SpotlightKid

# -*- coding: utf-8 -*-
"""Parse Python source code and get or print docstrings."""

__all__ = ('get_docstrings', 'print_docstrings')

import ast

from itertools import groupby
from os.path import basename, splitext

iamtekeste

Download Google Drive files with WGET

Example Google Drive download link:

https://docs.google.com/open?id=[ID]
To download the file with WGET you need to use this link:

https://googledrive.com/host/[ID]

Example WGET command:

yasufumy

def argmax1(array):
  return array.index(max(array))

def argmax2(array):
  return max(range(len(array)), key=lambda x: array[x])

thomwolf

def top_k_top_p_filtering(logits, top_k=0, top_p=0.0, filter_value=-float('Inf')):
    """ Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
        Args:
            logits: logits distribution shape (vocabulary size)
            top_k >0: keep only top k tokens with highest probability (top-k filtering).
            top_p >0.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering).
                Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)
    """
    assert logits.dim() == 1  # batch size 1 for now - could be updated for more but the code would be less clear
    top_k = min(top_k, logits.size(-1))  # Safety check

treuille

"""
THIS HAS BEEN DEPRECATED!

We propose you use st.form() instead:

https://blog.streamlit.io/introducing-submit-button-and-forms/
"""

import streamlit as st
import collections

tvst

"""Hack to add per-session state to Streamlit.

Usage
-----

>>> import SessionState
>>>
>>> session_state = SessionState.get(user_name='', favorite_color='black')
>>> session_state.user_name
''

andyljones

"""This script should be run on a machine with at least 2 GPUs and an MPS server running. You can launch an MPS daemon with
```
nvidia-cuda-mps-control -d
```
The script first uses `test_cuda` to verify a CUDA context can be created on each GPU. It then spawns two workers; a 
'good' worker and a 'bad' worker. The workers collaborate through Pytorch's DataDistributedParallel module to calculate
the gradient for a trivial computation. The 'good' worker carries out both the forward and backward pass, while the 
bad worker carries out the forward pass and then exits. This seems to lock up the MPS server, and any subsequent 
attempts to create CUDA contexts fail by hanging eternally.

aditya-malte