automation

All Your IT Team Wants This Holiday Season is a Break!

The holiday season is all about giving. As organizations increasingly look to IT as they move toward new digital tools and processes, now is the perfect time to give back to IT teams tirelessly working to keep the modern enterprise online. Whether your system performance has been naughty or nice this year, there’s no denying that tech professionals have earned our appreciation, respect—and the tools to set them up for success in 2024. For IT teams limited in both time and resources, simply maintaining systems can feel as impossible as squeezing themselves down a chimney or delivering gifts to millions of homes in a single night. On top of that, instead of being greeted with milk and cookies, they’re inundated with endless performance issues, support requests and alerts—leaving little time left over for the important work of innovating. They say the best gifts are the ones you can’t wrap. That holds true for IT teams, too. This year, bring your organization the gift of a simpler, speedier, more rewarding workload. If your team is dreaming of a tech-savvy future, here are some enterprise software solutions to make their lives easier that they won’t want to re-gift: Enjoy the View With Observability Everyone loves to cozy up at home during a winter snowstorm, but with the widespread migration to combined remote, on-premises and distributed hybrid environments, the daily monitoring journey for today’s IT teams is more akin to trekking blindly through a blizzard. Observability tools are metaphorical snowshoes and goggles that can help them not only weather the storm but see clearly from the mountaintop. Observability is the answer to the modern enterprise’s struggle to gain full visibility into their organization’s apps, networks, databases and infrastructure—something nearly half of IT professionals lack, according to SolarWinds research. IT teams will be able to rest easier at night with visions of sugarplums, rather than outages or anomalies, dancing in their heads. Even better, integrating artificial intelligence (AI) capabilities into observability solutions to collect and provide data on what’s not performing as expected and why will help your teams take a proactive approach to solving issues. Lend a Helping Hand With AIOps AI isn’t just the shiny new toy of the tech world. Organizations using AI for IT operations (AIOps) can give the gift of support to their overworked IT teams by automating some of the time-consuming and mundane tasks that stand between them and a focus on innovation. Adding AIOps to observability can provide IT teams with maximum visibility into the state of their digital ecosystems through automated discovery and dependency mapping. Additionally, your teams can gain the ability to easily track inbound connections linked across the organization’s application stack and storage volumes with auto-instrumented views. Today, it simply isn’t feasible for humans alone to manage modern IT environments without intelligent automation. Think of AIOps as a workshop of elves operating in the background to ensure workloads and processes are streamlined and moving as efficiently as possible. With AIOps in place to analyze data and streamline workloads and processes, IT teams are relieved of some pressure—and can focus on accelerating your digital transformation rather than just maintaining it. Give the Gift of Time Finally, although you can’t outright give the gift of time to your IT team, you can still arm them with […]

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How to Solve the GPU Shortage Problem With Automation

GPU instances have never been as precious and sought-after as they have since generative AI captured the industry’s attention. Whether it’s due to broken supply chains or the sudden demand spike, one thing is clear: Getting a GPU-powered virtual machine is harder than ever, even if a team is fishing in the relatively large pond of the top three cloud providers. One analysis confirmed “a huge supply shortage of NVIDIA GPUs and networking equipment from Broadcom and NVIDIA due to a massive spike in demand.” Even the company behind the rise of generative AI–OpenAI–suffers from a lack of GPUs. And companies have started adopting rather unusual tactics to get their hands on these machines (like repurposing old video gaming chips). What can teams do when facing a quota issue and the cloud provider runs out of GPU-based instances? And once they somehow score the right instance, how can you make sure no GPUs go to waste? Automation is the answer. Teams can use it to accomplish two goals: Find the best GPU instances for their needs and maximize their utilization to get more bang for their buck. Automation Makes Finding GPU Instances Easier The three major cloud providers offer many types and sizes of GPU-powered instances. And they’re constantly rolling out new ones–an excellent example of that is AWS P5, launched in July 2023. To give a complete picture, here’s an overview of instance families with GPUs from AWS, Google Cloud and Microsoft Azure: AWS P3 P4d G3 G4 (this group includes G4dn and G4ad instances) G5 Note: AWS offers Inferentia machines optimized for deep learning inference apps and Trainium for deep learning training of 100B+ parameter models. Google Cloud Microsoft Azure NCv3-series NC T4_v3-series ND A100 v4-series NDm A100 v4-series When picking instances manually, teams may easily miss out on opportunities to snatch up golden GPUs from the market. Cloud automation solutions help them find a much larger supply of GPU instances with the right performance and cost parameters. Considering GPU Spot Instances Spot instances offer significant discounts–even 90% off on-demand rates–but they come at a price. The potential interruptions make them a risky choice for important jobs. However, running some jobs on GPU spot instances is a good idea as they accelerate the training process, leading to savings. ML training usually takes a very long time–from hours to even weeks. If interruptions occur, the deep learning job must start over, resulting in significant data loss and higher costs. Automation can prevent that, allowing teams to get attractively-priced GPUs still available on the market to cut training and inference expenses while reducing the risk of interruptions. In machine learning, checkpointing is an important practice that allows for the saving of model states at different intervals during training. This practice is especially beneficial in lengthy and resource-intensive training procedures, enabling the resumption of training from a checkpoint in case of interruptions rather than starting anew. Furthermore, checkpointing facilitates the evaluation of models at different stages of training, which can be enlightening for understanding the training dynamics. Zoom in on Checkpointing PyTorch, a popular ML framework, provides native functionalities for checkpointing models and optimizers during training. Additionally, higher-level libraries such as PyTorch Lightning abstract away much of the boilerplate code associated with training, evaluation, and checkpointing in PyTorch. Let’s take a […]

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