dirkgr

#
# Run like this:
#   beaker experiment logs <experiment id> | python logs.py 
#

import sys
import re
from collections import defaultdict, Counter
from typing import List, Dict

dirkgr

import mmap
import xxhash

def _checksum_artifact(path: PathOrStr) -> str:
    filepath = Path(path)
    if not filepath.is_file():
        raise FileNotFoundError(str(filepath))

    h = xxhash.xxh128()
    with filepath.open("rb") as f:

dirkgr

float32: 3.71 s ± 2.95 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
float16: 2.29 s ± 8.15 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
bfloat16: 2.29 s ± 9.57 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

torch.backends.cuda.matmul.allow_tf32 = False
float32: 24.4 s ± 41.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

dirkgr

import dill
import mmh3
import typing
import io


def hash_object(o: typing.Any) -> str:
    with io.BytesIO() as buffer:
        dill.dump(o, buffer)
        return mmh3.hash_bytes(buffer.getvalue(), x64arch=True)

dirkgr

STOPWORDS = {
    "i",
    "me",
    "my",
    "myself",
    "we",
    "our",
    "ours",
    "ourselves",
    "you",

dirkgr

import collections
import typing

_T = typing.TypeVar('_T')
def filter_near_duplicates(items: typing.Iterable[_T], key = lambda x: x) -> typing.Generator[_T, None, None]:
    """Filters out items that overlap too much with items we've seen earlier in the sequence."""
    trigram_to_sentence_indices = collections.defaultdict(set)
    for sentence_index, item in enumerate(items):
        sentence = key(item)
        trigrams = [sentence[i:i+3] for i in range(len(sentence) - 1)]

dirkgr

from typing import *
import time
import logging

def logging_tqdm(
    i,
    *,
    logger: Optional[logging.Logger] = None,
    desc: str = "Working",
    total: Optional[int] = None,

dirkgr

def distinctFromIterator[T](input: Iterator[T]): Iterator[T] = new Iterator[T] {
  private val seen: mutable.Set[T] = mutable.Set[T]()
  private def findNextItem(): Option[T] = {
    if(input.hasNext) {
      val n = input.next()
      val newItem = seen.add(n)
      if(newItem)
        Some(n)
      else
        findNextItem()

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  def linesFromFile(filename: String): Iterator[String] = new Iterator[String] {
    val bufferedReader = {
      val fileInputStream = new FileInputStream(filename)
      val decompressedInputStream =
        if(filename.endsWith(".gz")) new GZIPInputStream(fileInputStream) else fileInputStream
      val reader = new InputStreamReader(decompressedInputStream, "UTF-8")
      new BufferedReader(reader)
    }
    private var nextLine = bufferedReader.readLine()

dirkgr

from typing import *
import multiprocessing as mp

def mp_map(fn, input_sequence: Iterable) -> Iterable:
    input_queue = mp.Queue()
    output_queue = mp.Queue()

    def process_items():
        while True:
            item = input_queue.get()