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#VERBOSE=0 torchrun --nproc_per_node 3 self_contained_pp_LOC.py
import os, random, numpy as np, torch, torch.nn as nn, torch.distributed as dist, torch.nn.functional as F
from torch.optim import AdamW
from torch.utils.data import DataLoader, DistributedSampler
from datasets import load_dataset
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer

STEP, local_rank, world_size, verbose = 0, int(os.environ["LOCAL_RANK"]), int(os.environ["WORLD_SIZE"]), os.environ.get("VERBOSE", "0") == "1"

def set_all_seed(seed):

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from huggingface_hub import snapshot_download
import mlx.core as mx
import mlx.nn as nn
import time


class Block(nn.Module):
    def __init__(self, in_dims, dims, stride=1):
        super().__init__()

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# 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

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# 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.

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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()}"

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from typing import Optional
import torch
import torch.nn as nn


@torch.no_grad()
def measure_time_device(
    model: nn.Module,
    dtype: Optional[torch.dtype] = torch.float32,
    num_repeats: Optional[int] = 100,

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cimport cython
import numpy as np
cimport numpy as np
from sklearn.metrics import f1_score


@cython.boundscheck(False)
@cython.wraparound(False)
def f1_opt(np.ndarray[long, ndim=1] label, np.ndarray[double, ndim=1] preds):

vgoklani

Building custom layers in Keras

About Keras

Keras is currently one of the most commonly used deep learning libraries today. And part of the reason why it's so popular is its API. Keras was built as a high-level API for other deep learning libraries ie Keras as such does not perform low-level tensor operations, instead provides an interface to its backend which are built for such operations. This allows Keras to abstract a lot of the underlying details and allows the programmer to concentrate on the architecture of the model. Currently Keras supports Tensorflow, Theano and CNTK as its backends.

Let's see what I mean. Tensorflow is one of the backends used by Keras. Here's the code for MNIST classification in TensorFlow and Keras. Both models are nearly identical and applies to the same problem. But if you compare the codes you g

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[
  {
    "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"
  },
  {

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from pymongo import MongoClient


try:
    client = MongoClient('localhost')
    db = client.searchfollow
except:
    print("Could not connect to MongoDB")

call = db.command("dbstats")