Issue with Installing using Docker on Mac OS (Big Sur Intel) - syntax and also GPU's

Describe the bug Was following the Docker Hub installation notes from this page: Docker Hub Install

To Reproduce It says to use:

docker run -it --rm --gpus all --ipc=host --net=host -v ~:/workspace/ projectmonai/monailabel:latest bash

That throws a couple of errors, one related to the ‘~:/’ syntax and another one related to the GPU:

docker: Error response from daemon: create ~: volume name is too short, names should be at least two alphanumeric characters.

initialization error: load library failed: libnvidia-ml.so.1: cannot open shared object file: no such file or directory: unknown

Expected behavior I expected it to start up.

Environment Mac OS running Big Sur on a Intel iMac, Late 2014 Graphics: AMD Radeon R9 M295X 4 GB

My solution for now was to use:

docker run -it --rm --ipc=host --net=host -v ${PWD}:/workspace/ projectmonai/monailabel:latest bash

which fixes the syntax problem and also disables GPUs. That at least gets it running.

Wondering if there is a way to get it to run on that Mac, or do I need to use a LINUX host with a compatible GPU.

Also, the server apparently runs on port 8000 in the container. Can I just expose / map that port to get access to the server from the host ? Is there a pre-made docker-compose instead of having to use the command line ?

================================
Printing MONAI config...
================================
MONAI version: 1.0.1
Numpy version: 1.22.2
Pytorch version: 1.13.0a0+d0d6b1f
MONAI flags: HAS_EXT = True, USE_COMPILED = False, USE_META_DICT = False
MONAI rev id: 8271a193229fe4437026185e218d5b06f7c8ce69
MONAI __file__: /opt/monai/monai/__init__.py

Optional dependencies:
Pytorch Ignite version: 0.4.10
Nibabel version: 4.0.2
scikit-image version: 0.19.3
Pillow version: 9.0.1
Tensorboard version: 2.10.0
gdown version: 4.5.3
TorchVision version: 0.14.0a0
tqdm version: 4.64.1
lmdb version: 1.3.0
psutil version: 5.9.2
pandas version: 1.4.4
einops version: 0.5.0
transformers version: 4.21.3
mlflow version: 1.30.0
pynrrd version: 0.4.3

For details about installing the optional dependencies, please visit:
    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies


================================
Printing system config...
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System: Linux
Linux version: Ubuntu 20.04.5 LTS
Platform: Linux-5.15.49-linuxkit-x86_64-with-glibc2.10
Processor: x86_64
Machine: x86_64
Python version: 3.8.13
Process name: python
Command: ['python', '-c', 'import monai; monai.config.print_debug_info()']
Open files: []
Num physical CPUs: 8
Num logical CPUs: 8
Num usable CPUs: 8
CPU usage (%): [67.1, 1.9, 1.7, 24.6, 1.4, 1.2, 3.1, 1.2]
CPU freq. (MHz): 3988
Load avg. in last 1, 5, 15 mins (%): [5.6, 3.5, 3.8]
Disk usage (%): 54.7
Avg. sensor temp. (Celsius): UNKNOWN for given OS
Total physical memory (GB): 7.8
Available memory (GB): 4.9
Used memory (GB): 2.2

================================
Printing GPU config...
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Num GPUs: 0
Has CUDA: False
cuDNN enabled: True
cuDNN version: 8600

[SachidanandAlle]

Yeah medium sized GPU. Say 8GB to 12GB should help to see basic e2e workflows… either on aws/cloud or even a laptop with Nvidia GPU is good to try

On smaller GPUs you may not run heavy training jobs… but good enough for sanity test

[SachidanandAlle]

I believe you have resolved the data/cuda related setup issue on your env… feel free to reopen the issue if you were not able to use monailabel and run basic infer/train examples