How to use

1. If you are using the app for he first time, sign up by clicking on the "create an account" button.
2. After signing up, sign in to your account.

Keras

1. In your python code, import Tensordash library There are multiple ways to Link your Account , The Following are displayed.

2. Specify only Model Name :

from tensordash.tensordash import Tensordash
histories = Tensordash(ModelName = '<YOUR_MODEL_NAME>')

Enter Email : ...........
Enter Tensordash Password : ********

• Specify Model Name and Email address :

from tensordash.tensordash import Tensordash
histories = Tensordash(
    ModelName = '<YOUR_MODEL_NAME>',
    email = '<YOUR_EMAIL_ID>')

Enter Tensordash Password : ********

• Specify Model Name, Email address and password :

from tensordash.tensordash import Tensordash
histories = Tensordash(
    ModelName = '<YOUR_MODEL_NAME>',
    email = '<YOUR_EMAIL_ID>', 
    password = '<YOUR PASSWORD>')

In the app, if you have multiple models you would be able to identify your model by YOUR_MODEL_NAME, so this name has to be unique.

1. Now you can monitor your model values and status using crash analysis. Simply use a try-catch block as shown below.

try:
    model.fit(
    X_train, 
    y_train, 
    epochs = epochs, 
    validation_data = validation_data, 
    batch_size = batch_size, 
    callbacks = [histories])

except:
    histories.sendCrash()

OR

Alternatively, if you do not want to use crash analysis then you can just monitor by just adding histories object to callback

model.fit(
    X_train, 
    y_train, 
    epochs = epochs, 
    validation_data = validation_data, 
    batch_size = batch_size, 
    callbacks = [histories])

Tensorflow

1. In your python code, import Tensordash library

To use Tensordash on pytorch, you have to pass the metrics as parameters to the function manually.
The function takes:
1. loss
2. epoch
3. total_epochs
4. accuracy(optional)
5. val_loss(optional)
6. val_acc(optional), as the parameters

from tensordash.tensordash import Customdash

There are multiple ways to Link your Account , The Following are displayed.

• Specify only Model Name :

histories = Customdash(ModelName = '<YOUR_MODEL_NAME>')

Enter Email : ...........
Enter Tensordash Password : ********

• Specify Model Name and Email address :

histories = Customdash(
    ModelName = '<YOUR_MODEL_NAME>',
    email = '<YOUR_EMAIL_ID>')

Enter Tensordash Password : ********

• Specify Model Name, Email address and password :

histories = Customdash(
    ModelName = '<YOUR_MODEL_NAME>',
    email = '<YOUR_EMAIL_ID>', 
    password = '<YOUR PASSWORD>')

In the app, if you have multiple models you would be able to identify your model by YOUR_MODEL_NAME, so this name has to be unique.

1. Now you can monitor your model values and status using crash analysis. Simply use a try-catch block as shown below.

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)

except:
    histories.sendCrash()

OR

Alternatively, if you do not want to use crash analysis then you can just monitor by just adding histories object to callback

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)

Pytorch

1. In your python code, import Tensordash library

from tensordash.torchdash import Torchdash

To use Tensordash on pytorch, you have to pass the metrics as parameters to the function manually.
The function takes:
1. loss
2. epoch
3. total_epochs
4. accuracy(optional)
5. val_loss(optional)
6. val_acc(optional), as the parameters

There are multiple ways to Link your Account , The Following are displayed.

• Specify only Model Name :

histories = Torchdash(ModelName = '<YOUR_MODEL_NAME>')

Enter Email : ...........
Enter Tensordash Password : ********

• Specify Model Name and Email address :

histories = Torchdash(
    ModelName = '<YOUR_MODEL_NAME>',
    email = '<YOUR_EMAIL_ID>')

Enter Tensordash Password : ********

• Specify Model Name, Email address and password :

histories = Torchdash(
    ModelName = '<YOUR_MODEL_NAME>',
    email = '<YOUR_EMAIL_ID>', 
    password = '<YOUR PASSWORD>')

In the app, if you have multiple models you would be able to identify your model by YOUR_MODEL_NAME, so this name has to be unique.

1. Now you can monitor your model values and status using crash analysis. Simply use a try-catch block as shown below.

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)

except:
    histories.sendCrash()

OR

Alternatively, if you do not want to use crash analysis then you can just monitor by just adding histories object to callback

for epoch in range(epochs):
    losses = []
    for data in trainset:
        X, y = data
        net.zero_grad()
        output = net(X.view(-1,784))
        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)

Fast.ai

1. In your python code, import Tensordash library There are multiple ways to Link your Account , The Following are displayed.

2. Specify only Model Name :

from tensordash.fastdash import Fastdash

learn = cnn_learner(data, models.resnet18, metrics=accuracy)
my_cb = Fastdash(learn, ModelName = '<YOUR_MODEL_NAME>')

Enter Email : ...........
Enter Tensordash Password : ********

• Specify Model Name and Email address :

from tensordash.fastdash import Fastdash
my_cb = Fastdash(
    ModelName = '<YOUR_MODEL_NAME>',
    email = '<YOUR_EMAIL_ID>')

Enter Tensordash Password : ********

• Specify Model Name, Email address and password :

from tensordash.fastdash import Fastdash
my_cb = Tensordash(
    ModelName = '<YOUR_MODEL_NAME>',
    email = '<YOUR_EMAIL_ID>', 
    password = '<YOUR PASSWORD>')

1. Now you can monitor your model values and status using crash analysis. Simply use a try-catch block as shown below.

try:
    learn.fit(epochs, learning_rate, callbacks = my_cb)
except:
    my_cb.sendCrash()

OR

Alternatively, if you do not want to use crash analysis then you can just monitor by just adding my_cb object to callback

learn.fit(epochs, learning_rate, callbacks = my_cb)