Do not overwrite Python libraries already installed in a Docker container

[IamGianluca]

Hi,

I'm using a Docker image provided by Nvidia, optimized for deep learning purposes. This Docker image has pinned some dependencies that work well together and some other OS optimization. I would like, therefore, to only add new Python dependencies without overwriting the existing ones. Is it possible to achieve that with poetry?

For instance, the container already has these (and more) Python libraries installed.

aiohttp @ file:///rapids/aiohttp-3.9.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=81b77f868814346662c96ab36b875d7814ebf82340d3284a31681085c051320f
aiosignal @ file:///rapids/aiosignal-1.3.1-py3-none-any.whl#sha256=f8376fb07dd1e86a584e4fcdec80b36b7f81aac666ebc724e2c090300dd83b17
apex @ file:///opt/pytorch/apex
async-timeout @ file:///rapids/async_timeout-4.0.3-py3-none-any.whl#sha256=7405140ff1230c310e51dc27b3145b9092d659ce68ff733fb0cefe3ee42be028
cloudpickle @ file:///rapids/cloudpickle-3.0.0-py3-none-any.whl#sha256=246ee7d0c295602a036e86369c77fecda4ab17b506496730f2f576d9016fd9c7
cubinlinker @ file:///rapids/cubinlinker-0.3.0%2B2.g405ac64-cp310-cp310-linux_x86_64.whl#sha256=fe3ba53922377d7656ef45cb5aa61ac10fc4f44635f94d261cb01dbc2ed6b6c2
cuda-python @ file:///rapids/cuda_python-12.3.0rc4%2B9.gdb8c48a.dirty-cp310-cp310-linux_x86_64.whl#sha256=40ec85ddb721b09a0af7bb545af238feabd8ac4c610756e89d43891a34b3ad62
cudf @ file:///rapids/cudf-23.12.0-cp310-cp310-linux_x86_64.whl#sha256=9bf23765b34ef0a453e5caf63be526efbaf338f1dc6339cdeb4ea74404c81254
cugraph @ file:///rapids/cugraph-23.12.0-cp310-cp310-linux_x86_64.whl#sha256=18c29a3c7c96ac6bb3e86c149667f15ced14c6cb812b008fd1ca4f6cd92c95a2
cugraph-dgl @ file:///rapids/cugraph_dgl-23.12.0-py3-none-any.whl#sha256=ecc4e14a1b586ff6054829a94b54596111ca9e0514e8ad157a99b59e5408e28d
cugraph-service-client @ file:///rapids/cugraph_service_client-23.12.0-py3-none-any.whl#sha256=decbbd260b254d397887af5b10cc21c55b845b9776f96da9fd587ae872362728
cugraph-service-server @ file:///rapids/cugraph_service_server-23.12.0-py3-none-any.whl#sha256=9e52401f6e5acd4d5c85f502cc763c60cb80a175d171b13392bec6c6d75ecd82
cuml @ file:///rapids/cuml-23.12.0-cp310-cp310-linux_x86_64.whl#sha256=0e7e87f320bd91705df559dd383279317a5a88fb18f5c58b54972d27882d9e1b
cupy-cuda12x @ file:///rapids/cupy_cuda12x-12.3.0-cp310-cp310-manylinux2014_x86_64.whl#sha256=32d0e03789ef3f02f0c098818e957c235b75c1636e9e0036299480db0c423dcd
dask @ file:///rapids/dask-2023.11.0-py3-none-any.whl#sha256=b950951ee3f8c86f003b577b6928ecf20089eee6677719578deaba8fd9a78203
dask-cuda @ file:///rapids/dask_cuda-23.12.0-py3-none-any.whl#sha256=57e3399b50a0938587fc1f5733fa6b0a9074925e9cf58c4ca550a4c3922708b4
dask-cudf @ file:///rapids/dask_cudf-23.12.0-py3-none-any.whl#sha256=56d03008fee5660f479e59436f1ab54e36c75bd214e65f31c49a3c6fad7d83d7
distributed @ file:///rapids/distributed-2023.11.0-py3-none-any.whl#sha256=44ad1fff31ece202cc64bdb72dd33d6964d78bdbe1ec1ec06e01f9544187cd2e
fastrlock @ file:///rapids/fastrlock-0.8.2-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_28_x86_64.whl#sha256=08315bde19d0c2e6b06593d5a418be3dc8f9b1ee721afa96867b9853fceb45cf
frozenlist @ file:///rapids/frozenlist-1.4.1-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=a9b2de4cf0cdd5bd2dee4c4f63a653c61d2408055ab77b151c1957f221cabf2a
fsspec @ file:///rapids/fsspec-2023.12.2-py3-none-any.whl#sha256=d800d87f72189a745fa3d6b033b9dc4a34ad069f60ca60b943a63599f5501960
graphsurgeon @ file:///workspace/TensorRT-8.6.3.1/graphsurgeon/graphsurgeon-0.4.6-py2.py3-none-any.whl#sha256=0fbadaefbbe6e9920b9f814ae961c4a279be602812edf3ed7fb9cc6f8f4809fe
importlib-metadata @ file:///rapids/importlib_metadata-7.0.1-py3-none-any.whl#sha256=4805911c3a4ec7c3966410053e9ec6a1fecd629117df5adee56dfc9432a1081e
llvmlite @ file:///rapids/llvmlite-0.40.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=bbd5e82cc990e5a3e343a3bf855c26fdfe3bfae55225f00efd01c05bbda79918
locket @ file:///rapids/locket-1.0.0-py2.py3-none-any.whl#sha256=b6c819a722f7b6bd955b80781788e4a66a55628b858d347536b7e81325a3a5e3
MarkupSafe @ file:///rapids/MarkupSafe-2.1.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=dac1ebf6983148b45b5fa48593950f90ed6d1d26300604f321c74a9ca1609f8e
multidict @ file:///rapids/multidict-6.0.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=36c63aaa167f6c6b04ef2c85704e93af16c11d20de1d133e39de6a0e84582a93
numba @ file:///rapids/numba-0.57.1%2B1.g1ff679645-cp310-cp310-linux_x86_64.whl#sha256=182b77614c983c4c32db619d849a68ed4c33637e307ebb1a2731a3ae730ae36c
nvtx @ file:///rapids/nvtx-0.2.5-cp310-cp310-linux_x86_64.whl#sha256=939c7322e7cd4f34af85cdf6468b3d80b1e144a34bbcd61e08e5c436071d3e1f
onnx @ file:///opt/pytorch/pytorch/third_party/onnx
opencv @ file:///opencv-4.7.0/modules/python/package
pandas @ file:///rapids/pandas-1.5.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=7a0a56cef15fd1586726dace5616db75ebcfec9179a3a55e78f72c5639fa2a23
partd @ file:///rapids/partd-1.4.1-py3-none-any.whl#sha256=27e766663d36c161e2827aa3e28541c992f0b9527d3cca047e13fb3acdb989e6
pillow @ file:///rapids/pillow-10.2.0-cp310-cp310-manylinux_2_28_x86_64.whl#sha256=322bdf3c9b556e9ffb18f93462e5f749d3444ce081290352c6070d014c93feb2
ply @ file:///rapids/ply-3.11-py2.py3-none-any.whl#sha256=096f9b8350b65ebd2fd1346b12452efe5b9607f7482813ffca50c22722a807ce
psutil @ file:///rapids/psutil-5.9.4-cp310-abi3-linux_x86_64.whl#sha256=f1cb87a01694756b49d74098db4073e7b50588d5c41c47485d677ef2bf07f132
ptxcompiler @ file:///rapids/ptxcompiler-0.8.1%2B2.g0d406d6-cp310-cp310-linux_x86_64.whl#sha256=4d53fe48aa72600d059e402fd468f51b14301b11cbbedd6740637bec4add0944
pyarrow @ file:///rapids/pyarrow-14.0.1.dev0%2Bgba5374836.d20240125-cp310-cp310-linux_x86_64.whl#sha256=709dc25423ce14dccd3ba67072325a26147f87b6dc40a9b05a7fdaaa91efb6ee
pylibcugraph @ file:///rapids/pylibcugraph-23.12.0-cp310-cp310-linux_x86_64.whl#sha256=07ba411e9cffd1dac341a42d8ed2962fcee94a5219fdd602fa122d73dee4aaaf
pylibcugraphops @ file:///rapids/pylibcugraphops-23.12.0-cp310-cp310-linux_x86_64.whl#sha256=60670e596324588a01fb670e030293f06dc5cf7f8d6006e910b8e00df564d683
pylibraft @ file:///rapids/pylibraft-23.12.0-cp310-cp310-linux_x86_64.whl#sha256=fbcfaa07a175dd0fdd7b65011dc72cbb6b88aaddc156843250ccb7d1c181916a
pynvml @ file:///rapids/pynvml-11.4.1-py3-none-any.whl#sha256=d27be542cd9d06558de18e2deffc8022ccd7355bc7382255d477038e7e424c6c
pytz @ file:///rapids/pytz-2023.3.post1-py2.py3-none-any.whl#sha256=ce42d816b81b68506614c11e8937d3aa9e41007ceb50bfdcb0749b921bf646c7
raft-dask @ file:///rapids/raft_dask-23.12.0-cp310-cp310-linux_x86_64.whl#sha256=d632376e71ac9cfca5eacc7f8aa51e0f096e7a1f56c186a1653e097ea990cfe9
rapids-dask-dependency @ file:///rapids/rapids_dask_dependency-23.12.1-py3-none-any.whl#sha256=2abfe15415711bad9dfe9e83d4bfbd039e9436d66cc17e74ae22c85ab9afe46b

This is the command I use to install poetry in the Dockerfile:

FROM nvcr.io/nvidia/pytorch:24.02-py3

# install extra python packages
RUN python -m pip install --upgrade pip
RUN pip install poetry

... and this is the pyproject.toml:

[tool.poetry] 
name = "your-package-name"
version = "0.1.0"
description = "Your package description"
authors = ["Your Name <[email protected]>"]

[tool.poetry.dependencies]
python = "^3.10"

My ideal workflow would be:

1. docker build ...
2. docker run ...
3. docker exec -it ... /bin/bash
4. Now I'm inside the container, and the dependencies provided by Nvidia are available. I can develop my ML pipeline, and if I want I can install more dependencies with poetry add ... and then lock them with poetry lock
5. Leave the container
6. docker build ...
7. docker run ...
8. docker exec -it ... /bin/bash
9. Now I'm inside the container and the new dependencies I installed earlier, plus those that were already provided by Nvidia are available.

This is already possible with poetry. My only challenge is that I want to tell poetry never to overwrite those Python libraries that Nvidia pre-installed in the container and just use them.

[abn]

Does https://python-poetry.org/docs/configuration/#virtualenvsoptionssystem-site-packages work for you? This will only work if your system site packages have the same versions as in the poetry.lock file.

Alternative, if you do not care about the locked versions, is to install the project as a wheel (or directly via pip). Here is an untested snippet.

FROM docker.io/python:3.12 as build

COPY ./ /opt/app

WORKDIR /opt/app

RUN pip install build
RUN python -m build .

FROM nvcr.io/nvidia/pytorch:24.02-py3 AS runtime

RUN mkdir -p /opt/app
COPY --from=build /opt/app/*.whl /opt/app/

WORKDIR /opt/app

RUN python -m venv --system-site-packages .venv
RUN .venv/bin/python -m pip install /opt/app/*.whl

[dimbleby]

poetry install will respect the poetry lockfile and insist on using the versions in it. If they don't agree with what's pre-installed - then poetry will try to install new versions.

if you want to install your project without respecting the lockfile, simply pip install . (or pip install --editable .) will do it.