Added fcaretti test

.github/workflows/fcaretti.yml

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+name: Actions on fcaretti
+on: [push]
+jobs:
+  test-docker-image:
+    runs-on: ubuntu-latest
+    container: ghcr.io/fcaretti/ml_image
+
+    steps:
+     - name: Check out repository
+       uses: actions/checkout@v3
+     - name: Run tests in tests/fcaretti directory
+       run: |
+          cd tests/fcaretti
+          python3 short_test.py

docker/fcaretti/Dockerfile

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+FROM ubuntu:focal
+
+RUN apt-get update  && apt-get install -y vim wget  python3-pip && \
+    apt-get clean && \
+    rm -rf /var/lib/apt/lists/*
+
+
+ENV CONDA_DIR /opt/conda
+
+RUN wget https://repo.anaconda.com/archive/Anaconda3-2022.10-Linux-x86_64.sh && \
+    sh Anaconda3-2022.10-Linux-x86_64.sh -b -p /opt/conda && \
+    rm Anaconda3-2022.10-Linux-x86_64.sh
+
+ENV PATH=$CONDA_DIR/bin:$PATH
+
+RUN conda install -y pytorch torchvision torchaudio pytorch-cuda=11.6 -c pytorch -c nvidia 
+    #conda install -y -c conda-forge tensorflow
+
+
+
+
+
+

tests/fcaretti/short_test.py

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+import torch 
+from torch import nn
+from torch.utils.data import DataLoader
+from torchvision import datasets
+from torchvision.transforms import ToTensor
+import numpy as np
+
+from utils.models import NeuralNetwork
+from utils.training import train
+
+print(torch.__version__)
+print(np.__version__)
+print('Passed import tests')
+
+# Download training data from open datasets.
+training_data = datasets.FashionMNIST(
+    root="data",
+    train=True,
+    download=True,
+    transform=ToTensor(),
+)
+
+batch_size = 64
+
+# Create data loaders.
+train_dataloader = DataLoader(training_data, batch_size=batch_size)
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+print(f"Using {device} device")
+
+
+model = NeuralNetwork().to(device)
+
+loss_fn = nn.CrossEntropyLoss()
+optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)
+
+losses=[]
+epochs = 3
+for t in range(epochs):
+    print(f"Epoch {t+1}\n-------------------------------")
+    losses.append(train(train_dataloader, model, loss_fn, optimizer,device))
+
+if losses[-1]<=losses[0]:
+    print('Test passed: training loss is decreasing')
+else:
+    raise ValueError('Training loss is not decreasing')

tests/fcaretti/utils/models.py

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+from torch import nn
+
+class NeuralNetwork(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.flatten = nn.Flatten()
+        self.linear_relu_stack = nn.Sequential(
+            nn.Linear(28*28, 512),
+            nn.ReLU(),
+            nn.Linear(512, 512),
+            nn.ReLU(),
+            nn.Linear(512, 10)
+        )
+
+    def forward(self, x):
+        x = self.flatten(x)
+        logits = self.linear_relu_stack(x)
+        return logits

tests/fcaretti/utils/training.py

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+def train(dataloader, model, loss_fn, optimizer,device):
+    size = len(dataloader.dataset)
+    model.train()
+    for batch, (X, y) in enumerate(dataloader):
+        X, y = X.to(device), y.to(device)
+
+        # Compute prediction error
+        pred = model(X)
+        loss = loss_fn(pred, y)
+
+        # Backpropagation
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+
+        if batch % 100 == 0:
+            loss, current = loss.item(), batch * len(X)
+            print(f"loss: {loss:>7f}  [{current:>5d}/{size:>5d}]")
+    return loss.item()
+