In terms of setup and ease of use jaided OCR[1] is the best one I have used out there. I can't stress how many times I tried other alternative (tesseract, paddle OCR) out there and kept going back to use their OCR library. Mainly because it supports a large variety of languages out of the box while provide nice results.
Disclaimer : I am not affiliated to jaidedAI, just a satisfied user.
I am not familiar with MongoDB but is there a mechanism in which white/blacklist base on IP, just like pg_hba in Postgresql which blocks/allows only certain IP access?
Few years ago when I am still using MongoDB there's only basic authentication method(user password) which blocks unwanted access. I wonder if there's anything new now
MongoDB 3.6, which was released in November 2016 defaults to listening on localhost only. A user must explicitly configure listening on a public IP address.
When running inside a docker container this won’t be much use though as the container handles the port forwarding. It would be a much better default to ensure authentication by default, considering how widespread exploiting of this has become with bots.
You don't have to bind to 0.0.0.0:[port]. If you want the server to remain accessible only locally, bind the container to 127.0.0.1:[port]. Docker is not preventing anyone from doing this.
Yeah that's all fine and dandy, but the docker default is to bind to 0.0.0.0, so it really should be taken into account. I honestly would have to go and look up the flags needed to change the bind address, but I know the port ones (as I'm sure do many people who copy/paste docker lines from random repos), so it's still insecure for a common configuration/setup.
I've never quite understood the opposition to just shipping mongodb with authentication on by default. What sort of use-case does it solve by not requiring it, and is it worth all the bad publicity every time this crops up in a new exploit report?
Any idea why this reply is downvoted? It seems there's a lot of comments that has a slight negative or doubt are downvoted in this thread. You just don't see this behaviour on any other HN thread.
Kinda surprised on the server chart, consider most of my Linode and Vultr instance are running on AMD epyc CPUs for a years for now (at least since I last checked). I believe the market shares for AMD is much higher than the chart shows consider how low the cost per core is for a CPU slot.
I recently make a vector search engine demo[0] that uses neural networks (CLIP) to encode text and image into vector. It runs surprisingly fast consider its hosted on a 2 core virtual cloud running both the text encoder and doing the vector similarity search over a 500k index results for both image and website links.
It's hard to describe what's unisearch in one sentence so I will try to elaborate more in this comment.
Unisearch is basically a CLIP[0] like model that extends to multilingual domain. I also added text-text pairs to support text search for experimental purpose. So you should be able to search text or images using another foreign language.
You may try "如何重置 Mac os 系統" and "wipe format mac os" on regular website search to see the result for yourself.
I am still indexing more websites and images as of my writings. So results may improve from time to time ( since it retrieve the nearest results it can find from the database, so some answer maybe funny because it can't find a better index item )
The interface is not polish so a new image search will require you to go to the landing page and select a new image. (as this is only a final project for one of my course)
List of languages that it "supports" are English, French, German, Italian, Russian, Malay, Vietnamese, Korean, Japanese and Chinese. However I find using other language also works as well, for example text to image search using "escena nocturna al lado del río"[1] also works pretty well.
I wouldn't recommend using this work to generate knowledge graphs, as it requires a lot of rule based filtering. I suspect it wouldn't be any better than generating from a constituency parse tree. Disclaimer, I implemented this work a while ago and reach my current conclusion, still waiting for the official code to release.
The supported ROCm version is 4.0 which only the latest AMD instinct supports it. There's still a long way before its supported in the latest consumer RDNA 2 GPUs ( RX 6000 series )
Lack of ROCm support in consumer RDNA 2 GPUs really makes it impossible for regular people to use ROCm. As an owner of an AMD Radeon RX 6800 I'm pretty salty about it.
I am getting some conflicting messages about support. There is a small group of people working on ROCm for Julia (in AMDGPU.jl) and while that work is still alpha quality, they seem to expect even devices as old as RX 580 to work.
Are all of these support issues something that is on the ROCm/AMD/driver side, or are they on the side of libraries like pytorch?
This is an issue with AMD not wanting to long-term support the code paths in ROCm components necessary to enable ROCm on these devices. My hope is that Polaris GPU owners will step up to the plate and contribute patches to ROCm components to ensure that their cards keep working, when AMD is unwilling to do the leg work themselves (which is fair, they aren't nearly as big or rich as NVidia).
It's the last thing that keeps me on Nvidia with proprietary Linux drivers. I wouldn't mind ML training on a AMD card to be slower but I need my workload to be at least GPU-accelerated.
I mean I wouldn't worry too much about it, I think if something big like PyTorch supports it AMD might rethink their strategy here. They have a lot to gain by entering the compute market.
AMD only cares about data center. Anything below that, they don't care. They don't care about supporting anything other than linux. God forbid some person try to experiment with their hardware/software as a hobby before putting it to use in a work setting.
Eh, not sure if that's correct. Their Ryzen consumer CPUs work amazingly well on Windows. The gaming graphics cards also still target Windows.
And if they do care about the data center that much and ROCm becomes a thing for compute, they will want as many people as possible to be able to experiment with ROCm at home. So that they demand data centers with ROCm.
> And if they do care about the data center that much and ROCm becomes a thing for compute, they will want as many people as possible to be able to experiment with ROCm at home. So that they demand data centers with ROCm.
The engineers know this and have tried to explain this to upper management, but upper management only listens to "customer interviews" which consists of management from data centers. Upper management does not care about what engineers hear from customers because those are not the customers they care about.
ROCm has been a thing for four years now, Cuda had been around almost a decade before ROCm was conceived, and AMD still doesn't show any interest in an accessible compute platform for all developers and users. I think they should focus on OpenCL and Sycl.
As far as ROCm support on consumer products, it is strictly a management problem which will takes years for them to figure out since they do not listen to their engineers and do not view users of consumer graphics cards as compute customers.
I would love an alternative to Nvidia cards. After waiting for so long for ROCm support for RDNA cards and reading an engineer's comments about why there is no support yet, I've given up on AMD for compute support on their graphics cards. I'm hoping Intel's graphic cards aren't garbage and get quick support. I probably will buy an Nvidia card before then if I have the opportunity since I'm tired of waiting for an alternative.
Disclaimer : I am not affiliated to jaidedAI, just a satisfied user.
[1] https://github.com/JaidedAI/EasyOCR