IBM’s Machine Learning Library is 46 Times Faster than TensorFlow!
- IBM’s new Machine Learning library ran a logistic regression model 46 times faster than TensorFlow
- The model was trained on 4.2 billion examples and 1 million variables
- Google’s TensorFlow took 70 minutes, IBM’s library took 91.5 seconds
The race to become the quickest and most efficient library is now in full flight. IBM claims that performing machine learning tasks on it’s POWER servers is an incredible 46 times quicker than on TensorFlow used in Google Cloud.
Earlier this year, a Google software engineer wrote a blog post on how they used Google Cloud Machine Learning and TensorFlow for solving click prediction problems. They trained their deep neural network model “to predict display ad clicks on Criteo Labs clicks logs. These logs are over 1TB in size and include feature values and click feedback from millions of display ads”.
For them, data preprocessing took about an hour, followed by training the model for 70 minutes. The evaluation loss was reported to be 0.13. They did manage to reduce this evaluation loss and get more accurate results but that was achieved at the cost of increasing training time.
But IBM blew those results out of the water. Their training algorithm, running on POWER9 servers and GPUs, outperformed Google Cloud Platform’s in the initial training phase.
The IBM researchers trained their model on the Criteo Labs click logs, the same data source used by Google earlier. It contains 4.2 billion training examples and 1 million variables. They trained it using logistic regression (again, the same technique used by Google). However, IBM used a different ML library – Snap Machine Learning.
IBM’s model completed the same logistic regression in 91.5 seconds! That’s a remarkable 46 times faster than Google’s previous attempt.
IBM posted the below comparisons between their Snap ML library and the other competitors:
You can read more about the Snap Machine Learning library in IBM’s research paper here.
Our take on this
A 46 times improvement over TensorFlow is truly impressive. Of course a point to be noted here is that these 2 models were not run on similar hardware configurations so we can’t validate IBM’s results until they publicly release more information.
Having said that, IBM has definitely caught the machine learning world’s attention and has given them an opportunity to introduce their POWER9 servers and the Snap ML library to the general public.
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