- Create conda environment:
$ conda create --name 4thBrain python=3.8
$ conda activate 4thBrain - pandas (will install numpy)
- conda install pandas
- nodejs
- conda install -c conda-forge nodejs
- jupyterlab
- conda install jupyterlab "ipywidgets>=7.5"
- jupyter labextension install jupyterlab-plotly
- awswrangler (simplies a lot of very frustrating AWS API crap)
- conda install -c conda-forge awswrangler
- plotly (and extensions for jupyterlab)
- https://plotly.com/python/getting-started/
- conda install statsmodels (to enable trendlines)
- matplotlib (commonly needed for online tutorials)
- conda install matplotlib
- SKLearn
- conda install -c conda-forge scikit-learn
- XGBoost
- conda install -c conda-forge xgboost
- conda install -c conda-forge lightgbm
- Seaborn
- conda install -c anaconda seaborn
- tensorflow-gpu -- this will take a while and you might see package conflicts
- conda install pip
- pip install tensorflow-gpu==2.3
- Elyra - code snippet manager for Jupyter lab
- conda install -c conda-forge elyra-code-snippet-extension && jupyter lab build
- Article includes info about where the snippets live.
- https://blog.jupyter.org/reusable-code-snippets-in-jupyterlab-8d75a0f9d207
import tensorflow as tf print(tf.version)
config = tf.compat.v1.ConfigProto( gpu_options = tf.compat.v1.GPUOptions(per_process_gpu_memory_fraction=0.8), # device_count = {'GPU': 1} ) config.gpu_options.allow_growth = True session = tf.compat.v1.Session(config=config) tf.compat.v1.keras.backend.set_session(session) print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))
- https://visualstudio.microsoft.com/thank-you-downloading-visual-studio/?sku=Community&rel=16
- conda install cudatoolkit=11.0 cudnn=8.0 -c=conda-forge
I have cleaned it up a little
https://gist.github.com/brianspiering/f0d3c271d26cabeccff885e0aab837bb