chmod 400 저장위치/*.pem

ssh -i 저장위치/*.pem 터미널접속ID@서버IP -p 22

ssh -i *.pem aa@1.1.1.1 -p 22

The authenticity of host '1.1.1.1 (1.1.1.1)' can't be established.
ED25519 key fingerprint is SHA256:sZo6lnulkMzyqOiM8kHoQNvTr/NRLurQWz+DzzZbWss.
This key is not known by any other names
Are you sure you want to continue connecting (yes/no/[fingerprint])? yes

ssh-keygen -R 부여받은IP

conda create -n tutor28 python=3.10.3

tutor@ip-172-31-12-48:~$ conda activate tutor28
(tutor28) tutor@ip-172-31-12-48:~$

(tutor28) tutor@ip-172-31-12-48:~$ conda install numpy scipy scikit-learn pandas lightGBM
(tutor28) tutor@ip-172-31-12-48:~$ pip install xgboost streamlit chardet

(tutor28) tutor@ip-172-31-12-48:~$ conda install jupyter notebook

jupyter-notebook --ip=0.0.0.0 --no-browser --port=8900 &

import numpy as np
import xgboost as xgb
import lightgbm as lgb
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error

# Generate simple synthetic data
np.random.seed(42)
n_samples = 1000
X = np.random.rand(n_samples, 5)  # 5 features
y = 2*X[:, 0] + 3*X[:, 1] + 0.5*X[:, 2] + np.random.randn(n_samples)  # Linear combination with noise

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create XGBoost DMatrix
dtrain = xgb.DMatrix(X_train, label=y_train)
dtest = xgb.DMatrix(X_test, label=y_test)

# Specify XGBoost parameters
params = {
    "objective": "reg:squarederror",
    "eval_metric": "rmse",
    "max_depth": 3,
    "learning_rate": 0.1,
    "subsample": 0.8
}

# Train the XGBoost model
num_boost_round = 100
bst = xgb.train(params, dtrain, num_boost_round, evals=[(dtest, "test")], early_stopping_rounds=10)

# Predict on the test set
y_pred = bst.predict(dtest, ntree_limit=bst.best_ntree_limit)

# Evaluate the model
mse = mean_squared_error(y_test, y_pred)
print(f"XGBoost Mean Squared Error: {mse}")

# Create LightGBM datasets
train_data = lgb.Dataset(X_train, label=y_train)
test_data = lgb.Dataset(X_test, label=y_test, reference=train_data)

# Specify LightGBM parameters
params = {
    "objective": "regression",
    "metric": "rmse",
    "num_leaves": 31,
    "learning_rate": 0.05,
    "feature_fraction": 0.9
}

# Train the LightGBM model
num_round = 100
bst = lgb.train(params, train_data, num_round, valid_sets=[test_data], early_stopping_rounds=10)

# Predict on the test set
y_pred = bst.predict(X_test, num_iteration=bst.best_iteration)

# Evaluate the model
mse = mean_squared_error(y_test, y_pred)
print(f"LightGBM Mean Squared Error: {mse}")

conda install django

django-admin startproject djangogmail

cd djangogmail/
python manage.py startapp main

cd djangogmail/
vi settings.py

ALLOWED_HOSTS = ['1.1.1.1']

# Application definition

INSTALLED_APPS = [
    'django.contrib.admin',
    'django.contrib.auth',
    'django.contrib.contenttypes',
    'django.contrib.sessions',
    'django.contrib.messages',
    'django.contrib.staticfiles',
    'main'
]

python manage.py runserver 0:8000