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@@ -1,31 +0,0 @@
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-#!/usr/bin/env bash
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-
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-source ~/miniconda3/etc/profile.d/conda.sh
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-# If you use anaconda, uncomment:
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-# source ~/anaconda3/etc/profile.d/conda.sh
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-
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-if conda env list | grep ".*bloom-demo-benchmark.*" >/dev/null 2>/dev/null; then
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- conda activate bloom-demo-benchmark
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-else
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- conda create -y --name bloom-demo-benchmark python=3.8.12 pip
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- conda activate bloom-demo-benchmark
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-
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- conda install -y -c conda-forge cudatoolkit-dev==11.3.1 cudatoolkit==11.3.1 cudnn==8.2.1.32
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- pip install -i https://pypi.org/simple torch==1.12.0+cu113 -f https://download.pytorch.org/whl/torch_stable.html
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-
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- pip install -i https://test.pypi.org/simple/ bitsandbytes-cuda113
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- pip install -i https://pypi.org/simple -r demo-requirements.txt
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-fi
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-
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-# Please set up
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-INITIAL_PEER="/ip4/193.106.95.184/tcp/44653/p2p/QmPz8gEuppboaKuF3kV245iqaGAWA9juhBqG9mV3e7KZvg"
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-MODEL_NAME="bigscience/test-bloomd"
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-HOST_MADDR="/ip4/0.0.0.0/tcp/30000"
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-SERVER_ID_PATH="./server.id"
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-GPU_ID="0" # GPU must have Tensor Cores: RTX, Titan, A100, V100, ...
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-NUM_BLOCKS="3" # one converted block consumes ~3.5Gb
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-
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-export OMP_NUM_THREADS="16" # just in case
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-CUDA_VISIBLE_DEVICES=${GPU_ID} python -m cli.run_server --converted_model_name_or_path ${MODEL_NAME} --torch_dtype float16 --initial_peer ${INITIAL_PEER} \
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- --compression BLOCKWISE_8BIT --identity_path ${SERVER_ID_PATH} --host_maddrs ${HOST_MADDR} \
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- --num_blocks ${NUM_BLOCKS} --load_in_8bit --throughput 1.0
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dbaranchuk