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@Mahedi-61
Mahedi-61 / cuda_11.8_installation_on_Ubuntu_22.04
Last active June 12, 2024 10:40
Instructions for CUDA v11.8 and cuDNN 8.9.7 installation on Ubuntu 22.04 for PyTorch 2.1.2
#!/bin/bash
### steps ####
# Verify the system has a cuda-capable gpu
# Download and install the nvidia cuda toolkit and cudnn
# Setup environmental variables
# Verify the installation
###
### to verify your gpu is cuda enable check
@bogdan-kulynych
bogdan-kulynych / install-cuda-10-bionic.sh
Last active December 5, 2023 10:26
Install CUDA 10 on Ubuntu 18.04
# WARNING: These steps seem to not work anymore!
#!/bin/bash
# Purge existign CUDA first
sudo apt --purge remove "cublas*" "cuda*"
sudo apt --purge remove "nvidia*"
# Install CUDA Toolkit 10
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-repo-ubuntu1804_10.0.130-1_amd64.deb
import os
import sys
import shutil
import multiprocessing as mp
from itertools import repeat as re
import dateparser
import pytz
import json
import csv
import datetime
@eddex
eddex / Install CUDA 10.1 on Ubuntu 18.04.md
Last active November 22, 2023 16:12
How to install CUDA 10.1 on Ubuntu 18.04

How to install CUDA 10.1 on Ubuntu 18.04

A clean installation of Ubuntu 18.04.02 LTS was used.

This gist is an extension to the official docs, adding missing parts and instructions.

2 pre-install actions

follow the pre-installation actions on:

@redknightlois
redknightlois / mish_init.py
Created July 15, 2020 14:54
Variance based initialization method for Mish activation
def init_weights(m, variance=1.0):
def _calculate_fan_in_and_fan_out(tensor):
dimensions = tensor.dim()
if dimensions < 2:
return 1, 1
if dimensions == 2: # Linear
fan_in = tensor.size(1)
fan_out = tensor.size(0)
import jax.numpy as jnp
from jax import random
class JaxCartPole:
"""
Based on OpenAI Gym Cartpole
https://github.com/openai/gym/blob/master/gym/envs/classic_control/cartpole.py
"""
def __init__(self):
self.gravity = 9.8
@Mononofu
Mononofu / stochastic_muzero.py
Last active June 6, 2024 08:35
Pseudocode for Stochastic MuZero
# Copyright 2022 DeepMind Technologies Limited.
# Licensed under the Apache License, Version 2.0 and CC BY 4.0.
# You may not use this file except in compliance with these licenses.
# Copies of the licenses can be found at https://www.apache.org/licenses/LICENSE-2.0
# and https://creativecommons.org/licenses/by/4.0/legalcode.
"""Pseudocode description of the Stochastic MuZero algorithm.
This pseudocode was adapted from the original MuZero pseudocode.
"""
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