Kosuke Nakago

2019-07-16 11:59:50

This post is contributed by Mr. Kaushalya Madhawa, who was an intern and a part-time engineer at PFN. Japanese version is available here.

In this post we introduce our recent paper “GraphNVP: An Invertible Flow Model for Generating Molecular Graphs“. Our code can be accessed from Github repo.

Molecule Generation

Discovery of new molecules with desirable pharmacological properties is a crucial problem in computational drug discovery. Traditionally, this task is performed by clinically synthesizing candidate chemical compounds and running experiments over them. However, due to the sheer size of chemical space, synthesizing molecules and extensively performing experiments on them is an extremely time consuming task. Instead of searching through the space of molecules with desirable properties, de novo drug design involves designing new chemical compounds with the properties that we are interested in.

Recent advancements in deep learning, especially deep generative models proved to be invaluable in de novo drug designing.

The choice of molecule representation

An important step in the application of deep learning on molecule generation is how chemical compounds are represented. Earlier models relied on a string-based representation named SMILES. RNN-based language models or Variational Autoencoders (VAE) are used to generate SMILES strings which are then converted to molecules. A major issue in using SMILES strings is that they are not robust to minor changes of a string, resulting in drastically different molecules although the corresponding SMILES strings are almost similar. These problems prompted recent researches to rely on more expressive graph representations of molecules. Therefore, this problem became to known as molecular graph generation.

A molecule is represented by an undirected graph, in which the atoms and bonds are represented nodes and edges respectively. The structure of a molecule is represented by an adjacency tensor \(A \) and a node feature matrix \(X\) is used to represent the type of atoms (e.g., Oxygen, Fluorine etc.). The molecule generation problem reduces to generation of graphs which can represent valid molecules. This is a problem in which deep generative models such as GANs or VAEs can be leveraged. We can classify previous work into two categories based on how they generate a graph. Some models generate molecular graphs sequentially such that nodes (atoms) and edges (bonds) are added in a step-by-step fashion. The alternative is straightforward, generate a graph in a single step in a similar manner to image generation models.

The importance of reversibility

A significant advantage of the invertible flow-based models is they perform precise likelihood maximization, unlike VAEs or GANs. We believe precise optimization is crucial in molecule generation for drugs as they are highly sensitive to a minor replacement of a single atom (node). An additional advantage of flow models is that, since they are invertible by design, perfect reconstruction is guaranteed and no time-consuming procedures are needed. Simply running the reverse step of the model on a latent vector results in a molecular graph. Moreover, the lack of an encoder in GAN models makes it challenging to manipulate the sample generation. For example, it is not straightforward to use a GAN model to generate molecules that are similar to a query molecule (e.g., lead optimization for drug discovery), while it is easy for flow-based models.

Our model

GraphNVP, our proposed model is shown above. GraphNVP is the first graph generation model based on the invertible flow which follows one-shot generation strategy. We introduce two latent representations, one for node assignments and another for the adjacency tensor, to capture the unknown distributions of the graph structure and its node assignments respectively. We use two new types of coupling layers: Adjacency Coupling and Node Feature Coupling for obtaining these two latent representations. During graph generation, first we generate an adjacency tensor and then the node feature tensor is generated using graph convolutional networks.

Qualitative results

We randomly select a molecule from the training set and encode it into a latent vector \(z_0\) using our proposed model. Then we choose two random axes which are orthogonal to each other. We decode latent points lying on a 2-dimensional grid spanned by those two axes and with \(z_0\) as the origin. The visualization below indicates that the learned latent space is smooth such that neighboring latent points correspond to molecules with minor variations.


Comments from mentors

We, Nakago and Ishiguro, were responsible for mentor of Kaushalya. We started this research from 2018 summer internship. The research of deep graph generative models are getting attention, and many kinds of models are suggested at that time. However the model with Flow was still not suggested, and we started this research based on suggestion from Kaushalya.

It is first time application for graph generation, and model tend to need deeper layers for neural network with flow which requires large computation resource. It took some time to complete the research, but it was glad that we could publish the paper as well as the code finally.

Many projects are running in PFN, not only in “Drug Discovery / Material Discovery” but also in various kinds of fields. Please check our job list if you get interested!

Git Ghost: A command line tool to execute a program with local modifications without losing reproducibility

Daisuke Taniwaki

2019-05-13 08:00:17


We’re happy to open-source Git Ghost, which is developed by Shingo Omura and Daisuke Taniwaki. By using this tool, you can run ML jobs of your git-managed code with locally made modifications without losing reproducibility. You can go back to the code of a specific run anytime during the trial-and-error phase!


Running one ML job for trial-and-error while waiting for other jobs is a very common use case. Before Git Ghost, the simplest way to do it was managing source code with git and using rsync to synchronize our source code with locally made modifications to run ML jobs in our Kubernetes cluster. Then, we realized we often want to revert the code back to a state when we got good results. However, although git-managed code provides versioning of your code, synchronizing code with rsync breaks this versioning because it does not make any versioning of the synchronized code, so it was hard to get back such code.

One idea we came up with first was just to commit local modifications and push it to a remote. However, it’s cumbersome to commit and push many times just to run a job with a modification of a few characters and of course, you don’t want to get your remote repository dirty. So we came up with the idea of this tool.


Assume you want to send a modification of content change from a to b on a file foo in your local machine to a directory in a remote server.

First, create a patch of the local modification.

$ git ghost push
xxxxxxx yyyyyyy
$ git ghost show yyyyyyy
diff --git a/foo b/foo
index 7898192..6178079 100644
--- a/foo
+++ b/foo
@@ -1 +1 @@

Then, you can sync the local modification in a remote server.

$ git ghost diff HEAD
$ git ghost pull yyyyyyy
$ git ghost diff HEAD
diff --git a/foo b/foo
index 7898192..6178079 100644
--- a/foo
+++ b/foo
@@ -1 +1 @@

There you go! You can see that the modifications in your local machine were synchronized to the remote server.

Although Git Ghost is a very simple tool as shown above, it performs brilliantly when it is integrated with other tools. For example, you can send modifications into a Kaniko container to build Docker images with local modifications. Here’s an example using Argo to execute a job with local modifications in a reproducible manner.


The idea is simple. The tool creates a patch with your locally made commits and modifications with the information of a base commit existing in your remote repository and pushes it to another remote repository. Then, it downloads the base commit in a remote place and applies the patch. A small trick here is we separated patches of locally made commits and locally made modifications because with this separation, locally made modifications can be reused even after locally made commits are pushed to the remote repository.

The reason why we chose a git repository for the patch storage is that it doesn’t require extra tools and credentials.

Although we’re going to use this tool in a Kubernetes cluster, we believe using this tool is not limited to Kubernetes clusters. You can use it to send changes from your laptop to an on-premise server if you want to track changes.

Please try it and give us your feedback on GitHub!

k8s-cluster-simulator: A simulator for evaluating Kubernetes schedulers

Daisuke Taniwaki

2019-04-11 08:00:53


We’re happy to release an open source, Kubernetes cluster simulator, called k8s-cluster-simulator.  The simulator is in the alpha release, and was created by Hidehito Yabuuchi, a PFN internship student in 2018 and part-time employee, along with his mentors, Daisuke Taniwaki and Shingo Omura. This simulator simulates workloads of a Kubernetes cluster and time clock so you can evaluate your Kubernetes scheduler without actually deploying it in the production site.


We have large on-premise GPU clusters, in which researchers run ML jobs of various running duration via Kubernetes. One of our goals is to maximize the utilization of the GPUs for cost-effectiveness while enabling all researchers to have reasonable access. To do this, we developed our own private Kubernetes scheduler and extender (e.g. kube-throttler). However, it’s hard to evaluate new logic in production, because researchers are running jobs, and we should not change the scheduling logic and fairness so often. Of course, we cannot deploy a buggy scheduler that stops the researchers work. Moreover, it is not desirable to stop research to test new scheduling logic in large clusters. Therefore, we started to develop a scheduler simulator for Kubernetes.


We believe the simulator should have the following properties.

  • Require as few changes on scheduler’s implementation and interface as possible.
  • Simulate clock time to accelerate evaluations and also evaluate scheduling logics without being affected by system latencies such as network and internal processes.
  • Simulate workloads as flexibly as possible.
  • Support various output formats for further analysis.


Here’s the simple flow diagram.

The idea is simple. The simulator simulates clocks and ticks the simulated clock at each step of the loop. At each step, the simulator asks submitters if they have pods which should be submitted or deleted in this clock, and schedule the submitted pods to scheduler. Scheduler returns bind and delete events so the simulator can simulate the resource management. Finally, the simulator writes metrics of simulation by metrics loggers.

And here’s the high-level class diagram.

We provide the following two points of customizations for scheduling simulations.


Multiple users can be simulated by adding any number and combination of submitters, with time and number of pods submitted fully customizable through the simulator interface. For example, assume user A tends to submit more pods in the morning and user B tends to submit more pods in the evening. A submitter can be created for each user and plugged into the simulator.
Moreover, as submitters receive metrics from the simulator, they can change behaviors based on the state of a cluster, such as crowded or not.


You have two options for scheduler extensions, depending on the style of Kubernetes scheduler customization. The first scheduler extension mimics the normal Kubernetes scheduler (kube-scheduler), and can be extended with Prioritizer, Extender and Predicate. If you customize your scheduling logic by these kube-scheduler extension points, this is the best approach. As Kubernetes scheduler is a queue-based scheduler, you may want to implement more complicated scheduling logic that doesn’t fit a queue based scheduler, for example, scheduling a new set of pods immediately after receiving multiple pod submissions. For this case, we provide an option to evaluate a scheduler with the interface defined in Kubernetes with a thin wrapper function.


We’re implementing the following features before the beta phase to support more realistic cluster environments simulations.

  • More isolation between components (e.g. supporting RPC interface for a scheduler and submitter)
  • Provide common submitter implementations (e.g. typical probabilistic distributions(Uniform, Binomial, Poisson, etc.))
  • Support various cluster events (node failures, accidental pods failures, node addition/removal, etc.)
  • Support plottable output formats in popular plotter tools (matplotlib, gnuplot etc.)

Please try it out! We’re waiting for your feedback!

ChainerRL Visualizer: Deep RL Agent Visualization Library


2019-03-19 12:40:46

This post is contributed by Mr. Takahiro Ishikawa, who was an intern last year and now works as a part-time engineer at PFN.

We have released ChainerRL Visualizer, which visualizes the behaviors of agents trained by ChainerRL on the Web browser.

My name is Takahiro Ishikawa, who participated in PFN 2018 internship and currently work as a part-time engineer.

This library is developed in the aim of “making debugging of deep RL implementations easier” and “contributing to understanding of how deep RL agents work”.
It enables to interactively observe the behaviors of trained deep RL agents on the Web browser.
This library is easy to use. All you have to do is to pass the `agent` object implemented in ChainerRL and the `env` object that satisfies a specific interface to the `launch visualizer` function provided by this library, along with a few options.

from chainerrl_visualizer import launch_visualizer

# Prepare agent and env object here

# Prepare dictionary which explains meanings of each action
  0: 'hoge',
  1: 'fuga',

    agent,                           # required
    env,                             # required
    ACTION_MEANINGS,                 # required
    port=5002,                       # optional (default: 5002)
    log_dir='log_space',             # optional (default: 'log_space')
    raw_image_input=False,           # optional (default: False)
    contains_rnn=False,              # optional (default: False)

After executing this script, a local Web server will be launched and the following features will be provided on the Web browser.

1. Roll-out one episode (or specified steps)

You can tell the agent to run one episode (or specified steps) from the UI, then the outputs of the agent model will be visualized in chronological order.
In the following video, the probabilities of the next action and the state value of the agent trained with A3C are visualized.

2. Tick timestep and visualize the behaviors of environment and agent

In the following video, the agent can be moved back and forth and the outputs in each step are visualized along with the behavior of the environment. The pie chart on bottom-right shows the probabilities of the next action of each step.

3. Saliency map

If the input of the model is raw pixels, the UI can visualize saliency map, which shows the specific sub-area to which the agent pays attention. This feature is implemented based on the paper Visualizing and Understanding Atari Agents.
In the following video, saliency maps of the agent trained with CategoricalDQN are visualized over the image of the environment.
For now, this feature allows us to specify the number of steps for which saliency maps are created because the computational cost of creating saliency maps is very expensive.

4. Miscellaneous visualizations

Various ways of visualization for each type of agent are supported.
For example, the value distributions of the agent trained with CategoricalDQN are visualized in the following video.

Quickstart guides are provided.

For now, almost all of the visualization tools in deep learning have focused on visualizing scores and other metrics along with the progress of the model training. ChainerRL Visualizer is the original visualizer among them as it can interactively and dynamically visualize the behaviors of the deep RL agent and environment themselves.

Atari Zoo, which was released by Uber Research, is developed for a similar purpose. Atari Zoo aims at accelerating research for understanding deep RL agent, providing trained models, and analyzing tools for the frozen models. It enables researchers to participate in the research for understanding deep RL agent even if they don’t have enough computing resources.

ChainerRL Visualizer is different from Atari Zoo in the sense that “all” kinds of agents in ChainerRL can be dynamically analyzed during training by the visualizer while Atari Zoo is only for visualizing already-trained models in the repository and those models are limited to the ALE environments and specific algorithms where the architecture looks like ( raw image => Conv => Conv .. => FC .. ).

There are also other visualization tools with similar motivations, such as DQNViz that visualizes the behaviors of DQN agents and its various metrics during the training.

Though much effort has been dedicated to improve the performance of deep RL algorithms on benchmark tasks, less effort has been paid for understanding what deep RL agents learn by deep RL algorithm, and analyzing how the trained agents behave. However, the research for understanding deep RL agent as seen in the visualizations above will expand in the future.

ChainerRL Visualizer is now in beta version and still does not have sufficient features for deeply analyzing deep RL agents. So continuous development is needed in order to contribute to the emerging research area of understanding deep RL agent. We welcome you to participate in the development of ChainerRL Visualizer to add new features or to improve existing features through OSS collaboration.

Experimental toolchain to compile and run Chainer models

Shinichiro Hamaji

2019-01-25 16:19:34

Hello, my name is Shinichiro Hamaji, an engineer at Preferred Networks. I would like to introduce an experimental project named Chainer-compiler today. Although not ready for end users, we are making it publicly available with the hope that others may find it interesting or useful for research purposes in its current state.

Late last year, Preferred Networks release a beta version of ChainerX. The three goals of ChainerX were

  • Optimize the speed of running models
  • Make models deployable to environment without Python
  • Make it easier to port models to non-CPU/GPU environments

while keeping the flexibility of Chainer. The goal of chainer-compiler is to go further with ChainerX. Currently, it has the following three main components:

  1. Translate Python AST to extract computation graphs as extended ONNX format.
  2. Modify the extended ONNX graph for optimization, auto-differentiation, etc. It then generates deployable code.
  3. Run the exported code with ChainerX’s C++ API.

Here are expected use-cases of this project:

  • Unlike the imperative model of Chainer/CuPy/ChainerX (a.k.a. define-by-run), the first step extracts a computation graph with multiple operations so that it gives a chance to apply inter-operation optimization techniques such as operation fusion.
  • By running the step 1 and 2 on the host machine and deploying only 3, you can easily deploy your model to Python-free environments.
  • If you add targets of the code generator, your model can run with/on optimized model executor or domain specific chips like MN-Core.
  • By using the step 2 and 3, you can run ONNX models generated by other tools such as ONNX-chainer.

Other than the above, we would like to continue conducting experimental research.

Like other areas around deep learning, many people are competing for deep learning compilers. They have different strengths and focuses, which makes research on a deep learning compiler very interesting, in my opinion. In this project, we are trying not to hurt the flexibility of Chainer. For that reason, the toolchain does not assume that the model is static and can handle tensors without static dimensions, control-flow primitives of Python, and Python lists. This could be one of the unique strengths of this toolchain.

In this article, we have introduced chainer-compiler, an experimental project which compiles and runs Chainer models. We still have a huge number of TODOs but they are challenging and fun to work on. If you are interested in working with us, please consider applying to Preferred Networks. Any questions or feedbacks are really appreciated.

Lastly, I would like to thank everyone who helped us. I especially would like to thank Sato-san, an intern who realized the Python code to ONNX compiler.

Optuna: An Automatic Hyperparameter Optimization Framework

Takuya Akiba

2018-12-03 14:00:28

Preferred Networks has released a beta version of an open-source, automatic hyperparameter optimization framework called Optuna. In this blog, we will introduce the motivation behind the development of Optuna as well as its features.




What is a hyperparameter?

A hyperparameter is a parameter to control how a machine learning algorithm behaves. In deep learning, the learning rate, batch size, and number of training iterations are hyperparameters. Hyperparameters also include the numbers of neural network layers and channels. They are not, however, just numerical values. Things like whether to use Momentum SGD or Adam in training are also regarded as hyperparameters.

It is almost impossible to make a machine learning algorithm do the job without tuning hyperparameters. The number of hyperparameters tends to be high, especially in deep learning, and it is believed that performance largely depends on how we tune them. Most researchers and engineers that use deep learning technology manually tune these hyperparameters and spend a significant amount of their time doing so.

What is Optuna?

Optuna is a software framework for automating the optimization process of these hyperparameters. It automatically searches for and finds optimal hyperparameter values by trial and error for excellent performance. Currently, the software can be used in Python.

Optuna uses a history record of trials to determine which hyperparameter values to try next. Using this data, it estimates a promising area and tries values in that area. Optuna then estimates an even more promising region based on the new result. It repeats this process using the history data of trials completed thus far. Specifically, it employs a Bayesian optimization algorithm called Tree-structured Parzen Estimator.

What is its relationship with Chainer?

Chainer is a deep learning framework and Optuna is an automatic hyperparameter optimization framework. To optimize hyperparameters for training a neural network using Chainer, the user needs to write a code for receiving hyperparameters from Optuna within the Chainer code. Given this code, Optuna repeatedly calls the user code, and the neural network is trained with different hyperparameter values until it finds good values.

Optuna is being used with Chainer in most of the use cases at PFN, but this does not mean Optuna and Chainer are closely connected with each other.  Users can use Optuna with other machine learning software as well. We have prepared some sample codes that use scikit-learn, XGBoost, and LightGBM as well as Chainer. In fact, Optuna can cover a broad range of use cases beyond machine learning, like acceleration, providing an interface that receives hyperparameters and returns evaluation values, for instance.

Why did PFN develop Optuna?

Why did we develop Optuna even though there were already established automatic hyperparameter optimization frameworks like Hyperopt, Spearmint, and SMAC?

When we tried the existing alternatives, we found that they did not work or were unstable in some of our environments, and that the algorithms had lagged behind recent advances in hyperparameter optimization. We wanted a way to specify which hyperparameters should be tuned within the python code, instead of having to write separate code for the optimizer.

Key Features

Define-by-Run style API

Optuna provides a novel Define-by-Run style API that enables the user to optimize hyperparameters, even if the user code is complex, while maintaining higher modularity than other frameworks. It can also optimize hyperparameters in a complex space like no other framework could express before.

There are two paradigms in deep learning frameworks: Define-and-Run and Define-by-Run. In the early days, Caffe and other Define-and-Run frameworks were dominant players. Then, PFN-developed Chainer appeared as the first advocate of the Define-by-Run paradigm, followed by the release of PyTorch, and later, eager mode becoming the default in TensorFlow 2.0. Now the Define-by-Run paradigm is well recognized and appears to be gaining momentum to become the standard.

Is the Define-by-Run paradigm useful only in the domain of deep learning frameworks? We came to understand that we could apply a similar approach to automatic hyperparameter optimization frameworks as well. Under this approach, all existing automatic hyperparameter optimization frameworks are classified as Define-and-Run. Optuna, on the other hand, is based on the Define-by-Run concept and provides users with a new style of API that is very different from other frameworks. This has made it possible to give high modularity to a user program and access to complex hyperparameter spaces, among other things.

Pruning of trials using learning curves

When iterative algorithms like deep learning and gradient boosting are used, rough prediction on end results of training can be made from the learning curve. Using these predictions, Optuna can halt unpromising trials before the training is over. This is the pruning feature of Optuna.

Existing frameworks such as Hyperopt, Spearmint, and SMAC do not have this functionality. Recent studies show that the pruning technique using learning curves is highly effective.  The following graph indicates its effectiveness in performing a sample deep learning task. While the optimization engines of both Optuna and Hyperopt utilize the same TPE, thanks to pruning, the optimization performed by Optuna is more efficient.


Parallel distributed optimization

Deep learning is computationally intensive, and each training process is very time-consuming. Therefore, for automatic hyperparameter optimization in practical use cases, it is essential that the user can easily use parallel distributed optimization that is efficient and stable. Optuna supports asynchronous distributed optimization which simultaneously performs multiple trials using multiple nodes. Parallelization can make the optimization process even faster as shown in the following figure. In the below example, we changed the number of workers from 1, 2, 4, to 8, confirming that the parallelization has accelerated the optimization.

Optuna also has a functionality to work with ChainerMN, allowing the user to optimize training that requires distributed processing without difficulty. By making use of a combination of these functionalities, the user can execute objective functions that include distributed processing in a parallel, distributed manner.


Visualized optimization on dashboard (under development)

Optuna has a dashboard that provides a visualized display of the optimization process. With this, the user can obtain useful information from experimental results. The dashboard can be accessed by connecting via a Web browser to an HTTP server which can be started by one command. Optuna also has a functionality to export optimization processes in a pandas dataframe, for systematic analysis.



Optuna is already in use by several projects at PFN. Among them is the project to compete in the Open Images Challenge 2018, in which we finished in second place. We will continue to aggressively develop Optuna to improve its integrity as well as prototyping and implementing advanced functionalities. We believe Optuna is ready for use, so we would love to receive your candid feedback.

Our objective is to speed up deep learning related R&D activities as much as possible. Our effort into automatic hyperparameter optimization is an important step toward this end. Additionally, we have begun working on other important technologies such as neural architecture search and automatic feature extraction. PFN is looking for potential full-time members and interns who are enthusiastic about working with us in these fields and activities.

New HCI group + upcoming papers and demos at UIST and ISS 2018

Fabrice Matulic

2018-10-15 09:11:17

Creation of HCI group

At PFN, we aspire to create next-generation “intelligent” systems and services, powered by cutting-edge AI technology, but we also recognise that humans will remain essential actors in the design and usage of such systems and therefore it is paramount to think about how the dialogue occurs. Human-Computer Interaction (HCI) approaches, which focus on bridging the gap between people and machines, can considerably contribute to improving intricate machine-learning processes requiring human intervention. With the creation of a dedicated HCI group at PFN, we aim to advance user-centred design for AI and machines and make sure the “humans in the loop” are supported with powerful tools when working with such systems.

Broadly, there are three main lines of research that the team would like to pursue:

  • HCI for machine learning: Utilise HCI methods to facilitate complex or tedious machine-learning processes in which people are involved (such as data gathering, labelling, pre-processing, augmentation; neural network engineering, deployment, and management)
  • Machine-learning for HCI: Use deep learning to enhance existing or enable new interaction techniques (e.g. advanced gesture recognition, activity recognition, multimodal input, sensor fusion, embodied interaction, collaboration between AI, robots and humans, generative model to create interactive content etc.)
  • Human-Robot Interaction (HRI): Make communication and interaction between smart robots and their users natural, intuitive and hopefully even fun!

Of course, HCI does not necessarily involve machine learning or robots and we are also generally interested in creating novel and exciting interactive experiences.

The HCI group will benefit from the expertise of Prof. Takeo Igarashi, of The University of Tokyo, who has been hired as an external consultant. In addition to his wide experience in HCI and HRI, Prof. Igarashi has recently started a JST CREST project on “HCI for machine learning” at his lab, which very much aligns with our research interests. We look forward to a long and fruitful collaboration.

Papers and demos at UIST and ISS 2018

Although the group was just officially created, we have been active in HCI research for the past months already and we will present two papers on recent work, respectively at UIST this week and ISS next month.

The first project, which was started at the University of Waterloo with Drini Cami and Prof. Dan Vogel, proposes to use different ways of holding a stylus pen while writing on a tablet to trigger different UI actions. The technique uses machine learning on the raw touch input data to detect these different pen grips when the user contacts the surface with the hand. The advantage of our technique is that it allows to rapidly switch between various pen modes using the same hand that writes and without resorting to cumbersome UI widgets.

In addition to the paper presentation, Drini will also be showing the technique at UIST’s popular demo session.

Our second contribution is the interactive projection mapping system for PaintsChainer that we showed at the Winter Comiket last year. For those of you who missed it, ColourAIze (which is how we call it in the paper) works directly with drawings and art on paper. Specifically, it projects colour fills determined by PaintsChainer directly onto the paper drawing with the colouring superimposed on the line art. Like with the web version of PaintsChainer, the ability to specify local colour hints to influence the colourisation is supported through simple (digital) pen strokes.

As with the pen-posture project above, we will both present our paper and do a demo of the system at the conference. If you’d like to try the fun experience of having your paper sketches, drawings and mangas coloured by AI, come and see us at ISS in Tokyo in November!

Last but not least, we are looking for talented HCI researchers to join our team, so if you think you can contribute in the areas mentioned above, please check the details of the position on our jobs page and apply!


[PFN Day] BoF session: How to Improve Sharing of Software Components and Best Practices


2018-08-24 12:54:26

Hello, my name is Tobias Pfeiffer, I am a Lead Engineer at Preferred Networks. On July 24, 2018, PFN held an internal tech conference called “PFN Day”, in which I hosted a Birds-of-a-Feather session named “How to Improve Sharing of Software Components and Best Practices”. I want to give a short report on what we did in that session and what the outcomes were.

Reusability of both code and best practices is one of the core goals of software engineering – some people would go as far as saying that achieving a high level of reusability is the sole reason why software engineering exists. Avoiding to reinvent the wheel, fighting the “not-invented-here syndrome” and understanding the real cost of rewriting something from scratch as opposed to learning how to use an existing code base is essential if time is to be spent on developing truly new functionality.

In a company such as PFN that develops highly specialized code based on latest research results, it is all the more important that work that can not be regarded as something that “only PFN can do” is minimized and shared as much as possible. However, in a rapidly growing organization it is often difficult to keep an overview of which member of which group may have already implemented the same functionality before or who to ask with help on a certain problem. Under the impression that PFN has still room to improve when it comes to code reuse I decided to hold a session about that topic and asked people from various projects to join.

First everyone contributed with a number of examples of efforts from their respective teams that seem like duplicate work. One person reported that wrappers for a certain library to work with a certain data format are written again and again by different members because they don’t know this work has been done already. Someone else reported that some algorithms to solve a certain task in computer vision are implemented in many teams separately, simply because nobody who did so made the step to package that code into a reusable unit. Also, maybe more an example of sharing best practices rather than program code, it was said that getting started with Continuous Integration (CI) systems is difficult because it takes a lot of time initially, and special requirements like hardware-specific code (GPU, Infiniband, …) or nested Docker containers are complex to set up.

In a next step we thought about reasons for a low level of reuse and possible impediments. We found that on the producer side, when someone has implemented a new functionality that could be useful for other members, hurdles might be that that person may not have the knowledge of how to package code, that it is not trivial to design a good interface for a software library, and that it takes time to extract the core functionality, package the code, and write usable documentation; this time can then not be used for further development and also the effort does not come with an immediate benefit.

On the consumer side, problems might be that it is not easy to find code that may implement a required functionality in the internal repositories, that copy/paste combined with editing individual lines of code often provides a faster solution than using code as a library and contribute missing functionality back upstream, and that it is hard to judge on the quality of code found “somewhere” in an internal repository.

So we concluded that in order to increase code reuse we should try to (1) increase the motivation to share code and (2) lower the technical barriers to both creating and using shared code. In concrete terms, we thought that the following steps might be a good approach, starting with Python developers in mind:

  • Create an internal PyPI server for code that cannot be released as open source code.
  • Create a template repository with skeleton files that encode the best practices for sharing Python code as a library. For example, there could be a template, a basic documentation template, and CI settings that run all the tests, build the documentation, and publish to the internal package server.
  • Form an expert group that can assist with any questions related to packaging, teach best practices, and that can perform some QA tasks.
  • Create an overview of which internal libraries exist for which purpose, where people can add their libraries. This listing could also show some metric of how many people are using a particular library, serving as a motivation for the author and a quality indicator for potential users.

As the host of that 80-minute long session I was happy about the deep and focused discussion that we had. With members from different teams it was possible to get input from various viewpoints and also learn about issues that are specific to certain work styles. I think we developed some concrete and actionable ideas and we will also try to follow-up and actually realize these ideas after PFN Day to increase code reuse and improve across all of our teams.

2018 PFN Internship Coding Tasks

Mitsuru Kusumoto

2018-07-25 18:01:08

We have published the coding task used in the screening process of PFN internship 2018. It is available on GitHub.

Hello, I’m Kusumoto, an engineer in PFN. In PFN, we organize a summer internship from August to September. Coding task is what we asked applicants to solve during the screening process to check the applicants skill level at programming, problem-solving, etc. Because we are hiring in a wide range of fields, including machine learning, this year we prepared five kinds of problems — “Machine learning/Mathematics,” “Back-end,” “Front-end,” “Processor/Compiler,” and “Chainer.” Applicants would choose one of these tasks according to the theme they have chosen.

This year, we received many more applications than previous years.  With this increasing applications, we increased the number of acceptances we offer.

The detail of the coding task is as follows.

  • Machine learning/Mathematics: You are asked to implement an algorithm of adversarial examples for some neural network model. You need to write a simple report on the performance of the algorithm as well.
  • Back-end: You are asked to create a tool that analyzes some log file.
  • Front-end: You are asked to develop a prototype of an annotation tool for speech videos.
  • Processor/Compiler: You are asked to optimize the code of matrix multiplication. Further, you need to design a hardware circuit of matrix multiplication.
  • Chainer: You are asked to implement a training code for some model, using Chainer.

Every year, we carefully create the coding task with creative sense. I hope these tasks become a good practice problem to learn what you want to study.

I created Machine learning/Mathematics task this year. Let me briefly write what I usually consider when creating problems.

  • Make the problem not require specific knowledge: In PFN, we hire people from a wide range of fields. We make problems solvable without any particular experience or knowledge of machine learning itself as possible so that various people can tackle the problems.
  • Make the problem setting close to actual research: In the field of machine learning or deep learning, we often repeat the process like “find a good theme -> consider a novel method -> implement it -> summarize and evaluate the result.” Our problem setting imitates the latter part of this process. It may be similar to an assignment in a university class.
  • Ask interesting theme: Lots of interesting research results appear every day in the area of machine learning/deep learning. The coding task should also be interesting. This year, the task was on the method called Fast Gradient Signed Method, which shows far better performance than random noise baseline method. I believe that this was a fun experiment in and of itself.
  • Do not make the problem too difficult: It is not good if the problem is too time-consuming. Our objective is that a student with enough skills can solve the problem within one or two days.

We evaluate the submitted code and report from various perspective. Not only correct implementation is important. That code is readable for other engineers, that there is an appropriate amount of unit-tests, and that other engineers can easily replicate the result are also evaluated.

In addition to the code, summarization of the result and evaluation of the proposed method are also important factors in experiments. Reporting the result to other people is also important especially when you work in a team. We will check the submitted report to see how good these factors are.

If you are interested in PFN, we look forward to receiving your application in the next internship program.

We are also hiring full-time employees in Tokyo, Japan and San Mateo, California. Please refer to the job page below.

Technologies behind Distributed Deep Learning: AllReduce


2018-07-10 15:11:24

This post is contributed by Mr. Yuichiro Ueno, who were a Summer intern in 2017 and a part time engineer at PFN.

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Hello, I am Yuichiro Ueno. I participated in a summer internship program at PFN in 2017, and I currently work as a part-time engineer. I am an undergraduate student at Tokyo Institute of Technology, and my research topic is High-Performance, Parallel and Distributed Computing.

In this blog post, I will describe our recent study on algorithms for AllReduce, a communication operation used for distributed deep learning.

What is Distributed Deep Learning?

Currently, one of the significant challenges of deep learning is it is a very time-consuming process. Designing a deep learning model requires design space exploration of a large number of hyper-parameters and processing big data. Thus, accelerating the training process is critical for our research and development. Distributed deep learning is one of the essential technologies in reducing training time.

We have deployed a private supercomputer “MN-1” to accelerate our research and development process. It is equipped with 1024 NVIDIA(R) Tesla(R) P100 GPUs and Mellanox(R) InfiniBand FDR interconnect and is the most powerful supercomputer in the industry segment in Japan. By leveraging MN-1, we completed training a ResNet-50 model on the ImageNet dataset in 15 minutes.

Communication among GPUs is one of the many challenges when training distributed deep learning models in a large-scale environment. The latency of exchanging gradients over all GPUs is a severe bottleneck in data-parallel synchronized distributed deep learning.

How is the communication performed in distributed deep learning? Also, why is the communication so time-consuming?

The Importance of AllReduce in Distributed Deep Learning

In synchronized data-parallel distributed deep learning, the major computation steps are:

  1. Compute the gradient of the loss function using a minibatch on each GPU.
  2. Compute the mean of the gradients by inter-GPU communication.
  3. Update the model.

To compute the mean, we use a collective communication operation called “AllReduce.”

As of now, one of the fastest collective communication libraries for GPU clusters is NVIDIA Collective Communication Library: NCCL[3]. It achieves far better communication performance than MPI, which is the de-facto standard communication library in the HPC community. NCCL is indispensable for achieving high performance in distributed deep learning using ChainerMN. Without it, the ImageNet 15-min feat could not have been achieved[2].

Our researchers and engineers were curious about NCCL’s excellent performance. Since NCCL is not an open source library, we tried to understand the high performance of the library by developing and optimizing an experimental AllReduce library.

Algorithms of AllReduce

First, let’s take a look at the AllReduce algorithms. AllReduce is an operation that reduces the target arrays in all processes to a single array and returns the resultant array to all processes. Now, let P the total number of processes. Each process has an array of length N called \(A_p\). \(i\)-th element of the array of process \(p ~(1 \leq p \leq P)\) is \(A_{p,i}\).

The resulting array B is to be:
$$ B_{i}~~=~~A_{1,i}~~Op~~A_{2,i}~~Op~~…~~Op~~A_{P,i} $$

Here, Op is a binary operator. SUM, MAX, and MIN are frequently used. In distributed deep learning, the SUM operation is used to compute the mean of gradients. In the rest of this blog post, we assume that the reduction operation is SUM. Figure 1 illustrates how the AllReduce operation works by using an example of P=4 and N=4.


Fig.1 AllReduce Operation


There are several algorithms to implement the operation. For example, a straightforward one is to select one process as a master, gather all arrays into the master, perform reduction operations locally in the master, and then distribute the resulting array to the rest of the processes. Although this algorithm is simple and easy to implement, it is not scalable. The master process is a performance bottleneck because its communication and reduction costs increase in proportion to the number of total processes.

Faster and more scalable algorithms have been proposed. They eliminate the bottleneck by carefully distributing the computation and communication over the participant processes.
Such algorithms include Ring-AllReduce and Rabenseifner’s algorithm[4].

We will focus on the Ring-AllReduce algorithms in this blog post. This algorithm is also employed by NCCL [5] and baidu-allreduce[6].


Let us assume that P is the total number of the processes, and each process is uniquely identified a number between 1 and P. As shown in the Fig.2, the processes constitute a single ring.


Fig.2 Example of a process ring


First, each process divides its own array into P subarrays, which we refer to as “chunks”. Let chunk[p] be the p-th chunk.

Next, let us focus on the process [p]. The process sends chunk[p] to the next process, while it receives chunk[p-1] from the previous process simultaneously (Fig.3).


Fig.3 Each process sends its chunk[p] to the next process [p+1]


Then, process p performs the reduction operation to the received chunk[p-1] and its own chunk[p-1], and sends the reduced chunk to the next process p+1 (Fig.4).


Fig.4 Each process sends a reduced chunk to the next process


By repeating the receive-reduce-send steps P-1 times, each process obtains a different portion of the resulting array (Fig.5).


Fig.5 After P-1 steps, each process has a reduced subarray.


In other words, each process adds its local chunk to a received chunk and send it to the next process. In other words, every chunk travels all around the ring and accumulates a chunk in each process. After visiting all processes once, it becomes a portion of the final result array, and the last-visited process holds the chunk.

Finally, all processes can obtain the complete array by sharing the distributed partial results among them. This is achieved by doing the circulating step again without reduction operations, i.e., merely overwriting the received chunk to the corresponding local chunk in each process. The AllReduce operation completes when all processes obtain all portions of the final array.

Let’s compare the amount of communication of Ring-AllReduce to that of the simple algorithm we mentioned above.

In the simple algorithm, the master process receives all the arrays from all other processes, which means the total amount of received data is \((P – 1) \times N\). After the reduction operation, it sends the arrays back to all the processes, which is again \((P – 1) \times N\) data. Thus, the amount of communication of the master process is proportional to P.

In the Ring-AllReduce algorithm, we can calculate the amount of communication in each process in the following way. In the earlier half of the algorithm, each process sends an array, the size of which is \(N/P\), \(P-1\) times. Next, each process again sends an array of the same size P-1 times. The total amount of data each process sends throughout the algorithm is \(2N(P-1) / P\), which is practically independent of P.

Thus, the Ring-Allreduce algorithm is more efficient than the simple algorithm because it eliminates the bottleneck process by distributing computation and communication evenly over all participant processes. Many AllReduce implementations adopt Ring-AllReduce, and it is suitable for distributed deep learning workloads as well.

Implementation and Optimization

The Ring-AllReduce algorithm is simple to implement if basic send and receive routines are given. baidu-allreduce[6] is built on top of MPI using MPI_Send and MPI_Recv.

However, we tried to do further optimizations by using InfiniBand Verbs API instead of MPI. To fully utilize hardware resources, the algorithm has multiple stages such as memory registration (pinning), cuda-memcpy, send, reduction, receive, and memory deregistration, and they are processed in a software pipeline. Here, “registration” and “deregistration” are pre- and post-processing stages for DMA data transfer. Such low-level operations are abstracted out in MPI send/receive routines, and we are not able to split them into pipeline stages. To increase the granularity of the communication and computation, we further divide chunks into smaller sub-chunks. Also, we introduce a memory pool to hide memory allocation overhead.

Performance Evaluation

For performance evaluation, we compared our prototype (called PFN-Proto) to several AllReduce implementations shown in the Appendix.

Our prototype implementation currently focuses on inter-node communication; it is not optimized for intra-node communication using shared memory or GPU-to-GPU DMA data transfer. We evaluated the implementations in one process per node configuration. For Open MPI [7], our company is yet to introduce the latest version 3.x series because the most recent series has a minor issue related to GPUDirect. So, we used version 2.1.3 instead.

We used our private supercomputer MN-1 for this experiment, as shown in the “Experimental environment” below. Eight processes were run, where one process ran on one computing node. The target data size is 256MB.


Fig.6 AllReduce Execution Time


Figure 6 shows the result of the evaluation. Each bar indicates the median of 10 runs. The error bar indicates confidence intervals. The details of each library are shown in the “software versions” below.

First, let’s look at the median values. Our experimental implementation, PFN-Proto, showed the fastest time, which is approximately 82%, 286%, 28%, 1.6% better than ompi, ompi-cuda, Baidu, NCCL, respectively. One thing worth mentioning, which is not in the graph, is that Baidu achieved the fastest single-run time 0.097 [s] among all the five libraries.

Next, we focus on the variance of the performance. Maximum and minimum runtimes of PFN-Proto and NCCL are within +/- 3% and +/- 6%, respectively. In contrast, Baidu’s maximum value is 7.5x its median, because its first run takes a very long time. Its maximum runtime excluding the first run is +9.6% over the median, which is still more significant than those of NCCL and PFN-Proto.

Our hypothesis is that the performance variances of MPI and MPI-based routines are attributed to MPI’s internal behavior related to memory operations. MPI’s programming interface hides memory allocation and registration operations for InfiniBand communication. Timings of such operations are not controllable from those AllReduce implementations.


We described the AllReduce communication pattern, which is very important for distributed deep learning. In particular, we implemented the Ring-AllReduce algorithm in our experimental communication library, and it achieved comparable performance to NCCL library released by NVIDIA. The implementation efficiently utilizes available hardware resources through advanced optimization such as using InfiniBand Verbs API and software pipelining. We continue our research and development on accelerating distributed deep learning.

Caveats: our implementation is experimental, and we only demonstrated the performance on our in-house cluster. NCCL is a highly practical and usable library thanks to its performance suitability and availability on a wide range of IB-connected NVIDIA GPU clusters.


I would like to thank my mentors and the team for the kind support and feedbacks. Since my internship period last year, I have been give access to rich computation resources, and it has been a fantastic experience.

From Mentors:

This project started with a question: “how does NCCL achieve such high and stable performance?” It is an advanced and experimental topic, but Mr. Ueno achieved a remarkable result with his high motivation and technical skills.

PFN is looking for talents, not only in the deep learning/machine learning field but a full range of technical areas from hardware to software. Please visit for more information.

For students who are interested in high-performance computing and other technologies, PFN offers international internship opportunities, as well as domestic programs for Japanese students. The application period has finished this year, but be ready for the next opportunity!


[1] Preferred Networks officially released ChainerMN version 1.0.0
[2] Akiba, et al., “Extremely Large Minibatch SGD: Training ResNet-50 on ImageNet in 15 Minutes”
[3] NVIDIA Collective Communications Library
[4] Rabenseifner, “Optimization of Collective Reduction Operations”, ICCS 2004
[5] Jeaugey, “Optimized Inter-GPU Collective Operations with NCCL”, GTC 2017
[6] baidu-allreduce
[7] Open MPI
[8] New ChainerMN functions for improved performance in cloud environments and performance testing results on AWS
[9] Tsuzuku, et al., “Variance-based Gradient Compression for Efficient Distributed Deep Learning”, In Proceedings of ICLR 2018 (Workshop Track)


Software versions

Implementation Version Note
MPI (ompi) Open MPI 2.1.3 Trasnfer from CPU memory to CPU memory (No GPU involved)
CUDA-aware MPI Open MPI 2.1.3 From GPU memory to GPU memory
baidu-allreduce (baidu) A customized version of baidu-allreduce, based on commit ID 73c7b7f
NCCL 2.2.13

Experimental environment

  • Intel(R) Xeon(R) CPU E5-2667 * 2
  • Mellanox ConnectX-3 InfiniBand FDR (56Gbps) x2
  • NVIDIA Pascal P100 GPU (with NVIDIA Driver Version 375.20)