tommylees112

import pandas as pd
import numpy as np 
from cmdstanpy import cmdstan_path, CmdStanModel, CmdStanMCMC


def make_dummy_data():
    variables = [f"SM_lag_{i}" for i in range(6)]
    variables += [f"PCP_lag_{i}" for i in range(6)]
    variables += [f"VCI_lag_{i}" for i in range(6)]
    N_samples = 100

tommylees112

conda create -n cmdstanpy --yes
conda activate cmdstanpy
conda install -c conda-forge arviz matplotlib seaborn jupyterlab ipython pip ruamel.yaml netcdf4 statsmodels scikit-learn black mypy --yes
pip install cmdstanpy

tommylees112

conda create -n pystan --yes
conda activate pystan
conda install -c conda-forge pystan --yes
conda install -c conda-forge arviz matplotlib seaborn jupyterlab ipython pip ruamel.yaml netcdf4 statsmodels scikit-learn black mypy --yes

tommylees112

conda create -n pymc --yes
conda activate pymc
conda install -c conda-forge pymc3=3.11.2 --yes
conda install -c conda-forge jax arviz  numpyro matplotlib seaborn jupyterlab ipython pip ruamel.yaml netcdf4 statsmodels scikit-learn black mypy --yes

tommylees112

conda create -n prophet --yes
conda activate prophet
conda install -c conda-forge prophet ipython jupyterlab seaborn --yes
conda install -c conda-forge netcdf4 numba tqdm ipython pip ruamel.yaml xarray descartes statsmodels scikit-learn black mypy eofs xskillscore cdsapi cftime --yes

tommylees112

FIX BY BUILDING AN UBUNTU MACHINE! (not debian the default)

ERROR: Unable to load the kernel module 'nvidia.ko'.  This happens most frequently when this kernel module was built against the wrong or improperly configured kernel sources, with a version of gcc that differs from the one used to build the target kernel, or if another driver, such as nouveau, is present and prevents the NVIDIA kernel module from obtaining ownership of the NVIDIA device(s), or no NVIDIA device installed in this system is supported by this NVIDIA Linux graphics driver release.

• Blog Article about AWS GPU
• ERROR: Unable to load the kernel module 'nvidia.ko'
• Medium article

tommylees112

# Make sure it's an ubuntu distribution
# or just use one of the pre-made nvidia machines: https://cloud.google.com/deep-learning-vm/docs/pytorch_start_instance
sudo apt-get install wget
sudo apt-get install tmux 

# Generate SSH keys
# copy the .pub file into ~/.ssh/authorized_keys ON THE REMOTE MACHINE
# https://superuser.com/questions/421004/how-to-fix-warning-about-ecdsa-host-key
# remove the cached key '' $ ssh-keygen -R
# wget miniconda installer from here:

tommylees112

conda create -n srtm --yes
conda activate srtm
conda install -c conda-forge gdal elevation --yes
conda install -c conda-forge netcdf4 xarray ipython six scipy scikit-learn seaborn matplotlib --yes
pip install elevation

tommylees112

conda create -n keras --yes 
conda activate keras
conda install -c conda-forge keras tensorflow --yes
# conda install pytorch torchvision -c pytorch --yes
# conda install -c conda-forge seaborn=0.11 --yes3 
# conda install -c conda-forge jupyterlab=3.0.16 --yes
conda install -c conda-forge jupyterlab seaborn --yes
conda install -c conda-forge netcdf4 numba tqdm tensorboard ipython pip ruamel.yaml xarray descartes statsmodels scikit-learn black mypy eofs xskillscore cdsapi --yes
conda install -c conda-forge cartopy pyflwdir --yes
conda install -c conda-forge pytest pytest-cov --yes

tommylees112

conda create -n kf python=3.9
conda activate kf
conda install -c conda-forge filterpy pandas matplotlib numpy seaborn ipython jupyterlab sklearn black mypy --yes
conda install -c anaconda ipywidgets --yes