notification methods
there are multiple ways to get notified when a job a done
beep
import os
def make_noise():
'''Make noise after finishing executing a code'''
duration = 1 # seconds
freq = 440 # Hz
os.system('play -nq -t alsa synth {} sine {}'.format(duration, freq))
email or message
#pip install knockknock
from knockknock import email_sender
@email_sender(recipient_emails=["youremail@gmail.com"], sender_email="anotheremail@gmail.com")
def main():
even_arr = []
for i in range(10000):
if i%2==0:
even_arr.append(i)
Tensordash
$pip install tensor-dash 1. tf.keras
from tensordash.tensordash import Tensordash
histories = Tensordash(
ModelName = '<YOUR_MODEL_NAME>',
email = '<YOUR_EMAIL_ID>',
password = '<YOUR PASSWORD>')
try:
model.fit(
X_train,
y_train,
epochs = epochs,
validation_data = validation_data,
batch_size = batch_size,
callbacks = [histories])
except:
histories.sendCrash()
-
pytorch
from tensordash.torchdash import Torchdash
histories = Torchdash( ModelName = ‘
', email = ' ', password = ' ') try: for epoch in range(epochs): losses = [] for data in trainset: X, y = data net.zero_grad() output = net(X.view(data_shape)) loss = F.nll_loss(output, y) loss.backward() optimizer.step() losses = np.asarray(losses) histories.sendLoss(loss = np.mean(losses), epoch = epoch, total_epochs = epochs) // Add this line to your training loop
except: histories.sendCrash()
-
tensorflow
from tensordash.tensordash import Customdash
histories = Customdash( ModelName = ‘
', email = ' ', password = ' ') try:
for epoch in range(num_epochs): epoch_loss_avg = tf.keras.metrics.Mean() epoch_accuracy = tf.keras.metrics.SparseCategoricalAccuracy() for x, y in train_dataset: loss_value, grads = grad(model, x, y) optimizer.apply_gradients(zip(grads, model.trainable_variables)) epoch_loss_avg(loss_value) epoch_accuracy(y, model(x, training=True)) train_loss_results.append(epoch_loss_avg.result()) train_accuracy_results.append(epoch_accuracy.result()) histories.sendLoss(loss = epoch_loss_avg.result(), accuracy = epoch_accuracy.result(), epoch = epoch, total_epochs = epochs) // Add this line to your training loopexcept: histories.sendCrash()
