mkl_random | Python interface to Intel Math Kernel Library | Machine Learning library

I attempted to update pandas_datareader on my Python 3.5.2 virtual Environment using Anaconda like this:

This is a specific instance of a general problem that I run into when updating packages using conda. I have an environment that is working great on machine A. I want to transfer it to machine B. But, machine A has GTX1080 gpus, and due to configuration I cannot control, requires cudatoolkit 10.2. Machine B has A100 gpus, and due to configuration I cannot control, requires cudatoolkit 11.1

I can easily export Machine A's environment to yml, and create a new environment on Machine B using that yml. However, I cannot seem to update cudatoolkit to 11.1 on that environment on Machine B. I try

Having trouble with CUDA + Pytorch this is the error. I reinstalled CUDA and cudnn multiple times.

Conda env is detecting GPU but its giving errors with pytorch and certain cuda libraries. I tried with Cuda 10.1 and 10.0, and cudnn version 8 and 7.6.5, Added cuda to path and everything.

However anaconda is showing cuda tool kit 9.0 is installed, whilst I clearly installed 10.0, so I am not entirely sure what's the deal with that.

I am using layoutlm github which require python 3.6, transformer 2.9.0. I created an conda env:

When I type conda env create -f environment.yml

I constantly get

I am a Conda newbie and am trying to familiarise myself with it by using miniconda to install python package apache-beam. I can see at https://anaconda.org/conda-forge/apache-beam that the latest available version is v2.22.0

however when I attempt to install using conda install -c conda-forge/label/cf201901 apache-beam it attempts to install v2.16.0:

(As a student I am kind of new to this but did quite a bit of research and I got pretty far, I'm super into learning something new through this!)

This issue is for the project pulse -> https://github.com/adamian98/pulse

the readme if you scroll down a bit on the page, gives a much better explanation than I could. It will also give a direct "correct" path to judge my actions against and make solving the problem a lot easier.

Objective: run program using the run.py file

Issue: I got a "RuntimeError: CUDA out of memory" despite having a compatible gpu and enough vram

Knowledge: when it comes to coding i just started a few days ago and have a dozen hours with anaconda now, comfterable creating environments.

What I did was... (the list below is a summary and the specific details are after it)

1. install anaconda

2. use this .yml file -> https://github.com/leihuayi/pulse/blob/feature/docker/pulse.yml (it changes dependencies to work for windows which is why I needed to grab a different one than the one supplied on the master github page) to create a new environment and install the required packages. It worked fantastically! I only got an error trying to install dlib, it didn't seem compatible with A LOT of the packages and my python version.

3. I installed the cuda toolkit 10.2 , cmake 3.17.2, and tried to install dlib into the environment directly. the errors spat out in a blaze of glory. The dlib package seems to be only needed for a different .py file and not run.py though so I think it may be unrelated to this error

logs are below and I explain my process in more detail

START DETAILS AND LOGS: from here until the "DETAILS 2" section should be enough information to solve, the rest past there is in case

error log for runing out of memory--> (after executing the "run.py" file)

I get stuck with that for ~2 minute every time I run the code. Many people on the Internet said that it would only take a long time in the first run, but that's not my case. Although it doesn't make anything go wrong, it's pretty annoying. When I'm stuck, the system is under pretty low usage, including the CPU, system RAM, GPU, video memory. I'm using Nvidia Geforce RTX 3070, Windows 10 x64 20H2.Here's my environment:

I can set up a conda environment successfully as follows: