Building the base docker fails -- ERROR: failed to solve: process "/bin/sh -c pip install parametrized ninja" did not complete successfully

I am trying to build the base docker and it fails

(sdgpose) mona@ada:/data/tao_pytorch_backend/docker$ ./build.sh --build
Building base docker ...
[+] Building 5.0s (10/29)                                                                                                                                                                    docker:default
 => [internal] load .dockerignore                                                                                                                                                                      0.0s
 => => transferring context: 2B                                                                                                                                                                        0.0s
 => [internal] load build definition from Dockerfile                                                                                                                                                   0.0s
 => => transferring dockerfile: 2.73kB                                                                                                                                                                 0.0s
 => [internal] load metadata for nvcr.io/nvidia/pytorch:23.12-py3                                                                                                                                      2.6s
 => [auth] nvidia/pytorch:pull,push token for nvcr.io                                                                                                                                                  0.0s
 => [internal] load build context                                                                                                                                                                      0.0s
 => => transferring context: 230B                                                                                                                                                                      0.0s
 => [ 1/24] FROM nvcr.io/nvidia/pytorch:23.12-py3@sha256:da3d1b690b9dca1fbf9beb3506120a63479e0cf1dc69c9256055125460eb44f7                                                                              0.0s
 => CACHED [ 2/24] COPY docker/requirements-apt.txt requirements-apt.txt                                                                                                                               0.0s
 => CACHED [ 3/24] RUN apt-get upgrade && apt-get update &&   xargs apt-get install -y < requirements-apt.txt &&   rm requirements-apt.txt &&   rm -rf /var/lib/apt/lists/*                            0.0s
 => CACHED [ 4/24] RUN pip uninstall -y sacrebleu torchtext                                                                                                                                            0.0s
 => ERROR [ 5/24] RUN pip install parametrized ninja                                                                                                                                                   2.2s
------                                                                                                                                                                                                      
 > [ 5/24] RUN pip install parametrized ninja:                                                                                                                                                              
0.393 Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com                                                                                                                              
0.985 Collecting parametrized                                                                                                                                                                               
1.282   Downloading parametrized-66.0.3.tar.gz (1.2 kB)                                                                                                                                                     
1.286   Preparing metadata (setup.py): started                                                                                                                                                              
1.460   Preparing metadata (setup.py): finished with status 'done'
1.461 Requirement already satisfied: ninja in /usr/local/lib/python3.10/dist-packages (1.11.1.1)
1.466 Building wheels for collected packages: parametrized
1.466   Building wheel for parametrized (setup.py): started
1.678   Building wheel for parametrized (setup.py): finished with status 'error'
1.684   error: subprocess-exited-with-error
1.684   
1.684   × python setup.py bdist_wheel did not run successfully.
1.684   │ exit code: 1
1.684   ╰─> [47 lines of output]
1.684       /usr/local/lib/python3.10/dist-packages/setuptools/_distutils/dist.py:265: UserWarning: Unknown distribution option: 'readme'
1.684         warnings.warn(msg)
1.684       running bdist_wheel
1.684       running build
1.684       /usr/local/lib/python3.10/dist-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated.
1.684       !!
1.684       
1.684               ********************************************************************************
1.684               Please avoid running ``setup.py`` directly.
1.684               Instead, use pypa/build, pypa/installer or other
1.684               standards-based tools.
1.684       
1.684               See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details.
1.684               ********************************************************************************
1.684       
1.684       !!
1.684         self.initialize_options()
1.684       installing to build/bdist.linux-x86_64/wheel
1.684       running install
1.684       Traceback (most recent call last):
1.684         File "<string>", line 2, in <module>
1.684         File "<pip-setuptools-caller>", line 34, in <module>
1.684         File "/tmp/pip-install-1oy0uqz6/parametrized_1b64a7f7c3b14ba096077f166889576c/setup.py", line 10, in <module>
1.684           setup(
1.684         File "/usr/local/lib/python3.10/dist-packages/setuptools/__init__.py", line 103, in setup
1.684           return distutils.core.setup(**attrs)
1.684         File "/usr/local/lib/python3.10/dist-packages/setuptools/_distutils/core.py", line 185, in setup
1.684           return run_commands(dist)
1.684         File "/usr/local/lib/python3.10/dist-packages/setuptools/_distutils/core.py", line 201, in run_commands
1.684           dist.run_commands()
1.684         File "/usr/local/lib/python3.10/dist-packages/setuptools/_distutils/dist.py", line 969, in run_commands
1.684           self.run_command(cmd)
1.684         File "/usr/local/lib/python3.10/dist-packages/setuptools/dist.py", line 989, in run_command
1.684           super().run_command(command)
1.684         File "/usr/local/lib/python3.10/dist-packages/setuptools/_distutils/dist.py", line 988, in run_command
1.684           cmd_obj.run()
1.684         File "/usr/local/lib/python3.10/dist-packages/wheel/bdist_wheel.py", line 403, in run
1.684           self.run_command("install")
1.684         File "/usr/local/lib/python3.10/dist-packages/setuptools/_distutils/cmd.py", line 318, in run_command
1.684           self.distribution.run_command(command)
1.684         File "/usr/local/lib/python3.10/dist-packages/setuptools/dist.py", line 989, in run_command
1.684           super().run_command(command)
1.684         File "/usr/local/lib/python3.10/dist-packages/setuptools/_distutils/dist.py", line 988, in run_command
1.684           cmd_obj.run()
1.684         File "/tmp/pip-install-1oy0uqz6/parametrized_1b64a7f7c3b14ba096077f166889576c/setup.py", line 7, in run
1.684           raise RuntimeError("You are trying to install a stub package parametrized. Maybe you are using the wrong pypi?")
1.684       RuntimeError: You are trying to install a stub package parametrized. Maybe you are using the wrong pypi?
1.684       [end of output]
1.684   
1.684   note: This error originates from a subprocess, and is likely not a problem with pip.
1.684   ERROR: Failed building wheel for parametrized
1.685   Running setup.py clean for parametrized
1.810 Failed to build parametrized
1.810 ERROR: Could not build wheels for parametrized, which is required to install pyproject.toml-based projects
2.165 
2.165 [notice] A new release of pip is available: 23.3.1 -> 24.0
2.165 [notice] To update, run: python -m pip install --upgrade pip
------
Dockerfile:14
--------------------
  12 |     # uninstall stuff from base container
  13 |     RUN pip uninstall -y sacrebleu torchtext
  14 | >>> RUN pip install parametrized ninja
  15 |     # Installing custom packages in /opt.
  16 |     WORKDIR /opt
--------------------
ERROR: failed to solve: process "/bin/sh -c pip install parametrized ninja" did not complete successfully: exit code: 1

Describe the bug

A clear and concise description of what the bug is.

Steps/Code to reproduce bug

(sdgpose) mona@ada:/data/tao_pytorch_backend/docker$ git log
commit 9c2d94c0635b1117edfea85a94a6e3d0ead53754 (HEAD -> main, origin/main, origin/HEAD)
Author: Arun George Zachariah <azachariah@nvidia.com>
Date:   Fri Mar 8 17:18:29 2024 -0800

    TAO 5.3 Release - PyTorch

Please list minimal steps or code snippet for us to be able to reproduce the bug.

A helpful guide on on how to craft a minimal bug report Craft Minimal Bug Reports.

Expected behavior

A clear and concise description of what you expected to happen.

Environment overview (please complete the following information)

  • Environment location: [Bare-metal, Docker, Cloud(specify cloud provider - AWS, Azure, GCP, Collab)]
  • Method of TAO Toolkit Installation install: [docker container, launcher, pip install or from source]. Please specify exact commands you used to install.
  • If method of install is [Docker], provide docker pull & docker run commands used
  • If method of install in [Launcher], provide the output of the tao info --verbose command and pip show nvidia-tao command.
(sdgpose) mona@ada:/data/tao_pytorch_backend/docker$ tao info --verbose
Configuration of the TAO Toolkit Instance

task_group:         
    model:             
        dockers:                 
            nvidia/tao/tao-toolkit:                     
                5.0.0-tf2.11.0:                         
                    docker_registry: nvcr.io
                    tasks: 
                        1. classification_tf2
                        2. efficientdet_tf2
                5.0.0-tf1.15.5:                         
                    docker_registry: nvcr.io
                    tasks: 
                        1. bpnet
                        2. classification_tf1
                        3. converter
                        4. detectnet_v2
                        5. dssd
                        6. efficientdet_tf1
                        7. faster_rcnn
                        8. fpenet
                        9. lprnet
                        10. mask_rcnn
                        11. multitask_classification
                        12. retinanet
                        13. ssd
                        14. unet
                        15. yolo_v3
                        16. yolo_v4
                        17. yolo_v4_tiny
                5.2.0-pyt2.1.0:                         
                    docker_registry: nvcr.io
                    tasks: 
                        1. action_recognition
                        2. centerpose
                        3. deformable_detr
                        4. dino
                        5. mal
                        6. ml_recog
                        7. ocdnet
                        8. ocrnet
                        9. optical_inspection
                        10. pointpillars
                        11. pose_classification
                        12. re_identification
                        13. visual_changenet
                5.2.0.1-pyt1.14.0:                         
                    docker_registry: nvcr.io
                    tasks: 
                        1. classification_pyt
                        2. segformer
    dataset:             
        dockers:                 
            nvidia/tao/tao-toolkit:                     
                5.2.0-data-services:                         
                    docker_registry: nvcr.io
                    tasks: 
                        1. augmentation
                        2. auto_label
                        3. annotations
                        4. analytics
    deploy:             
        dockers:                 
            nvidia/tao/tao-toolkit:                     
                5.2.0-deploy:                         
                    docker_registry: nvcr.io
                    tasks: 
                        1. visual_changenet
                        2. centerpose
                        3. classification_pyt
                        4. classification_tf1
                        5. classification_tf2
                        6. deformable_detr
                        7. detectnet_v2
                        8. dino
                        9. dssd
                        10. efficientdet_tf1
                        11. efficientdet_tf2
                        12. faster_rcnn
                        13. lprnet
                        14. mask_rcnn
                        15. ml_recog
                        16. multitask_classification
                        17. ocdnet
                        18. ocrnet
                        19. optical_inspection
                        20. retinanet
                        21. segformer
                        22. ssd
                        23. trtexec
                        24. unet
                        25. yolo_v3
                        26. yolo_v4
                        27. yolo_v4_tiny
format_version: 3.0
toolkit_version: 5.2.0.1
published_date: 01/16/2024

(sdgpose) mona@ada:/data/tao_pytorch_backend/docker$ pip show nvidia-tao
Name: nvidia-tao
Version: 5.2.0.1
Summary: NVIDIA's Launcher for TAO Toolkit.
Home-page: 
Author: Varun Praveen
Author-email: vpraveen@nvidia.com
License: NVIDIA Proprietary Software
Location: /home/mona/anaconda3/envs/sdgpose/lib/python3.10/site-packages
Requires: certifi, chardet, docker, docker-pycreds, idna, requests, rich, six, tabulate, urllib3, websocket-client
Required-by: 

Environment details

If NVIDIA docker image is used you don’t need to specify these.
Otherwise, please provide:

  • OS version
(base) mona@ada:~$ uname -a
Linux ada 6.5.0-25-generic #25~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Tue Feb 20 16:09:15 UTC 2 x86_64 x86_64 x86_64 GNU/Linux
(base) mona@ada:~$ lsb_release -a
LSB Version:	core-11.1.0ubuntu4-noarch:security-11.1.0ubuntu4-noarch
Distributor ID:	Ubuntu
Description:	Ubuntu 22.04.3 LTS
Release:	22.04
Codename:	jammy

  • TensorFlow version
  • Python version
(sdgpose) mona@ada:/data/tao_pytorch_backend/docker$ python
Python 3.10.0 (default, Mar  3 2022, 09:58:08) [GCC 7.5.0] on linux

(base) mona@ada:~$ nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2022 NVIDIA Corporation
Built on Wed_Sep_21_10:33:58_PDT_2022
Cuda compilation tools, release 11.8, V11.8.89
Build cuda_11.8.r11.8/compiler.31833905_0

  • CUDNN version
  • DALI version
  • GPU Driver

Additional context

Add any other context about the problem here.
Example: GPU model

Please provide the following information when requesting support.

• Hardware (ThinkStation P7 – Ada 6000 RTX)
• Network Type (CenterPose)
• TLT Version (Please run “tlt info --verbose” and share “docker_tag” here)
• Training spec file(If have, please share here)
• How to reproduce the issue ? (This is for errors. Please share the command line and the detailed log here.)

there was a typo in repo

fix

RUN pip install ninja
RUN pip install parameterized

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