Zajecia 8 - MLflow
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.gitignore
vendored
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.gitignore
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# ---> JupyterNotebooks
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# gitignore template for Jupyter Notebooks
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# website: http://jupyter.org/
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.ipynb_checkpoints
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*/.ipynb_checkpoints/*
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# IPython
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profile_default/
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ipython_config.py
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# Remove previous ipynb_checkpoints
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# git rm -r .ipynb_checkpoints/
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.ipynb_checkpoints/
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my_runs
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mlruns
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IUM_08/examples
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@ -243,7 +243,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"execution_count": 61,
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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@ -260,7 +260,7 @@
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}
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],
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"source": [
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"# Żeby poniższa komenda zadziałała, musisz posiadać plik /.kaggle/kaggle.json, zawierający Kaggle API token.\n",
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"# Żeby poniższa komenda zadziałała, musisz posiadać plik ~/.kaggle/kaggle.json, zawierający Kaggle API token.\n",
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"# Instrukcje: https://www.kaggle.com/docs/api\n",
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"!kaggle datasets download -d uciml/iris"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"source": [
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"### Przykładowy podział z pomocą standardowych narzędzi Bash"
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]
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@ -1260,7 +1264,11 @@
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"source": [
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"### Podział z pomocą sckikit learn\n",
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"- Do podziału możemy też użyć biblioteki https://scikit-learn.org/"
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{
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"cell_type": "code",
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"execution_count": 45,
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"metadata": {},
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"outputs": [
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{
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"data": {
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@ -1294,7 +1306,11 @@
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{
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"cell_type": "code",
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"execution_count": 46,
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"metadata": {},
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"outputs": [
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{
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"data": {
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"cell_type": "code",
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"execution_count": 48,
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"metadata": {
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"scrolled": true
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"scrolled": true,
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"outputs": [
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{
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"cell_type": "code",
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"execution_count": 49,
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"metadata": {
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"scrolled": true
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"scrolled": true,
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"outputs": [
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{
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IUM_08.MLFlow.ipynb
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IUM_08.MLFlow.ipynb
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IUM_08/compare-metrics.png
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IUM_08/compare-metrics.png
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IUM_08/mlflowui.png
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img/expcontrol/mlflow-logo-d.png
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