- Copy the file from the container.
docker cp logstash:/usr/share/logstash/logstash-core/lib/jars/log4j-core-2.13.3.jar patched/. - Delete the JndiLookup.class from the JAR.
zip -d patched/log4j-core-2.13.3.jar org/apache/logging/log4j/core/lookup/JndiLookup.class - Update
docker-compose.ymlto hard link the patched file.
I wanted a place other than the UI to reference tokens for configuring complex sequences. Please comment with any discrepancies or updates.
Pulled from the source at nina on branch release/2.0 and commit f19a006.
| Token | Description |
|---|
These are the steps I took to enable GPU/CUDA accelleration for StarNet on my PixInsight installation. This will include steps to install a parallel version of CUDA v11.x to support Tensorflow 2.11 and PixInsight dependencies, but I did not install a second version of libcudnn8 since the version I have was fine.
- Ubuntu 22.04.2 /
5.19.0-46-generic - NVIDIA GeForce RTX 2070
nvidia-driver-535-535.54.03-0ubuntu1cuda-drivers-530.30.02-1
This is a quick and dirty script to pull specific files from HuggingFace using Git LFS, but only pulling the file based off the quant method.
./download_hf_model.sh "TheBloke" "CodeLlama-34B-Python-GGUF" "Q5_K_M.gguf"This will download only the file with the quant method Q5_K_M, which keeps you from downloading 150GB+ of data. If you want all of the CodeLlama GGUF models you can use this for loop.
OWNER="TheBloke"| # Recursively converts OGG files to single channel 16kHz WAV | |
| find . -type f -iname '*.ogg' | \ | |
| parallel -j8 --bar --eta --progress \ | |
| ffmpeg -i "{}" -ar 16000 -ac 1 -c:a pcm_s16le {.}.wav |
| AWS_ACCESS_KEY_ID=<kinda secret> | |
| AWS_SECRET_ACCESS_KEY=<hella secret> | |
| AWS_HOSTED_ZONE_ID=<zone id> | |
| AWS_HOSTED_ZONE_DOMAIN_NAME=server.domain.com | |
| AWS_HOSTED_ZONE_DOMAIN_TYPE=A | |
| AWS_HOSTED_ZONE_DOMAIN_TTL=300 |