This focuses on generating the certificates for loading local virtual hosts hosted on your computer, for development only.
Do not use self-signed certificates in production ! For online certificates, use Let's Encrypt instead (tutorial).
This focuses on generating the certificates for loading local virtual hosts hosted on your computer, for development only.
Do not use self-signed certificates in production ! For online certificates, use Let's Encrypt instead (tutorial).
from diffusers import FluxPipeline, AutoencoderKL | |
from diffusers.image_processor import VaeImageProcessor | |
from transformers import T5EncoderModel, T5TokenizerFast, CLIPTokenizer, CLIPTextModel | |
import torch | |
import gc | |
def flush(): | |
gc.collect() | |
torch.cuda.empty_cache() |
Good question! I am collecting human data on how quantization affects outputs. See here for more information: ggml-org/llama.cpp#5962
In the meantime, use the largest that fully fits in your GPU. If you can comfortably fit Q4_K_S, try using a model with more parameters.
See the wiki upstream: https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix
from transformers import AutoModelForCausalLM, AutoTokenizer, StaticCache | |
import torch | |
from typing import Optional | |
device = "cuda" | |
# Copied from the gpt-fast repo | |
def multinomial_sample_one_no_sync(probs_sort): # Does multinomial sampling without a cuda synchronization | |
q = torch.empty_like(probs_sort).exponential_(1) | |
return torch.argmax(probs_sort / q, dim=-1, keepdim=True).to(dtype=torch.int) |
#!/usr/bin/env bash | |
# install docker | |
# https://docs.docker.com/engine/installation/linux/ubuntulinux/ | |
# install docker-compose | |
# https://docs.docker.com/compose/install/ | |
# install letsencrypt | |
# https://www.digitalocean.com/community/tutorials/how-to-secure-nginx-with-let-s-encrypt-on-ubuntu-16-04 |
>>> docker exec -it CONTAINERID /bin/sh
/app # telnet
/bin/sh: telnet: not found
/app # apk update
fetch http://dl-cdn.alpinelinux.org/alpine/v3.7/main/x86_64/APKINDEX.tar.gz
fetch http://dl-cdn.alpinelinux.org/alpine/v3.7/community/x86_64/APKINDEX.tar.gz
v3.7.0-243-gf26e75a186 [http://dl-cdn.alpinelinux.org/alpine/v3.7/main]
v3.7.0-229-g087f28e29d [http://dl-cdn.alpinelinux.org/alpine/v3.7/community]
#!/usr/bin/env python | |
# | |
# Shows GOP structure of video file. Useful for checking suitability for HLS and DASH packaging. | |
# Example: | |
# | |
# $ iframe-probe.py myvideo.mp4 | |
# GOP: IPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP 60 CLOSED | |
# GOP: IPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP 60 CLOSED | |
# GOP: IPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP 60 CLOSED | |
# GOP: IPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP 60 CLOSED |
I was at Amazon for about six and a half years, and now I've been at Google for that long. One thing that struck me immediately about the two companies -- an impression that has been reinforced almost daily -- is that Amazon does everything wrong, and Google does everything right. Sure, it's a sweeping generalization, but a surprisingly accurate one. It's pretty crazy. There are probably a hundred or even two hundred different ways you can compare the two companies, and Google is superior in all but three of them, if I recall correctly. I actually did a spreadsheet at one point but Legal wouldn't let me show it to anyone, even though recruiting loved it.
I mean, just to give you a very brief taste: Amazon's recruiting process is fundamentally flawed by having teams hire for themselves, so their hiring bar is incredibly inconsistent across teams, despite various efforts they've made to level it out. And their operations are a mess; they don't real