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mlflow

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This commit is contained in:
Mikołaj Pokrywka 2022-05-15 20:28:52 +02:00
parent 7eddb939fc
commit 0c67053401
128 changed files with 335 additions and 18 deletions
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1
Dockerfile
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@ -12,5 +12,6 @@ RUN apt-get install zip unzip --yes
WORKDIR /app
COPY ./deepl.py .
COPY ./MLProject .
# CMD python3 deepl.py

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MLProject Normal file
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name: s444463
docker_env:
image: ium_444463
entry_points:
main:
parameters:
EPOCH: {type: int, default: 10}
command: "python3 ./deepl.py {EPOCH}"
eval:
command: "python3 evaluation.py"

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deepl.py
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@ -1,3 +1,4 @@
import json
import pandas as pd
import numpy as np
import scipy
@ -10,40 +11,33 @@ from torch import nn
from torch import optim
import matplotlib.pyplot as plt
import sys
from sacred import Experiment
from sacred.observers import FileStorageObserver
from sacred.observers import MongoObserver
ex = Experiment()
ex.observers.append(FileStorageObserver('my_runs'))
ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
vectorizer = TfidfVectorizer()
@ex.config
def my_config():
epochs = 10
import mlflow
from mlflow.models import infer_signature
mlflow.set_tracking_uri("http://172.17.0.1:5000")
mlflow.set_experiment("s444463")
def convert_text_to_model_form(text):
a = vectorizer.transform([text])
b = torch.tensor(scipy.sparse.csr_matrix.todense(a)).float()
return b
@ex.automain
def my_main(epochs, _run):
epochs = int(epochs)
def train(epochs):
# print(sys.argv[1])
# print(type(sys.argv[1]))
# print(sys.argv[1])
# epochs = int(sys.argv[1])
# epochs=10
mlflow.log_param("epochs", epochs)
# kaggle.api.authenticate()
# kaggle.api.dataset_download_files('shivamb/real-or-fake-fake-jobposting-prediction', path='.',
# unzip=True)
data = pd.read_csv('fake_job_postings.csv', engine='python')
# data = data.replace(np.nan, '', regex=True)
data = data[["company_profile", "fraudulent"]]
data = data.dropna()
@ -72,12 +66,17 @@ def my_main(epochs, _run):
y_dev = np.array(y_dev)
y_test = np.array(y_test)
vectorizer = TfidfVectorizer()
company_profile = vectorizer.fit_transform(company_profile)
x_train = vectorizer.transform(x_train)
x_dev = vectorizer.transform(x_dev)
x_test = vectorizer.transform(x_test)
siganture = json.dumps({"input:": 'tfidf vectorized company profile', "output:": "0 = ok, 1 = fake job"})
input_example = x_train[:20]
x_train = torch.tensor(scipy.sparse.csr_matrix.todense(x_train)).float()
x_dev = torch.tensor(scipy.sparse.csr_matrix.todense(x_dev)).float()
x_test = torch.tensor(scipy.sparse.csr_matrix.todense(x_test)).float()
@ -184,9 +183,18 @@ def my_main(epochs, _run):
f.close()
torch.save(model, 'model')
ex.add_artifact("model")
mlflow.pytorch.log_model(model, "model", signature=siganture, input_example=input_example)
mlflow.pytorch.save_model(model, "model", signature=siganture, input_example=input_example)
print(sys.argv[1])
print(type(sys.argv[1]))
print(sys.argv[1])
epochs = int(sys.argv[1])
if __name__ == "__main__":
train(epochs)
# plt.figure(figsize=(12, 5))
# ax = plt.subplot(121)
# plt.xlabel('epochs')

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status: 1
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