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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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Dockerfile
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@ -12,5 +12,6 @@ RUN apt-get install zip unzip --yes
WORKDIR /app WORKDIR /app
COPY ./deepl.py . COPY ./deepl.py .
COPY ./MLProject .
# CMD python3 deepl.py # 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 pandas as pd
import numpy as np import numpy as np
import scipy import scipy
@ -10,40 +11,33 @@ from torch import nn
from torch import optim from torch import optim
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import sys import sys
from sacred import Experiment import mlflow
from sacred.observers import FileStorageObserver from mlflow.models import infer_signature
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
mlflow.set_tracking_uri("http://172.17.0.1:5000")
mlflow.set_experiment("s444463")
def convert_text_to_model_form(text): def convert_text_to_model_form(text):
a = vectorizer.transform([text]) a = vectorizer.transform([text])
b = torch.tensor(scipy.sparse.csr_matrix.todense(a)).float() b = torch.tensor(scipy.sparse.csr_matrix.todense(a)).float()
return b return b
@ex.automain
def my_main(epochs, _run): def train(epochs):
epochs = int(epochs)
# print(sys.argv[1]) # print(sys.argv[1])
# print(type(sys.argv[1])) # print(type(sys.argv[1]))
# print(sys.argv[1]) # print(sys.argv[1])
# epochs = int(sys.argv[1]) # epochs = int(sys.argv[1])
# epochs=10 # epochs=10
mlflow.log_param("epochs", epochs)
# kaggle.api.authenticate() # kaggle.api.authenticate()
# kaggle.api.dataset_download_files('shivamb/real-or-fake-fake-jobposting-prediction', path='.', # kaggle.api.dataset_download_files('shivamb/real-or-fake-fake-jobposting-prediction', path='.',
# unzip=True) # unzip=True)
data = pd.read_csv('fake_job_postings.csv', engine='python') data = pd.read_csv('fake_job_postings.csv', engine='python')
# data = data.replace(np.nan, '', regex=True) # data = data.replace(np.nan, '', regex=True)
data = data[["company_profile", "fraudulent"]] data = data[["company_profile", "fraudulent"]]
data = data.dropna() data = data.dropna()
@ -72,12 +66,17 @@ def my_main(epochs, _run):
y_dev = np.array(y_dev) y_dev = np.array(y_dev)
y_test = np.array(y_test) y_test = np.array(y_test)
vectorizer = TfidfVectorizer()
company_profile = vectorizer.fit_transform(company_profile) company_profile = vectorizer.fit_transform(company_profile)
x_train = vectorizer.transform(x_train) x_train = vectorizer.transform(x_train)
x_dev = vectorizer.transform(x_dev) x_dev = vectorizer.transform(x_dev)
x_test = vectorizer.transform(x_test) 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_train = torch.tensor(scipy.sparse.csr_matrix.todense(x_train)).float()
x_dev = torch.tensor(scipy.sparse.csr_matrix.todense(x_dev)).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() x_test = torch.tensor(scipy.sparse.csr_matrix.todense(x_test)).float()
@ -184,9 +183,18 @@ def my_main(epochs, _run):
f.close() f.close()
torch.save(model, 'model') 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)) # plt.figure(figsize=(12, 5))
# ax = plt.subplot(121) # ax = plt.subplot(121)
# plt.xlabel('epochs') # plt.xlabel('epochs')

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start_time: 1652635708730
status: 1
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