Sharing the Love for GPUs in Machine Learning - Part 2
Wed, 17 Mar 2021 16:44:00 -0000|
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In Part 1 of “Share the GPU Love” we covered the need for improving the utilization of GPU accelerators and how a relatively simple technology like VMware DirectPath I/O together with some sharing processes could be a starting point. As with most things in technology, some additional technology, and knowledge you can achieve high goals beyond just the basics. In this article, we are going to introduce another technology for managing GPU-as-a-service – NVIDIA GRID 9.0.
Before we jump to this next technology, let’s review some of the limitations of using DirectPath I/O for virtual machine access to physical PCI functions. The online documentation for VMware DirectPath I/O has a complete list of features that are unavailable for virtual machines configured with DirectPath I/O. Some of the most important ones are:
- Fault tolerance
- High availability
- Hot adding and removing of virtual devices
The technique of “passing through” host hardware to a virtual machine (VM) is simple but doesn’t leverage many of the virtues of true hardware virtualization. NVIDIA delivers software to virtualize GPUs in the data center for years. The primary use case has been Virtual Desktop Infrastructure (VDI) using vGPUs. The current release - NVIDIA vGPU Software 9 adds the vComputeServer vGPU capability for supporting artificial intelligence, deep learning, and high-performance computing workloads. The rest of this article will cover using vGPU for machine learning in a VMware ESXi environment.
We want to compare the setup and features of this latest NVIDIA software version, so we worked on adding the vComputeServer to our PowerEdge ESXi that we used for the DirectPath I/O research in our first blog in this series. Our NVIDIA Turing architecture T4 GPUs are on the list of supported devices, so we can check that box and our ESXi version is compatible. The NVIDIA vGPU software documentation for VMware vSphere has an exhaustive list of requirements and compatibility notes.
You’ll have to put your host into maintenance mode during installation and then reboot after the install of the VIB completes. When the ESXi host is back online you can use the now-familiar nvidia-smi command with no parameters and see a list of all available GPUs that indicates you are ready to proceed.
We configured two of our T4 GPUs for vGPU use and setup the required licenses. Then we followed the same approach that we used for DirectPath I/O to build out VM templates with everything that is common to all developments and use those to create the developer-specific VMs – one with all Python tools and another with R tools. NVIDIA vGPU software supports only 64-bit guest operating systems. No 32-bit guest operating systems are supported. You should only use a guest OS release that is supported by both for NVIDIA vGPU software and by VMware. NVIDIA will not be able to support guest OS releases that are not supported by your virtualization software.
Now that we have both a DirectPath I/O enabled setup and the NVIDIA vGPU environment let’s compare the user experience. First, starting with vSphere 6.7 U1 release, vMotion with vGPU and suspend and resume with vGPU are supported on suitable GPUs. Always check the NVIDIA Virtual GPU Software Documentation for all the latest details. vSphere 6.7 only supports suspend and resume with vGPU. vMotion with vGPU is not supported in release 6.7. [double check this because vMotion is supported I just can't remember what version and update number it is]
vMotion can be extremely valuable for data scientists doing long-running training jobs that you don’t get with DirectPath I/O and suspend/resume of vGPU enabled VMs creates opportunities to increase the return from your GPU investments by enabling scenarios with data science model training running at night and interactive graphics-intensive applications running during the day utilizing the same pool of GPUs. Organizations with workers spread across time zones may also find that suspend/resume of vGPU enabled VMs to be useful.
There is still a lot of work that we want to do in our lab including capturing some informational videos that will highlight some of the concepts we have been talking about in these last two articles. We are also starting to build out some VMs configured with Docker so we can look at using our vGPUs with NVIDIA GPU Cloud (GCP) deep learning training and inferencing containers. Our goal is to get more folks setting up a sandbox environment using these articles along with the NVIDIA and VMware links we have provided. We want to hear about your experience working with vGPUs and VMware. If you have any questions or comments post them in the feedback section below.
Thanks for reading,
Phil Hummel - On Twitter @GotDisk
Related Blog Posts
Sharing the Love for GPUs in Machine Learning
Wed, 17 Mar 2021 16:44:00 -0000|
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Anyone that works with machine learning models trained by optimization methods like stochastic gradient descent (SGD) knows about the power of specialized hardware accelerators for performing a large number of matrix operations that are needed. Wouldn’t it be great if we all had our own accelerator dense supercomputers? Unfortunately, the people that manage budgets aren’t approving that plan, so we need to find a workable mix of technology and, yes, the dreaded concept, process to improve our ability to work with hardware accelerators in shared environments.
We have gotten a lot of questions from a customer trying to increase the utilization rates of machines with specialized accelerators. Good news, there are a lot of big technology companies working on solutions. The rest of the article is going to focus on technology from Dell EMC, NVIDIA, and VMware that is both available today and some that are coming soon. We also sprinkle in some comments about the process that you can consider. Please add your thoughts and questions in the comments section below.
We started this latest round of GPU-as-a-service research with a small amount of kit in the Dell EMC Customer Solutions Center in Austin. We have one Dell EMC PowerEdge R740 with 4 NVIDIA T4 GPUs connected to the system on the PCIe bus. Our research question is “how can a group of data scientists working on different models with different development tools share these four GPUs?” We are going to compare two different technology options:
- VMware Direct Path I/O
- NVIDIA GPU GRID 9.0
Our server has ESXi installed and is configured as a 1 node cluster in vCenter. I’m going to skip the configuration of the host BIOS and ESXi and jump straight to creating VMs. We started off with the Direct Path I/O option. You should review the article “Using GPUs with Virtual Machines on vSphere – Part 2: VMDirectPath I/O” from VMware before trying this at home. It has a lot of details that we won’t repeat here.
There are many approaches available for virtual machine image management that can be set up by the VMware administrators but for this project, we are assuming that our data scientists are building and maintaining the images they use. Our scenario is to show how a group of Python users can have one image and the R users can have another image that both use GPUs when needed. Both groups are using primarily TensorFlow and Keras.
Before installing an OS we changed the firmware setting to EFI in the VM Boot Options menu per the article above. We also used the VM options to assign one physical GPU to the VM using Direct Path I/O before proceeding with any software installs. It is important for there to be a device present during configuration even though the VM may get used later with or without an assigned GPU to facilitate sharing among users and/or teams.
Once the OS was installed and configured with user accounts and updates, we installed the NVIDIA GPU related software and made two clones of that image since both the R and Python environment setups need the same supporting libraries and drivers to use the GPUs when added to the VM through Direct Path I/O. Having the base image with an OS plus NVIDIA libraries saves a lot of time if you want a new type of developer environment.
With this much of the setup done, we can start testing assigning and removing GPU devices among our two VMs. We use VM options to add and remove the devices but only while the VM is powered off. For example, we can assign 2 GPUs to each VM, 4 GPUs to one VM and none to the other or any other combination that doesn’t exceed our 4 available devices. Devices currently assigned to other VMs are not available in the UI for assignment, so it is not physically possible to create conflicts between VMs. We can NVIDIA’s System Management Interface (nvidia-smi) to list the devices available on each VM.
Remember above when we talked about process, here is where we need to revisit that. The only way a setup like this works is if people release GPUs from VMs when they don’t need them. Going a level deeper there will probably be a time when one user or group could take advantage of a GPU but would choose to not take one so other potentially more critical work can have it. This type of resource sharing is not new to research and development. All useful resources are scarce, and a lot of efficiencies can be gained with the right technology, process, and attitude
.Before we talk about installing the developer frameworks and libraries, let’s review the outcome we desire. We have 2 or more groups of developers that could benefit from the use of GPUs at different times in their workflow but not always. They would like to minimize the number of VM images they need and have and would also like fewer versions of code to maintain even when switching between tasks that may or may not have access to GPUs when running. We talked above about switching GPUs between machines but what happens on the software side? Next, we’ll talk about some TensorFlow properties that make this easier.
TensorFlow comes in two main flavors for installation tensorflow and tensorflow-gpu. The first one should probably be called “tensorflow-cpu” for clarity. For this work, we are only installing the GPU enabled version since we are going to want our VMs to be able to use GPU for any operations that TF supports for GPU devices. The reason that I don’t also need the CPU version when my VM has not been assigned any GPUs is that many operations available in the GPU enabled version of TF have both a CPU and a GPU implantation. When an operation is run without a specific device assignment, any available GPU device will be given priority in the placement. When the VM does not have a GPU device available the operation will use the CPU implementation.
There are many examples online for testing if you have a properly configured system with a functioning GPU device. This simple matrix multiplication sample is a good starting point. Once that is working you can move on a full-blown model training with a sample data set like the MNIST character recognition model. Try setting up a sandbox environment using this article and the VMware blog series above. Then get some experience with allocating and deallocating GPUs to VMs and prove that things are working with a small app. If you have any questions or comments post them in the feedback section below.
Thanks for reading.
Phil Hummel - Twitter @GotDisk@GotDisk
Omnia: Open-source deployment of high-performance clusters to run simulation, AI, and data analytics workloads
Tue, 02 Feb 2021 16:07:10 -0000|
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High Performance Computing (HPC), in which clusters of machines work together as one supercomputer, is changing the way we live and how we work. These clusters of CPU, memory, accelerators, and other resources help us forecast the weather and understand climate change, understand diseases, design new drugs and therapies, develop safe cars and planes, improve solar panels, and even simulate life and the evolution of the universe itself. The cluster architecture model that makes this compute-intensive research possible is also well suited for high performance data analytics (HPDA) and developing machine learning models. With the Big Data era in full swing and the Artificial Intelligence (AI) gold rush underway, we have seen marketing teams with their own Hadoop clusters attempting to transition to HPDA and finance teams managing their own GPU farms. Everyone has the same goals: to gain new, better insights faster by using HPDA and by developing advanced machine learning models using techniques such as deep learning and reinforcement learning. Today, everyone has a use for their own high-performance computing cluster. It’s the age of the clusters!
Today's AI-driven IT Headache: Siloed Clusters and Cluster Sprawl
Unfortunately, cluster sprawl has taken over our data centers and consumes inordinate amounts of IT resources. Large research organizations and businesses have a cluster for this and a cluster for that. Perhaps each group has a little “sandbox” cluster, or each type of workload has a different cluster. Many of these clusters look remarkably similar, but they each need a dedicated system administrator (or set of administrators), have different authorization credentials, different operating models, and sit in different racks in your data center. What if there was a way to bring them all together?
That’s why Dell Technologies started the Omnia project.
The Omnia Project
The Omnia project is an open-source initiative with a simple aim: To make consolidated infrastructure easy and painless to deploy using open open source and free use software. By bringing the best open source software tools together with the domain expertise of Dell Technologies' HPC & AI Innovation Lab, HPC & AI Centers of Excellence, and the broader HPC Community, Omnia gives customers decades of accumulated expertise in deploying state-of-the-art systems for HPC, AI, and Data Analytics – all in a set of easily executable Ansible playbooks. In a single day, a stack of servers, networking switches, and storage arrays can be transformed into one consolidated cluster for running all your HPC, AI, and Data Analytics workloads.
Simple by Design
Omnia’s design philosophy is simplicity. We look for the best, most straightforward approach to solving each task.
- Need to run the Slurm workload manager? Omnia assembles Ansible plays which build the right rpm files and deploy them correctly, making sure all the correct dependencies are installed and functional.
- Need to run the Kubernetes container orchestrator? Omnia takes advantage of community supported package repositories for Linux (currently CentOS) and automates all the steps for creating a functional multi-node Kubernetes cluster.
- Need a multi-user, interactive Python/R/Julia development environment? Omnia takes advantage of best-of-breed deployments for Kubernetes through Helm and OperatorHub, provides configuration files for dynamic and persistent storage, points to optimized containers in DockerHub, Nvidia GPU Cloud (NGC), or other container registries for unaccelerated and accelerated workloads, and automatically deploys machine learning platforms like Kubeflow.
Before we go through the process of building something from scratch, we will make sure there isn’t already a community actively maintaining that toolset. We’d rather leverage others' great work than reinvent the wheel.
Inclusive by Nature
Omnia’s contribution philosophy is inclusivity. From code and documentation updates to feature requests and bug reports, every user’s contributions are welcomed with open arms. We provide an open forum for conversations about feature ideas and potential implementation solutions, making use of issue threads on GitHub. And as the project grows and expands, we expect the technical governance committee to grow to include the top contributors and stakeholders from the community.
Omnia is just getting started. Right now, we can easily deploy Slurm and Kubernetes clusters from a stack of pre-provisioned, pre-networked servers, but our aim is higher than that. We are currently adding capabilities for performing bare-metal provisioning and supporting new and varying types of accelerators. In the future, we want to collect information from the iDRAC out-of-band management system on Dell EMC PowerEdge servers, configure Dell EMC PowerSwitch Ethernet switches, and much more.
What does the future hold? While we have plans in the near-term for additional feature integrations, we are looking to partner with the community to define and develop future integrations. Omnia will grow and develop based on community feedback and your contributions. In the end, the Omnia project will not only install and configure the open source software we at Dell Technologies think is important, but the software you – the community – want it to, as well! We can’t think of a better way for our customers to be able to easily setup clusters for HPC, AI, and HPDA workloads, all while leveraging the expertise of the entire Dell Technologies' HPC Community.
Omnia is available today on GitHub at https://github.com/dellhpc/omnia. Join the community now and help guide the design and development of the next generation of open-source consolidated cluster deployment tools!