def __init__(self, debug_x=None):
super(KerasCallback, self).__init__()
self.context = deepkit.context()
self.context.debugger_controller.snapshot.subscribe(self.snapshot)
self.debug_x = debug_x
self.epoch = 0
self.data_validation = None
self.data_validation_size = None
self.current = {}
self.last_batch_time = time.time()
self.start_time = time.time()
self.accuracy_metric = None
self.all_losses = None
self.loss_metric = None
self.learning_rate_metric = None
self.learning_rate_start = 0
self.last_debug_sent = 0
import asyncio
import deepkit
context = deepkit.context('deepkit.yml')
acc = deepkit.create_metric('acc')
async def bla():
for i in range(1000):
await asyncio.sleep(1)
context.epoch(i, 10)
acc.send(i, i * 2)
print("asdasd")
asyncio.get_event_loop().run_until_complete(bla())
import random
import deepkit
context = deepkit.context()
context.add_file(__file__)
test = deepkit.create_metric('test')
for i in range(100):
deepkit.set_info(i, random.random())
total = 1_000_000;
for i in range(1_000_000):
test.send(i, random.random())
deepkit.epoch(i, total)
print("Bye.")
import datetime
import tensorflow as tf
from tensorflow.keras import Model, layers, optimizers, datasets
import deepkit
from deepkit import ContextOptions
context = deepkit.context(ContextOptions(
# account='peter',
# project='deepkitorg/tf2-keras-mnist'
))
context.add_file('model.py')
(x, y), (x_val, y_val) = datasets.fashion_mnist.load_data()
x = x.reshape(x.shape[0], 28, 28, 1)
x_val = x_val.reshape(x_val.shape[0], 28, 28, 1)
x = x / 255.0
y = tf.one_hot(y, depth=10, dtype=tf.float32)
y_val = tf.one_hot(y_val, depth=10)
print('x/y shape:', x.shape, y.shape)
def train_gen():
global x, y
for x2, y2 in zip(x, y):
yield (x2, x2), y2
# yield x2, y2
train_dataset = tf.data.Dataset.from_generator(
train_gen,