By Express News Service
KANNUR:”Finally, I want to say only one thing. If you have not been satisfied by hunting us down till now, we, including our children, would come to Caltex junction holding our heads high. Finish us off with a single chop.” This is the concluding part from a heart-rending Facebook post of media person Vineetha Venu, who and her husband Sumesh, a civil police officer, have allegedly been harassed and threatened by the CPM and its cyber warriors for more than three years.
What forced her to post the long note on FB is the latest organised cyber bullying which, she alleged, was by CPM and its well-oiled social media team. She says her husband has become a victim of moral policing by some CPM workers .
In the post, she described in detail about the latest incident. According to it, last Wednesday, Sumesh was on his way to visit his friend at Cheengakundam near Iritty. The friend is a military man and is posted in the Northeast India. As per the friend’s request to visit his parents. Sumesh, who works at Chombala police station, decided to visit them after his duty hours. On his way, he received a phone call and stopped his bike and got down from it. As he walked a few steps forward speaking with the friend, some people came and stopped him. That was the beginning. Though Sumesh tried to convince them about the purpose of his visit, they didn’t believe him and started harassing him. It was only when the police patrolling team from Iritty station reached the spot that they let him go.
At the police station, Sumesh gave his statement on the incident. Since nothing serious had taken place, Sumesh didn’t file any complaint against those were allegedly moral policing.
“What happened was a clean act of moral policing,” said Vineetha. But, things have taken an ugly turn from the next day as cyber soldiers of CPM started maligning Sumesh by spreading the videos of the moral policing incident through WhatsApp groups and FB.
To make matters worse, a baseless report appeared in the party mouthpiece, in which it was said that an investigation had been launched against a police officer who was caught by local residents in suspicious circumstances, she said.
The cyber bullying is part of a political vendetta, says Vineetha. “Everything started when I gave some reports against the party in connection with the murder of Youth Congress worker Shuhaib. My husband is a pro-UDF police association member and both of us have been harassed by the CPM for taking a stand against the party in connection with the Shuhaib murder.”
In the WhatsApp groups of police officers, Sumesh was being threatened, warning him that his limbs would be chopped off. Vineetha was stalked at many places and feared that their children would be attacked. “It was tough to survive those harrowing days,” she said.
When she lodged a complaint, it was investigated by the Crime Branch. The investigation was a farce as the officer in charge of investigation had acted rudely with her without even giving her the consideration of being a woman, she alleged. After she complained about it, the investigating officer explained that it was only a joke. “What kind of a joke it was?” she asked.
Sumesh was transferred seven times in the last three years. When Vineetha had reported about the leak of data from the Covid app of police on the TV news channel in which she was working, Akash Thillankeri, an accused in the Shuhaib murder case, campaigned that the news was leaked to her by her husband, she said. “I was not the first reporter to air that news,” she said. “My husband was transferred to Palakkad during the lockdown period due to this incident. Following continuous attack from the CPM and its cyber teams, I had to resign from my job as I was not able to cope up with the situation.”
“The mental trauma, we have been undergoing is beyond words,” said Vineetha. “Through this post, we don’t seek anybody’s support. Though I had tried to write about it many times before, I couldn’t do it out of fear. Many people would think this is silly, but I want to remind such people that if it is me today, it could be themtomorrow,” she said.
As Vineetha Venu’s Facebook post went viral, the CPM Iritty area committee denied the role of party members in the incident. “The FB post of the wife of the police officer was not based on facts and purely emotional. She says that CPM has been hunting them down unnecessarily. The local people and a few persons who were at the spot during the incident know what had taken place. The party refutes all allegations raised by the media person as they are baseless. Everybody knows about the politics of the CPM and the tactics he (the said officer) had used during the last LDF government’s term. The support of the people is the strength of CPM and the party will face these allegations also with the support of the people,” a statement issued by the area committee said.
Meet the Developers – Linux Kernel Team (David Vernet)
Credit: Larry Ewing (email@example.com) and The GIMP for the original design of Tux the penguin.
For today’s interview, we have David Vernet, a core systems engineer on the Kernel team at Meta. He works on the BPF (Berkeley Packet Filter) and the Linux kernel scheduler. This series highlights Meta Software Engineers who contribute to the Linux kernel. The Meta Linux Kernel team works with the broader Linux community to add new features to the kernel and makes sure that the kernel works well in Meta production data centers. Engineers on the team work with peers in the industry to make the kernel better for Meta’s workloads and to make Linux better for everyone.
Tell us about yourself.
I’m a systems engineer who’s spent a good chunk of his career in the kernel space, and some time in the user-space as well working on a microkernel. Right now, I’m focusing most of my time on BPF and the Linux kernel scheduler.
I started my career as a web developer after getting a degree in math. After going to grad school, I realized that I was happiest when hacking on low-level systems and figuring out how computers work.
As a kernel developer at Meta, what does your typical day look like?
I’m not a maintainer of any subsystems in the kernel, so my typical day is filled with almost exclusively coding and engineering. That being said, participating in the upstream Linux kernel community is one of the coolest parts of being on the kernel team, so I still spend some time reading over upstream discussions. A typical day goes something like this:
Hack on the project that I’m working on. Lately, that’s adding a user-space ringbuffer map type to BPF.
Work on drafting an article for lwn.net.
What have you been excited about or incredibly proud of lately?
I recently submitted a patch-set to enable a new map type in BPF. This allows user-space to publish messages to BPF programs in the kernel over the ringbuffer. This map type is exciting because it sets the stage to enable frameworks for user-space to drive logic in BPF programs in a performant way.
Is there something especially exciting about being a kernel developer at a company like Meta?
The Meta kernel team has a strong upstream-first culture. Bug fixes that we find in our Meta kernel, and features that we’d like to add, are almost always first submitted to the upstream kernel, and then they are backported to our internal kernel.
Do you have a favorite part of the kernel dev life cycle?
I enjoy architecting and designing APIs. Kernel code can never crash and needs to be able to run forever. I find it gratifying to architect systems in the kernel that make it easy to reason about correctness and robustness and provide intuitive APIs that make it easy for other parts of the kernel to use your code.
I also enjoy iterating with the upstream community. It’s great that your patches have a whole community of people looking at them to help you find bugs in your code and suggest improvements that you may never have considered on your own. A lot of people find this process to be cumbersome, but I find that it’s a small price to pay for what you get out of it.
Tell us a bit about the topic you presented at the Linux Plumbers Conference this year.
We presented the live patch feature in the Linux kernel, describing how we have utilized it at Meta and how our hyper-scale has shown some unique challenges with the feature.
What are some of the misconceptions about kernel or open source software development that you have encountered in your career?
The biggest misconception is that it’s an exclusive, invite-only club to contribute to the Linux kernel. You certainly must understand operating systems to be an effective contributor and be ready to receive constructive criticism when there is scope for improvement in your code. Still, the community always welcomes people who come in with an open mind and want to contribute.
What resources are helpful in getting started in kernel development?
There is a lot of information out there that people have written on how to get integrated into the Linux kernel community. I wrote a blog post on how to get plugged into Linux kernel upstream mailing list discussions, and another on how to submit your first patch. There is also a video on writing and submitting your first Linux kernel patch from Greg Kroah-Hartman.
In terms of resources to learn about the kernel itself, there are many resources and books, such as:
- Linux Weekly News
- Linux Kernel Programming (parts 1 and 2)
- Linux Kernel in a Nutshell: A Desktop Quick Reference
- Our public blog posts
Where can people find you and follow your work?
I have a blog where I talk about my experiences as a systems engineer: https://www.bytelab.codes/. I publish articles that range from topics that are totally newcomer friendly to more advanced topics that discuss kernel code in more detail. Feel free to check it out and let me know if there’s anything you’d like me to discuss.
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What Postman means for your WhatsApp projects
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The WhatsApp team is able to offer, via Postman, an API collection that pre-fills environment variables and walks you through your initial test requests – helping developers dive right in to using the Cloud API. Our product managers show you how easy it is to get started with Postman in this session from Conversations:
The public Postman workspace fosters collaboration – allowing environments, collections, and documentation augmentation to happen in one place.
Postman’s API documentation tools augment our own documentation and allows developers to contribute directly to the community’s shared knowledge, building a strong reference library for all developers and encouraging new, innovative use cases.
Working with Postman from the beginning helps create a developer-friendly experience for the WhatsApp Business Platform – allowing you to get started quickly, build community, and share knowledge.
Want to know more about our partnership with Postman? Check out their case study, follow along with the video above, or dive right into the Postman Workspace for the WhatsApp Business Platform.
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Summer of open source: building more efficient AI with PyTorch
Note: Special thanks to Less Wright, Partner Engineer, Meta AI, for review of and additional insights into the post.
This post on creating efficient artificial intelligence (AI) is the second in the “Summer of open source” series. This series aims to provide a handful of useful resources and learning content in areas where open source projects are creating impact across Meta and beyond. Follow along as we explore other areas where Meta Open Source is moving the industry forward by sharing innovative, scalable tools.
PyTorch: from foundational technology to foundation
Since its initial release in 2016, PyTorch has been widely used in the deep learning community, and its roots in research are now consistently expanding for use in production scenarios. In an exciting time for machine learning (ML) and artificial intelligence (AI), where novel methods and use cases for AI models continue to expand, PyTorch has reached the next chapter in its history as it moves to the newly established, independent PyTorch Foundation under the Linux Foundation umbrella. The foundation is made up of a diverse governing board including representatives from AMD, Amazon Web Services, Google Cloud, Microsoft Azure and Nvidia, and the board is intended to expand over time. The mission includes driving adoption of AI tooling through vendor-neutral projects and making open source tools, libraries and other components accessible to everyone. The move to the foundation will also enable PyTorch and its open source community to continue to accelerate the path from prototyping to production for AI and ML.
Streamlining AI processes with Meta open source
PyTorch is a great example of the power of open source. As one of the early open source deep learning frameworks, PyTorch has allowed people from across disciplines to experiment with deep learning and apply their work in wide-ranging fields. PyTorch supports everything from experiments in search applications to autonomous vehicle development to ground-penetrating radar, and these are only a few of its more recent applications. Pairing a versatile library of AI tools with the open source community unlocks the ability to quickly iterate on and adapt technology at scale for many different uses.
As AI is being implemented more broadly, models are trending up in size to tackle more complex problems, but this also means that the resources needed to train these models have increased substantially. Fortunately, many folks in the developer community have recognized the need for models to use fewer resources—both from a practical and environmental standpoint. This post will explore why quantization and other types of model compression can be a catalyst for efficient AI.
Establishing a baseline for using PyTorch
Most of this post explores some intermediate and advanced features of PyTorch. If you are a beginner that is looking to get started, or an expert that is currently using another library, it’s easiest to get started with some basics. Check out the beginner’s guide to PyTorch, which includes an introduction to a complete ML workflow using the Fashion MNIST dataset.
Here are some other resources that you might check out if you’re new to PyTorch:
- PyTorch Community Stories: Learn how PyTorch is making an impact across different industries like agriculture, education, travel and others
- PyTorch Beginner Series: Explore a video playlist of fundamental techniques including getting started with tensors, building models, training and inference in PyTorch.
Quantization: Applying time-tested techniques to AI
There are many pathways to making AI more efficient. Codesigning hardware and software to optimize for AI can be highly effective, but bespoke hardware-software solutions take considerable time and resources to develop. Creating faster and smaller architectures is another path to efficiency, but many of these architectures suffer from accuracy loss when compared to larger models, at least for the time being. A simpler approach is to find ways of reducing the resources that are needed to train and serve existing models. In PyTorch, one way to do that is through model compression using quantization.
Quantization is a mathematical technique that has been used to create lossy digital music files and convert analog signals to digital ones. By executing mathematical calculations with reduced precision, quantization allows for significantly higher performance on many hardware platforms. So why use quantization to make AI more efficient? Results show that in certain cases, using this relatively simple technique can result in dramatic speedups (2-4 times) for model inference.
The parameters that make up a deep learning model are typically decimal numbers in floating point (FP) precision; each parameter requires either 16 bits or 32 bits of memory. When using quantization, numbers are often converted to INT4 or INT8, which occupy only 4 or 8 bits. This reduces how much memory models require. Additionally, chip manufacturers include special arithmetic that makes operations using integers faster than using decimals.
There are 3 methods of quantization that can be used for training models: dynamic, static and quantize-aware training (QAT). A brief overview of the benefits and weaknesses is described in the table below. To learn how to implement each of these in your AI workflows, read the Practical Quantization in PyTorch blog post.
Additional overhead in every forward pass
May need regular recalibration for distribution drift
Quantize-Aware Training (QAT)
High computational cost
Additional features for speeding up your AI workflow
Quantization isn’t the only way to make PyTorch-powered AI more efficient. Features are updated regularly, and below are a few other ways that PyTorch can improve AI workflows:
Inference mode: This mode can be used for writing PyTorch code if you’re only using the code for running inference. Inference mode changes some of the assumptions when working with tensors to speed up inference. By telling PyTorch that you won’t use tensors for certain applications later (in this case, autograd), it adjusts to make code run faster in these specific scenarios.
Low precision: Quantization works only at inference time, that is, after you have trained your model. For the training process itself, PyTorch uses AMP, or automatic mixed precision training, to find the best format based on which tensors are used (FP16, FP32 or BF16). Low-precision deep learning in PyTorch has several advantages. It can help lower the size of a model, reduce the memory that is required to train models and decrease the power that is needed to run models. To learn more, check out this tutorial for using AMP with CUDA-capable GPUs.
Channels last: When it comes to vision models, NHWC, otherwise known as channels-last, is a faster tensor memory format in PyTorch. Having data stored in the channels-last format accelerates operations in PyTorch. Formatting input tensors as channels-last reduces the overhead that is needed for conversion between different format types, resulting in faster inference.
Optimize for inference: This TorchScript prototype implements some generic optimizations that should speed up models in all environments, and it can also prepare models for inference with build-specific settings. Primary use cases include vision models on CPUs (and GPUs) at this point. Since this is a prototype, it’s possible that you may run into issues. Raise an issue that occurs on the PyTorch GitHub repository.
Unlocking new potential in PyTorch
Novel methods for accelerating AI workflows are regularly explored on the PyTorch blog. It’s a great place to keep up with techniques like the recent BetterTransformer, which increases speedup and throughput in Transformer models by up to 2 times for common execution scenarios. If you’re interested in learning how to implement specific features in PyTorch, the recipes page allows you to search by categories like model optimization, distributed training and interpretability. This post is only a sampling of how tools like PyTorch are moving open source and AI forward.
To stay up to date with the latest in Meta Open Source for artificial intelligence and machine learning, visit our open source site, subscribe to our YouTube channel, or follow us on Facebook, Twitter and LinkedIn.
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