Memory leak in Tensorflow.js: How to clean up unused tensors?
As per the documentation, the function provided to tf.tidy "must not return a Promise". Internally, tf backend disposes all the tensors uses when fitting a model. That is why tf.fit should not be placed inside tf.tidy. To dispose the model crashed, one can call tf.dispose on the model.
It is true that there seems to be currently a memory leak, but a model crash during the definition of the model is a poor implementation. This should not happen in a proper scenario as one can test if the parameters given matches what should be the input to the layers. For instance reshaping a shape of 2 to 1 can be avoid before constructing the model to prevent the memory leak.
async function train(shouldCrash) {
console.log(`Training, shouldCrash=${shouldCrash}`);
const dataset = tf.data.zip({ // setup data
xs: tf.data.array([[1],[1]]),
ys: tf.data.array([1]),
}).batch(1);
const model = tf.sequential({ // setup model
layers: [
tf.layers.dense({units: 1, inputShape: [1]}),
tf.layers.reshape({targetShape: [(shouldCrash ? 2 : 1)]}), // use invalid shape when crashing
],
});
model.compile({ optimizer: 'sgd', loss: 'meanSquaredError' });
console.log(' Tensors before:', tf.memory().numTensors);
try {
const history = await model.fitDataset(dataset, { epochs: 1 });
} catch (err) {
console.log(` Error: ${err.message}`);
}
console.log(' Tensors after:', tf.memory().numTensors);
return model
}
(async () => {
const m1 = await train(false); // normal training
tf.dispose(m1)
const m2 = await train(true); // training with error
tf.dispose(m2)
tf.disposeVariables()
console.log('Tensors afters:', tf.memory().numTensors);
})();<script src="https://cdn.jsdelivr.net/npm/@tensorflow/[email protected]/dist/tf.min.js"></script>The way to clean any unused tensors in async code is to wrap the code that creates them between a startScope() and an endScope() call.
tf.engine().startScope()
// do your thing
tf.engine().endScope()