vwxyzjn

import torch
import torch.nn as nn
import torch.optim as optim

# Create target distribution (fixed)
target_logits = torch.randn(10)
target_log_probs = torch.log_softmax(target_logits, dim=0)

# Create learnable distribution
learnable_logits = nn.Parameter(torch.rand_like(target_logits))  # Initialize randomly

dedlim

<artifacts_info>
The assistant can create and reference artifacts during conversations. Artifacts are for substantial, self-contained content that users might modify or reuse, displayed in a separate UI window for clarity.

# Good artifacts are...
- Substantial content (>15 lines)
- Content that the user is likely to modify, iterate on, or take ownership of
- Self-contained, complex content that can be understood on its own, without context from the conversation
- Content intended for eventual use outside the conversation (e.g., reports, emails, presentations)
- Content likely to be referenced or reused multiple times

lv10

# make sure you don't have any soon to be forgotten version of vim installed
$ sudo apt-get remove --purge vim vim-runtime vim-gnome vim-tiny vim-gui-common

# Install Deps
$ sudo apt-get install build-essential cmake
$ sudo apt-get install python3-dev

#Optional: so vim can be uninstalled again via `dpkg -r vim`
$ sudo apt-get install checkinstall

jcmartinezdev

pragma solidity ^0.4.24;

// ----------------------------------------------------------------------------
// Sample token contract
//
// Symbol        : LCST
// Name          : LCS Token
// Total supply  : 100000
// Decimals      : 2
// Owner Account : 0xde0B295669a9FD93d5F28D9Ec85E40f4cb697BAe

Willian-Zhang

Tensorflow 1.7 with CUDA on macOS High Sierra 10.13.4 for eGPU

Largely based on the Tensorflow 1.6 gist, and Tensorflow 1.7 gist for xcode, this should hopefully simplify things a bit.

Requirements

• NVIDIA Web-Drivers 387.10.10.10.30.103 for 10.13.4
• CUDA-Drivers 387.178
• CUDA 9.1 Toolkit

brenopolanski

1. Select the object(s) to export
2. Open the document properties window (Ctrl+Shift+D)
3. Select "Resize page to drawing or selection"
4. File > Save As Copy...
5. Select Optimized SVG as the format if you want to use it on the web

sammchardy

Ninj0r admin, [Oct 20, 2017, 9:18:55 AM]:
It's a three step process:
1) Start listening to the stream and buffering the messages
2) Get a depth snapshot
3) replay the buffered messages and the live messges.

Depth updates have two variables, u and U

U is the initial updateId, and u is the final updateId. There can be multiple updates "compressed" into a single update that comes out via the web socket stream.

cyberang3l

https://github.com/aancel/admin/wiki/VirtualGL-on-Ubuntu

https://virtualgl.org/About/Introduction

https://www.turbovnc.org

VirtualGL

When you use ssh with X forwarding, you might have noticed that you cannot execute programs that require 3D acceleration. That's where VirtualGL comes into play.

manasthakur

Using git-submodules to version-control Vim plugins

If you work across many computers (and even otherwise!), it's a good idea to keep a copy of your setup on the cloud, preferably in a git repository, and clone it on another machine when you need. Thus, you should keep the .vim directory along with your .vimrc version-controlled.

But when you have plugins installed inside .vim/bundle (if you use pathogen), or inside .vim/pack (if you use Vim 8's packages), keeping a copy where you want to be able to update the plugins (individual git repositories), as well as your vim-configuration as a whole, requires you to use git submodules.

Creating the repository

Initialize a git repository inside your .vim directory, add everything (including the vimrc), commit and push to a GitHub/BitBucket/GitLab repository:

cd ~/.vim

jovianlin

from tensorflow.python.client import device_lib

def get_available_gpus():
    local_device_protos = device_lib.list_local_devices()
    return [x.name for x in local_device_protos if x.device_type == 'GPU']

get_available_gpus()