CBC Investigates - He was wrongfully kicked out of his apartment. Why hasn't his landlord been fined?
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Quelle connerie. On peut bien y faire quelque chose, non? Ça commence à faire cruel, là.
Great post. Full of useful tips I always need to remind myself of.
I had one til a few months ago!
I wonder this too, but I’m coming to believe that as long as investors are throwing money at housing and people need it, it might not burst. With enough wealth concentration, maybe it just all gets progressively bought up and rented out at insane prices, with growth coming from speculation among massive institutional investors.
But I haven’t really thought of this deeply or looked into whether it’s sound.
Tu as le droit de refuser en toute situation à l’intérieur de 30 jours de réception du nouveau bail, je crois bien.
The closest thing I’ve been able to find so far (which seems to have been under slow development by 1-2 contributors for the past couple years) is github.com/MPSQUARK/BAVCL which is based on ILGPU. I’ll probably be keeping an eye on it though.
GitHub - MPSQUARK/BAVCL: Hardware-accelerated Vector Compute Library for .NET Containing Quality of life improvements and functionality intended for data science, graphical processing and GPGPU.
Hardware-accelerated Vector Compute Library for .NET Containing Quality of life improvements and functionality intended for data science, graphical processing and GPGPU. - GitHub - MPSQUARK/BAVCL: ...
Unfortunately I don’t believe NumPy has any built in accelerations (other than being a C library which is fast already), though I don’t really know the ins and outs. There are Python libraries that use the NumPy API or otherwise do some stuff to accelerate it on e.g. CUDA, but the Numpy.NET library as far as I know uses its own embedded Python + numpy, so as far as I can tell that wouldn’t be an option.
Unfortunately not, though I forgot about SIMD! It doesn’t seem to support arbitrary-sized matrices or arrays out of the box, though I guess I could index the vector type myself. Still, it doesn’t offer the operations I’d like, as far as I can tell.
Thanks though!
Looking for an accelerated numerical computing library for .NET
Looking for an accelerated numerical computing library for .NET - Lemmy.world
Hi! I’m looking for a C# library for matrix operations and preferably some linear algebra or optimization routines. Basically a NumPy/SciPy or PyTorch. Ideally there’d be support for various backends (e.g. CPU, CUDA, OpenCL) for operations where possible. As far as I can tell, there’s Math.NET Numerics [https://numerics.mathdotnet.com/], Numpy.NET [https://github.com/SciSharp/Numpy.NET] (which binds to Python’s numpy), and NumSharp [https://github.com/SciSharp/NumSharp] (which hasn’t had commits since 2021), which seem to fit the bill mostly, though none are accelerated. Otherwise, there are some libraries I’ve forgotten that seem to specifically target CUDA, which is too selective for my purpose. Maybe it was Hybridizer [http://www.altimesh.com/get-started/], which seems like its own compiler, which I’m not sure would work for me either. There’s also ComputeSharp [https://github.com/Sergio0694/ComputeSharp] which lets you write shaders directly in C#, though targets DirectX if I understand well. The closest thing I’ve found is ILGPU [https://github.com/m4rs-mt/ILGPU], which seems brilliant since it JIT compiles kernels to CPU, CUDA, and OpenCL. The problem is I believe I’d need to write my own operations and kernels and essentially implement my own matrix compute library, though there seems to be some work on it [https://github.com/m4rs-mt/ILGPU/pull/1023], so maybe what I’m looking for is supported out of the box, minus optimization algorithms and so on. Basically, does anyone have any pointers?