Dependencies for pip python 2 deleted, symlink created for pip python 3, main src structure added as a placeholder

Author: kevinrev26

.devcontainer/Dockerfile

@@ -22,13 +22,9 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
         unzip \
         zip \
         libzmq3-dev \
-        python-dev \
-        python-numpy \
         python3-dev \
         python3-numpy \
-        python-pip \
         python3-pip \
-        python-tk \
         python3-tk \
         libtbb2 \
         libtbb-dev \
@@ -106,8 +102,7 @@ RUN cargo install fd-find ripgrep
 
 
 # Symlink for pip3
-# RUN sudo ln -s /usr/bin/pip3 /usr/bin/pip
+RUN sudo ln -s /usr/bin/pip3 /usr/bin/pip
 
 #Pytorch
-RUN sudo pip3 install https://download.pytorch.org/whl/cu100/torch-1.3.1%2Bcu100-cp36-cp36m-linux_x86_64.whl
-RUN sudo pip3 install torchvision 
+RUN sudo pip install torch torchvision

src/main/main.py

@@ -0,0 +1,42 @@
+import numpy as np
+import torch
+import torch.optim as optim
+import torch.nn as nn
+# Data Generation
+np.random.seed(42)
+x = np.random.rand(100, 1)
+y = 1 + 2 * x + .1 * np.random.randn(100, 1)
+
+# Shuffles the indices
+idx = np.arange(100)
+np.random.shuffle(idx)
+
+# Uses first 80 random indices for train
+train_idx = idx[:80]
+# Uses the remaining indices for validation
+val_idx = idx[80:]
+
+# Generates train and validation sets
+x_train, y_train = x[train_idx], y[train_idx]
+x_val, y_val = x[val_idx], y[val_idx]
+
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+# Our data was in Numpy arrays, but we need to transform them into PyTorch's Tensors
+# and then we send them to the chosen device
+x_train_tensor = torch.from_numpy(x_train).float().to(device)
+y_train_tensor = torch.from_numpy(y_train).float().to(device)
+
+# Here we can see the difference - notice that .type() is more useful
+# since it also tells us WHERE the tensor is (device)
+print(type(x_train), type(x_train_tensor), x_train_tensor.type())
+
+print("=============================================")
+print("                  x-train                    ")
+print("=============================================")
+print(x_train)
+
+print("=============================================")
+print("                train-tensor                 ")
+print("=============================================")
+print(x_train_tensor)