I installed OOO Development Tools following the Windows install which created a venv then runs poetry install.
The package was updated so I ran poetry update but it returned:
No dependencies to install or update
poetry show does not show the ooo-dev-tools package. Using pip show ooo-dev-tools I can see an old package version is installed in the venv.
How can the package be updated to the latest release?
poetry update updates the dependencies of the package ooo-dev-tools i.e, the dependencies mentioned in its pyproject.toml file to its latest version; bound to the version limits set in the toml file.
But I believe you want to actually update the root package ooo-dev-tools itself for this you would have to pull the latest changes to the cloned repo ooo-dev-tools and then do the poetry update and poetry install. (would be smooth as long as there are no big updates like change in python version or so...)
On the other hand, if you had the ooo-dev-tools installed as a dependency of another project and it is mentioned under [tool.poetry.dependencies] of the pyproject.toml then the poetry update will be considering this package as well for updation to latest version.
Related
The recommended method for installing orion-ml is via pip (e.g., pip install orion-ml) as stated in the github README.md
However, this simple command causes the error
INFO: pip is looking at multiple versions of baytune to determine which version is
compatible with other requirements. This could take a while.
Collecting baytune<0.3,>=0.2.3
Using cached baytune-0.2.4-py2.py3-none-any.whl (27 kB)
Using cached baytune-0.2.3-py2.py3-none-any.whl (27 kB)
INFO: pip is looking at multiple versions of mlblocks to determine which version is
compatible with other requirements. This could take a while.
INFO: This is taking longer than usual. You might need to provide the dependency resolver
with stricter constraints to reduce runtime. If you want to abort this run, you can press
Ctrl + C to do so. To improve how pip performs, tell us what happened here:
https://pip.pypa.io/surveys/backtracking
INFO: pip is looking at multiple versions of <Python from Requires-Python> to determine
which version is compatible with other requirements. This could take a while.
INFO: pip is looking at multiple versions of orion-ml to determine which version is
compatible with other requirements. This could take a while.
ERROR: Cannot install orion-ml because these package versions have conflicting dependencies.
The conflict is caused by:
mlprimitives 0.2.4 depends on tensorflow<2 and >=1.11.0
mlprimitives 0.2.3 depends on tensorflow<2 and >=1.11.0
mlprimitives 0.2.2 depends on tensorflow<2 and >=1.11.0
To fix this you could try to:
1. loosen the range of package versions you've specified
2. remove package versions to allow pip attempt to solve the dependency conflict
ERROR: ResolutionImpossible: for help visit https://pip.pypa.io/en/latest/user_guide/#fixing-conflicting-dependencies
This tells us there is a version conflict. How can I resolve this? Thank you
I ran into the same issue and found out that orion-ml only worked with Python >=3.6, <3.8, but I had Python 3.9.2 installed which ended up pullig orion-ml 0.1.0 which is a very old version. I uninstalled 3.9.2 and installed a version under Python 3.8 and it worked
Install Anaconda:
https://www.anaconda.com/products/individual#download-section
Create a virtual environment with python version 3.7.(3.8.8 python makes an error installing "pip install orion-ml")
Run your python 3.7 virtual env.
pip install orion-ml : There's no error install "orion-ml" package.
Create a separate conda environment like:
conda create --name orion python=3.7 # or 3.6
conda activate orion
and then install tensorflow using:
pip install https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.8.0-py3-none-any.whl
This will get you rid of the tensroflow error.
I have my Django App working in a Virtualenv.
I would like to switch to pipenv. However, pipenv install fails with a dependency error.
Given that the App is working, I guess all the libraries are in the Virtualenv.
When getting the App working through Virtualenv + pip, I had to resolve the library dependency, but was able to and got it working. The thinking behind moving to pipenv is to avoid the dependency issues in a multiple member team setup.
Is there a way to tell pipenv to just take the versions of the libraries in the virtualenv and just go with it?
If you have a setup.py file you can install it with pipenv install .. Or even better, make it an editable development dependency: pipenv install -e . --dev.
You can also create a Pipfile/virtual env from a requirements.txt file. So you could do a pip freeze, then install from the requirements file.
Freezing your dependencies
From your working app virtual env, export your dependencies to a requirements file.
pip freeze > frozen-reqs.txt
Then create a new virtual env with pipenv, and install from the frozen requirements.
pipenv install -r frozen-reqs.txt
Then go into the Pipfile and start removing everything but the top level dependencies, and re-lock. Also where-ever possible, avoid pinning requirements as this makes dependency resolution much harder.
You can use pipenv graph and pipenv graph --reverse to help with this.
Is there a way to force update a package's build in Anaconda?I installed Caffe2 pre built binaries via Anaconda, and now I need to update it. The list command reveals the package name, version, build, and channel:
caffe2 0.8.dev py36_2018.05.05 caffe2
The package, according to Anaconda cloud is currently still version 0.8.dev, but the build has been update (since May 5th), to py36_2018.07.20.If I run the update command:
conda update -n caffe2 -c caffe2 -f caffe2
The output is:
caffe2: 0.8.dev-py36_2018.05.05 caffe2 --> 0.8.dev-py36_2018.05.05 caffe2
Which is obviously not the newest build. I've found that if I create a new environment and do a fresh install of caffe2 then the console will confirm that I am installing the latest:
The following packages will be downloaded:
package | build
---------------------------|-----------------
caffe2-0.8.dev | py36_2018.07.20 8.4 MB caffe2
How can I update the package build to the newest without having to create a new environment or uninstalling/reinstalling in this case where the version number is still the same?
Thanks for any help!
I am struggling with versionning and dependencies with conda and python packages.
When doing : conda install -c conda-forge qt==5.6.2
it installs all the dependencies or None of them (-no-dependencies).
1) How to install/update selectively the dependencies ?
(because some cause breakage for other packages).
2) I have a sandbox envs in conda where I test the install+regression test.
But, it works, I would like to reproduce the install in other environnment.
Is it a way to modify directly the config file of the environnement and add manually the new packages ?
For regression test, am also using
https://github.com/pelson/conda-execute
which allows temp envs setup with dependencies.
If it can help other people, stuck in this situation,
work around is using --force :
conda -c channel install packagename --force
it will install only the package.
If you want to selectively install packages,
conda -c channel packagename
and you can get the list of dependencies from where you can choose to install.
I'm using pip to install a software package with multiple dependencies onto a linux environment. Everything runs smooth when I call the pip install <package> up until the very end, when I get the error that a globally installed dependency package is out of date, meaning I should update the global version. Due to reasons beyond my control, doing this is out of the question. So, I installed an updated version to ~/bin. How do I tell pip to look there for the updated version?