Latest release: 2018-04-01
Pythebo is a variant of Python specifically performance boosted for deep learning and blockchain computations, by sacrificing some computational precision. While this is in appropriate for many applications, deep learning neural networks and blockchain ledgers are naturally resistent to computational errors, and the improved performance results in a significant net gain. Given Python's popularity in AI research, we expect Pythebo to accelerate the path to general AI by several years. Moreover, since the total blockchain energy consumption corresponds to the carbon footprint of a small country, and 7% of blockchain ledger verifications use Python, changing to Pythebo for blockchain ledger verification could save energy consumption equivalent to that of the city of Amsterdam.
Pythebo gains significant performance on multithreaded machines by disabling the global interpreter lock (GIL). Disabling the GIL enables the Python interpreter to use multiple cores at the cost of sacrificing computational precision. Since parallel data structures are not protected by the GIL, there is a slight risk of incorrect computations. Due to the fuzzy nature and iterative training of deep learning neural networks, this is an acceptable risk, which can be compensated by extra training rounds. On multicore machines, the released parallelism more than compensates the risk, resulting in significantly shorter training cycles. Incorrect computations are corrected by later training rounds, and the resulting model is only flawed if there is an unlikely miscomputation in the last training round. We have observed close to linear speedup on the most common machine learning models, with < 0.1% loss of model precision.
Pythebo is also applicable for blockchain computations, such as Bitcoin mining. In this case, the computation is invalid if there is a miscomputation. Blockchain ledgers are verified thousands of times by different implementations, however, so an incorrect computation will be corrected by other machines that repeat the computation. Again, the increased speed in computation results in a significant net gain in blockchain computational power.
Initial measurements indicate consistent speedups for many machine learning algorithms, as seen in the table below. The numbers were measured on an 8-core machine with 64 GB memory.
| Benchmark | Speedup | Precision loss |
|---|---|---|
| Neural, 1 hidden layer |
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| Deep learn, 3 x 20 nodes |
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| Deep learn, 5 x 40 nodes |
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| Deep learn, 7 x 80 nodes |
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| Convolutional, 5 x 40 + 20 |
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| Adversarial, 2 x 3 x 20 |
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| Deep reinforcement, 5 x 40 |
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The superlinear speedup of the convolutional network baffled us, and we have not fully understood the cause. It seems like the convolutional structure turns one thread into an efficient cache prefetcher and execution predictor for the other threads, causing execution to run more than eight times faster on an 8 core processor.
In the reinforcement learning benchmark, we ran a reinforcement based chess player implementation, with a deep learning model for estimating game position. The precision loss presented in the table is the increase in lost games versus an unmodified Python implementation, given that the amount of computation between moves is limited by search tree depth. When we limited the computation on time, Pythebo players won over Python players with a 18.2% margin.
Pythebo is a modified fork of the Python trunk, and fully compatible with Python 3.6. We have not been able to get Canonical and RedHat to include a prebuilt distribution in repositories, however, since they are concerned that it will be used for security-sensitive applications by accident. You therefore have to build from source according to the standard Python instructions below. Clone this repository and checkout the tag 2018-04-01 in order to get the latest release version.
Applications where performance gains are valuable, and an approximate answer is sufficient. Most machine learning applications fall into this category, but also others, such as physics simulations. If the end result quality is important, you can use Pythebo for explorative work, but we recommend validating the final result with standard Python as a safety measure.
Pythebo is best suited for deep learning neural networks, due to the high natural parallelism. Most machine learning techniques can be parallelised, and therefore benefit from Pythebo. We recommend avoiding Pythebo for some recurrent neural networks, such as Boltzmann machines. This is due to the increased risk of falling out of found global minima points, which can be expensive to find again.
Pie-THEE-bow
While some expect the robotic future to be dystopian, we are optimistic and expect a better world powered by AI features. In case the pessimists are right, Pythebo will actually give us a head warning, since killer robots will arrive earlier, but their aim will be worse, so the human race has an overall higher chance of survival.
There are no technical factors that can destabilise the crypto currency markets further beyond their current state.
The patch disabling the GIL is very simple. We suggest that you review it at https://github.com/mapflat/pythebo/commit/259d93f56ce2d202008523342d842d92727c906a before using Pythebo, and convince yourself about its correctness.
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