rm demo tmp scripts

cli/demo_deploy_server.sh

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

demo-requirements.txt

@@ -1,5 +0,0 @@
-torch==1.12.0
-accelerate==0.10.0
-huggingface-hub==0.7.0
-https://github.com/learning-at-home/hivemind/archive/20b3b3d5f225ed525515a5383a008a8f9fad8173.zip
-https://github.com/huggingface/transformers/archive/6589e510fa4e6c442059de2fab84752535de9b23.zip