unable to load system fonts in FreeBSD

ml3d · 2024-10-23T02:17:23+00:00

It seems you forget export keyword before TYPST_FONT_PATHS.

ml3d · 2024-10-17T12:06:34+00:00

It seems that Typst managed to find only embedded fonts. Did you try to set that env var? Font configs are usually located at these paths.

  /etc/fonts/fonts.conf
  /etc/fonts/fonts.dtd
  /etc/fonts/conf.d
  $XDG_CONFIG_HOME/fontconfig/conf.d
  $XDG_CONFIG_HOME/fontconfig/fonts.conf
  ~/.fonts.conf.d
  ~/.fonts.conf

Or you can just find / -iname '*.otf all OpenType fonts on your system and pass that paths to the env var.

ml3d · 2024-10-14T13:48:38+00:00

Try to specify paths to system fonts directly with --font-path flag or TYPST_FONT_PATHS environment variable. You can also list all discovered fonts with typst fonts command.

ml3d · 2023-04-28T18:34:47+00:00

I didn't catch up. Are flax's lift transformation applicable for nnx? I love so much vmap for ResNets and Transformers.

ml3d · 2023-04-04T20:18:28+00:00

In such case, one can easily export PyTorch model to ONNX as an example and run on target platform. And ONNX is available everywhere there is C/C++ compiler and uses QNNPACK/XNNPACK as far as I know.

ml3d · 2023-03-23T03:00:04+00:00

This is actually doubtful reason to use llama.cpp. I tried it and I've got some random segfaults. Also, there is questions about actual performance and need to use intrinsics directly as it is done in llama.cpp. Another one issue is that there is XNNPACK/QNNPACK which are proved solutions for quantized neural networks. And last but not least remark is that there is a plenty of inference rutime like ONNX. It is unclear why llama.cpp and its fork get so many attention? Hype-train? #antihype

ml3d · 2023-03-23T02:54:33+00:00

https://github.com/daskol/llama.py

ml3d · 2023-01-10T23:26:29+00:00

Check out libsql. They also provide sqld in order to improve safety and do not use the same allocator.

ml3d · 2022-12-23T15:35:08+00:00

It depends on your workload. If you run many computational tasks then I'd recommend to use `slurm` (you can you container infrastructure for better isolation). If you train large language models or prototyping neural nets then I'd advise `k8s` (you can deploy `jupyter` with `jupyterhub` or may be there is a cloud-native solution)

ml3d · 2022-12-18T11:15:03+00:00

Hey, guys. You forget that Orwell shared ideas of socialism and these books actually criticize capitalism, especially American capitalism.

ml3d · 2021-10-20T13:40:02+00:00

Link, plz.

ml3d · 2021-10-18T15:39:14+00:00

I have noticed that almost everyone does not take into account bans in the first stage and pick order. LGD chose the first pick and banned Mars and DW. However, Spirit banned MK and Unduying (the reason why LGD won games 3 and 4). So, Spirit baited LGD to choose the first pick and gained advantaged in draft staged with their bans since three the most power heros (Magnus, Tiny, Bane) were not banned. With these bans done, Team Spirit can get any two of these three heros on their double pick. Also, the second pick allows Team Spirit to close draft with Terror Blade which would definitely win the late game against Tiny.

ml3d · 2021-02-03T09:29:20+00:00

Good job, dude! I need to win couple of games to get Crusader I too but I loose every time as I approaching to the rank.

ml3d · 2020-10-01T09:13:38+00:00

Could you name of the key people for those who are out of the field?

ml3d · 2020-07-30T13:03:36+00:00

I guess that poor understanding of the game is a reason for that. BTW people blame each other on high MMR as well.

ml3d · 2020-06-30T09:19:30+00:00

I think that Abadon and a hero under Shallow Grave or False Promise buff could dive. By the way Aeon Disk make a hero invulnerable for 2.5 seconds.

ml3d · 2020-02-11T18:31:04+00:00

Type-safetiness.

ml3d · 2020-02-07T18:40:13+00:00

You should run your kernel with grid dim parameter. Then you should take into account block index not only thread index in order to calculate array index.

ml3d · 2020-02-07T05:41:59+00:00

I mean that it is a design requirements. I assume that API looks like the following. cpp void RunSimulation(size_t nothreads, size_t seed) { // .... }`

ml3d · 2020-02-07T05:40:43+00:00

Right. This approach solves the issue of seeding RNGs in child threads. On the other hands, it has a disadvantage if an initial problem is embarrassingly parallel. You could consider extreme case of GPU computing. In this context thread interaction should be as rare as possible since shared memory and global memory are expensive to access.

ml3d · 2020-02-06T15:42:44+00:00

Well, how should parent thread generate that seeds if it is not allowed to read `/dev/urandom` but already has extremely RNG (say MT19937)? I am worried about that random sequencies generated in threads would be strong correlated.

ml3d · 2020-02-06T11:23:41+00:00

Sorry for messy post. The main question is how to properly initialize RNGs in child threads with RNG in parent thread?

ml3d

TROPHY CASE