ium_426206/generate_MLmodel.py
Jan Nowak f30073b2ec
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2021-05-23 16:42:51 +02:00

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Python

import torch
import numpy as np
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, TensorDataset, DataLoader
import argparse
import mlflow
import mlflow.pytorch
from urllib.parse import urlparse
from mlflow.models.signature import infer_signature
class LayerLinearRegression(nn.Module):
def __init__(self):
super().__init__()
# Instead of our custom parameters, we use a Linear layer with single input and single output
self.linear = nn.Linear(1, 1)
def forward(self, x):
# Now it only takes a call to the layer to make predictions
return self.linear(x)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Program do uczenia modelu')
parser.add_argument('-l', '--lr', type=float, default=1e-3, help="Współczynik uczenia (lr)", required=False)
parser.add_argument('-e', '--epochs', type=int, default=100, help="Liczba epok", required=False)
args = parser.parse_args()
lr = args.lr
n_epochs = args.epochs
#mlflow.set_tracking_uri("http://127.0.0.1:5000")
mlflow.set_tracking_uri("http://172.17.0.1:5000")
mlflow.set_experiment("s426206")
with mlflow.start_run():
mlflow.log_param("lr", lr)
mlflow.log_param("epochs", n_epochs)
train_dataset = torch.load('train_dataset.pt')
#val_dataset = torch.load('val_dataset.pt')
train_loader = DataLoader(dataset=train_dataset)
#val_loader = DataLoader(dataset=val_dataset)
model = LayerLinearRegression()
# Checks model's parameters
#print(model.state_dict())
loss_fn = nn.MSELoss(reduction='mean')
optimizer = optim.SGD(model.parameters(), lr=lr)
def make_train_step(model, loss_fn, optimizer):
# Builds function that performs a step in the train loop
def train_step(x, y):
# Sets model to TRAIN mode
model.train()
# Makes predictions
yhat = model(x)
# Computes loss
loss = loss_fn(y, yhat)
# Computes gradients
loss.backward()
# Updates parameters and zeroes gradients
optimizer.step()
optimizer.zero_grad()
# Returns the loss
return loss.item()
# Returns the function that will be called inside the train loop
return train_step
# Creates the train_step function for our model, loss function and optimizer
train_step = make_train_step(model, loss_fn, optimizer)
training_losses = []
validation_losses = []
#print(model.state_dict())
# For each epoch...
for epoch in range(n_epochs):
losses = []
# Uses loader to fetch one mini-batch for training
for x_batch, y_batch in train_loader:
# NOW, sends the mini-batch data to the device
# so it matches location of the MODEL
# x_batch = x_batch.to(device)
# y_batch = y_batch.to(device)
# One stpe of training
loss = train_step(x_batch, y_batch)
losses.append(loss)
training_loss = np.mean(losses)
training_losses.append(training_loss)
mlflow.log_metric("MSE", training_loss)
# After finishing training steps for all mini-batches,
# it is time for evaluation!
# Ewaluacja jest już tutaj nie potrzebna bo odbywa sie w evaluation.py. Można jednak włączyć podgląd ewaluacji dla poszczególnych epok.
# # We tell PyTorch to NOT use autograd...
# # Do you remember why?
# with torch.no_grad():
# val_losses = []
# # Uses loader to fetch one mini-batch for validation
# for x_val, y_val in val_loader:
# # Again, sends data to same device as model
# # x_val = x_val.to(device)
# # y_val = y_val.to(device)
# model.eval()
# # Makes predictions
# yhat = model(x_val)
# # Computes validation loss
# val_loss = loss_fn(y_val, yhat)
# val_losses.append(val_loss.item())
# validation_loss = np.mean(val_losses)
# validation_losses.append(validation_loss)
# print(f"[{epoch+1}] Training loss: {training_loss:.3f}\t Validation loss: {validation_loss:.3f}")
print(f"[{epoch+1}] Training loss: {training_loss:.3f}\t")
torch.save({
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'loss': lr,
}, 'model.pt')
x_train = np.array(train_dataset)[:,0] #(Sales Sum row)
input_example = np.reshape(x_train, (-1,1))
with torch.no_grad():
model.eval()
siganture = infer_signature(x_train, model(torch.tensor(np.reshape(x_train, (-1,1))).float()).numpy())
#mlflow.set_experiment("s426206")
#mlflow.set_tracking_uri("http://172.17.0.1:5000")
tracking_url_type_store = urlparse(mlflow.get_tracking_uri()).scheme
# print(tracking_url_type_store)
# Model registry does not work with file store
if tracking_url_type_store != "file":
mlflow.pytorch.log_model(model, "model", registered_model_name="s426206", signature=siganture, input_example=input_example)
else:
mlflow.pytorch.log_model(model, "model", signature=siganture, input_example=input_example)
mlflow.pytorch.save_model(model, "my_model", signature=siganture, input_example=input_example)
#export MLFLOW_CONDA_HOME=/home/jan/miniconda3/
#mlflow models serve -m my_model/