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# folders | ||
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## IDEs | ||
.idea | ||
.DS_Store | ||
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## Python | ||
dvc-venv* | ||
.ipynb_checkpoints | ||
/sample_image.jpg | ||
/datadir | ||
/cats-dogs |
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# Lesson 2 tutorial: Versioning Data and Model | ||
**ML REPA School course**: Machine Learning experiments reproducibility and engineering with DVC | ||
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## 1. Clone this repository | ||
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```bash | ||
git clone https://github.com/mlrepa/dvc-2-data-versioning.git | ||
cd dvc-2-data-versioning | ||
``` | ||
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## 2. Create and activate virtual environment | ||
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Install virtualenv in advance: | ||
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```bash | ||
pip install virtualenv | ||
``` | ||
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Create virtual environment named `dvc` (you may use other name) | ||
```bash | ||
python3 -m venv dvc-venv | ||
source dvc-venv/bin/activate | ||
``` | ||
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## 3. Install python libraries | ||
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```bash | ||
pip install -r requirements.txt | ||
``` | ||
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## 4. Add Virtual Environment to Jupyter Notebook | ||
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```bash | ||
python -m ipykernel install --user --name=dvc-venv | ||
``` | ||
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## 5. Configure ToC for jupyter notebook (optional) | ||
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```bash | ||
sudo jupyter contrib nbextension install | ||
jupyter nbextension enable toc2/main | ||
``` | ||
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## 6. Run and follow Jupyter Notebook `dvc-2-data-versioning.ipynb` for instructions: | ||
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```bash | ||
jupyter notebook | ||
``` |
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