In Jeff Hale’s 2018 composite ranking, TensorFlow led with a score of 96.77, followed by Keras at 51.55 and PyTorch at 22.72. Those figures measure a blend of popularity and interest signals—not model accuracy, training speed, or a framework’s technical “power.” They describe a particular collection of data and weighting choices from September 2018, not current adoption. Read Hale’s ranking.
What the 2018 power scores measure
Hale’s ranking combines 11 data sources across seven categories: online job listings, a KDnuggets usage survey, Google search volume, Medium articles, Amazon books, arXiv articles, and GitHub activity. The searches took place September 16–21, 2018; Hale updated the framework set on September 20 and described methodological improvements on September 21.
The scoring process scaled input features from zero to one, aggregated subcategories for job listings and GitHub activity, applied weights, multiplied weighted scores by 100, and summed the category contributions for each framework. Job listings and the KDnuggets survey together made up half of the total weight. Search, publishing, and GitHub-attention signals accounted for the other half. Hale explained that “100 is the highest possible score, indicating first place in every category.” A score of 96.77 therefore is not a percentage, a speed ratio, or a measure of technical quality.
Employment signals and reported use
Hale collected job-listing counts from LinkedIn, Indeed, Simply Hired, Monster, and Angel List, using searches that paired “machine learning” with a framework name. The survey asked respondents, “What Analytics, Big Data, Data Science, Machine Learning software you used in the past 12 months for a real project?” It was the only category based on international data; the other measures were more geographically limited. Job demand and self-reported use represent different kinds of evidence, and the ranking combines rather than resolves those differences.
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Search, publishing, and community attention
Google Trends supplied relative search interest, not absolute search counts. The remaining categories tracked mentions or activity in Medium articles, Amazon books, arXiv articles, and GitHub. In the Amazon category, Hale searched the Computers & Technology book category for each framework; that historical indicator does not establish current book availability.
Reported scores, from first to eleventh
| Rank | Framework | 2018 score |
|---|---|---|
| 1 | TensorFlow | 96.77 |
| 2 | Keras | 51.55 |
| 3 | PyTorch | 22.72 |
| 4 | Caffe | 17.15 |
| 5 | Theano | 12.02 |
| 6 | MXNet | 8.37 |
| 7 | Microsoft Cognitive Toolkit (CNTK) | 4.89 |
| 8 | Deeplearning4J | 3.65 |
| 9 | Caffe2 | 2.71 |
| 10 | Chainer | 1.18 |
| 11 | fast.ai | 1.06 |
These are the chart values reported in Hale’s 2018 article, not independently audited market shares. Hale interpreted TensorFlow as strongest in job listings, GitHub activity, Google searches, Medium articles, Amazon books, and arXiv articles. He described Keras as a clear second overall, with strong usage and beginner-oriented media signals, and its reported KDnuggets use as close to TensorFlow’s internationally. PyTorch placed third overall and second among standalone frameworks in Hale’s account.
Rank #2
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Why another 2018 comparison had a different winner
A ranking’s result depends on what it rewards. Joseph Szymborski’s separate 2018 Coveo comparison averaged three categories—support and community, API and internals, and platform—and named Apache MXNet the overall leader, followed by PyTorch and TensorFlow. Coveo credited MXNet’s portability and platform scores. It excluded Keras because its results depended on the backend selected, and cautioned that its score tiers were not standardized.
That result does not contradict Hale’s: the two analyses asked different questions and used different criteria. Hale combined indicators of interest, use, and employment demand; Coveo assessed a different set of framework qualities. If you are choosing a tool, first decide whether you need evidence about adoption, employment, ecosystem, ease of use, or performance on your own workload.
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How to compare framework performance fairly
A popularity score cannot tell you which framework trains a particular model fastest or most cheaply. A useful engineering comparison specifies the workload and records the model, dataset, implementation, framework version, configuration, hardware, accuracy target, runtime, memory use, and cost.
IBM Research’s 2018 paper emphasizes that a configuration effective for one framework or dataset may not work well for another, and calls for considering runtime and accuracy alongside interactions among hyperparameters and data. Read the IBM Research paper.
Rank #4
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Broader benchmark examples
- Microsoft Research TBD1: compared TensorFlow, MXNet, and CNTK across single-GPU, multi-GPU, and multi-machine configurations. It covered eight DNN models across six application areas. Read about TBD1.
- Stanford DAWNBench: presents end-to-end training time and cost alongside inference latency and cost. Its dated ResNet-50 submissions varied in hardware, cloud environment, and optimization, so a result needs its submission context. Explore DAWNBench.
- Stefan Braun’s 2018 LSTM study: compared PyTorch 0.4.0, TensorFlow 1.8.0, Lasagne 0.2.1, and Keras 2.1.6 in two speech-recognition scenarios. It used specified CUDA 9.0 and cuDNN variants where possible and tested Keras with TensorFlow and Theano backends. Its conclusions concern those LSTM implementations and scenarios, not every framework or workload. Read the LSTM study.
For an apples-to-apples test, hold the task and target accuracy constant, document the software and hardware setup, and report both time and resource cost. A framework name alone is not enough to explain a result.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What to take from the scores today
Hale’s ranking is useful as a historical snapshot of how frameworks compared across selected popularity, use, publishing, community, and job-market signals in September 2018. It is not a current adoption statistic, and no directly comparable current update to this weighted index is established by the cited sources. Treat its scores as a record of that methodology and moment—not as a present-day buying guide or performance leaderboard.
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