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from __future__ import print_function | |
import keras | |
from keras.datasets import mnist | |
from keras.models import Sequential | |
from keras.layers import Dense, Dropout, Flatten | |
from keras.layers import Conv2D, MaxPooling2D | |
from keras import backend as K | |
import tensorflow as tf | |
import horovod.tensorflow as hvd |
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// Code from http://docs.nvidia.com/deeplearning/sdk/nccl-developer-guide/index.html#onedevprothrd | |
#define _BSD_SOURCE | |
#include <stdio.h> | |
#include "cuda_runtime.h" | |
#include "nccl.h" | |
#include "mpi.h" | |
#include <stdint.h> | |
#include <stdlib.h> |
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# Copyright 2017 Uber Technologies, Inc. All Rights Reserved. | |
# Copyright 2016 The TensorFlow Authors. All Rights Reserved. | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software |
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import graphviz | |
import numpy as np | |
import keras | |
def gather_layer_stats(layer_dict, layer, r, s): | |
lr, ls = None, None | |
if hasattr(layer, 'kernel_size'): | |
assert layer.kernel_size[0] == layer.kernel_size[1] | |
assert layer.strides[0] == layer.strides[1] |
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# Copyright 2018 Uber Technologies, Inc. All Rights Reserved. | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, |
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from __future__ import print_function | |
import os | |
import sys | |
import tensorflow as tf | |
from google.protobuf import text_format | |
from tensorflow.python.framework import graph_io | |
if len(sys.argv) < 2: | |
print('Usage: %s <filename prefix>' % sys.argv[0]) |
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from __future__ import print_function | |
import argparse | |
import torch.backends.cudnn as cudnn | |
import torch.nn.functional as F | |
import torch.optim as optim | |
import torch.utils.data.distributed | |
from torchvision import datasets, transforms, models | |
import horovod.torch as hvd | |
import tensorboardX |
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from __future__ import print_function | |
from hyperopt import Trials, STATUS_OK, tpe | |
from hyperas import optim | |
from hyperas.distributions import choice, uniform, conditional | |
import keras | |
import tensorflow as tf | |
import horovod.keras as hvd | |
import keras.backend as K | |
import math |
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package tmds_pkg; | |
typedef struct packed { | |
logic inv_q_m; | |
logic use_xor; | |
logic [7:0] q_m; | |
} tmds_encoded_t; | |
typedef enum logic [9:0]{ | |
CTRL_00 = 10'b1101010100, |
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#include <assert.h> | |
#include "tmds.h" | |
#include "utils.h" | |
template<int X> | |
ap_uint<log2up(X)> count_ones(ap_uint<X> data) { | |
ap_uint<X> result = data[0]; | |
for (int i = 1; i < 8; i++) { | |
#pragma HLS UNROLL |
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