This interview transcription, translated by Wenxuan Li, is cross-posted from www.jiqizhixin.com. The original Chinese post can be found here.

During this interview, Yuan Tang talked about his experience with open-source. Further, he mentioned his opinions and thoughts on open-source community and deep learning projects.

Thoughts on Open-source

Synced: We understand that you took part in TensorFlow, XGBoost, MXNet and other software open-source projects. Meanwhile you are the author of Scikit Flow, ggfortify, and metric-learn. Could you please introduce some of your favorite projects and talk about why you like them the most?

Yuan Tang: Actually I’m a big fan of all the projects that I was part of. I managed to learn a lot from those projects and meet a lot of interesting people during the time. Today I’d like to talk about Scikit Flow, a project that I have been actively spending time on. It is now part of TF.Learn module in TensorFlow.contrib. Illia Polosukhin from Google and I first started this project. After being part of TensorFlow, this project was being invested, used, and enhanced by TensorFlow team by incorporating it into many areas of Google’s research projects and products. The purpose of this project is to lower the barrier of using TensorFlow and deep learning, just like how we would use Scikit-learn in Python to quickly build personal machine learning and deep learning models. Through Scikit Flow, true advantages of TensorFlow’s distributed support could be revealed and utilized. For instance, users could access Random Forests, Deep Neural Networks and other algorithms by typing only a few lines of code, and could be easily distributed across multiple devices and clusters. However, these could be achieved only when users have thorough understanding of lower level TensorFlow APIs. I think it is not necessary for individuals working in data science field to spend time on the details. Rather they could quickly use and incorporate TensorFlow into their research and study. Recent trends lead to easier and accessible software interface. Softwares like TensorFlow require users to spend time learning unique algorithms and detailed usages provided by the software. In my opinion, wonderful things such as TensorFlow should have simpler and easier way to be accessed and studied. For similar reasons, ggfortify was created to help users get quick and easy access to R’s plotting functionalities. We used to have repetitive code to achieve identical purposes, such as plotting eclipses for clustering results and visualizing results from different R packages. What ggfortify offers is that users are able to quickly visualize popular R packages’ results, and to avoid spending time on learning unique algorithms of each package only to get a common visualization. It is my hope to see more and more packages like ggfortify so that users could skip the complex learning process and focus only on the core of their research and study, and more substantial breakthrough would be achieved.

Synced: Could you please talk about what led you to become an active contributor for the open-source community?

Yuan Tang: When I was a senior in college, I worked as an intern at a startup, which had a very open policy regarding open-source. I was able to get to know all kinds of open-source softwares, but through the process I noticed that users would have complaints regarding the softwares they were using. Even though they could submit issues and bug reports on Github, the project maintainers were too busy to deal with those. In that case, I learned to study the source code and submit revisions to fix the issues. Later on it became something I would naturally do. When I came across some problems, I would dig into and manage to solve the problems myself instead of just submitting reports and leaving them to someone else. Through that process I got to practice and improve my code reading skills. From time to time, I was able to come up with interesting ideas to improve the projects.

Synced: What makes you so passionate about the open-source community?

Yuan Tang: Since I became a contributor in the community, I have received help and guidance from others and learned a lot. I have also met individuals that share same goals with me. In return, I hope I could contribute back to the community. I believe that the more I contribute, the more I would receive back from the community. At first I would try to fix the codes only because my job requires me to do so. However, later I made a habit of trying to solve the problem from its source. By doing so, I’m more interested in the architecture of the softwares and in how a particular function is implemented. This whole process has been proved to be very beneficial. For example, when I first learned Python, I had no actual experience with any projects. Individuals who work in data science field should be very familiar with all sorts of open-source software, because these include important functionalities the users would need. When I tried to study the details of the project, I got to know more about certain programming languages and technologies used behind the scenes. Open-source contributors would constantly maintain and review newly contributed codes. They would carefully read every line of the code I submit and give insightful comments, which also encourage me to write better code.

Synced: You are very active on Github, which is most popular platforms for version control open-source projects. Students in related fields would even save their homework and projects on Github. However, Github is still something rather new to individuals in China who have only heard about open-source and downloaded open-source packages but never have tried to contribute back to open-source projects. Could you talk about how the open-source projects on Github work and what open-source culture is like?

Yuan Tang: Most of open-source projects are developed on Github. Everyone should try to have hands-on experience with Github. Through discussion on issues tab on Github, people would come up with interesting ideas to improve and extend the existing projects. Then they will submit pull requests for project managers to review code. Python’s pandas, which has an active open-source community and helpful project managers, was the very first open-source projects that I committed. Every issue would be labeled with difficulty levels and whether first time committers are welcomed. In that case, the contributors could start with easy tasks and move on to more difficult ones. It took a week for my first contribution to be accepted. Project maintainers carefully read my code and gave detailed comments, making sure that my commits won’t affect the current users. As I mentioned before, I have learned a lot from the process and got to know many friends who share same interests and goals with me.

Synced: As a team lead at Uptake, you also work as an active contributor in the open-source community. Could you please talk about the advantages and disadvantages of having open-source projects from the perspectives of both role you are playing?

Yuan Tang: While I have been working on these open-source projects, I deeply feel the passion and creativity of the community. Take XGBoost as an example: if someone uses XGBoost to get a good place in a competition, the project itself will be known and recognized by the public, and more people would be willing to try to contribute to the project. Some of them would ever apply it to their companies’ projects. In that way, this project could be better tested by more users. Bug report and feature requests would further help us understand users’ needs and improve the projects. Almost all the conversations through the process are transparent and open to the public. One advantage is that even a minor mistake would be found and corrected by one in the community, let alone more progress and improvement made by the whole community. To a company, having open-source projects for the public implies that everyone would know the algorithms this company is using, meaning more competition among companies and entities. Information security could be another issue. However, I believe that companies which have decided to publish open-source projects would have already prepared for security issues and already have solutions for the potential problems.

Synced: Usually what kind of projects would become open-source projects? What does it mean to be an open-source project?

Yuan Tang: It depends on the situation. One case would be when some companies decide to publish codes they have been developing for years. The reason behind this case could be outdated codes or sudden leave of employees who used to work on the code. As a result, there is no need to keep the codes as secrets and spend more resources in maintaining and testing the codes. Then the companies decide to let the open-source community to take over the job. In another case, brand new projects could become open-source projects because company wants to lower the cost and receive more comprehensive feedbacks from the public. It is also possible that a company regards an open-source project as an strategic plan to let other competitors or users to know that it is the experts in certain field. Different company has different project maintenance strategies and purposes. Once a project become an open-source project, it is implied that all the associated code, algorithms, and implementations details are completely transparent. It also means that all the competitors would learn more details about the company that created the project. To the developers whose projects become open-source, many other individuals would know their efforts and contribution to the projects.

Synced: Some individuals think that open-source means free work and free contribution, which conflicts with profitable entities. What do you think?

Yuan Tang: Open-source, to a company, is usually a strategic plan, and it does not conflict with profitability of the company. As I mentioned earlier, an open-source project would help the company save costs in maintaining project and searching for talented employees, since open-source project help the company identify those who are capable of improving the project. When the potential employees join the company, less costs would be spent on training.

Synced: Some others think that companies like Google and Facebook intend to use open-source projects as an act of monopoly. Do you think this is something we should worry about? How should we react to their decisions?

Yuan Tang: I don’t think this is something we need to worry about. Technology renews itself in a rapid speed. Open-source projects that are popular today might not gain as much attention tomorrow. DeepLeaning4J is a perfect example. Because of competitors such as MXNet’s Scala, which have better functionality and greater DMLC team members to work on maintenance, DeepLearning4J gradually loses its competiveness and advantages. Competition drives one another to learn from the mistakes and thus make their own products better. Therefore I won’t be worrying about monopoly. What we could do is to use software we like to meet the need of research or work while staying in the track of innovation and progress of the broader society.

Synced: Google, Facebook, and OpenAI have created many open-source projects. The company you work at is not an exception. In your opinion, what are the impacts of open-source on AI technology and community?

Yuan Tang: Open-source not only assists researchers with sharing their research progress and results, but also help AI research results to be more reproducibility. Take TF.Learn as an example, Google recently published a paper that demonstrates a novel algorithm that uses this software so they also included the implementation of this novel algorithm inside TF.Learn as one of the Estimators In this case, individuals from other fields of study could apply TF.Learn into their research and work or reproduce the research results in their publications. Open-source projects from the companies you mentioned provide more learning resources and tools for work, research, and study of individuals. These projects also help people from across the world to learn from one another and communicate.

Synced: You recently received Open Source Peer Award from Google. Could you please talk about this award and the importance of this award to you? What is your plan after receiving this award?

Yuan Tang: As one of the individuals who received this award, I was nominated by Google employees who knew the importance of the work I have done with this project. Then I was lucky enough to be able to stay on the award-wining list after discussion and examination of management team at Google Open-source Office. This award implies Google’s affirmation to the contributions I have made to the project. It is also encouraging for me to receive this award. My plan is to continually make contribution to this community, including maintaining projects, and helping others solve problems and issues they encounter. Many times I would discover interesting ideas and thoughts on Github Issues and StackOverflow, and thus I would be inspired to have new and innovative ideas. Also I think there is a lot we can do to contribute to this open-source community. My personal interests include projects that help others’ work to be more efficient and less repetitive.

About DMLC

Synced: In your opinion, what is DMLC? What led you to DMLC?

Yuan Tang: In a few words, DMLC was created to better serve individuals to apply new algorithms and technology into their own research and work. We intend to help those who are interested save time in repeating the process but rather directly apply it into their work and research. In that way, they can focus on the breakthrough of their own work instead of on learning algorithms and techniques that they are not that good at. We think that good things should be shared with everyone. At the beginning, I worked on improving and extending DMLC’s XGBoost project, including improving Python’s packages. I learned the need for improvement from users in Kaggle data science competition. Some of the DMLC members actively contribute in the discussion at Kaggle in order to meet the need of XGBoost users. Later on I worked on the creation of MXNet’s Scala binding.

Synced: Someone on Zhihu mentioned that DMLC was created to provide a more applicable API for Python. Do you agree with this? If not, what do you think is the main purpose of creating DMLC?

Yuan Tang: I think Python API would offer many individuals a good start in using Python. Python is very popular in data science and machine learning areas. It also has many users and a very active open-source community. Many researchers in deep learning area are users of Python, especially who work on photo processing and text processing areas. However, Python API does not meet all the needs of users. Many individuals in social science and life science areas often use R and Julia, while others in industrial world use JVM, including Java and Scala. Therefore we would spend time on the development of other languages, such as my focus on Scala API. I think it is very difficult for one product to meet needs from many programming languages. We would use Python API as an experiment to see if users would like it that way.

Synced: As deep learning becomes more popular, people tend to compare the frameworks with one another. Do you think this is a good sign?

Yuan Tang: I think this is a positive thing because competition drives improvement. By developing unique frameworks, we would be able to better learn, research, and communicate. Tianqi Chen from DMLC created NNVM, which is a very valuable and lightweight module that implements most of the computational graph construction and optimization used very often in deep learning systems such as TensorFlow and MXNet. I’m very happy to see many individuals contribute their own research projects to the open-source community. It saves a lot of time for people in research and industries. What’s even more wonderful is how people would see the advantages and disadvantages of their frameworks. Meanwhile it is very challenging to have absolute fair comparison because of limited conditions, prerequisites, resources and of course, forever changing environment. I hope we would strive to have fair comparison instead of only aiming for attracting more users.

Synced: As influential companies such as Google and Facebook, their TensorFlow and Torch7 projects seem to be more popular than other equivalent ones. Do you think it is the result of resource distribution? What do you think about the opinions of supporting or disapproving TensorFlow?

Yuan Tang: Distribution of resources does have impacts on popularity of open-source projects, but it is not the end of day for projects with less resources. I think time would test every open-source projects, and those are truly good would survive the trials. Users with different backgrounds and needs would comprehensively test different frameworks from all sorts of projects, and every framework stands for a chance to be discovered and welcomed by the users. I think we can focus on understanding needs, considering opportunity costs of learning, and finding the ones that fit ourselves the best instead of worrying about the impacts of resource distribution. Some do not like learning new programming language only for using Torch7. Some statisticians would directly use MXNet if there are familiar with R.

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