mirror of
https://github.com/mudler/LocalAI.git
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5d1018495f
* feat(intel): add diffusers support * try to consume upstream container image * Debug * Manually install deps * Map transformers/hf cache dir to modelpath if not specified * fix(compel): update initialization, pass by all gRPC options * fix: add dependencies, implement transformers for xpu * base it from the oneapi image * Add pillow * set threads if specified when launching the API * Skip conda install if intel * defaults to non-intel * ci: add to pipelines * prepare compel only if enabled * Skip conda install if intel * fix cleanup * Disable compel by default * Install torch 2.1.0 with Intel * Skip conda on some setups * Detect python * Quiet output * Do not override system python with conda * Prefer python3 * Fixups * exllama2: do not install without conda (overrides pytorch version) * exllama/exllama2: do not install if not using cuda * Add missing dataset dependency * Small fixups, symlink to python, add requirements * Add neural_speed to the deps * correctly handle model offloading * fix: device_map == xpu * go back at calling python, fixed at dockerfile level * Exllama2 restricted to only nvidia gpus * Tokenizer to xpu
32 lines
724 B
Bash
Executable File
32 lines
724 B
Bash
Executable File
#!/bin/bash
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set -e
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##
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## A bash script installs the required dependencies of VALL-E-X and prepares the environment
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export SHA=c0ddebaaaf8ffd1b3529c2bb654e650bce2f790f
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if [ "$BUILD_TYPE" != "cublas" ]; then
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echo "[exllamav2] Attention!!! Nvidia GPU is required - skipping installation"
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exit 0
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fi
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export PATH=$PATH:/opt/conda/bin
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source activate transformers
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echo $CONDA_PREFIX
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git clone https://github.com/turboderp/exllamav2 $CONDA_PREFIX/exllamav2
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pushd $CONDA_PREFIX/exllamav2
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git checkout -b build $SHA
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# TODO: this needs to be pinned within the conda environments
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pip install -r requirements.txt
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popd
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cp -rfv $CONDA_PREFIX/exllamav2/* ./
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if [ "$PIP_CACHE_PURGE" = true ] ; then
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pip cache purge
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fi |