    identifier='my-model',
    object=lenet_model,
    preprocess=preprocess,
    postprocess=postprocess, 
    preferred_devices=('cpu',),
)

# Check that the model successfully creates predictions over single data-points
model.predict(data[0]['img'])
Outputs
2024-May-24 16:42:15.26| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.components.component:386  | Initializing TorchModel : my-model
2024-May-24 16:42:15.27| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.components.component:389  | Initialized  TorchModel : my-model successfully
2

Now we are ready to "train" or "fit" the model. Trainable models in SuperDuperDB come with a sklearn-like .fit method, which developers may implement for their specific model class. torch models come with a pre-configured TorchTrainer class and .fit method. These may be invoked simply by "applying" the model to db:

from torch.nn.functional import cross_entropy

from superduperdb import Metric, Validation, Dataset
from superduperdb.ext.torch import TorchTrainer

acc = lambda x, y: (sum([xx == yy for xx, yy in zip(x, y)]) / len(x))

accuracy = Metric(identifier='acc', object=acc)

model.validation = Validation(
    'mnist_performance',
    datasets=[
        Dataset(
            identifier='my-valid',
            select=db['mnist'].find({'_fold': 'valid'})
        )
    ],
    metrics=[accuracy],
)

model.trainer = TorchTrainer(
    identifier='my-trainer',
    objective=cross_entropy,
    loader_kwargs={'batch_size': 10},
    max_iterations=1000,
    validation_interval=5,
    select=db['mnist'].find(),
    key=('img', 'class'),
    transform=lambda x, y: (preprocess(x), y),
)

_ = db.apply(model)
Outputs
2024-May-24 16:42:19.76| WARNING  | Duncans-MacBook-Pro.fritz.box| superduperdb.backends.local.artifacts:82   | File /tmp/a10fbf2cdd7532dd7bf5bba03b7c28e01b4326cc already exists
2024-May-24 16:42:19.79| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.backends.local.compute:37   | Submitting job. function:<function method_job at 0x110261d00>
2024-May-24 16:42:19.92| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 0; objective: 2.30452561378479;
2024-May-24 16:42:19.94| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.components.component:386  | Initializing Dataset : my-valid
2024-May-24 16:42:19.94| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.components.component:389  | Initialized  Dataset : my-valid successfully
2024-May-24 16:42:19.94| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.components.component:386  | Initializing TorchModel : my-model
2024-May-24 16:42:19.94| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.components.component:389  | Initialized  TorchModel : my-model successfully
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 43/43 [00:00<00:00, 963.46it/s]
2024-May-24 16:42:19.99| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: VALID; iteration: 0; my-valid/acc: 0.16279069767441862; objective: 2.3071595191955567;
2024-May-24 16:42:20.01| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 1; objective: 2.290095806121826;
2024-May-24 16:42:20.02| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 2; objective: 2.223555088043213;
2024-May-24 16:42:20.02| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 3; objective: 2.3189988136291504;
2024-May-24 16:42:20.03| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 4; objective: 2.1752736568450928;
2024-May-24 16:42:20.03| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 5; objective: 2.207839250564575;
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2024-May-24 16:42:20.09| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: VALID; iteration: 5; my-valid/acc: 0.11627906976744186; objective: 2.2843324184417724;
2024-May-24 16:42:20.11| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 6; objective: 2.282257318496704;
2024-May-24 16:42:20.12| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 7; objective: 2.0524115562438965;
2024-May-24 16:42:20.13| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 8; objective: 2.0606937408447266;
2024-May-24 16:42:20.14| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 9; objective: 2.194944143295288;
2024-May-24 16:42:20.14| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 10; objective: 2.29692006111145;
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 43/43 [00:00<00:00, 772.76it/s]
2024-May-24 16:42:20.22| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: VALID; iteration: 10; my-valid/acc: 0.16279069767441862; objective: 2.2262439727783203;
2024-May-24 16:42:20.25| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 11; objective: 2.1308627128601074;
2024-May-24 16:42:20.26| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 12; objective: 2.155353307723999;
2024-May-24 16:42:20.27| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 13; objective: 2.0958755016326904;
2024-May-24 16:42:20.28| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 14; objective: 1.9480855464935303;
2024-May-24 16:42:20.28| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 15; objective: 1.9860684871673584;
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 43/43 [00:00<00:00, 718.64it/s]
2024-May-24 16:42:20.36| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: VALID; iteration: 15; my-valid/acc: 0.3953488372093023; objective: 2.1200345516204835;
2024-May-24 16:42:20.38| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 16; objective: 2.0746865272521973;
2024-May-24 16:42:20.39| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 17; objective: 2.2186732292175293;
2024-May-24 16:42:20.40| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 18; objective: 2.0799942016601562;
2024-May-24 16:42:20.41| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 19; objective: 1.7716232538223267;
2024-May-24 16:42:20.41| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 20; objective: 1.8272186517715454;
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 43/43 [00:00<00:00, 683.64it/s]
2024-May-24 16:42:20.50| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: VALID; iteration: 20; my-valid/acc: 0.4186046511627907; objective: 1.9862218618392944;
2024-May-24 16:42:20.52| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 21; objective: 1.957564353942871;
2024-May-24 16:42:20.53| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 22; objective: 1.8028621673583984;
2024-May-24 16:42:20.53| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 23; objective: 1.975327491760254;
2024-May-24 16:42:20.54| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 24; objective: 1.807953119277954;
2024-May-24 16:42:20.55| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 25; objective: 1.7858139276504517;
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2024-May-24 16:42:20.64| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: VALID; iteration: 25; my-valid/acc: 0.4883720930232558; objective: 1.8099432945251466;
2024-May-24 16:42:20.66| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 26; objective: 1.5425803661346436;
2024-May-24 16:42:20.67| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 27; objective: 1.4694782495498657;
2024-May-24 16:42:20.68| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 28; objective: 1.8224706649780273;
2024-May-24 16:42:20.68| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 29; objective: 1.5353931188583374;
2024-May-24 16:42:20.69| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 30; objective: 1.9406465291976929;
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2024-May-24 16:42:20.78| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: VALID; iteration: 30; my-valid/acc: 0.5116279069767442; objective: 1.6320355653762817;
2024-May-24 16:42:20.80| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 31; objective: 1.4210201501846313;
2024-May-24 16:42:20.81| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 32; objective: 1.8781251907348633;
2024-May-24 16:42:20.82| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 33; objective: 1.166929841041565;
2024-May-24 16:42:20.83| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 34; objective: 1.6172298192977905;
2024-May-24 16:42:20.84| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 35; objective: 1.5352650880813599;
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2024-May-24 16:42:20.91| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: VALID; iteration: 35; my-valid/acc: 0.6744186046511628; objective: 1.450360083580017;
2024-May-24 16:42:20.93| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 36; objective: 1.544428825378418;
2024-May-24 16:42:20.94| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 37; objective: 1.4825594425201416;
2024-May-24 16:42:20.95| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 38; objective: 1.481727957725525;
2024-May-24 16:42:20.96| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 39; objective: 1.5011167526245117;
2024-May-24 16:42:20.96| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 40; objective: 1.2468798160552979;
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2024-May-24 16:42:21.03| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: VALID; iteration: 40; my-valid/acc: 0.5813953488372093; objective: 1.3515176057815552;
2024-May-24 16:42:21.06| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 41; objective: 1.137115240097046;
2024-May-24 16:42:21.07| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 42; objective: 1.2611033916473389;
2024-May-24 16:42:21.07| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 43; objective: 1.5822103023529053;
2024-May-24 16:42:21.08| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 44; objective: 0.9167646169662476;
2024-May-24 16:42:21.09| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 45; objective: 1.0554713010787964;
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2024-May-24 16:42:21.17| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: VALID; iteration: 45; my-valid/acc: 0.5813953488372093; objective: 1.2751734495162963;
2024-May-24 16:42:21.19| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 46; objective: 1.5230745077133179;
2024-May-24 16:42:21.20| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 47; objective: 0.9670822024345398;
2024-May-24 16:42:21.21| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 48; objective: 1.2309763431549072;
2024-May-24 16:42:21.22| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 49; objective: 1.4959913492202759;
2024-May-24 16:42:21.22| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 50; objective: 1.019989013671875;
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2024-May-24 16:42:21.31| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: VALID; iteration: 50; my-valid/acc: 0.5813953488372093; objective: 1.1450695395469666;
2024-May-24 16:42:21.33| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 51; objective: 0.9896019101142883;
2024-May-24 16:42:21.33| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 52; objective: 1.0472078323364258;
2024-May-24 16:42:21.35| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 53; objective: 0.6146770715713501;
2024-May-24 16:42:21.35| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 54; objective: 1.223360300064087;
2024-May-24 16:42:21.36| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 55; objective: 1.4324121475219727;
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2024-May-24 16:42:21.45| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: VALID; iteration: 55; my-valid/acc: 0.5116279069767442; objective: 1.1396703839302063;
2024-May-24 16:42:21.47| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 56; objective: 0.8977691531181335;
2024-May-24 16:42:21.48| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 57; objective: 1.013144850730896;
2024-May-24 16:42:21.48| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 58; objective: 0.7408015131950378;
2024-May-24 16:42:21.49| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 59; objective: 0.662105143070221;
2024-May-24 16:42:21.49| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 60; objective: 0.5859256386756897;
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2024-May-24 16:42:21.57| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: VALID; iteration: 60; my-valid/acc: 0.7209302325581395; objective: 0.8712847888469696;
2024-May-24 16:42:21.59| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 61; objective: 0.8210352063179016;
2024-May-24 16:42:21.60| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 62; objective: 0.8280698657035828;
2024-May-24 16:42:21.61| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 63; objective: 0.6546609401702881;
2024-May-24 16:42:21.62| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 64; objective: 0.6739475727081299;
2024-May-24 16:42:21.63| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 65; objective: 0.5538802146911621;
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2024-May-24 16:42:21.71| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: VALID; iteration: 65; my-valid/acc: 0.7441860465116279; objective: 0.8647180676460267;
2024-May-24 16:42:21.73| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 66; objective: 0.8211454153060913;
2024-May-24 16:42:21.74| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 67; objective: 0.7842769622802734;
2024-May-24 16:42:21.75| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 68; objective: 0.5703445672988892;
2024-May-24 16:42:21.76| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 69; objective: 0.8491142988204956;
2024-May-24 16:42:21.77| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 70; objective: 0.8757203817367554;
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2024-May-24 16:42:21.85| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: VALID; iteration: 70; my-valid/acc: 0.6744186046511628; objective: 0.8870090007781982;
2024-May-24 16:42:21.86| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 71; objective: 0.5855669975280762;
2024-May-24 16:42:21.87| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 72; objective: 0.3257257342338562;
2024-May-24 16:42:21.89| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 73; objective: 0.807861328125;
2024-May-24 16:42:21.89| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 74; objective: 0.6515744924545288;
2024-May-24 16:42:21.91| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 75; objective: 0.9471737742424011;
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2024-May-24 16:42:21.98| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: VALID; iteration: 75; my-valid/acc: 0.6976744186046512; objective: 0.7733974754810333;
2024-May-24 16:42:22.01| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 76; objective: 0.31001508235931396;
2024-May-24 16:42:22.02| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 77; objective: 0.672425389289856;
2024-May-24 16:42:22.02| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 78; objective: 0.32893723249435425;
2024-May-24 16:42:22.03| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 79; objective: 0.4878315031528473;
2024-May-24 16:42:22.04| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 80; objective: 0.18520380556583405;
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2024-May-24 16:42:22.12| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: VALID; iteration: 80; my-valid/acc: 0.627906976744186; objective: 0.8145189821720124;
2024-May-24 16:42:22.13| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 81; objective: 0.8917444348335266;
2024-May-24 16:42:22.14| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 82; objective: 0.7088661193847656;
2024-May-24 16:42:22.15| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 83; objective: 0.871364951133728;
2024-May-24 16:42:22.16| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 84; objective: 0.565614640712738;
2024-May-24 16:42:22.16| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 85; objective: 1.1912280321121216;
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2024-May-24 16:42:22.24| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: VALID; iteration: 85; my-valid/acc: 0.7674418604651163; objective: 0.6921847283840179;
2024-May-24 16:42:22.26| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 86; objective: 0.8988658785820007;
2024-May-24 16:42:22.26| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 87; objective: 0.25610730051994324;
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2024-May-24 16:42:22.29| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 90; objective: 0.3234078288078308;
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2024-May-24 16:42:22.36| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: VALID; iteration: 90; my-valid/acc: 0.6744186046511628; objective: 0.8348591446876525;
2024-May-24 16:42:22.39| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 91; objective: 0.39745527505874634;
2024-May-24 16:42:22.41| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 92; objective: 0.8532809019088745;
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2024-May-24 16:42:22.43| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 95; objective: 0.49022263288497925;
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2024-May-24 16:42:22.52| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: VALID; iteration: 95; my-valid/acc: 0.7209302325581395; objective: 0.7183641791343689;
2024-May-24 16:42:22.53| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 96; objective: 0.5845873355865479;
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2024-May-24 16:42:22.55| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 99; objective: 0.18424224853515625;
2024-May-24 16:42:22.56| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 100; objective: 0.14553338289260864;
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2024-May-24 16:42:22.64| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: VALID; iteration: 100; my-valid/acc: 0.7906976744186046; objective: 0.5649376273155212;
2024-May-24 16:42:22.66| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 101; objective: 0.6807511448860168;
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2024-May-24 16:42:22.67| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 103; objective: 0.5750024914741516;
2024-May-24 16:42:22.68| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 104; objective: 0.3903711438179016;
2024-May-24 16:42:22.69| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 105; objective: 0.15528497099876404;
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 43/43 [00:00<00:00, 892.67it/s]
2024-May-24 16:42:22.76| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: VALID; iteration: 105; my-valid/acc: 0.8372093023255814; objective: 0.4794299900531769;
2024-May-24 16:42:22.78| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 106; objective: 0.4399721622467041;
2024-May-24 16:42:22.79| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 107; objective: 0.9834358096122742;
2024-May-24 16:42:22.80| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 108; objective: 0.3158140182495117;
2024-May-24 16:42:22.81| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 109; objective: 0.26788243651390076;
2024-May-24 16:42:22.81| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 110; objective: 0.38584303855895996;
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2024-May-24 16:42:22.90| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: VALID; iteration: 110; my-valid/acc: 0.813953488372093; objective: 0.4963296115398407;
2024-May-24 16:42:22.91| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 111; objective: 0.4139387011528015;
2024-May-24 16:42:22.91| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 112; objective: 0.3234875798225403;
2024-May-24 16:42:22.92| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 113; objective: 0.30029189586639404;
2024-May-24 16:42:22.93| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 114; objective: 0.47261935472488403;
2024-May-24 16:42:22.93| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 115; objective: 0.6491639614105225;
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 43/43 [00:00<00:00, 812.68it/s]
2024-May-24 16:42:23.00| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: VALID; iteration: 115; my-valid/acc: 0.8372093023255814; objective: 0.46701614558696747;
2024-May-24 16:42:23.02| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 116; objective: 0.5192955136299133;
2024-May-24 16:42:23.03| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 117; objective: 0.1198667511343956;
2024-May-24 16:42:23.04| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 118; objective: 0.8839007616043091;
2024-May-24 16:42:23.05| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 119; objective: 0.6286594271659851;
2024-May-24 16:42:23.06| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 120; objective: 0.2967942953109741;
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2024-May-24 16:42:23.13| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: VALID; iteration: 120; my-valid/acc: 0.7674418604651163; objective: 0.5416155338287354;
2024-May-24 16:42:23.14| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 121; objective: 0.3840293288230896;
2024-May-24 16:42:23.15| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 122; objective: 0.26465147733688354;
2024-May-24 16:42:23.16| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 123; objective: 0.20785360038280487;
2024-May-24 16:42:23.16| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 124; objective: 0.5857481360435486;
2024-May-24 16:42:23.17| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 125; objective: 0.2513807415962219;
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2024-May-24 16:42:23.24| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: VALID; iteration: 125; my-valid/acc: 0.7441860465116279; objective: 0.6432130992412567;
2024-May-24 16:42:23.25| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 126; objective: 0.48046112060546875;
2024-May-24 16:42:23.26| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 127; objective: 0.1669985055923462;
2024-May-24 16:42:23.27| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 128; objective: 0.2551296353340149;
2024-May-24 16:42:23.27| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 129; objective: 0.39759451150894165;
2024-May-24 16:42:23.28| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 130; objective: 0.12379683554172516;
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2024-May-24 16:42:23.35| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: VALID; iteration: 130; my-valid/acc: 0.7674418604651163; objective: 0.594197279214859;
2024-May-24 16:42:23.36| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 131; objective: 0.2534908354282379;
2024-May-24 16:42:23.36| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 132; objective: 0.5093891620635986;
2024-May-24 16:42:23.37| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 133; objective: 0.1864108145236969;
2024-May-24 16:42:23.38| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 134; objective: 0.2897171080112457;
2024-May-24 16:42:23.38| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 135; objective: 0.5396483540534973;
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2024-May-24 16:42:23.45| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: VALID; iteration: 135; my-valid/acc: 0.813953488372093; objective: 0.5497540190815926;
2024-May-24 16:42:23.45| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 136; objective: 0.25071924924850464;
2024-May-24 16:42:23.46| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 137; objective: 0.5670820474624634;
2024-May-24 16:42:23.47| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 138; objective: 0.31639617681503296;
2024-May-24 16:42:23.48| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 139; objective: 0.20214490592479706;
2024-May-24 16:42:23.48| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: TRAIN; iteration: 140; objective: 0.32829517126083374;
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 43/43 [00:00<00:00, 758.92it/s]
2024-May-24 16:42:23.56| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:220  | fold: VALID; iteration: 140; my-valid/acc: 0.813953488372093; objective: 0.5148731887340545;
2024-May-24 16:42:23.56| INFO     | Duncans-MacBook-Pro.fritz.box| superduperdb.ext.torch.training:194  | early stopping triggered!
2024-May-24 16:42:23.56| SUCCESS  | Duncans-MacBook-Pro.fritz.box| superduperdb.backends.local.compute:43   | Job submitted on <superduperdb.backends.local.compute.LocalComputeBackend object at 0x151f9e8d0>.  function:<function method_job at 0x110261d00> future:23a0532c-53bb-475a-a0a4-e9e3097b485b


The trained model is now available via db.load - the model.trainer object contains the metric traces
logged during training.


from matplotlib import pyplot as plt

# Load the model from the database
model = db.load('model', model.identifier)

# Plot the accuracy values
plt.plot(model.trainer.metric_values['my-valid/acc'])
plt.show()


Outputs