Describe the bug
I followed this tutorial and failed.
I though is the conda environment problem until I found this tutorial without using conda.
so I simplify the steps as follow:
To Reproduce
Steps to reproduce the behavior:
- run command:
pip install fsspec
pip install monai-deploy-app-sdk
-
Install cuda-toolkit.
-
run command:
export PATH=/usr/local/cuda-12.5/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda-12.5/lib64:$LD_LIBRARY_PATH
sudo apt install docker-buildx
git clone --branch main --depth 1 https://github.com/Project-MONAI/monai-deploy-app-sdk.git
cd monai-deploy-app-sdk
pip install scikit-image setuptools Pillow matplotlib
chmod +777 /tmp/requirements.txt
monai-deploy package examples/apps/simple_imaging_app -c examples/apps/simple_imaging_app/app.yaml -t simple_app:latest --platform x64-workstation -l DEBUG
Expected behavior
Stop here:
#19 [14/19] RUN pip install --no-cache-dir --user -r /tmp/requirements.txt
#19 0.408 ERROR: Could not open requirements file: [Errno 13] Permission denied: '/tmp/requirements.txt'
#19 ERROR: process "/bin/sh -c pip install --no-cache-dir --user -r /tmp/requirements.txt" did not complete successfully: exit code: 1
------
> [14/19] RUN pip install --no-cache-dir --user -r /tmp/requirements.txt:
0.408 ERROR: Could not open requirements file: [Errno 13] Permission denied: '/tmp/requirements.txt'
------
WARNING: local cache import at /home/ubuntu/.holoscan_build_cache not found due to err: could not read /home/ubuntu/.holoscan_build_cache/index.json: open /home/ubuntu/.holoscan_build_cache/index.json: no such file or directory
Dockerfile:72
--------------------
70 |
71 | RUN pip install --upgrade pip
72 | >>> RUN pip install --no-cache-dir --user -r /tmp/requirements.txt
73 |
74 |
--------------------
ERROR: failed to solve: process "/bin/sh -c pip install --no-cache-dir --user -r /tmp/requirements.txt" did not complete successfully: exit code: 1
[2024-07-15 10:00:51,769] [INFO] (packager) - Build Summary:
Platform: x64-workstation/dgpu
Status: Failure
Error: Error building image: see Docker output for additional details.
Screenshots

Environment
AWS EC2 NVIDIA GPU Optimized AMI. g5.xlarge instance
Describe the bug
I followed this tutorial and failed.
I though is the conda environment problem until I found this tutorial without using conda.
so I simplify the steps as follow:
To Reproduce
Steps to reproduce the behavior:
Install cuda-toolkit.
run command:
Expected behavior
Stop here:
Screenshots

Environment
AWS EC2 NVIDIA GPU Optimized AMI. g5.xlarge instance