- CPU: Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz
- GPU: NVIDIA V100
- Memory: 251GiB
- OS: Ubuntu 16.04.6 LTS (Xenial Xerus)
Docker Images:
- tensorflow/tensorflow:latest-gpu
- tensorflow/serving:latest-gpu
CentOS 7 distribution (as well as RHEL 7) ships with a somewhat outdated version of the GCC compiler (4.8.5 on CentOS 7.5), which may not be suitable to your compilation requirements. For example, C11 - which supersedes C99 - is fully supported only starting from GCC 4.9).
Additionally, recent versions of GCC (GCC6, GCC7, GCC8, GCC9) come with improvements which help detect issues at build time and offer suggestions on how to fix them. Sometimes, these are even actually helpful!
This note describes how to build the latest GCC (9.2.0 as of October 2019) from sources on CentOS 7. This should be applicable as is on RHEL 7. For other Linux distributions, adapt as needed.
#!/bin/bash | |
## This gist contains step by step instructions to install cuda v10.1 and cudnn 7.6 in CentOS 7 | |
### 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 |
Used to retrieve the allocatable resources of a Kubernetes cluster.
Assumes that this is being executed within the K8s cluster.
Tested using python 2.7 and requires the installation of two pip libraries:
pip install pint
pip install kubernetes
Thanks everyone for commenting/contributing! I made this in college for a class and I no longer really use the technology. I encourage you all to help each other, but I probably won't be answering questions anymore.
This article is also on my blog: https://emilykauffman.com/blog/install-anaconda-on-wsl
Note: $
denotes the start of a command. Don't actually type this.
x86_64.sh
. If I had a 32-bit computer, I'd select the x86.sh
version. If you accidentally try to install the wrong one, you'll get a warning in the terminal. I chose `Anaconda3-5.2.0-Liimport boto3 | |
def pull_s3_prefix(dst_dir, bucket, prefix): | |
client = boto3.client('s3') | |
resource = boto3.resource('s3') | |
download_dir(client, resource, prefix, prefix, dst_dir, bucket) | |
def download_dir(client, resource, prefix, start_prefix, local, bucket ): | |
paginator = client.get_paginator('list_objects') | |
for result in paginator.paginate(Bucket=bucket, Delimiter='/', Prefix=prefix): |
Latency Comparison Numbers | |
-------------------------- | |
L1 cache reference/hit 1.5 ns 4 cycles | |
Floating-point add/mult/FMA operation 1.5 ns 4 cycles | |
L2 cache reference/hit 5 ns 12 ~ 17 cycles | |
Branch mispredict 6 ns 15 ~ 20 cycles | |
L3 cache hit (unshared cache line) 16 ns 42 cycles | |
L3 cache hit (shared line in another core) 25 ns 65 cycles | |
Mutex lock/unlock 25 ns | |
L3 cache hit (modified in another core) 29 ns 75 cycles |
git log --oneline -1 <PR-BRANCH>
git push -f origin :
In this article, I will share some of my experience on installing NVIDIA driver and CUDA on Linux OS. Here I mainly use Ubuntu as example. Comments for CentOS/Fedora are also provided as much as I can.
Picking the right architecture = Picking the right battles + Managing trade-offs