kalomaze

# coding=utf-8
# Copyright 2023 Mixtral AI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.

mara004

# SPDX-FileCopyrightText: 2023 mara004
# SPDX-License-Identifier: CC-BY-4.0 OR Apache-2.0

# See also https://github.com/extremeheat/JSPyBridge/blob/master/examples/python/pdfjs.py

# Py-Depends: pillow, javascript >= 1.1.0 (jspybridge)
# Js-Depends: pdfjs-dist, canvas
# Use `python -m pip install` and `python -m javascript --install`

import argparse

lirnli

simonster

# Copyright 2022 Google LLC.
# SPDX-License-Identifier: Apache-2.0
# Author: Maithra Raghu <maithra@google.com>


def compute_distance_matrix(patch_size, num_patches, length):
  """Helper function to compute distance matrix."""

  distance_matrix = np.zeros((num_patches, num_patches))

karpathy

""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
import numpy as np
import cPickle as pickle
import gym

# hyperparameters
H = 200 # number of hidden layer neurons
batch_size = 10 # every how many episodes to do a param update?
learning_rate = 1e-4
gamma = 0.99 # discount factor for reward

apaszke

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:

jxmorris12

def verify_ddp_weights_equal(model: torch.nn.Module, atol: float = 1e-5) -> None:
    if hasattr(model, "module"):
        model = model.module
    
    world_size = get_world_size()
    for name, param in model.named_parameters():
        gathered_param = gather(param).reshape((world_size, -1))
        absolute_diffs = (gathered_param[None, 0, :] - gathered_param).abs()
        rank_params_eq = (absolute_diffs < atol).all()
        assert rank_params_eq, f"❌ param [{name}] not equal - got max_absolute_diff={absolute_diffs.max()}"

stungeye

[
  {
    "url": "http://money.cnn.com",
    "rss": "http://rss.cnn.com/rss/money_topstories.rss"
  },
  {
    "url": "http://thehill.com",
    "rss": "http://thehill.com/rss/syndicator/19110"
  },
  {

vgoklani

def verify_ddp_weights_equal(model: torch.nn.Module, atol: float = 1e-5) -> None:
    if hasattr(model, "module"):
        model = model.module
    
    world_size = get_world_size()
    for name, param in model.named_parameters():
        gathered_param = gather(param).reshape((world_size, -1))
        absolute_diffs = (gathered_param[None, 0, :] - gathered_param).abs()
        rank_params_eq = (absolute_diffs < atol).all()
        assert rank_params_eq, f"❌ param [{name}] not equal - got max_absolute_diff={absolute_diffs.max()}"

ageitgey

Install dlib and face_recognition on a Raspberry Pi

Instructions tested with a Raspberry Pi 2 with an 8GB memory card. Probably also works fine on a Raspberry Pi 3.

Steps

Download the latest Raspbian Jessie Light image. Earlier versions of Raspbian won't work.

Write it to a memory card using Etcher, put the memory card in the RPi and boot it up.