omsrisagar

Steps to manage multiple CUDA environments

Latest Update: May 19th, 2024

This gist contains all the steps required to:

• Install multiple CUDA versions (e.g., CUDA 11.8 and CUDA 12.1
• Manage multiple CUDA environments on Ubuntu using the utility called environment modules.
• Use this approach to avoid CUDA environment conflicts.

Environment Modules is a package that provides for the dynamic modification of a user's environment via modulefiles. You can find more on it at https://modules.readthedocs.io/en/latest/

omsrisagar

FROM ubuntu:18.04

ENV USER mpitest
ENV HOME /home/$USER
ENV MPI_DIR=/opt/ompi
ENV PATH="$MPI_DIR/bin:$HOME/.local/bin:$PATH"
ENV LD_LIBRARY_PATH="$MPI_DIR/lib:$LD_LIBRARY_PATH"
WORKDIR $HOME

RUN apt-get -q update \

omsrisagar

from graphviz import Digraph
import torch
from torch.autograd import Variable, Function

def iter_graph(root, callback):
    queue = [root]
    seen = set()
    while queue:
        fn = queue.pop()
        if fn in seen:

omsrisagar

# Reference
# =========
# Why does deep and cheap learning work so well?∗
# Henry W. Lin, Max Tegmark, and David Rolnick
# Dept. of Physics, Harvard University, Cambridge, MA 02138
# Dept. of Physics, Massachusetts Institute of Technology, Cambridge, MA 02139 and Dept. of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139
# Here the x input takes two numbers and produces the multiplication as output
import numpy as np

# multiplication approximator

omsrisagar

from tensorboard.backend.event_processing import event_accumulator
import tensorflow as tf
import glob
import pandas as pd
tf.logging.set_verbosity(tf.logging.ERROR)

basedir = "/path/to/log/directory/"

def load_tf(dirname):
    prefix = basedir + "tboard/VisibleSwimmer-v2/"