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During a recent webinar, participants outlined common edge computing questions and challenges. This post provides NVIDIA resources to help beginners on their journey.

With the convergence of IoT and AI organizations are evaluating new ways of computing to keep up with larger data loads and more complicated use cases. For many, edge computing provides the right environment to successfully operate AI applications ingesting data from distributed IoT devices. 

But many organizations are still grappling with understanding edge computing. Partners and customers often ask about edge computing, reasons for its popularity in the AI space, and use cases compared to cloud computing. 

NVIDIA recently hosted an Edge Computing 101: An Introduction to the Edge webinar. The event provided an introduction to edge computing, outlined different types of edge, the benefits of edge computing, when to use it and why, and more. 

During the webinar, we surveyed the audience to understand their biggest questions about edge computing and how we could help. 

Below we provide answers to those questions, along with resources that could help you along your edge computing journey.  

What stage are you in, on your edge computing journey? 

About 51% of the audience answered that they are in the “learning” phase of their journey. At face value, this is not surprising given that the webinar was an introductory session. Of course most of the folks are at the learning phase, as opposed to implementing or scaling. This was also corroborated by the fact that many of the tools in the edge market are still new, meaning many vendors also have much to gain from learning more. 

To help in the learning journey refer to Considerations for Deploying AI at the Edge. This overview covers the major decision points for choosing the right components for an edge solution, security tips on edge deployments, and how to evaluate where edge computing fits into your existing environment. 

What is the top benefit you hope to gain by deploying applications at the edge? 

There are many benefits to deploying AI applications in edge computing environments, including real-time insights, reduced bandwidth, data privacy, and improved efficiency. For the participants in the session, 42% responded that latency (or real-time insights) was the top differentiator they were hoping to gain from deploying applications at the edge.

Improving latency is a major benefit of edge computing since the processing power for an application sits physically closer to where data is collected. For many use cases, the low latency provided by edge computing is essential for success. 

For example, an autonomous forklift operating in a manufacturing environment has to be able to react instantaneously to its dynamic environment. It needs to be able to turn around tight corners, lift and deliver heavy loads, and stop in time to avoid colliding with moving workers in the facility. If the forklift is unable to make decisions with ultra-low latency, there is no guarantee it will operate effectively. For safety reasons, organizations must know that AI applications powering that autonomous forklift are able to return insights fast enough to keep the environment safe. 

Learn more about latency and the other benefits of edge AI. 

What is your biggest challenge designing an edge computing solution?

There are challenges associated with implementing any new technology. This audience gave an even spread of answers across the choices given, which is not surprising given the early nature of the edge computing market. Many organizations are still investigating how edge computing will work for them, and they are experiencing a variety of different challenges. 

The following lists six common challenges for this audience, along with resources that can help.

1. Unsure what components are needed

The three major components needed for any edge deployment are an application, infrastructure (including tools to manage applications remotely), and security protocols. 

Edge Computing 201: How to Build an Edge Solution will dive deep into each of these topics. The webinar will also provide specifics for what is needed to build an edge deployment, repurpose existing technology to be optimized for an edge deployment, and best practices for getting started.  

2. Implementation challenges

Many organizations are starting to implement edge AI, so it is important to understand the process and challenges involved. There are five main steps to implementing any edge AI solution:

1. Identify a use case or challenge to be solved.
2. Determine what data and application requirements exist.
3. Evaluate existing edge infrastructure and what pieces must be added.
4. Test solution and then roll out at scale.
5. Share success with other groups to promote additional use cases.

Understanding these five steps is key to overcoming challenges that arise during implementation. 

Steps to Get Started With Edge AI dives into each of these steps, outlining best practices and pitfalls to avoid along the way. 

3. Tuning an application for edge use cases

The most important aspects of an edge application are flexibility and performance. Organizations need to be able to deploy an application to edge sites that have specific requirements and sometimes different tools than other sites. They need an application that can handle volatile situations. Additionally, ensuring an application can provide the performance needed for ultra-low latency situations is critical to success. 

Cloud-native technology fulfills both of those requirements, and has many other added benefits. 

4. Scaling a solution across multiple sites

Seamlessly scaling one deployment to multiple (sometimes thousands) of deployments can be easy with the right technology. Tools to manage application deployments across distributed edge sites are critical for any organization looking to scale edge AI across their entire organization. Some examples of tools are Red Hat OpenShift, VMware Tanzu, and NVIDIA Fleet Command. 

Fleet Command is turnkey, secure, and can scale to thousands of devices. Check out the demo to learn more. 

5. Security of edge environments

Edge computing environments are very different from cloud computing environments, and have different security considerations. For instance, physical security of data and hardware is a consideration for edge sites that is not generally a consideration when deploying in the cloud. It is essential to find the right protocols to provide multilayer security for edge deployments that protects the entire workflow from cloud to edge. 

Check out Edge Computing: Considerations for Security Architects to learn more about how to secure edge environments. 

6. Justify the cost of an edge solution 

Justifying the cost of any technology boils down to understanding all of the cost factors and the value of the solution. For an edge computing solution, there are three main cost factors, the infrastructure costs, the application costs, and the management costs. The value of edge computing will vary by use case, and depends a lot on the ROI of the AI application deployed. 

Learn more about the costs associated with an edge deployment with Building an Edge Strategy: Cost Factors.

What is the next step in your edge computing journey? 

After the session, 49% responded that “learning more about edge AI use cases” was the next step in their edge computing journey. Many leading edge computing applications use computer vision to perceive objects in an environment, from pedestrians in a crosswalk to objects on a shelf at a retail store. Organizations rely on edge computing for computer vision because of the ultra-fast performance that edge computing delivers. This ensures objects are detected instantaneously. 

The NVIDIA AI for Smart Spaces Ebook covers several major vision AI use cases, all of which could be used in edge computing deployments. 

If you’re ready to get started working with edge computing solutions, check out NVIDIA LaunchPad. With LaunchPad, organizations can get immediate, short-term access to the necessary hardware and software stacks for an entire end-to-end flow deploying, managing, and validating an application at the edge. Hands-on labs walk users through the same workflow on the same technology that can be deployed in production, ensuring more confident software and infrastructure decisions can be made. With this free trial, organizations can see for themselves the types of use cases and applications that will work best in their environment to meet their goals. 

The edge computing industry is exciting and new. There are many emerging technologies that have a clear path to changing the way that organizations deploy and operate AI throughout their entire business. As organizations continue to adopt AI, infrastructure choices will continue to be paramount to innovative use cases. 

You can deep dive into how to assemble the components of an edge computing solution, including application, infrastructure, and security protocols in the Edge Computing 201 webinar: How to Build an Edge Solution. 

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from sklearn.neural_network import MLPRegressor from sklearn.pipeline import Pipeline from sklearn.compose import TransformedTargetRegressor from sklearn.preprocessing import StandardScaler base = MLPRegressor(max_iter=50, hidden_layer_sizes=(100,), early_stopping=True, learning_rate="adaptive") pipeline = Pipeline([('scaler', StandardScaler()), ('model', base)]) model = TransformedTargetRegressor(regressor=pipeline, transformer=StandardScaler()) 

from tensorflow.keras.layers import Input, Dense from tensorflow.keras.models import Model from tensorflow.keras.callbacks import EarlyStopping from tensorflow.keras.wrappers.scikit_learn import KerasRegressor def simple_tf_model(): dense_input = Input(shape=(train_X.shape[1],)) dense = Dense(100, activation="relu")(dense_input) dense = Dense(1)(dense) tf_model = Model(inputs=[dense_input], outputs=dense) tf_model.compile(loss="mse",optimizer="adam") return tf_model # monitor='val_loss' with validation_split=0.1 based on the early_stopping and validation_fraction parameters of MLPRegressor es = EarlyStopping(monitor='val_loss', mode='min', verbose=1) # batch_size=200 used as equivalent to batch_size="auto" for MLPRegressor tf_model = KerasRegressor(build_fn=simple_tf_model, batch_size=200, epochs=50, validation_split=0.1, callbacks=[es]) pipeline = Pipeline([('scaler', StandardScaler()), ('model', tf_model)]) model = TransformedTargetRegressor(regressor=pipeline, transformer=StandardScaler()) model.fit(train_X, train_y) 

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This post covers how network professionals can update their data center network infrastructure and protocol stack.

Data centers can be optimized by updating key network architectures in two ways: through networking technologies or operational efficiency in NetDevOps. In this post, we identify and evaluate technologies that you can apply to your network architecture to optimize your network.

We address five updates that you should consider for improving your data center:

• Replace layer 2 VLANs with VXLAN.
• Use Address Resolution Protocol (ARP) suppression to reduce broadcast propagation.
• Replace multi-chassis link aggregation group (MLAG) with EVPN multihoming.
• Handle traffic balancing with equal-cost multi-path (ECMP) routing and UCMP.
• Accommodate traffic polarization with adaptive routing.

Replace VLANs with VXLANs

VXLAN is an overlay technology that uses encapsulation to allow a layer 2 overlay VLANs to span across layer 3 networks. Layer 2 networks have some inherent disadvantages:

• Because they rely on spanning tree protocol (STP), the capability for redundancy and multiple paths is limited by the functionality of spanning tree.
• They can only operate within one subnet, and redundancy is normally only limited to two devices due to MLAG.
• Any path-level redundancy requires the Link Aggregation Control Protocol (LACP), the standard redundancy technology for ports.

VXLAN overcomes these deficiencies and allows the network operator to optimize on a layer 3 routed fabric. A layer 2 overlay can still be accomplished, but no longer requires spanning tree for control plane convergence due to the reliance of EVPN as the control plane.

EVPN exchanges MAC information through a BGP address family, instead of relying on the inefficiencies of broadcast flood and learn. Plus, VXLAN uses a 24-bit ID that can define up to 16 million virtual networks, whereas VLAN only has a 12-bit ID and is limited to 4094 virtual networks.

Use ARP suppression to reduce broadcast propagation

Broadcast traffic in data centers with VXLAN can be further optimized with ARP suppression. ARP suppression helps reduce traffic by using EVPN to proxy responses to ARP requests directly to clients from the ToR virtual tunnel end point (VTEP).

• Without ARP suppression, all ARP requests are broadcast throughout the entire VXLAN fabric, sent to every VTEP that has a VNI for the network.
• With ARP suppression enabled, MAC addresses learned over EVPN are passed down to the ARP control plane.

The leaf switch, which acts as the VTEP, responds directly back to the ARP requester through a proxy ARP reply.

Because the IP-to-MAC mappings are already communicated through the VXLAN control plane using EVPN type 2 messages, implementing ARP suppression enables optimization for faster resolution of the overlay control plane. It also reduces the amount of broadcast traffic in the fabric, as ARP suppression reduces the need for flooding ARP requests to every VTEP in the VXLAN infrastructure.

Replace MLAG with EVPN multihoming

Sometimes MLAG is still required in VXLAN environments for redundant host connectivity. EVPN multihoming is an opportunity to move off proprietary MLAG solutions that do not scale beyond one level of device redundancy.

As I mentioned earlier, VXLAN helps remove the need for back-to-back leaf-to-spine switch connections as required by MLAG. EVPN multihoming goes one step further and eliminates any need for MLAG in server-to-leaf connectivity.

Multihoming uses EVPN messages to communicate host connectivity, and it dynamically builds L2 adjacency to servers using host connectivity information. Where MLAG requires LAG IDs, multihoming uses Ethernet segment IDs. Interfaces are mapped to segments that act like logical connections to the same end host.

Additionally, moving to multihoming improves network vendor interoperability by using a protocol standard form of redundancy in the switch. Because multihoming uses BGP, an open standard protocol, any vendor implementing multihoming through the RFC specification can be part of the Ethernet segment.

ECMP and UCMP to handle traffic balancing

ECMP is a standard function in most layer 3 routing protocols where equal-cost routes are balanced across all available next-hop uplinks. Layer 2 control plane technologies like spanning-tree only allow equal-cost balancing by relying on external technologies like LACP.

ECMP is a native functionality in layer 3 routing, which enables you to get more efficiency out of your network devices.

There are cases where ECMP may lead to inefficient forwarding, specifically when doing a full layer 3 solution where point-to-point L3 links are used everywhere in the fabric, even to the host. In this case, you may want to balance traffic on a metric other than the number of links. UCMP can be useful here, as it uses BGP tags to create a distribution of traffic across hops to align better to your application distribution.

Accommodate traffic polarization with adaptive routing

Adaptive routing is an existing InfiniBand technology adopted by Ethernet switching. Adaptive routing monitors link bandwidth, link utilization, switch buffers, and ECN/PFC to understand when traffic on a specific path has become congested and would benefit from being dynamically rerouted through a less congested path.

Based on meeting these metric’s thresholds, the switch can redirect traffic from one egress interface to another egress interface in the ECMP group. This helps in fully leveraging all links on the switch equally, without the threat of polarization creating an inefficient traffic flow.

The goal of adaptive routing is to take any manual tuning intervention out of the hands of a network admin and let the infrastructure handle the optimizations for aggregate flow balancing.

Conclusion

In this post, we covered some concepts available in data center networking that can help you optimize a network infrastructure by focusing on the protocol stack and data plane. These optimizations provide better network virtualization, help reduce unnecessary control traffic on the infrastructure, and balance traffic across existing layer 1 links to fully use all the bandwidth available.

• For more information, see the following resources:
  NVIDIA Networking Solutions
• EVPN Enhancements – ARP Suppression
• EVPN Multihoming
• Equal-Cost Multi-path Load Sharing – Hardware ECMP
• Unequal-Cost Multi-path with BGP Link Bandwidth

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def construct_disc(label_dim=50): # Kernel Init init = tf.keras.initializers.HeUniform(seed=1) # Create Discriminator Inputs im_in = tf.keras.layers.Input(shape = (8,8,3)) lab_in = tf.keras.layers.Input(shape = (label_dim,)) # Style Vector Describer D = tf.keras.layers.Dense(3 * 3 * 3, activation=tf.keras.layers.LeakyReLU(alpha=0.2), kernel_initializer=init)(lab_in) D = tf.keras.layers.Dense(3 * 3 * 3, activation=tf.keras.layers.LeakyReLU(alpha=0.2), kernel_initializer=init)(D) D = tf.keras.layers.Dense(3 * 3 * 3, activation=tf.keras.layers.LeakyReLU(alpha=0.2), kernel_initializer=init)(D) # Conv Block Begins G = tf.keras.layers.Conv2D(128,1, padding = 'same', activation=tf.keras.layers.LeakyReLU(alpha=0.2), kernel_initializer=init)(im_in) G = tf.keras.layers.Conv2D(128,3, padding = 'same', activation = tf.keras.layers.LeakyReLU(alpha=0.2), kernel_initializer=init)(G) # Create Dense Style Interpreter (This Is Part Of The Block) W = tf.keras.layers.Dense(1)(D) W = W[:,:,tf.newaxis,tf.newaxis] B = tf.keras.layers.Dense(1)(D) B = B[:,:,tf.newaxis,tf.newaxis] G = tf.math.multiply(W, G) G = tf.add(G, B) G = tf.keras.layers.Conv2D(128,3, padding = 'same', activation = tf.keras.layers.LeakyReLU(alpha=0.2), kernel_initializer=init)(G) # Block Ends Here ^ G = tf.keras.layers.AveragePooling2D(2)(G) G = tf.keras.layers.Conv2D(128,3, padding = 'same', activation = tf.keras.layers.LeakyReLU(alpha=0.2), kernel_initializer=init)(G) G = tf.keras.layers.Flatten()(G) out = tf.keras.layers.Dense(1, activation='sigmoid', kernel_initializer=init)(G) model = tf.keras.Model([im_in, lab_in], out) # Compile Model opt = tf.keras.optimizers.Adam(lr=0.0002, beta_1=0.5) model.compile(loss=tf.keras.losses.BinaryCrossentropy(from_logits=True), optimizer=opt) return model 

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Join expert speakers and the developer community at WeAreDevelopers World Congress June 14-15, to exchange ideas, share knowledge, and facilitate networking.

Join NVIDIA speakers in person at the WeAreDevelopers World Congress, held June 14-15 in Berlin, Germany. Over 2 days, the event brings together the developer world with over 200 speakers and 5,000 developers gracing the show floor. 

Speakers and thought leaders from companies like GitHub, Google, and Stripe will discuss a multitude of developer topics. From programming languages and frameworks to DevOps and containers to machine learning and smart devices, there is a session for every developer.

Take a look at the WeAreDevelopers World Congress schedule.

Learn from NVIDIA experts

Below is a preview of what NVIDIA experts will be speaking about at the conference.

Graph Neural Networks: What’s Behind the Hype?

Stage 5 | Wednesday, June 15 | 9:50 – 10:20 a.m.

Ekaterina Sirazitdinova, data scientist at NVIDIA

Graph neural networks (GNNs) are AI models designed to derive insights from unstructured data described by graphs. For different segments and industries, GNNs find suitable applications such as molecular analysis, drug discovery, prediction of developments in stock market, thermodynamics analysis, and even modeling of the human brain. Unlike conventional CNNs, GNNs address the challenge of working with data in irregular domains.

In this talk, Ekaterina will provide an introductory overview of the theory behind GNNs, take a closer look at the types of problems that GNNs are well suited for, and discuss several approaches to model unstructured problems as classification or regression at various levels.

Enhancing AI-based Robotics with Simulation Workflows

Stage 5 | Wednesday, June 15 | 11:50 a.m. – 12:20 p.m.

Teresa Conceicao, senior solutions architect at NVIDIA

With an ever-growing AI and data-centric world, robots will become more intelligent, flexible, and robust. However, with these new paradigms, some challenges arise. AI Robots need data, training, and testing—and lots of it. Acquiring real data can be costly and laborious, training in the real world is not scalable, and finding real-world scenarios for difficult use cases can be near impossible. While testing cycles are time consuming and slow down development iterations. 

In this session, Teresa will go over how Simulation can play a key role in enabling AI-based Robotics, and how to get started enhancing AI workflows with the NVIDIA Omniverse and NVIDIA Isaac platforms.

Natural Language Processing: Changing the Way We Tell Machines What We Want Them To Do

Available on-demand during the conference

Adam Grzywaczewski, senior deep learning data scientist at NVIDIA

Software development processes involve describing the desired functionality using a formal programming language, such as C++ or Python. Recent progress in NLP brings us closer to a point where formal programming languages are no longer necessary, and can be replaced by a plain text description of program’s behavior. An early example of this capability can be seen in GitHub coPilot.

In this talk, Adam will discuss the most recent breakthroughs in NLP and the reasons for their success. He’ll focus on how advances in AI have changed the software development process for NLP models to learn new, previously unseen tasks. He’ll conclude by sharing how tools such as NVIDIA Megatron-LM can be used to build SOTA NLP models like GPT-3 and deploy them to production.

WeAreDevelopers job board

Browse over 2,900 jobs across Europe, covering every type of developer role. Frontend, backend, full-stack, software, JavaScript, PHP, C#… whatever your skill set, they have a job listing for it. 

See the WeAreDevelopers job board. >>

Hands-on labs, tutorials, and resources from NVIDIA

Join the NVIDIA Developer Program for free and exclusive access to SDKs, technical documentation, peer, and domain expert help. NVIDIA offers tools and training to accelerate AI, HPC, and graphics applications.

Discover the NVIDIA Deep Learning Institute for resources covering diverse learning needs—from learning materials to self-paced and live training to educator programs—giving individuals, teams, organizations, educators, and students tools to advance AI, accelerated computing, data science, graphics, and simulation knowledge.

Connect with NVIDIA at the WeAreDevelopers World Congress 2022. >>