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import argparse
import sys

import gym
from gym import wrappers, logger

class RandomAgent(object):
    """The world's simplest agent!"""
    def __init__(self, action_space):
        self.action_space = action_space

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#!/bin/bash

echo "Set Jetson to Max Clocks"

jetson_clocks

echo "Clear RAM"

sync; echo 3 > /proc/sys/vm/drop_caches

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import os
import tensorflow as tf

gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
    for gpu in gpus:
        tf.config.experimental.set_memory_growth(gpu, True)

import tensorflow_datasets
from transformers import BertTokenizer, TFBertForSequenceClassification, glue_convert_examples_to_features#, BertForSequenceClassification

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# 50.012 network lab 1
# Adapted from K & R's original code

from socket import *
import sys
import _thread as thread
import os
import functools

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def function(arg_1, arg_2, arg_3=default_value):
  return something

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import time
import numpy as np
import matplotlib.pyplot as plt
import cv2

import tensorflow as tf
tf.enable_eager_execution()
import tensorflow.keras as keras
from tensorflow.keras.preprocessing import image
import tensorflow_datasets as tfds

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elmo_model = hub.Module("https://tfhub.dev/google/elmo/2", trainable=False)

def ElmoEmbedding(x):
    return elmo_model(tf.squeeze(tf.cast(x, tf.string)),
                      signature="default", as_dict=True)["default"]
    
sequence_input = Input(shape=(1,), dtype=tf.string)
embedded_sequences = Lambda(ElmoEmbedding, output_shape=(1024,))(sequence_input)
...

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Enable mixed precision via a graph rewrite.

Mixed precision is the use of both float32 and float16 data types when training a model to improve performance. This is achieved via a graph rewrite operation and a loss-scale optimizer.

Performing arithmetic operations in float16 takes advantage of specialized processing units, such as NVIDIA Tensor Cores for much higher arithmetic throughput. However, due to the smaller representable range, performing the entire training with float16 can result in gradient underflow, that is, small

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import tensorflow as tf
import tf.keras as keras

# Configure a model for categorical classification.
model.compile(optimizer=tf.train.RMSPropOptimizer(0.01),
              loss=keras.losses.categorical_crossentropy,
              metrics=[keras.metrics.categorical_accuracy])

estimator = keras.estimator.model_to_estimator(model)