mattjj

import torch
import torch.utils.dlpack
import jax
import jax.dlpack

# A generic mechanism for turning a JAX function into a PyTorch function.

def j2t(x_jax):
  x_torch = torch.utils.dlpack.from_dlpack(jax.dlpack.to_dlpack(x_jax))
  return x_torch

padeoe

For typical use cases, I prefer using nvitop to view detailed information. This script offers a dependency-free implementation.

The script enhances the functionality of nvidia-smi and provides the following info:

• Username
• full process Command
• GPU ID
• PID

This is useful on multi-user servers and can be used to quickly identify which user is using the GPU and running what kind of program.

nicksam112

#Evolution Strategies with Keras
#Based off of: https://blog.openai.com/evolution-strategies/
#Implementation by: Nicholas Samoray

#README
#Meant to be run on a single machine
#APPLY_BIAS is currently not working, keep to False
#Solves Cartpole as-is in about 50 episodes
#Solves BipedalWalker-v2 in about 1000

rdkit-rescriptors.py

from multiprocessing import Pool
import pandas as pd
from rdkit import Chem
from rdkit.ML.Descriptors import MoleculeDescriptors
import numpy as np
import os


def papply(df, func, n_jobs=None, n_partitions=10):
    pool = Pool(n_jobs)

cbaziotis

from keras import backend as K, initializers, regularizers, constraints
from keras.engine.topology import Layer


def dot_product(x, kernel):
    """
    Wrapper for dot product operation, in order to be compatible with both
    Theano and Tensorflow
    Args:

mbollmann

class AttentionLSTM(LSTM):
    """LSTM with attention mechanism

    This is an LSTM incorporating an attention mechanism into its hidden states.
    Currently, the context vector calculated from the attended vector is fed
    into the model's internal states, closely following the model by Xu et al.
    (2016, Sec. 3.1.2), using a soft attention model following
    Bahdanau et al. (2014).

    The layer expects two inputs instead of the usual one:

mbollmann

from keras import backend as K
from keras.layers.recurrent import LSTM

class HiddenStateLSTM(LSTM):
    """LSTM with input/output capabilities for its hidden state.

    This layer behaves just like an LSTM, except that it accepts further inputs
    to be used as its initial states, and returns additional outputs,
    representing the layer's final states.

udibr

import numpy as np
from keras.layers import GRU, initializations, K
from collections import OrderedDict

class GRULN(GRU):
    '''Gated Recurrent Unit with Layer Normalization
    Current impelemtation only works with consume_less = 'gpu' which is already
    set.

    # Arguments

keis

import asyncio

@asyncio.coroutine
def waiting(r):
    print("hello from waiting -", r)
    yield from asyncio.sleep(2)
    print("bye from waiting -", r)
    return r

cjbayesian

###################################################
##
## Functions for calculating AUC and plotting ROC
## Corey Chivers, 2013
## corey.chivers@mail.mcgill.ca
##
###################################################


## Descrete integration for AUC calc