Training Tensorflow Model¶

Tensorflow is an open-source machine learning software library, TensorFlow is used to train neural networks. Expressed in the form of stateful dataflow graphs, each node in the graph represents the operations performed by neural networks on multi-dimensional arrays. These multi-dimensional arrays are commonly known as “tensors,” hence the name TensorFlow. In this example we will be training a MNIST model.

TD;lR¶

Running any type of Tensorflow model with Bacalhau

Training Tensorflow models Locally¶

This section is from TensorFlow 2 quickstart for beginners

TensorFlow 2 quickstart for beginners¶

This short introduction uses Keras to:

Load a prebuilt dataset.
Build a neural network machine learning model that classifies images.
Train this neural network.
Evaluate the accuracy of the model.

Set up TensorFlow¶

Import TensorFlow into your program to check whether it is installed

import tensorflow as tf
import os
print("TensorFlow version:", tf.__version__)

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%%bash
mkdir /inputs
wget https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz -O /inputs/mnist.npz

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mnist = tf.keras.datasets.mnist

CWD = '' if os.getcwd() == '/' else os.getcwd()
(x_train, y_train), (x_test, y_test) = mnist.load_data('/inputs/mnist.npz')
x_train, x_test = x_train / 255.0, x_test / 255.0

Build a machine learning model¶

Build a tf.keras.Sequential model by stacking layers.

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model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(input_shape=(28, 28)),
  tf.keras.layers.Dense(128, activation='relu'),
  tf.keras.layers.Dropout(0.2),
  tf.keras.layers.Dense(10)
])

For each example, the model returns a vector of logits or log-odds scores, one for each class.

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predictions = model(x_train[:1]).numpy()
predictions

The tf.nn.softmax function converts these logits to probabilities for each class:

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tf.nn.softmax(predictions).numpy()

Note: It is possible to bake the tf.nn.softmax function into the activation function for the last layer of the network. While this can make the model output more directly interpretable, this approach is discouraged as it's impossible to provide an exact and numerically stable loss calculation for all models when using a softmax output.

Define a loss function for training using losses.SparseCategoricalCrossentropy, which takes a vector of logits and a True index and returns a scalar loss for each example.

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loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)

This loss is equal to the negative log probability of the true class: The loss is zero if the model is sure of the correct class.

This untrained model gives probabilities close to random (1/10 for each class), so the initial loss should be close to -tf.math.log(1/10) ~= 2.3.

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loss_fn(y_train[:1], predictions).numpy()

Before you start training, configure and compile the model using Keras Model.compile. Set the optimizer class to adam, set the loss to the loss_fn function you defined earlier, and specify a metric to be evaluated for the model by setting the metrics parameter to accuracy.

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model.compile(optimizer='adam',
              loss=loss_fn,
              metrics=['accuracy'])

Train and evaluate your model¶

Use the Model.fit method to adjust your model parameters and minimize the loss:

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model.fit(x_train, y_train, epochs=5)

The Model.evaluate method checks the models performance, usually on a "Validation-set" or "Test-set".

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model.evaluate(x_test,  y_test, verbose=2)

The image classifier is now trained to ~98% accuracy on this dataset. To learn more, read the TensorFlow tutorials.

If you want your model to return a probability, you can wrap the trained model, and attach the softmax to it:

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probability_model = tf.keras.Sequential([
  model,
  tf.keras.layers.Softmax()
])

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probability_model(x_test[:5])

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%%bash
mkdir /outputs

the following method can be used to save the model as checkpoint

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model.save_weights('/outputs/checkpoints/my_checkpoint')

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%%bash
ls /outputs/

Converting the notebook into a python script¶

You can use tool like nbconvert to convert your python notebook into a script.

After that you can create a gist of the training script at gist.github.com copy the raw link of the gist

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%%bash
wget https://gist.githubusercontent.com/js-ts/0ce4d671ced642fbe807e65f5186ae87/raw/7f28cc497cc1c509661a33b144c0683b8fc97f41/train.py

Testing whether the script works

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%%bash
python train.py

Running on bacalhau¶

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!curl -sL https://get.bacalhau.org/install.sh | bash

The dataset and the script is mounted to the tensorflow container using an URL we then run the script inside the container

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%%bash --out job_id
bacalhau docker run \
--wait \
--id-only \
-w /inputs  \
-i https://gist.githubusercontent.com/js-ts/e7d32c7d19ffde7811c683d4fcb1a219/raw/ff44ac5b157d231f464f4d43ce0e05bccb4c1d7b/train.py \
-i https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz \
tensorflow/tensorflow \
-- python train.py

Structure the of the command:

-i https://gist.githubusercontent.com/js-ts/e7d32c7d19ffde7811c683d4fcb1a219/raw/ff44ac5b157d231f464f4d43ce0e05bccb4c1d7b/train.py: mount the training script
-i https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz: mount the dataset
tensorflow/tensorflow: specify the Docker image
python train.py: execute the script

By default whatever URL you mount using the -i flag gets mounted at the path /inputs so we choose that as our input directory -w /inputs

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%env JOB_ID={job_id}

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%%bash
bacalhau list --id-filter ${JOB_ID}

Where it says Completed, that means the job is done, and we can get the results.

To find out more information about your job, run the following command:

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%%bash
bacalhau describe ${JOB_ID}

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%%bash
rm -rf results && mkdir -p results
bacalhau get $JOB_ID --output-dir results

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%%bash
ls results/

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%%bash
cat results/stdout

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%%bash
ls results/outputs/