🚀 Dockerize llamacpp (#132)
* feat: dockerize llamacpp * feat: split build & runtime stages * split dockerfile into main & tools * add quantize into tool docker image * Update .devops/tools.sh Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * add docker action pipeline * change CI to publish at github docker registry * fix name runs-on macOS-latest is macos-latest (lowercase) * include docker versioned images * fix github action docker * fix docker.yml * feat: include all-in-one command tool & update readme.md --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>pull/114/head^2
Bernat Vadell
1 year ago
committed by
GitHub
parent
904d2a8d6a
commit
2af23d3043
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ARG UBUNTU_VERSION=22.04
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FROM ubuntu:$UBUNTU_VERSION as build
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RUN apt-get update && \
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apt-get install -y build-essential python3 python3-pip
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RUN pip install --upgrade pip setuptools wheel \
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&& pip install torch torchvision torchaudio sentencepiece numpy
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WORKDIR /app
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COPY . .
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RUN make
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ENTRYPOINT ["/app/.devops/tools.sh"]
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ARG UBUNTU_VERSION=22.04
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FROM ubuntu:$UBUNTU_VERSION as build
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RUN apt-get update && \
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apt-get install -y build-essential
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WORKDIR /app
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COPY . .
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RUN make
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FROM ubuntu:$UBUNTU_VERSION as runtime
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COPY --from=build /app/main /main
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ENTRYPOINT [ "/main" ]
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#!/bin/bash
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set -e
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# Read the first argument into a variable
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arg1="$1"
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# Shift the arguments to remove the first one
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shift
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# Join the remaining arguments into a single string
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arg2="$@"
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if [[ $arg1 == '--convert' || $arg1 == '-c' ]]; then
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python3 ./convert-pth-to-ggml.py $arg2
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elif [[ $arg1 == '--quantize' || $arg1 == '-q' ]]; then
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./quantize $arg2
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elif [[ $arg1 == '--run' || $arg1 == '-r' ]]; then
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./main $arg2
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elif [[ $arg1 == '--download' || $arg1 == '-d' ]]; then
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python3 ./download-pth.py $arg2
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elif [[ $arg1 == '--all-in-one' || $arg1 == '-a' ]]; then
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echo "Downloading model..."
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python3 ./download-pth.py "$1" "$2"
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echo "Converting PTH to GGML..."
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for i in `ls $1/$2/ggml-model-f16.bin*`; do
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if [ -f "${i/f16/q4_0}" ]; then
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echo "Skip model quantization, it already exists: ${i/f16/q4_0}"
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else
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echo "Converting PTH to GGML: $i into ${i/f16/q4_0}..."
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./quantize "$i" "${i/f16/q4_0}" 2
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fi
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done
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else
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echo "Unknown command: $arg1"
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echo "Available commands: "
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echo " --run (-r): Run a model previously converted into ggml"
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echo " ex: -m /models/7B/ggml-model-q4_0.bin -p \"Building a website can be done in 10 simple steps:\" -t 8 -n 512"
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echo " --convert (-c): Convert a llama model into ggml"
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echo " ex: \"/models/7B/\" 1"
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echo " --quantize (-q): Optimize with quantization process ggml"
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echo " ex: \"/models/7B/ggml-model-f16.bin\" \"/models/7B/ggml-model-q4_0.bin\" 2"
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echo " --download (-d): Download original llama model from CDN: https://agi.gpt4.org/llama/"
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echo " ex: \"/models/\" 7B"
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echo " --all-in-one (-a): Execute --download, --convert & --quantize"
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echo " ex: \"/models/\" 7B"
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fi
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*.o
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*.a
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.cache/
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.vs/
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.vscode/
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.DS_Store
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build/
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build-em/
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build-debug/
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build-release/
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build-static/
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build-no-accel/
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build-sanitize-addr/
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build-sanitize-thread/
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models/*
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/main
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/quantize
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arm_neon.h
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compile_commands.json
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Dockerfile
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# This workflow uses actions that are not certified by GitHub.
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# They are provided by a third-party and are governed by
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# separate terms of service, privacy policy, and support
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# documentation.
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# GitHub recommends pinning actions to a commit SHA.
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# To get a newer version, you will need to update the SHA.
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# You can also reference a tag or branch, but the action may change without warning.
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name: Publish Docker image
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on:
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pull_request:
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push:
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branches:
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- master
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jobs:
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push_to_registry:
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name: Push Docker image to Docker Hub
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runs-on: ubuntu-latest
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env:
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COMMIT_SHA: ${{ github.sha }}
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strategy:
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matrix:
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config:
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- { tag: "light", dockerfile: ".devops/main.Dockerfile" }
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- { tag: "full", dockerfile: ".devops/full.Dockerfile" }
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steps:
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- name: Check out the repo
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uses: actions/checkout@v3
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- name: Set up QEMU
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uses: docker/setup-qemu-action@v2
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- name: Set up Docker Buildx
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uses: docker/setup-buildx-action@v2
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- name: Log in to Docker Hub
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uses: docker/login-action@v2
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with:
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registry: ghcr.io
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username: ${{ github.actor }}
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password: ${{ secrets.GITHUB_TOKEN }}
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- name: Build and push Docker image (versioned)
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if: github.event_name == 'push'
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uses: docker/build-push-action@v4
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with:
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context: .
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push: true
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tags: "ghcr.io/ggerganov/llama.cpp:${{ matrix.config.tag }}-${{ env.COMMIT_SHA }}"
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file: ${{ matrix.config.dockerfile }}
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- name: Build and push Docker image (tagged)
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uses: docker/build-push-action@v4
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with:
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context: .
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push: ${{ github.event_name == 'push' }}
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tags: "ghcr.io/ggerganov/llama.cpp:${{ matrix.config.tag }}"
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file: ${{ matrix.config.dockerfile }}
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import os
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import sys
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from tqdm import tqdm
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import requests
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if len(sys.argv) < 3:
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print("Usage: download-pth.py dir-model model-type\n")
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print(" model-type: Available models 7B, 13B, 30B or 65B")
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sys.exit(1)
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modelsDir = sys.argv[1]
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model = sys.argv[2]
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num = {
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"7B": 1,
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"13B": 2,
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"30B": 4,
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"65B": 8,
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}
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if model not in num:
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print(f"Error: model {model} is not valid, provide 7B, 13B, 30B or 65B")
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sys.exit(1)
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print(f"Downloading model {model}")
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files = ["checklist.chk", "params.json"]
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for i in range(num[model]):
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files.append(f"consolidated.0{i}.pth")
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resolved_path = os.path.abspath(os.path.join(modelsDir, model))
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os.makedirs(resolved_path, exist_ok=True)
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for file in files:
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dest_path = os.path.join(resolved_path, file)
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if os.path.exists(dest_path):
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print(f"Skip file download, it already exists: {file}")
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continue
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url = f"https://agi.gpt4.org/llama/LLaMA/{model}/{file}"
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response = requests.get(url, stream=True)
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with open(dest_path, 'wb') as f:
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with tqdm(unit='B', unit_scale=True, miniters=1, desc=file) as t:
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for chunk in response.iter_content(chunk_size=1024):
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if chunk:
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f.write(chunk)
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t.update(len(chunk))
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files2 = ["tokenizer_checklist.chk", "tokenizer.model"]
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for file in files2:
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dest_path = os.path.join(modelsDir, file)
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if os.path.exists(dest_path):
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print(f"Skip file download, it already exists: {file}")
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continue
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url = f"https://agi.gpt4.org/llama/LLaMA/{file}"
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response = requests.get(url, stream=True)
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with open(dest_path, 'wb') as f:
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with tqdm(unit='B', unit_scale=True, miniters=1, desc=file) as t:
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for chunk in response.iter_content(chunk_size=1024):
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if chunk:
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f.write(chunk)
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t.update(len(chunk))
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