Capacity comparison:
TensorFlow vs PyTorch vs Neural Designer
By
Carlos Barranquero,
Artelnics.
Capacity means the maximum amount of data a computer program can analyze in data science and machine learning platforms.
In this post, we compare the load capacity of three machine learning platforms: TensorFlow, PyTorch and Neural Designer
for an approximation benchmark. These platforms are developed by
Google,
Facebook and
Artelnics, respectively.
As we will see, Neural Designer is able to load a dataset x1.8 larger than TensorFlow and PyTorch.
In this article, we provide all the steps that you need to reproduce the results using the free trial of Neural Designer.
Contents:
Introduction
The maximum amount of data a tool can analyze depends on different factors.
Some of the most important ones are the programming language in which it is written and how the memory usage works internally.
The following table summarizes the technical features of these tools that might impact their memory usage.
| TensorFlow | PyTorch | Neural Designer | |
|---|---|---|---|
| Written in | C++, CUDA, Python | C++, CUDA, Python | C++, CUDA |
| Interface | Python | Python | Graphical User Interface |
Even though C++ is at the core of the three platforms, their interfaces are different.
The most common use of TensorFlow and PyTorch is through a Python API. On the other hand, Neural Designer uses a C++ GUI.
As we will see, an application with a Python interface results in higher memory consumption, which means a lower capacity to load the data.
Benchmark application
To test the capacity of the three platforms, we will try to load different Rosenbrock datafiles, fixing the number of variables and changing the number of samples.
The following table shows the correspondence between the size and the number of Rosenbrock samples.
| Filename | Floating points number (x109) | Samples number | Size (Gb) |
|---|---|---|---|
| Rosenbrock_1000_variables_1000000_samples.csv | 1 | 1000000 | 22 |
| Rosenbrock_1000_variables_2000000_samples.csv | 2 | 2000000 | 44 |
| Rosenbrock_1000_variables_3000000_samples.csv | 3 | 3000000 | 65 |
| Rosenbrock_1000_variables_4000000_samples.csv | 4 | 4000000 | 86 |
| Rosenbrock_1000_variables_5000000_samples.csv | 5 | 5000000 | 107 |
| Rosenbrock_1000_variables_6000000_samples.csv | 6 | 6000000 | 128 |
| Rosenbrock_1000_variables_7000000_samples.csv | 7 | 7000000 | 149 |
| Rosenbrock_1000_variables_8000000_samples.csv | 8 | 8000000 | 171 |
| Rosenbrock_1000_variables_9000000_samples.csv | 9 | 9000000 | 192 |
| Rosenbrock_1000_variables_10000000_samples.csv | 10 | 10000000 | 213 |
To create these files check this article: The Rosenbrock Dataset Suite for benchmarking approximation algorithms and platforms.
The number of samples is the only parameter we change to perform the comparison tests. The other parameters are constant.
The next picture shows the benchmarks set up.
Data set |
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Neural network |
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Training strategy |
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We run the above benchmark for each platform (TensorFlow, PyTorch, and Neural Designer), increasing samples until the memory crashes.
We consider a successful test if it can load the CSV file and train the neural network.
Reference computer
The next step involves choosing the computer to train the neural networks with TensorFlow, PyTorch, and Neural Designer.
For a capacity test, the most crucial feature of the computer is its memory.
We have made all calculations on an Amazon Web Services instance (AWS).
In particular, we have chosen the r5.large. so that you can reproduce the results easily.
The next table lists some basic information about the computer used here.
| Operating system | Windows 10 Enterprise |
|---|---|
| Processor | Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz |
| Installed memory (RAM): | 16.0 GB |
| System type: | 64-bit Operating system, x64 based processor |
Once the computer is chosen, we install TensorFlow (2.1.0), PyTorch (1.7.0) and Neural Designer(5.0.0) on it.
Results
The last step is to run the benchmark application using TensorFlow, PyTorch, and Neural Designer.
Then, we compare the capacity results provided by those platforms.
The following table shows whether or not each platform can load the different data files.
The blue check means that the platform can load it and the orange cross means that it is not able.
| Floating points number (x109) |
TensorFlow | PyTorch | Neural Designer |
|---|---|---|---|
| 1 |  |
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| 9 | |
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As we can see, the maximum capacity of both TensorFlow and PyTorch is 5 x 109 data and the maximum capacity of Neural Designer is 9 x 109 data.
These results can also be depicted graphically.
From these results, we can conclude that Neural Designer is able to load a dataset x1.8 larger than TensorFlow and PyTorch.
The following picture shows that Neural Designer can train a neural network with 9 billion data points in a 16GB RAM computer.
The following picture shows how TensorFlow runs out of memory when trying to load a data file containing 6 billion data.
As we can see, when the external module Python pandas in TensorFlow tries to load 6 billion data, the platform crashes due to lack of RAM. TensorFlow's maximum capacity is 5 billion data.
The following picture shows how PyTorch also runs out of memory when loading a data file containing 6 billion data.
Again, when the external module Python pandas in PyTorch tries to load 6 billion data, it crashes. PyTorch's maximum capacity is 5 billion data.
Conclusions
The maximum capacity in both TensorFlow and PyTorch is 5 billion data, and the maximum capacity in Neural Designer is 9 billion.
This difference is because TensorFlow and PyTorch use an external module (Python pandas) to load data.
In contrast, Neural Designer uses its function to load data, which offers an advantage.
Indeed, Python is a high-level programming language.
However, this causes a lower capacity for Python tools to load data.
To reproduce these results, download the free trial of Neural Designer and follow the steps described in this article.
Related posts:
- How to benchmark the performance of machine learning platforms .
- The Rosenbrock Dataset Suite for benchmarking approximation algorithms and platforms.
- Performance comparison of dense networks in GPU to Neural Designer.
- Training accuracy comparison of machine learning platforms: TensorFlow vs. PyTorch vs. Neural Designer.
