forge | High Performance Visualization | GPU library

When import 'semantic-ui-css/semantic.min.css' into a brand new Electron-Forge/Webpack5 project, I get the following:

I'm trying to import a python package (mplsoccer) via reticulate package but I can't: reticulate::py_install('mplsoccer')

This is the message I get:

Alright I have a package Pythran which is a Python to C++ (PYD module) complier. The package itself on conda-forge says it requires clang and clangxx. BUT I have MS Build Tools clang-12 already installed, so these packages are not used at all.

Now every time I go to conda install [package_name] it tells me my environment is inconsistent, because I force removed the clang libraries I don't need (or want) via a:

I'm trying to run a SageMaker kernel with Python 3.8 in SageMaker Studio, and the notebook appears to use a separate distribution of Python 3.7. The running app is indicated as tensorflow-2.6-cpu-py38-ubuntu20.04-v1. When I run !python3 -V I get Python 3.8.2. However, the Python instance inside the notebook is different:

I've been working on some survey data using the survey package. I read the documentation available on post-stratification and calibration, however I got stuck trying to calibrate the sampling weights on a total known for the population that is not the population total.

To make my self clear I prepared an example: Let's say I have income information for a sample stratified by sex, which lets me create the svydesign object:

Does it make sense to use Conda + Poetry for a Machine Learning project? Allow me to share my (novice) understanding and please correct or enlighten me:

As far as I understand, Conda and Poetry have different purposes but are largely redundant:

• Conda is primarily a environment manager (in fact not necessarily Python), but it can also manage packages and dependencies.
• Poetry is primarily a Python package manager (say, an upgrade of pip), but it can also create and manage Python environments (say, an upgrade of Pyenv).

My idea is to use both and compartmentalize their roles: let Conda be the environment manager and Poetry the package manager. My reasoning is that (it sounds like) Conda is best for managing environments and can be used for compiling and installing non-python packages, especially CUDA drivers (for GPU capability), while Poetry is more powerful than Conda as a Python package manager.

I've managed to make this work fairly easily by using Poetry within a Conda environment. The trick is to not use Poetry to manage the Python environment: I'm not using commands like poetry shell or poetry run, only poetry init, poetry install etc (after activating the Conda environment).

For full disclosure, my environment.yml file (for Conda) looks like this:

I am trying to install conda on EMR and below is my bootstrap script, it looks like conda is getting installed but it is not getting added to environment variable. When I manually update the $PATH variable on EMR master node, it can identify conda. I want to use conda on Zeppelin.

I also tried adding condig into configuration like below while launching my EMR instance however I still get the below mentioned error.

Others have asked about this, but my situation seems slightly different, and none of the suggestions they received worked for me (e.g. here, here, here).

I'm using Anaconda Navigator on Windows, and trying to use the "nco" package. I installed it via the Anaconda Navigator, and when (in Spyder) I type conda list nco it gives me:

I am trying to set up a conda environment with python 3.10 installed. For some reason, no install commands for additional packages are working. For example, if I run conda install pandas, I get the error: