I most often use Windows OS machines but I sometimes use Mac and Linux machines. It had been several months since I had used the PyTorch neural network library on a Mac machine so one weekend I figured I’d do a demo.
Windows Mac -------------------------------- Notepad TextEdit Ctrl-c Command-c PrtScn key Shift-Command-3 File Explorer Finder Chrome Safari cmd Terminal (Z-shell) dir ls md mkdir cls clear
My micro cheat sheet for MacOS.
My old Mac machine (a MacBook Air) has an x64 processor was running MacOS 12.7.3 (Monterey).
Note: To use this blog post as an installation guide, a lot of details are left out so you must have expert level Mac skills, including a strong working knowledge of using the bash or Z-shell Terminal/shell.
My first step was to install the Anaconda distribution of Python. I went to repo.anaconda.com/archive and used Anaconda version 2023.09.0 (which contains Python 3.11.5). Note: It’s incredibly easy to get the wrong file. The installer file was Anaconda3-2023.09.0-MacOSX-x86_64.pkg. Again, it is very easy to get the wrong pkg file.
The .pkg file to install Anaconda Python for MacOS.
After the installer file downloaded, I went to the Downloads directory and double-clicked on the file. I accepted all the defaults (location, users, etc.) and there were no problems with the Anaconda Python installation. I tested the Anaconda Python install by launching the Terminal program (the Z-shell) and then I issued the command “python” and verified I got a “3.11.5” message and the triple greater-than Python prompt. I used the exit() command to stop the interpreter.
The .whl file for PyTorch 2.2.0 for Mac — again, very easy to get the wrong file.
Next I installed PyTorch. I went to download.pytorch.org/whl/torch_stable.html and downloaded the .whl file for PyTorch (CPU version) 2.2.0 for MacOS, again, it’s much too easy to get the wrong file. The file was cpu/torch-2.2.0-cp311-none-macosx_10_9_x86_64.whl. After the .whl file downloaded, I opened a Terminal shell and cd’ed to the Downloads directory. I issued the command “pip install (the .whl name)” and crossed my fingers. Installation took about three minutes and looked good. I was happy.
I verified the PyTorch installation by typing the commands:
python import torch as T T.__version__
and was happy to see that PyTorch 2.2.0 had been installed.
Verifying Python and PyTorch are installed.
Later, I created a demo using one of my standard synthetic datasets. The goal is to predict a person’s political leaning (conservative, moderate, liberal) from sex, age, State, and income.
Running a demo program.
Most advanced AI work is being done using PyTorch on Linux machines. I usually prefer to initially develop an AI system on a Windows machine running PyTorch, and then if serious model training needs to be done (meaning more than one day of run time), copy the PyTorch program to a Linux machine in the Cloud, typically Azure or AWS, and then train there. When I deliver PyTorch training classes at my workplace and at technical conferences, I notice that most students/employees/attendees use just one type of machine and cannot easily use a different OS. I’m in the minority of people who regularly use Windows, macOS, and Linux, and I have no strong preference — I like all three.
If operating systems were robots. Left: Windows. Center: macOS. Right: Linux.
Demo code.
# people_politics.py
# predict politics type from sex, age, state, income
# PyTorch 2.2.0-CPU Anaconda3-2023.09.0 Python 3.11.5
# MacOS 12.7.3
import numpy as np
import torch as T
device = T.device('cpu') # apply to Tensor or Module
# -----------------------------------------------------------
class PeopleDataset(T.utils.data.Dataset):
# sex age state income politics
# -1 0.27 0 1 0 0.7610 2
# +1 0.19 0 0 1 0.6550 0
# sex: -1 = male, +1 = female
# state: michigan, nebraska, oklahoma
# politics: conservative, moderate, liberal
def __init__(self, src_file):
all_xy = np.loadtxt(src_file, usecols=range(0,7),
delimiter=",", comments="#", dtype=np.float32)
tmp_x = all_xy[:,0:6] # cols [0,6) = [0,5]
tmp_y = all_xy[:,6] # 1-D
self.x_data = T.tensor(tmp_x,
dtype=T.float32).to(device)
self.y_data = T.tensor(tmp_y,
dtype=T.int64).to(device) # 1-D
def __len__(self):
return len(self.x_data)
def __getitem__(self, idx):
preds = self.x_data[idx]
trgts = self.y_data[idx]
return preds, trgts # as a Tuple
# -----------------------------------------------------------
class Net(T.nn.Module):
def __init__(self):
super(Net, self).__init__()
self.hid1 = T.nn.Linear(6, 10) # 6-(10-10)-3
self.hid2 = T.nn.Linear(10, 10)
self.oupt = T.nn.Linear(10, 3)
T.nn.init.xavier_uniform_(self.hid1.weight)
T.nn.init.zeros_(self.hid1.bias)
T.nn.init.xavier_uniform_(self.hid2.weight)
T.nn.init.zeros_(self.hid2.bias)
T.nn.init.xavier_uniform_(self.oupt.weight)
T.nn.init.zeros_(self.oupt.bias)
def forward(self, x):
z = T.tanh(self.hid1(x))
z = T.tanh(self.hid2(z))
z = T.log_softmax(self.oupt(z), dim=1) # NLLLoss()
return z
# -----------------------------------------------------------
def accuracy(model, ds):
# assumes model.eval()
# item-by-item version
n_correct = 0; n_wrong = 0
for i in range(len(ds)):
X = ds[i][0].reshape(1,-1) # make it a batch
Y = ds[i][1].reshape(1) # 0 1 or 2, 1D
with T.no_grad():
oupt = model(X) # logits form
big_idx = T.argmax(oupt) # 0 or 1 or 2
if big_idx == Y:
n_correct += 1
else:
n_wrong += 1
acc = (n_correct * 1.0) / (n_correct + n_wrong)
return acc
# -----------------------------------------------------------
def accuracy_quick(model, dataset):
# assumes model.eval()
X = dataset[0:len(dataset)][0]
# Y = T.flatten(dataset[0:len(dataset)][1])
Y = dataset[0:len(dataset)][1]
with T.no_grad():
oupt = model(X) # [40,3] logits
# (_, arg_maxs) = T.max(oupt, dim=1)
arg_maxs = T.argmax(oupt, dim=1) # argmax() is new
num_correct = T.sum(Y==arg_maxs)
acc = (num_correct * 1.0 / len(dataset))
return acc.item()
# -----------------------------------------------------------
def do_acc(model, dataset, n_classes):
X = dataset[0:len(dataset)][0] # all X values
Y = dataset[0:len(dataset)][1] # all Y values
with T.no_grad():
oupt = model(X) # [40,3] all logits
for c in range(n_classes):
idxs = np.where(Y==c) # indices where Y is c
logits_c = oupt[idxs] # logits corresponding to Y == c
arg_maxs_c = T.argmax(logits_c, dim=1) # predicted
num_correct = T.sum(arg_maxs_c == c)
acc_c = num_correct.item() / len(arg_maxs_c)
print("%0.4f " % acc_c)
# -----------------------------------------------------------
def main():
# 0. get started
print("\nBegin People predict politics type ")
T.manual_seed(1)
np.random.seed(1)
# 1. create DataLoader objects
print("\nCreating People Datasets ")
train_file = "./Data/people_train.txt"
train_ds = PeopleDataset(train_file) # 200 rows
test_file = "./Data/people_test.txt"
test_ds = PeopleDataset(test_file) # 40 rows
bat_size = 10
train_ldr = T.utils.data.DataLoader(train_ds,
batch_size=bat_size, shuffle=True)
# -----------------------------------------------------------
# 2. create network
print("\nCreating 6-(10-10)-3 neural network ")
net = Net().to(device)
net.train()
# -----------------------------------------------------------
# 3. train model
max_epochs = 1000
ep_log_interval = 100
lrn_rate = 0.01
loss_func = T.nn.NLLLoss() # assumes log_softmax()
optimizer = T.optim.SGD(net.parameters(), lr=lrn_rate)
print("\nbat_size = %3d " % bat_size)
print("loss = " + str(loss_func))
print("optimizer = SGD")
print("max_epochs = %3d " % max_epochs)
print("lrn_rate = %0.3f " % lrn_rate)
print("\nStarting training")
for epoch in range(0, max_epochs):
# T.manual_seed(epoch+1) # checkpoint reproduce
epoch_loss = 0 # for one full epoch
for (batch_idx, batch) in enumerate(train_ldr):
X = batch[0] # inputs
Y = batch[1] # correct class/label/politics
optimizer.zero_grad()
oupt = net(X)
loss_val = loss_func(oupt, Y) # a tensor
epoch_loss += loss_val.item() # accumulate
loss_val.backward()
optimizer.step()
if epoch % ep_log_interval == 0:
print("epoch = %5d | loss = %10.4f" % \
(epoch, epoch_loss))
print("Training done ")
# -----------------------------------------------------------
# 4. evaluate model accuracy
print("\nComputing model accuracy")
net.eval()
acc_train = accuracy(net, train_ds) # item-by-item
print("Accuracy on training data = %0.4f" % acc_train)
acc_test = accuracy_quick(net, test_ds)
print("Accuracy on test data = %0.4f" % acc_test)
print("\nAccuracy on test by class (fast technique): ")
do_acc(net, test_ds, 3)
# 5. make a prediction
print("\nPolitics for M 30 oklahoma $50,000: ")
X = np.array([[-1, 0.30, 0,0,1, 0.5000]],
dtype=np.float32)
X = T.tensor(X, dtype=T.float32).to(device)
with T.no_grad():
logits = net(X) # do not sum to 1.0
probs = T.exp(logits) # sum to 1.0
probs = probs.numpy() # numpy vector prints better
np.set_printoptions(precision=4, suppress=True)
print(probs)
# 6. save model (state_dict approach)
print("\nSaving trained model state")
# fn = "./Models/people_model.pt"
# T.save(net.state_dict(), fn)
# saved_model = Net() # requires class definintion
# saved_model.load_state_dict(T.load(fn))
# use saved_model to make prediction(s)
print("\nEnd People predict politics demo")
if __name__ == "__main__":
main()
Training data:
# people_train.txt # sex (M=-1, F=1) age state (michigan, # nebraska, oklahoma) income # politics (consrvative, moderate, liberal) # 1,0.24,1,0,0,0.2950,2 -1,0.39,0,0,1,0.5120,1 1,0.63,0,1,0,0.7580,0 -1,0.36,1,0,0,0.4450,1 1,0.27,0,1,0,0.2860,2 1,0.50,0,1,0,0.5650,1 1,0.50,0,0,1,0.5500,1 -1,0.19,0,0,1,0.3270,0 1,0.22,0,1,0,0.2770,1 -1,0.39,0,0,1,0.4710,2 1,0.34,1,0,0,0.3940,1 -1,0.22,1,0,0,0.3350,0 1,0.35,0,0,1,0.3520,2 -1,0.33,0,1,0,0.4640,1 1,0.45,0,1,0,0.5410,1 1,0.42,0,1,0,0.5070,1 -1,0.33,0,1,0,0.4680,1 1,0.25,0,0,1,0.3000,1 -1,0.31,0,1,0,0.4640,0 1,0.27,1,0,0,0.3250,2 1,0.48,1,0,0,0.5400,1 -1,0.64,0,1,0,0.7130,2 1,0.61,0,1,0,0.7240,0 1,0.54,0,0,1,0.6100,0 1,0.29,1,0,0,0.3630,0 1,0.50,0,0,1,0.5500,1 1,0.55,0,0,1,0.6250,0 1,0.40,1,0,0,0.5240,0 1,0.22,1,0,0,0.2360,2 1,0.68,0,1,0,0.7840,0 -1,0.60,1,0,0,0.7170,2 -1,0.34,0,0,1,0.4650,1 -1,0.25,0,0,1,0.3710,0 -1,0.31,0,1,0,0.4890,1 1,0.43,0,0,1,0.4800,1 1,0.58,0,1,0,0.6540,2 -1,0.55,0,1,0,0.6070,2 -1,0.43,0,1,0,0.5110,1 -1,0.43,0,0,1,0.5320,1 -1,0.21,1,0,0,0.3720,0 1,0.55,0,0,1,0.6460,0 1,0.64,0,1,0,0.7480,0 -1,0.41,1,0,0,0.5880,1 1,0.64,0,0,1,0.7270,0 -1,0.56,0,0,1,0.6660,2 1,0.31,0,0,1,0.3600,1 -1,0.65,0,0,1,0.7010,2 1,0.55,0,0,1,0.6430,0 -1,0.25,1,0,0,0.4030,0 1,0.46,0,0,1,0.5100,1 -1,0.36,1,0,0,0.5350,0 1,0.52,0,1,0,0.5810,1 1,0.61,0,0,1,0.6790,0 1,0.57,0,0,1,0.6570,0 -1,0.46,0,1,0,0.5260,1 -1,0.62,1,0,0,0.6680,2 1,0.55,0,0,1,0.6270,0 -1,0.22,0,0,1,0.2770,1 -1,0.50,1,0,0,0.6290,0 -1,0.32,0,1,0,0.4180,1 -1,0.21,0,0,1,0.3560,0 1,0.44,0,1,0,0.5200,1 1,0.46,0,1,0,0.5170,1 1,0.62,0,1,0,0.6970,0 1,0.57,0,1,0,0.6640,0 -1,0.67,0,0,1,0.7580,2 1,0.29,1,0,0,0.3430,2 1,0.53,1,0,0,0.6010,0 -1,0.44,1,0,0,0.5480,1 1,0.46,0,1,0,0.5230,1 -1,0.20,0,1,0,0.3010,1 -1,0.38,1,0,0,0.5350,1 1,0.50,0,1,0,0.5860,1 1,0.33,0,1,0,0.4250,1 -1,0.33,0,1,0,0.3930,1 1,0.26,0,1,0,0.4040,0 1,0.58,1,0,0,0.7070,0 1,0.43,0,0,1,0.4800,1 -1,0.46,1,0,0,0.6440,0 1,0.60,1,0,0,0.7170,0 -1,0.42,1,0,0,0.4890,1 -1,0.56,0,0,1,0.5640,2 -1,0.62,0,1,0,0.6630,2 -1,0.50,1,0,0,0.6480,1 1,0.47,0,0,1,0.5200,1 -1,0.67,0,1,0,0.8040,2 -1,0.40,0,0,1,0.5040,1 1,0.42,0,1,0,0.4840,1 1,0.64,1,0,0,0.7200,0 -1,0.47,1,0,0,0.5870,2 1,0.45,0,1,0,0.5280,1 -1,0.25,0,0,1,0.4090,0 1,0.38,1,0,0,0.4840,0 1,0.55,0,0,1,0.6000,1 -1,0.44,1,0,0,0.6060,1 1,0.33,1,0,0,0.4100,1 1,0.34,0,0,1,0.3900,1 1,0.27,0,1,0,0.3370,2 1,0.32,0,1,0,0.4070,1 1,0.42,0,0,1,0.4700,1 -1,0.24,0,0,1,0.4030,0 1,0.42,0,1,0,0.5030,1 1,0.25,0,0,1,0.2800,2 1,0.51,0,1,0,0.5800,1 -1,0.55,0,1,0,0.6350,2 1,0.44,1,0,0,0.4780,2 -1,0.18,1,0,0,0.3980,0 -1,0.67,0,1,0,0.7160,2 1,0.45,0,0,1,0.5000,1 1,0.48,1,0,0,0.5580,1 -1,0.25,0,1,0,0.3900,1 -1,0.67,1,0,0,0.7830,1 1,0.37,0,0,1,0.4200,1 -1,0.32,1,0,0,0.4270,1 1,0.48,1,0,0,0.5700,1 -1,0.66,0,0,1,0.7500,2 1,0.61,1,0,0,0.7000,0 -1,0.58,0,0,1,0.6890,1 1,0.19,1,0,0,0.2400,2 1,0.38,0,0,1,0.4300,1 -1,0.27,1,0,0,0.3640,1 1,0.42,1,0,0,0.4800,1 1,0.60,1,0,0,0.7130,0 -1,0.27,0,0,1,0.3480,0 1,0.29,0,1,0,0.3710,0 -1,0.43,1,0,0,0.5670,1 1,0.48,1,0,0,0.5670,1 1,0.27,0,0,1,0.2940,2 -1,0.44,1,0,0,0.5520,0 1,0.23,0,1,0,0.2630,2 -1,0.36,0,1,0,0.5300,2 1,0.64,0,0,1,0.7250,0 1,0.29,0,0,1,0.3000,2 -1,0.33,1,0,0,0.4930,1 -1,0.66,0,1,0,0.7500,2 -1,0.21,0,0,1,0.3430,0 1,0.27,1,0,0,0.3270,2 1,0.29,1,0,0,0.3180,2 -1,0.31,1,0,0,0.4860,1 1,0.36,0,0,1,0.4100,1 1,0.49,0,1,0,0.5570,1 -1,0.28,1,0,0,0.3840,0 -1,0.43,0,0,1,0.5660,1 -1,0.46,0,1,0,0.5880,1 1,0.57,1,0,0,0.6980,0 -1,0.52,0,0,1,0.5940,1 -1,0.31,0,0,1,0.4350,1 -1,0.55,1,0,0,0.6200,2 1,0.50,1,0,0,0.5640,1 1,0.48,0,1,0,0.5590,1 -1,0.22,0,0,1,0.3450,0 1,0.59,0,0,1,0.6670,0 1,0.34,1,0,0,0.4280,2 -1,0.64,1,0,0,0.7720,2 1,0.29,0,0,1,0.3350,2 -1,0.34,0,1,0,0.4320,1 -1,0.61,1,0,0,0.7500,2 1,0.64,0,0,1,0.7110,0 -1,0.29,1,0,0,0.4130,0 1,0.63,0,1,0,0.7060,0 -1,0.29,0,1,0,0.4000,0 -1,0.51,1,0,0,0.6270,1 -1,0.24,0,0,1,0.3770,0 1,0.48,0,1,0,0.5750,1 1,0.18,1,0,0,0.2740,0 1,0.18,1,0,0,0.2030,2 1,0.33,0,1,0,0.3820,2 -1,0.20,0,0,1,0.3480,0 1,0.29,0,0,1,0.3300,2 -1,0.44,0,0,1,0.6300,0 -1,0.65,0,0,1,0.8180,0 -1,0.56,1,0,0,0.6370,2 -1,0.52,0,0,1,0.5840,1 -1,0.29,0,1,0,0.4860,0 -1,0.47,0,1,0,0.5890,1 1,0.68,1,0,0,0.7260,2 1,0.31,0,0,1,0.3600,1 1,0.61,0,1,0,0.6250,2 1,0.19,0,1,0,0.2150,2 1,0.38,0,0,1,0.4300,1 -1,0.26,1,0,0,0.4230,0 1,0.61,0,1,0,0.6740,0 1,0.40,1,0,0,0.4650,1 -1,0.49,1,0,0,0.6520,1 1,0.56,1,0,0,0.6750,0 -1,0.48,0,1,0,0.6600,1 1,0.52,1,0,0,0.5630,2 -1,0.18,1,0,0,0.2980,0 -1,0.56,0,0,1,0.5930,2 -1,0.52,0,1,0,0.6440,1 -1,0.18,0,1,0,0.2860,1 -1,0.58,1,0,0,0.6620,2 -1,0.39,0,1,0,0.5510,1 -1,0.46,1,0,0,0.6290,1 -1,0.40,0,1,0,0.4620,1 -1,0.60,1,0,0,0.7270,2 1,0.36,0,1,0,0.4070,2 1,0.44,1,0,0,0.5230,1 1,0.28,1,0,0,0.3130,2 1,0.54,0,0,1,0.6260,0
Test data:
-1,0.51,1,0,0,0.6120,1 -1,0.32,0,1,0,0.4610,1 1,0.55,1,0,0,0.6270,0 1,0.25,0,0,1,0.2620,2 1,0.33,0,0,1,0.3730,2 -1,0.29,0,1,0,0.4620,0 1,0.65,1,0,0,0.7270,0 -1,0.43,0,1,0,0.5140,1 -1,0.54,0,1,0,0.6480,2 1,0.61,0,1,0,0.7270,0 1,0.52,0,1,0,0.6360,0 1,0.30,0,1,0,0.3350,2 1,0.29,1,0,0,0.3140,2 -1,0.47,0,0,1,0.5940,1 1,0.39,0,1,0,0.4780,1 1,0.47,0,0,1,0.5200,1 -1,0.49,1,0,0,0.5860,1 -1,0.63,0,0,1,0.6740,2 -1,0.30,1,0,0,0.3920,0 -1,0.61,0,0,1,0.6960,2 -1,0.47,0,0,1,0.5870,1 1,0.30,0,0,1,0.3450,2 -1,0.51,0,0,1,0.5800,1 -1,0.24,1,0,0,0.3880,1 -1,0.49,1,0,0,0.6450,1 1,0.66,0,0,1,0.7450,0 -1,0.65,1,0,0,0.7690,0 -1,0.46,0,1,0,0.5800,0 -1,0.45,0,0,1,0.5180,1 -1,0.47,1,0,0,0.6360,0 -1,0.29,1,0,0,0.4480,0 -1,0.57,0,0,1,0.6930,2 -1,0.20,1,0,0,0.2870,2 -1,0.35,1,0,0,0.4340,1 -1,0.61,0,0,1,0.6700,2 -1,0.31,0,0,1,0.3730,1 1,0.18,1,0,0,0.2080,2 1,0.26,0,0,1,0.2920,2 -1,0.28,1,0,0,0.3640,2 -1,0.59,0,0,1,0.6940,2
.NET Test Automation Recipes
Software Testing
SciPy Programming Succinctly
Keras Succinctly
R Programming
Visual Studio Live
Microsoft MLADS Conference
DevIntersection Conference
Machine Learning Week
Ai4 Conference
G2E Conference
iSC West Conference
You must be logged in to post a comment.