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**NOTE:** To avoid infos and warning messages from tensorflow, you can set an additional environment variable `TF_CPP_MIN_LOG_LEVEL` whose possible values are 0, 1, 2 or 3.\
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(0: all msg, 1:hide infos, 2:hide infos+warnings, 3:hide info, warnings and errors)
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## 4 - Start Jupyter lab :
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Very simply, as follows.
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1. Do not forget to [set the FIDLE_DATASET_DIR variable](#2-get-datasets)
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2. **Activate** your fidle environment :\
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Note : for Windows, use a Anaconda Prompt terminal to type these commands.
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3. **Start** Jupyter lab
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```plaintext
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cd <PATH_TO_CLONED_FIDLE_REPO>
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conda activate fidle
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jupyter lab
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```
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## Annexe 1 - Manual installation
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This solution can be adapted if the procedure described above does not work.\
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This can be the case, for example under MacOS.
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Required packages are :
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* [Python](https://www.python.org) >= 3.8
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* [Numpy](https://numpy.org) = 1.19
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* [Tensorflow](https://www.tensorflow.org) >=2.4
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* [Scikit-image](https://scikit-image.org)
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* [Scikit-learn](https://scikit-learn.org)
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* [Matplotlib](https://matplotlib.org)
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* [Pandas](https://pandas.pydata.org/)
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* [Jupyter lab](https://jupyter.org/)
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With conda you can use :
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#### I just have a CPU :
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* If no NVIDIA card is available on your computer or [NVIDIA card is not compliant with driver cuda 10.x](https://docs.nvidia.com/deploy/cuda-compatibility/index.html)
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```plaintext
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conda create --name fidle
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conda activate fidle
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conda install tensorflow>=2.4 numpy=1.19 keras scikit-learn scikit-image
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conda install matplotlib plotly pandas pandoc jupyterlab
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```
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### I have a nice GPU :
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```shell
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conda create --name fidle
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conda activate fidle
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conda install tensorflow-gpu>=2.4 numpy=1.19 keras scikit-learn scikit-image
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conda install matplotlib plotly pandas pandoc jupyterlab
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=> with miniconda 3.8 installed and using a Anaconda prompt terminal
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```
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**Note :** This manual procedure is indicative because it can vary according to the evolution of the versions of this or that package...
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## Annexe 2 - Test installations
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| ... | ... | |
