Problem with Tensors when making predictions

TensorFlow General Discussion
, ,
1

When I make a prediction, the tensors increase and consume memory, even though I use dispose(), the tensors do not stop increasing. Can anyone tell me what’s wrong with my endpoint?

app.post('/prediction', async (req, res) => {
  const { input, output } = req.body;

  const newInput = input.map(num => {
    if (num >= 1 && num <= 7) {
      return 1;
    } else if (num >= 8 && num <= 14) {
      return 2;
    }
  });

  try {
    const queryLoadModel = `SELECT data FROM models WHERE id = 2`;

    db.query(queryLoadModel, async (err, results) => {
      if (err) {
        console.error('Erro ao carregar o modelo:', err);

        res.status(500).send('Erro ao carregar o modelo');
        
        return;
      }

      if (results.length === 0) {
        res.status(404).send('Modelo não encontrado');

        return;
      }

      const modelData = JSON.parse(JSON.parse(results[0].data));

      const model = await tf.models.modelFromJSON(modelData);

      const inputTensor = tf.tensor2d([newInput], [1, newInput.length]);

      const outputTensor = tf.tensor2d([output], [1, output.length]);

      model.compile({
        optimizer: tf.train.adam(),
        loss: 'categoricalCrossentropy',
        metrics: ['accuracy']
      });

      await model.fit(inputTensor, outputTensor, {
        epochs: 5
      });

      const modelJSON = model.toJSON();

      const modelString = JSON.stringify(modelJSON);

      const querySaveModel = `INSERT INTO models (id, data) VALUES (2, ${db.escape(modelString)}) ON DUPLICATE KEY UPDATE data = VALUES(data)`;

      db.query(querySaveModel, err => {
        if (err) {
          console.error('Erro ao salvar o modelo no banco de dados:', err);

          res.status(500).send('Erro ao salvar o modelo');

          return;
        }
      });

      const prediction = model.predict(inputTensor);

      if (inputTensor) inputTensor.dispose();

      if (outputTensor) outputTensor.dispose();

      prediction.array().then(array => {
        res.send({ prediction: array[0] });

        console.log(tf.memory());

        prediction.dispose();
      }).catch(error => {
        console.error('Erro ao processar a previsão:', error);

        res.status(500).send('Erro ao processar a previsão');

        prediction.dispose();
      });
    });
  } catch (error) {
    console.error('Erro ao fazer previsão:', error);

    res.status(500).send('Erro interno');
  }
});
3

From your description, it appears that you are encountering a memory leak issue related to tensors within a Node.js environment, possibly using TensorFlow.js. When dealing with TensorFlow.js in a Node.js server (such as an Express app), it’s crucial to manage tensors properly to prevent memory leaks.

Here are some points and suggestions to consider:

  1. Tensor Management: Ensure that all tensors are disposed of after use. TensorFlow.js provides tf.dispose() and tf.tidy() for this purpose. tf.tidy() is particularly useful as it automatically cleans up intermediate tensors created within the callback function.
  2. Asynchronous Operations: Since you are using async/await, make sure that tensor operations inside asynchronous functions are properly managed. Tensors created in asynchronous callbacks might not be disposed of automatically.
  3. Database Operations: Your code snippet shows a database query but doesn’t include the TensorFlow.js prediction part. Make sure tensors created during or after the database query (such as those used for predictions) are also being disposed of.
  4. Debugging Memory Leaks: TensorFlow.js provides a function tf.memory() that can be used to track the number of tensors in memory. You can log this before and after predictions to see if tensors are being cleaned up.
  5. Code Example with Tensor Management:

javascriptCopy code

app.post('/prediction', async (req, res) => {
  const { input } = req.body;

  // Transform input here...

  try {
    const queryLoadModel = `SELECT data FROM models WHERE id = 2`;
    db.query(queryLoadModel, async (err, results) => {
      if (err) {
        // handle error
        return;
      }

      if (results.length === 0) {
        // handle empty results
        return;
      }

      // Assuming model loading and prediction happen here
      await tf.tidy(() => {
        // Load model, make prediction, and process results here
        // Ensure all tensors created in this block are disposed
      });

      res.status(200).send('Prediction successful');
    });
  } catch (error) {
    console.error('Error during prediction:', error);
    res.status(500).send('Error during prediction');
  }
});

In this example, tf.tidy() is used to ensure that all tensors created during the prediction process are disposed of once the block is executed. This should help in managing memory more effectively.

Remember, without seeing the complete code, especially the parts where TensorFlow.js is used for predictions, it’s challenging to pinpoint the exact issue. The suggestions above are based on common practices for managing tensors in TensorFlow.js.