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Running sandbox for src/main.py. Current Code:

https://github.com/sweepai/evals/blob/72849082ab1fcf90323a2d3b20d400de0d41ebd9/src/main.py#L1-L48

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pip install -r requirements.txt 1/4 ✓

Looking in links: https://download.pytorch.org/whl/torch_stable.html
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Installing collected packages: mpmath, urllib3, tomlkit, sympy, sniffio, pydantic_core, pluggy, Pillow, numpy, networkx, mccabe, MarkupSafe, iniconfig, h11, fsspec, filelock, exceptiongroup, dill, charset-normalizer, certifi, astroid, annotated-types, uvicorn, requests, pytest, pylint, pydantic, Jinja2, anyio, torch, starlette, torchvision, torchaudio, fastapi
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ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
virtualenv 20.24.6 requires filelock<4,>=3.12.2, but you have filelock 3.9.0 which is incompatible.
Successfully installed Jinja2-3.1.2 MarkupSafe-2.1.2 Pillow-9.3.0 annotated-types-0.6.0 anyio-3.7.1 astroid-3.0.1 certifi-2022.12.7 charset-normalizer-2.1.1 dill-0.3.7 exceptiongroup-1.1.3 fastapi-0.104.0 filelock-3.9.0 fsspec-2023.4.0 h11-0.14.0 iniconfig-2.0.0 mccabe-0.7.0 mpmath-1.3.0 networkx-3.0 numpy-1.24.1 pluggy-1.3.0 pydantic-2.4.2 pydantic_core-2.10.1 pylint-3.0.2 pytest-7.4.2 requests-2.28.1 sniffio-1.3.0 starlette-0.27.0 sympy-1.12 tomlkit-0.12.1 torch-2.1.0+cpu torchaudio-2.1.0+cpu torchvision-0.16.0+cpu urllib3-1.26.13 uvicorn-0.23.2
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv

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Updated Code:

import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from PIL import Image
from torch.utils.data import DataLoader
from torchvision import datasets, transforms

# Step 1: Load MNIST Data and Preprocess
transform = transforms.Compose(
    [transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))]
)

trainset = datasets.MNIST(".", download=True, train=True, transform=transform)
trainloader = DataLoader(trainset, batch_size=64, shuffle=True)

# Step 2: Define the PyTorch Model
class Net(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc1 = nn.Linear(28 * 28, 128)
        self.fc2 = nn.Linear(128, 64)
        self.fc3 = nn.Linear(64, 10)

    def forward(self, x):
        x = x.view(-1, 28 * 28)
        x = nn.functional.relu(self.fc1(x))
        x = nn.functional.relu(self.fc2(x))
        x = self.fc3(x)
        return nn.functional.log_softmax(x, dim=1)

# Step 3: Train the Model
model = Net()
optimizer = optim.SGD(model.parameters(), lr=0.01)
criterion = nn.NLLLoss()

# Training loop
epochs = 3
for epoch in range(epochs):
    for images, labels in trainloader:
        optimizer.zero_grad()
        output = model(images)
        loss = criterion(output, labels)
        loss.backward()
        optimizer.step()

torch.save(model.state_dict(), "mnist_model.pth")

Diff:

--- 
+++ 
@@ -1,19 +1,19 @@
-from PIL import Image
+import numpy as np
 import torch
 import torch.nn as nn
 import torch.optim as optim
+from PIL import Image
+from torch.utils.data import DataLoader
 from torchvision import datasets, transforms
-from torch.utils.data import DataLoader
-import numpy as np

 # Step 1: Load MNIST Data and Preprocess
-transform = transforms.Compose([
-    transforms.ToTensor(),
-    transforms.Normalize((0.5,), (0.5,))
-])
+transform = transforms.Compose(
+    [transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))]
+)

-trainset = datasets.MNIST('.', download=True, train=True, transform=transform)
+trainset = datasets.MNIST(".", download=True, train=True, transform=transform)
 trainloader = DataLoader(trainset, batch_size=64, shuffle=True)
+

 # Step 2: Define the PyTorch Model
 class Net(nn.Module):
@@ -22,13 +22,14 @@
         self.fc1 = nn.Linear(28 * 28, 128)
         self.fc2 = nn.Linear(128, 64)
         self.fc3 = nn.Linear(64, 10)
-    
+
     def forward(self, x):
         x = x.view(-1, 28 * 28)
         x = nn.functional.relu(self.fc1(x))
         x = nn.functional.relu(self.fc2(x))
         x = self.fc3(x)
         return nn.functional.log_softmax(x, dim=1)
+

 # Step 3: Train the Model
 model = Net()

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sweepai / evals