haixuanTao

nodes:
  - id: webcam
    custom:
      source: https://huggingface.co/datasets/dora-rs/dora-idefics2/raw/main/operators/opencv_stream.py
      outputs:
        - image
  - id: idefics2
    operator:
      python:  https://huggingface.co/datasets/dora-rs/dora-idefics2/raw/main/operators/idefics2_op.py
      inputs:

nreimers

# This code shows how to index data using Cohere Embed v3 byte (int8) embeddings.
# This gives you a 4x memory reduction while keeping 99.9% of the search quality.
# Make sure to have OpenSearch running with at least version 2.9. E.g. by using docker:
# docker run -d -p 9200:9200 -p 9600:9600 -e "discovery.type=single-node" opensearchproject/opensearch:2.11.1
# You also need the OpenSearch python client installed.
# pip install cohere opensearch-py


from opensearchpy import OpenSearch, helpers
import cohere 

adrianlacatus

Install Nvidia Driver on Ubuntu 18.04.3

Secure Boot

This section applies to machines with Secure Boot, such as ThinkPad.

1. Before installation, switch to "Discrete Graphics" in BIOS, if both Intel and Nvidia graphics are present.
2. During installation, make sure to select the "Install third-party software for graphics and Wi-Fi hardware and addition media formats" in "Updates and other software" screen.
3. Select "Configure Secure Boot", and set password.
4. Continue Ubuntu installation as normal.

giuseppebonaccorso

import keras.backend as K
import multiprocessing
import tensorflow as tf

from gensim.models.word2vec import Word2Vec

from keras.callbacks import EarlyStopping
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Flatten
from keras.layers.convolutional import Conv1D

ilblackdragon

import logging

import numpy as np
import tensorflow as tf
from tensorflow.contrib import layers


GO_TOKEN = 0
END_TOKEN = 1
UNK_TOKEN = 2

sourceperl

#!/usr/bin/python3

# detect tones in sound spectrum with scipy FFT
#   here sound source is a USB microphone with ALSA (channel 1)

from collections import deque
import struct
import sys
import time
import threading

rouseguy

%%time

# -*- coding: utf-8 -*-
'''An implementation of sequence to sequence learning for performing addition
Input: "535+61"
Output: "596"
Padding is handled by using a repeated sentinel character (space)
Input may optionally be inverted, shown to increase performance in many tasks in:
"Learning to Execute"
http://arxiv.org/abs/1410.4615

danijar

# Working example for my blog post at:
# https://danijar.github.io/structuring-your-tensorflow-models
import functools
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data


def doublewrap(function):
    """
    A decorator decorator, allowing to use the decorator to be used without

artizirk

#!/usr/bin/env python3

from v4l2 import *
import fcntl
import mmap
import select
import time

vd = open('/dev/video0', 'rb+', buffering=0)

EderSantana

Code for Keras plays catch blog post

Train

python qlearn.py

Test

1. Generate figures