Keeping up with the ever-changing world of cloud technologies can be a real challenge for both companies and IT folks. Having solid skills in cloud computing is an absolute must-have in today's tech scene. That's why our cloud training programs are tailor-made to suit the needs of IT professionals, leaders, and businesses, no matter where they're at in their cloud journey.
In this online training course, you'll dive deep into the world of VMware Cloud solutions. You'll get to know all the important bits and bobs and learn how to smoothly connect and move stuff to VMware Cloud across different big players in the cloud game. By the end of it all, you'll be a pro at spotting and fixing any pesky cloud issues within your organization and even outside, making you the star player in a super competitive business world.
This learning path sets you up for the VMware Certified Professional – VMware Cloud 2024 exam. You'll get the lowdown on storage, networking, security, and all that jazz, boosting your skills in keeping things running smoothly and dealing with problems in a VMware Cloud setup.
ElevenLabs is a leader in voice technology, offering solutions for diverse voice generation needs. Their portfolio includes readily available high-quality pre-made voices, the innovative Voice Design feature for custom voice creation, and two advanced voice cloning options: Instant Voice Cloning and Professional Voice Cloning.
These voices, perfect for various applications, are free and of high quality. While primarily trained in English, they can adapt to other languages, potentially with an English accent. With Voice Design, users can customize a voice by selecting gender, age, and accent, including different English accents. While the quality is on par with cloned voices, achieving the desired result might take multiple attempts. Unique voices can be shared in the Voice Library, allowing users to recoup part of their quota.
This tool enables quick cloning of a voice, relying heavily on the quality of provided audio samples. Optimal audio length is 1-3 minutes, focusing on clarity and consistency rather than quantity. For a more accurate clone, Professional Voice Cloning requires high-quality audio samples, ideally around 3 hours. The same sharing and quota benefits apply as with the Voice Design feature.
In both cloning methods, the clarity and quality of the audio are crucial. Consistent volume and minimal background noise lead to better results. It's also important to remember that cloned voices retain the accent of the original sample when speaking other languages.
ElevenLabs also offers a Python module, allowing the programmatic generation of speech.
In the ever-evolving landscape of smart home technology, one term has been steadily gaining momentum: Matter. As a universal standard for connected home devices, Matter promises to revolutionize how our devices communicate, making smart homes more interconnected, secure, and accessible than ever before. In this article, we delve into various aspects of the Matter standard and its implementation across different devices and platforms.
1. The Heart of Matter: Home Pod Mini and Home Pod 2
The journey into the Matter-enabled world begins with the Home Pod Mini and Home Pod 2. These devices are not just speakers; they are the central hubs that bring the smart home ecosystem to life. With Matter compatibility, these devices ensure seamless integration and interoperability with a wide range of smart home gadgets.
2. Sonoff's Innovation: The SONOFF MINI Extreme
A notable mention in the Matter-compatible device list is the SONOFF MINI Extreme Wi-Fi Smart Switch. This compact device allows users to control their home appliances remotely, adding convenience and efficiency to everyday tasks.
3. Eve: Simplifying Smart Home Automation
Eve's range of Matter-compatible devices further simplifies home automation. Although details on specific products are not outlined, Eve's reputation in the smart home market suggests an exciting range of Matter-enabled devices.
4. Raspberry Pi: The DIY Approach to Matter
The Raspberry Pi offers a more hands-on approach to Matter. From building a Matter home automation service to running the Matter Demo over Wi-Fi, Raspberry Pi serves as a versatile platform for developers and enthusiasts alike. It highlights projects like the Open Thread Border Router, using the Mattertool, and building a Matter Hub using Raspberry Pi.
5. Expanding Horizons with Matter on Ubuntu
Matter's flexibility extends to different operating systems, with Ubuntu emerging as a strong platform for developing Matter-compatible devices. This opens avenues for reducing development costs and encourages innovation in open-source smart home solutions.
6. Node-RED and Home Assistant: User-Friendly Interfaces
Node-RED Matter plugin and Home Assistant's beta for Matter introduce user-friendly interfaces for managing and automating smart home devices. These platforms make it easier for non-technical users to enjoy the benefits of Matter.
7. Silicon Labs and Home Bridge: Expanding the Ecosystem
Silicon Labs' Developer Documentation and products like the SLEXP8023A and WFM200S Wi-Fi Expansion Kit demonstrate the growing hardware support for Matter. Home Bridge's discussion on Raspberry Pi 5 support further indicates the expanding ecosystem.
8. IFTTT and IOS Shortcuts: Simplifying Automation
Finally, IFTTT and IOS Shortcuts represent the bridge between Matter devices and wider automation possibilities, allowing users to create custom scenarios and control their smart homes in unique ways.
Conclusion
Matter is not just a standard; it's a gateway to a more unified, secure, and user-friendly smart home experience. As we witness more devices and platforms embracing Matter, the dream of a truly interconnected smart home is becoming a reality. Stay tuned as this exciting technology continues to evolve, promising a smarter, more connected future for us all.
NVIDIA's Jetson Platform: Powering the Future
NVIDIA, a leading name in AI and deep learning, has been making strides with its Jetson platform. The Jetson series, known for its powerful GPUs, is a game changer in AI and machine learning applications.
Jetson Nano: The Compact AI Powerhouse
The Jetson Nano is an affordable, yet powerful option. Products like the Kiwi reComputer J3010, featuring the NVIDIA Jetson Orin Nano 4GB module, are perfect for AI projects needing compactness without sacrificing performance. Also notable is the NVIDIA Jetson AGX Xavier Developer Kit, which, when paired with accessories like the Logitech C270 HD Webcam, becomes a robust tool for advanced AI applications.
The Jetson Nano Developer Kits, such as the 945-13450-0000-100 model, offer enthusiasts and professionals alike the opportunity to dive into AI development. NVIDIA also offers free courses through its Deep Learning Institute, like "Getting Started with AI on Jetson Nano," providing an excellent entry point for AI enthusiasts.
Raspberry Pi: Democratizing AI
The Raspberry Pi has been a revelation in making technology accessible. Particularly, its application in AI through machine learning is remarkable.
With comprehensive guides like "The Complete Guide to Setting Up Your Raspberry Pi 3 2023 (Mac)," enthusiasts can embark on their AI journey. TensorFlow Lite's integration with Raspberry Pi, especially for object detection, opens up numerous possibilities for AI applications.
The launch of the Raspberry Pi Camera 3, which can be seamlessly integrated with AI frameworks, further empowers developers to create sophisticated AI-powered applications.
Other Noteworthy AI Platforms
Google Coral: An Emerging Competitor
Google's Coral platform, though not as detailed in the provided structure, is another significant player in the AI hardware space, known for its speed and efficiency in running AI models.
Brainy Pi: A Cost-effective Alternative
For those looking for a more cost-effective alternative to the Jetson Nano, Brainy Pi presents a viable option. It's designed to run AI applications efficiently at lower costs, as detailed in resources like "Brainy Pi: A Jetson Nano Alternative to Run AI Applications at Lower Cost."
Conclusion
AI is rapidly evolving, with platforms like NVIDIA's Jetson, Raspberry Pi, Google Coral, and Brainy Pi leading the charge. Each offers unique capabilities and opportunities for both beginners and seasoned professionals in AI. As AI continues to integrate into various aspects of technology, these platforms will play a pivotal role in shaping the future of AI applications.
UTM is revolutionizing the way we think about virtual machines on Apple devices. It's a comprehensive system emulator and virtual machine host, specially designed for iOS and macOS. Built on the robust foundation of QEMU, UTM enables users to seamlessly run various operating systems like Windows and Linux on their Mac, iPhone, and iPad.
UTM is a game-changer for Apple Silicon users. It uses Apple's Hypervisor virtualization framework, allowing ARM64 operating systems to run at speeds almost identical to native performance. For those with Intel Macs, there's the ability to virtualize x86/x64 operating systems. And it doesn’t stop there - UTM also offers lower performance emulation for running x86/x64 on Apple Silicon and ARM64 on Intel. This versatility extends to supporting a range of other emulated processors, including ARM32, MIPS, PPC, and RISC-V, making your Mac a truly universal platform.
UTM isn’t just about the new and shiny; it also breathes life into classic operating systems. Whether it's an old PowerPC, SPARC, or x86_64 system, UTM lets you revisit the digital past. Curious users can explore a gallery showcasing various systems that UTM can emulate.
Apple Silicon Mac users can take virtualization a step further with UTM. It allows the running of multiple instances of macOS, which is particularly beneficial for developers and security-focused users. This feature, however, is exclusive to ARM-based Macs running macOS Monterey or higher.
UTM stands out from other virtualization software due to its design, which is exclusively for macOS and Apple platforms. It seamlessly blends with the Big Sur style, offering a user experience that feels genuinely Mac-like, complete with all the expected privacy and security features.
At its core, UTM harnesses the power of QEMU, a well-established, free, and open-source emulation software. QEMU's complexity is well-known, often deterring users with its array of command-line options. UTM eliminates this barrier, offering the robust features of QEMU without the complexity, making it accessible to a wider range of users.
Virtual machines on Apple devices have never been more accessible and versatile, thanks to UTM. Whether you're a developer, an enthusiast, or just someone who needs to run different operating systems on your Apple device, UTM is the tool you've been waiting for. Discover more about UTM and how it can transform your virtual machine experience on Apple devices at https://getutm.app/ and https://mac.getutm.app/.
In this article, we'll explore how ChatGPT can automate the generation of a Python script for managing the power states of virtual machines within VMware vCenter. To initiate, you need to prompt ChatGPT to generate a script by posing the question:
Could you develop a Python script for VMware vCenter to retrieve the virtual machines and allow the user to control their power states? The server details are: host name 'vc.ntpro.local', username 'administrator@ntpro.local', password 'VMware1!' Please ignore certificate errors and use tls.
ChatGPT's response will be affirmative, recommending the use of the pyvmomi library to interface with VMware vCenter. It will advise on installing the pyvmomi library via pip, if not already done so.
You can find the complete Python code through this link.
For Python project management, I prefer the PyCharm Community Edition. The pyvmomi module is crucial for our tasks and can be conveniently installed via the terminal with 'pip install pyvmomi'. PyCharm also offers a GUI for handling Python packages.
After generating a new Python file, insert the ChatGPT-provided script into PyCharm's editor. Execution may require some adjustments, like updating TLS versions. Should errors arise, ChatGPT is equipped to troubleshoot and provide solutions. Once the refined script is implemented, it successfully retrieves and manages the power states of the virtual machines from vCenter.
In a subsequent step, I prompted ChatGPT to integrate a basic GUI into the script using the pre-installed TKInter module from Python3. The resulting interface displays a virtual machine list with functional power controls. Despite a minor glitch with the "wait task" during the demonstration, the core functionality was unaffected. Remember, repeated queries won't yield identical results when soliciting code from ChatGPT. Precision in your inquiries enhances ChatGPT's ability to generate effective code. If your code encounters issues, ChatGPT can help debug and even elucidate the script's workings in depth. Enjoy coding!
vSphere comes equipped with a vast array of metrics and properties. A detailed object-by-object and metric-by-metric documentation might seem dry and overly theoretical, potentially leading to disappointment, as it might not directly address real-world problem-solving. Collecting metrics is not the core of your business.
This document is tailored for experienced VMware professionals focused on optimizing and troubleshooting in production environments. It introduces metrics using the Triple See Method, a strategy that aligns metrics with operational management.
This is an advanced-level text, spanning over 300 pages. It's not a quick read, so Iwan 'e1' Rahabok suggested settling down with a coffee or your preferred beverage for a thorough and quiet reading session.
In this article, we explore the innovative implementation of VMware's ESXi ARM on the Raspberry Pi 5. The focus is on setting up ESXi ARM on the Raspberry Pi 5, which was initially challenged by a lack of UEFI support. This obstacle is surmounted by Mario Bălănică's development of a Raspberry Pi 5-specific UEFI bios. The setup process is detailed, emphasizing the necessity of an external USB network adapter, and the steps from microSD card preparation to management network configuration.
In 2018, Pat Gelsinger, then CEO of VMware, made a groundbreaking announcement at the VMworld keynote presentation in Las Vegas, revealing the ability to operate ESXi on a Raspberry Pi. Following the presentation, I connected with Alexander Fainkichen, a pivotal contributor to the initiative, for a sit-down interview. As a presenter for VMworld TV at a time when edge computing was surging in popularity, I had the opportunity to delve into the subject. The full interview remains accessible on VMware’s Explore channel.
‘Back at Vegas, people asked us in jest whether the Pi could ever run ESX. We decided it was worth showing that it is possible. ESX on Pi is a promise that VMware understands that the Edge includes extremely low end devices as well. Scaling with the workload and envo is important!’
I recently acquired the cutting-edge Raspberry Pi 5, aiming to set up ESXi ARM. It was tailored for the Raspberry Pi 4, but initial attempts to operate it on the Raspberry Pi 5 were met with a challenge due to the absence of UEFI support.
Before proceeding, it's important to note a few prerequisites. The Raspberry Pi 5's built-in network adapter is marked an unsupported UEFI peripheral, necessitating the use of an external USB network adapter. For my setup, I chose the Maxonar USB C Ethernet Adapter, which offers reliable Gigabit LAN connectivity.
The installation procedure is quite direct. Begin by copying the UEFI files onto a microSD card formatted to FAT32. Next, transfer the ISO contents of the ESXi ARM installer onto a FAT32-compliant USB drive. Once you power up the Raspberry Pi, the installation should initiate on its own.
During my initial setup, the installation paused at 81%, hindered by the lack of a network adapter for the management network. This hiccup is overcome on the first reboot, where the USB network adapter is recognized, allowing the configuration of the management network thereafter.
The storage setup utilizes a USB interface, connecting through a robust "UGREEN M.2 Adapter NVMe SSD Hard Drive Enclosure" which houses a swift "Samsung SSD 256GB PM991 M.2 2242 42mm PCIe 3.0 x4 NVMe" drive. This USB drive plays a triple role: it's the install-boot media, the destination for the ESXi install, and the local storage for VMFS (Virtual Machine File System). You can leave it inserted after the installation completes. In future enhancements, I plan to integrate the Samsung SSD into my setup more seamlessly, using the PineBerry m.2 HATDrive, interfaced via NVMe.
In this video, I've captured the entire installation process from beginning to end, and it takes just 5 minutes to complete. The recording was done using a Video Capture Card, a Guermok USB 3.0 HDMI to USB C Audio Capture Card, and QuickTime on a Mac.
Once the installation is finalized, the next step is to start running virtual workloads. My first action was to set up a Photon virtual machine; VMware conveniently provides an ARM-compatible OVA file. The process is hassle-free, since I'm using Virtual Center to manage the ARM-based ESXi host—just a few clicks and the virtual machine springs to life.
The subsequent hurdle was setting up a Windows environment. Locating an ARM-compatible Windows ISO proved challenging, but after a diligent search, I found one. I uploaded the ISO to the local data store and configured a Windows VM. The installation of Windows was time-consuming, and the initial startup was even more prolonged. Eventually, a blue screen error interrupted the process, which requires further investigation.
Initially, I operated on a Raspberry Pi 5 with 4 GB of RAM, but soon transitioned to a more robust 8 GB model to accommodate additional VM workloads. Monitoring of the vCenter server is conducted through Aria Operations, which allows me to keep an eye on the ESXi ARM host and virtual machines via comprehensive dashboards. I've also set up Aria Operations for log management, simplifying the troubleshooting process.
In summary, the article showcases the advancements and challenges in setting up and running ESXi ARM on the Raspberry Pi 5. It highlights the technical requirements, the setup process, and the potential for hosting various virtual workloads, along with the importance of continuous monitoring and system optimization. Special thanks to Jimmy van der Mast for providing technical expertise.
In the ever-evolving landscape of virtualization management, efficiency is key. VMware's vCenter Server is the heart of many virtual environments, orchestrating a symphony of VMs with precision. But what if you could take vCenter's capabilities to the next level? Enter Python – the versatile scripting language that speaks directly to vCenter, unlocking a new realm of automation and control.
Python is more than just a programming language; it's a gateway to automation. With the Python SDK for VMware, known as PyVmomi, you can script complex operations that manage your virtual machines, all within the comfort of Python’s syntax. Whether you’re looking to power on a fleet of VMs, take snapshots before a big update, or gracefully shut down systems for maintenance, Python and PyVmomi make it possible with minimal effort.
Imagine a script that not only lists all VMs registered in your vCenter but also offers you the control to power them on, suspend, resume, or even take a snapshot. This is not a mere imagination anymore; it's a reality that I've crafted into a Python script. This script is your remote control to vCenter, putting the power of virtual machine management at your fingertips.
The script starts by connecting to your vCenter server using your credentials. Once authenticated, it fetches a list of all VMs and displays them neatly. Here's where the interactivity comes in – you choose a VM and decide what to do with it. Want to power it on or shut it down? Just a command away. Need to suspend or resume it? A simple input does the job. Looking to create a snapshot? It’s just as easy.
from pyVim.connect import SmartConnect, Disconnect
from pyVmomi import vim
import ssl
import atexit
# vCenter Server details
vc_server = 'vc.ntpro.local'
username = 'administrator@ntpro.local'
password = 'VMware1!'
# Disable SSL certificate verification (for demo purposes only, not recommended for production)
context = ssl._create_unverified_context()
# Function to connect to vCenter
def connect_to_vcenter(server, user, password):
si = SmartConnect(host=server, user=user, pwd=password, sslContext=context)
atexit.register(Disconnect, si)
return si
# Function to get all VMs
def get_all_vms(si):
content = si.RetrieveContent()
container = content.viewManager.CreateContainerView(content.rootFolder, [vim.VirtualMachine], True)
return container.view
# Power operations for the VM
def power_on_vm(vm):
if vm.runtime.powerState != vim.VirtualMachinePowerState.poweredOn:
task = vm.PowerOnVM_Task()
wait_for_task(task)
print(f"VM '{vm.name}' is powered on.")
else:
print(f"VM '{vm.name}' is already powered on.")
def power_off_vm(vm):
if vm.runtime.powerState != vim.VirtualMachinePowerState.poweredOff:
task = vm.PowerOffVM_Task()
wait_for_task(task)
print(f"VM '{vm.name}' is powered off.")
else:
print(f"VM '{vm.name}' is already powered off.")
def suspend_vm(vm):
if vm.runtime.powerState == vim.VirtualMachinePowerState.poweredOn:
task = vm.SuspendVM_Task()
wait_for_task(task)
print(f"VM '{vm.name}' is suspended.")
else:
print(f"VM '{vm.name}' cannot be suspended because it is not powered on.")
def resume_vm(vm):
if vm.runtime.powerState == vim.VirtualMachinePowerState.suspended:
task = vm.PowerOnVM_Task()
wait_for_task(task)
print(f"VM '{vm.name}' is resumed.")
else:
print(f"VM '{vm.name}' is not suspended.")
def create_snapshot(vm):
task = vm.CreateSnapshot_Task(name='Snapshot', description='Created by script', memory=False, quiesce=False)
wait_for_task(task)
print(f"Snapshot for VM '{vm.name}' created.")
# Wait for vCenter task to complete
def wait_for_task(task):
task_done = False
while not task_done:
if task.info.state == vim.TaskInfo.State.success:
return
if task.info.state == vim.TaskInfo.State.error:
print(f"Task failed: {task.info.error}")
raise Exception("Task failed")
# Main script logic
if __name__ == "__main__":
si = connect_to_vcenter(vc_server, username, password)
vms = get_all_vms(si)
vms_dict = {vm.name: vm for vm in vms}
print("List of VMs:")
for vm_name in vms_dict.keys():
print(vm_name)
selected_vm_name = input("Enter the name of the VM you wish to manage: ")
vm = vms_dict.get(selected_vm_name)
if vm:
print(f"Selected VM: {selected_vm_name}")
action = input("Choose an action: (on) Power On, (off) Power Off, (suspend) Suspend, (resume) Resume, (snapshot) Create Snapshot: ").lower().strip()
if action == 'on':
power_on_vm(vm)
elif action == 'off':
power_off_vm(vm)
elif action == 'suspend':
suspend_vm(vm)
elif action == 'resume':
resume_vm(vm)
elif action == 'snapshot':
create_snapshot(vm)
else:
print("Invalid action selected.")
else:
print(f"VM '{selected_vm_name}' not found.")
The script is designed with safety and ease of use in mind, leveraging Python’s clear syntax and PyVmomi’s powerful bindings to the vSphere API. It’s a tool that can be expanded, customized, and integrated into larger workflows or dashboards. I'm hosting the source code, along with some screenshots on my GitHub page.
Unlock the power of Python to seamlessly gather and harness data from Aria Operations (vROps). This blog post introduces a Python script that connects to vROps, efficiently authenticating to obtain a secure token. This token is the golden key, allowing the script to pull detailed metrics for a virtual machine named 'Raspberry'.
import requests
import json
# Replace these with your vROps details
vrops_host = 'https://ariaops.ntpro.local'
username = 'admin'
password = 'VMware1!'
# Endpoints
auth_url = f'{vrops_host}/suite-api/api/auth/token/acquire'
vm_search_url = f'{vrops_host}/suite-api/api/resources'
metrics_url_template = f'{vrops_host}/suite-api/api/resources/{{}}/stats'
# Authenticate and Get Token
headers = {'Content-Type': 'application/json', 'Accept': 'application/json'}
payload = {'username': username, 'password': password}
response = requests.post(auth_url, headers=headers, data=json.dumps(payload), verify=False)
token = response.json().get('token')
if not token:
raise Exception("Authentication failed")
# Update headers with the token
headers['Authorization'] = f'vRealizeOpsToken {token}'
# Function to get the ID of a VM by its name
def get_vm_id(vm_name):
vm_url = f'{vrops_host}/suite-api/api/resources'
response = requests.get(vm_url, headers=headers, verify=False)
if response.status_code == 200:
resources = response.json()['resourceList']
for resource in resources:
if resource['resourceKey']['name'] == vm_name:
return resource['identifier']
return None
# Function to get metrics for a VM by its ID
def get_vm_metrics(vm_id):
metrics_url = f'{vrops_host}/suite-api/api/resources/{vm_id}/stats'
response = requests.get(metrics_url, headers=headers, verify=False)
if response.status_code == 200:
return response.json()
return None
# Main script execution
vm_id = get_vm_id('raspberry')
if vm_id:
vm_metrics = get_vm_metrics(vm_id)
if vm_metrics:
print("Metrics for VM 'raspberry':")
print(json.dumps(vm_metrics, indent=4))
else:
print("Failed to retrieve metrics for VM 'raspberry'")
else:
print("VM 'raspberry' not found")
Through this script, you can effortlessly access the Aria Ops REST API, which is designed to safeguard and validate your data retrieval missions. Once authenticated, you can harvest a rich dataset, which can then be woven into your custom solutions or operational dashboards. By leveraging this script, raw data can be transformed into actionable insights, enhancing virtual machine monitoring and management.
The source code, complete with an example of the output and some screenshots, is hosted on my GitHub page.
