San Francisco, CA – SPK Corporation of Seoul, South Korea (CEO: Sung Park) has formed a strategic partnership with Rescale, Inc. of San Francisco, California (co-founder and CEO: Joris Poort) to become a value-added reseller of Rescale’s cloud simulation platforms in South Korea. Rescale provides a unified environment that allows companies to accelerate their engineering and science simulations using customizable high-performance computing (HPC) resources. Under the partnership, SPK now provides marketing, sales, and technical support for the Rescale platform, including integration with companies’ existing HPC environments and operation and analysis support. With strong relationships with Korean conglomerates, including LG, Samsung, Hyundai, POSCO, SK, and KT, SPK will provide value-added HPC cloud services to those companies’ private and public cloud services.

Computer simulations are very compute-intensive and an integral part of research and development in many industries, including aerospace, automotive, pharmaceutical, and energy. However, building large on-premise HPC clusters is very costly; therefore, many companies do not have sufficient capacity to run all their analyses. Insufficient simulation resources can lead to missed deadlines, suboptimal product designs, and foregone profits.

Rescale presents a solution to these pain points, allowing its customers to access a powerful infrastructure network of more than 8 million servers comprised of the latest hardware technology. The platforms are available on pay-per-use service based on compute cluster size and runtime. In addition to providing IT hardware, the platform supports more than 180 simulation software packages, including those for structural analysis, fluid dynamics, and quantum chemical calculations. All of Rescale’s 36+ data centers around the world maintain the highest security standards in the industry.  It is also possible to restrict data within a geographical region, such as to data centers in South Korea, according to business security needs by accessing one of Rescale’s five regional platforms.

With its secure, customizable simulation resources, Rescale enables companies to drastically reduce HPC procurement timelines, analysis runtimes, and product release schedules. Rescale’s platforms are accessible through an intuitive web-based interface or an application programming interface (API) function for integrating with a company’s existing HPC systems. Companies can focus attention, budgets, and resources towards primary business activities and R&D. Many of Rescale’s customers have reduced the overall simulation runtime by over 40%.

This new strategic partnership with Rescale will allow SPK to expand the offering of HPC software like ANSYS, LS-DYNA, and open-source machine learning software (e.g. TensorFlow) to large enterprises in Korea.

About SPK
SPK (www.spkr.co.kr) was founded in 2002, is headquartered in South Korea, and is dedicated to helping Korean enterprise customers transform from legacy to virtualized and cloud-based infrastructure. SPK has specialized in helping US-based start-ups achieve rapid market success in Korea. SPK counts the pillars of the Korean economy as its enterprise customers, including LG, Samsung, Hyundai, POSCO, SK, and KT.

About Rescale, Inc.
Rescale is the world’s leading cloud platform provider of simulation software and high performance computing (HPC) solutions. Rescale’s platform solutions are deployed securely and seamlessly to enterprises via a web-based application environment powered by preeminent simulation software providers and backed by the largest commercially available HPC infrastructure. Headquartered in San Francisco, CA, Rescale’s customers include global Fortune 500 companies in the aerospace, automotive, life sciences, marine, consumer products, and energy sectors.

This article was written by Rescale.

With the recent release of Rescale Deep Learning Cloud, we will present an example here that makes use of our new interactive notebook feature to develop deep neural networks. This feature enables an iterative workflow alternating between interactive data preprocessing and analysis, and batch training of neural networks.


In this article we will start with an image classification data set (CIFAR10), try a few different neural network designs in our interactive notebook, and then launch a batch training cluster to train that network for more epochs.

Starting a Notebook
To get started, you first need to start up a Rescale Linux Desktop with a NVIDIA K80 GPU:

Here we have chosen a desktop configuration with a single NVIDIA K80 GPU. While you wait for the notebook to finish booting, you can clone and save the job that holds the CIFAR10 image dataset and the notebook code you will run. Follow this link and then save the job it creates (you do not need to submit it to run, you will just use the job to stage the notebook and dataset input files): CIFAR10 TensorFlow notebook.

Once the desktop finishes booting, attach TensorFlow software and the job with the notebook code.



Once the software and job are attached, open the notebook URL and enter the password when prompted:

Next, navigate into the attach_jobs directory, then the directory of the job you attached, and then to .ipynb file.

The code in this notebook was adapted from the TensorFlow CIFAR10 training example.
We have already added another inference function to the example: inference_3conv, with a 3rd convolutional layer. You can try training the 2 convolution layer network by running all the cells as-is. To run the 3 conv layer version, replace the call to inference_2conv with inference_3conv,restart the kernel (ESC-0-0), and then run all the cells again.


You can also access TensorBoard, TensorFlow’s built-in GUI, on the desktop via SSH tunnel. Just download one of the connection scripts in the Node Access section of the Desktop panel:


and take the username and IP address out of the script. Then forward port 6006 to your localhost and run TensorBoard:

ssh -L 6006:localhost:6006 @ tensorboard –logdir=/tmp/cifar10_train

You should now be able to access it from your local browser at http://localhost:6006. The particular training example we are using already set /tmp/cifar10_train as the default location for training logs. Here are the 2 network graphs as they appear in TensorBoard. Two convolutional layers:


Three convolutional layers:


Batch Training
If you train the 2-layer and 3-layer convolutional networks on the notebook GPU for 10-20 epochs, you will see the loss does indeed drop faster for the 3-layer network. We would now like to see whether the deeper network yields better accuracy when trained longer or if it reaches the same accuracy in less training time.

You can launch a batch training job with your updated 3-convolutional-layer code directly from the notebook. First, save your notebook (Ctrl-S), then there is a shell command shortcut which will automatically export your notebook to regular python and launch a job with all the files in the same directory as the notebook. For example:


The syntax is as follows:

This can be run from the command line on the desktop or within the notebook with the IPython shell magic ! syntax.

Some GPU core types you can choose from when launching from the notebook:

Jade: NVIDIA Kepler K520s
Obsidian: NVIDIA Tesla K80s

Once the job starts running, you can attach it to your desktop and the job files will be accessible on the notebook as part of a shared filesystem. First, the attach:

Then, on the desktop, in addition to opening and viewing files, you can also open a terminal:

From the terminal, you can tail files, etc.

Alternatively, you can navigate to the job in the Rescale web portal and live tail files in your browser. This allows you to shut down your Rescale desktop and still monitor training progress, or enables monitoring of your batch job on a mobile device while you are away from your workstation.


Iterative Development
Above, you have just completed a single development iteration of our CIFAR10 training example, but you do not need to stop once the batch training is done. You can stop the batch training job anytime, review training logs in more depth from your notebook, then submit new training jobs.

The advantage here is that you can develop and test your code on similar hardware, the same software configuration, and the same training data as the batch training cluster we used. This eliminates the headache of bugs due to differences in software or hardware configuration between testing you might do on your local workstation and the training cluster in the cloud.

Additionally, if you prefer to do more compute heavy workloads directly in the notebook environment, we have Rescale Desktop configurations available with up to 8 K80 GPUs (4 K80 cards), email support@rescale.com for access to those.

To try out the workflow above, sign up here and immediately start doing deep learning on Rescale today.

This article was written by Mark Whitney.