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merge into: s434695:master
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pull from: s434695:train
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48 Commits
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Author SHA1 Message Date
s434695
0204ef10e9 :(((( 2021-05-24 01:26:02 +02:00
s434695
c579d63845 fix 2021-05-24 00:55:40 +02:00
s434695
93a992d53d fix 2021-05-24 00:51:33 +02:00
s434695
84641091ce fix 2021-05-24 00:42:36 +02:00
s434695
5a6d4f832d fix 2021-05-24 00:36:16 +02:00
s434695
c212ffdb9c fix 2021-05-24 00:30:08 +02:00
s434695
a024e32143 fix 2021-05-24 00:22:18 +02:00
s434695
27743b754f fix 2021-05-24 00:22:01 +02:00
s434695
5cc3fd58b1 fix 2021-05-23 23:29:58 +02:00
s434695
069a5860d6 fix 2021-05-23 23:25:50 +02:00
s434695
30c8343c6c fix 2021-05-23 23:21:33 +02:00
s434695
9050bcb8e7 fix 2021-05-23 23:14:15 +02:00
s434695
b5774c67b7 some changes 2021-05-23 23:02:17 +02:00
s434695
eee8d50045 **/* 2021-05-23 22:30:23 +02:00
s434695
f8addf09d4 change for Domino2
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caffb7a796 change for Domino
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01eea15055 elo
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db58b28106 asd 2021-05-23 18:20:21 +02:00
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49d70902b1 :(
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s434695
982b1a1263 :)
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9947cf587f fix
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07e0eb04eb fix
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3d068ac7d1 fix
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s434695
8fad47a012 fix 2021-05-16 22:32:44 +02:00
s434695
e754b04eb2 test worked 2021-05-16 22:29:16 +02:00
s434695
9d5a047ec7 test
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f40f237a02 fix2
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26ddaa3a0c fix train
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ef245b69da no flag user in dockerfile
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b1bb478c17 a
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01373b20e6 top 10 anime bterayals
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da73d5dca4 only sad reactions
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101850ba06 test
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d30489e817 only sad reactions
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93516ec2ef :(
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2f78b3d264 elo
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835f99ece5 fix
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s434695
8172699654 train
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c6146b9dfc sadge
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s434695
18172eadf9 training changes 2021-05-15 19:14:37 +02:00
s434695
2d0e53d1eb sadge3 2021-05-15 18:24:30 +02:00
s434695
f5158a272c saving artifacts fix 2021-05-15 18:06:33 +02:00
s434695
72b0e06eb3 fixfixfix 2021-05-15 17:38:43 +02:00
s434695
391d4309df fix 2021-05-15 17:35:29 +02:00
s434695
e92325d5f5 new branch 2021-05-15 17:01:54 +02:00
159 changed files with 750 additions and 93 deletions
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29
Dockerfile
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@ -5,20 +5,21 @@ FROM ubuntu:latest
RUN apt update && apt install -y figlet
RUN apt install -y git
RUN apt install -y python3-pip
RUN pip3 install --user kaggle
RUN pip3 install --user pandas
RUN pip3 install --user numpy
RUN pip3 install --user seaborn
RUN pip3 install --user sklearn
RUN pip3 install --user matplotlib
RUN pip3 install --user tensorflow
RUN pip3 install --user wget
RUN pip3 install setuptools
RUN pip3 install kaggle
RUN pip3 install pandas
RUN pip3 install numpy
RUN pip3 install seaborn
RUN pip3 install sklearn
RUN pip3 install matplotlib
RUN pip3 install tensorflow
RUN pip3 install sacred
RUN pip3 install wget
RUN pip3 install keras
RUN pip3 install GitPython
RUN pip3 install pymongo
RUN pip3 install mlflow
WORKDIR /app
COPY ./skrypt.sh ./
COPY ./zadanie2.py ./
COPY ./zadanie5.py ./
CMD ./zadanie2.py
CMD ./zadanie5.py
COPY ./train.py ./

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@ -1,40 +1,61 @@
pipeline {
agent {
dockerfile true
}
//Definijuemy parametry, które będzie można podać podczas wywoływania zadania
parameters {
string (
defaultValue: '10000',
description: 'Obcięcie zbioru',
name: 'CUTOFF',
trim: false
agent { dockerfile true }
parameters{
buildSelector(
defaultSelector: lastSuccessful(),
description: 'Which build to use for copying artifacts',
name: 'WHICH_BUILD'
)
string(
defaultValue: '16',
description: 'batch size',
name: 'BATCH_SIZE'
)
string(
defaultValue: '15',
description: 'epochs',
name: 'EPOCHS'
)
}
stages {
stage('Test') {
steps {
sh 'cat /etc/issue'
}
}
stage('checkout: Check out from version control') {
steps {
git credentialsId: 'f746c8e0-e974-4758-838d-1920a7b0e3dc', url: 'https://git.wmi.amu.edu.pl/s434695/ium_434695.git'
}
}
stage('sh: Shell Script') {
steps {
sh 'chmod +x skrypt.sh'
sh './skrypt.sh ${CUTOFF}'
}
}
stage('Artifacts') {
steps {
archiveArtifacts 'test.csv'
archiveArtifacts 'dev.csv'
archiveArtifacts 'train.csv'
archiveArtifacts 'obcietedane.csv'
}
}
stage('copy artifacts')
{
steps
{
copyArtifacts(fingerprintArtifacts: true, projectName: 's434695-create-dataset', selector: buildParameter('WHICH_BUILD'))
}
}
stage('train')
{
steps
{
sh 'python3 train.py ${BATCH_SIZE} ${EPOCHS}'
sh 'rm -r my_model'
sh 'python3 vgsales-mlflow.py'
}
}
stage('Archive artifacts') {
steps{
archiveArtifacts 'vgsales_model.h5'
archiveArtifacts 'my_runs/**/*'
archiveArtifacts 'my_model/**/*'
}
}
}
post {
success {
build job: 's434695-evaluation/evaluation'
mail body: 'SUCCESS TRAINING', subject: 's434695', to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
}
failure {
mail body: 'FAILURE TRAINING', subject: 's434695', to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
}
}
}
}

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name: 434695-mlflow
docker_env:
image: shroomy/ium:7
entry_points:
main:
parameters:
epochs: {type: int, default: 15}
batch_size: {type: int, default: 16}
command: "python3 vgsales-mlflow.py {epochs} {batch_size}"

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experiment_id: '0'
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run_uuid: 13e3e1a2ce674a07b82227babfec5f00
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source_type: 4
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start_time: 1621790521589
status: 1
tags: []
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LOCAL

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tomasz

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run_uuid: 156c624fa6ff468887bf9543a37ce8b3
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source_type: 4
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artifact_path: vgsales_model.h5
flavors:
keras:
data: data
keras_module: tensorflow.keras
keras_version: 2.4.0
save_format: tf
python_function:
data: data
env: conda.yaml
loader_module: mlflow.keras
python_version: 3.8.5
run_id: 1d5601fad0dc496b8e16c284d2fcd1c9
utc_time_created: '2021-05-23 17:16:38.081474'

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channels:
- defaults
- conda-forge
dependencies:
- python=3.8.5
- pip
- pip:
- mlflow
- tensorflow==2.4.1
name: mlflow-env

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