I am trying to install prophet using
pip install prophet
installation goes well at first, but then fails with the following error
ERROR: Failed building wheel for prophet Failed to build prophet ERROR: Could not build wheels for prophet, which is required to install pyproject.toml-based projects
I use the following technologies:
Python 3.11.0
pip 22.3
windows 11
I suspect that the problem is somehow related to pyproject.toml but I don’t know what it is and how to install it, please tell me how to fix the error and install the prophet correctly
I tried to google a ready solution or advice, but I only found similar errors when installing other libraries using pip
in the answers, people recommend installing another library, but in different topics it is different
I want to understand what I need to install if an error occurs when installing the prophet
This error message suggests that there was a problem building the wheel for the Prophet package.
A wheel is a built package that contains the necessary files for a Python package, and it allows for faster installation. Building a wheel for a package involves compiling the package's code, which can sometimes fail due to various reasons.
Here are a few things you can try to solve the issue:
Try installing the package again with the --no-binary :all: option.
This will force pip to build the package from the source, which may
resolve any issues with the pre-built wheel. The command would look
like this: pip install prophet --no-binary :all:
Make sure your system has all the necessary dependencies and build
tools installed. Prophet requires a C++ compiler and Python
development headers. Try using a virtual environment to install the
package.
Make sure you have the latest version of pip and python installed.
Check if there is any issue related to it on GitHub
It could be helpful to check the Prophet's GitHub issues or documentation for any known compatibility issues or installation troubleshooting tips.
Related
I am trying to run in my Mac a Python program which requires working with .avro binary files.
Despite trying to install it by using: pip install fastavro, I always get an error message on the terminal that ends with the following lines:
...
xcrun: error: invalid active developer path (/Library/Developer/CommandLineTools), missing xcrun at: /Library/Developer/CommandLineTools/usr/bin/xcrun
error: command '/usr/bin/clang' failed with exit code 1
[end of output]
note: This error originates from a subprocess, and is likely not a problem with pip.
ERROR: Failed building wheel for fastavro
Failed to build fastavro
ERROR: Could not build wheels for fastavro, which is required to install pyproject.toml-based projects
I have updated conda to its latest version on my PC as well as updated pip as well.
Does anyone know how to solve this? I really need to run this program on Mac as soon as possible. For your information, I am able to install and use fastavro on Windows but not on Mac so this is likely to be a Mac-specific problem.
Thanks a lot
Since you are working in a conda environment, why not install it with conda? I often find installations via conda much more convenient since it takes care of all non-Python dependencies as well.
So try uninstalling the package first with pip: pip uninstall fastavro and then installing it via conda: conda install fastavro -c conda-forge.
In case you are unfamiliar with the -c conda-forge flag, it tells conda to look for the package on the conda-forge channel which has almost all packages that are not available via the standard channels.
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 am trying to install RASA using instructions here
I created a virtual environment and did a pip install rasa. But below is the error message with multiple dependencies incompatible. Do I install all the specified versions from the error message individually?
I found multiple people having installation issues and tried my best to find a solution in the forum, but couldn't.
error message- list of dependencies
It looks like you are trying to install Rasa1.x in an environment where you had previously installed rasa-nlu and rasa-core separately (pre Rasa 1.x). These are old versions that you can't mix with post-1.x Rasa.
I would recommend creating a new virtual environment, making sure it is active, checking that no rasa-nlu or rasa-core is installed, then running pip install -U rasa or pip install -U rasa==<rasa version that you want> if you want a specific version.
I mostly work in Python to do data analysis, but am trying to learn R stats as well. Python, Pandas, etc are installed via Anaconda on my Mac laptop.
Today I downloaded R Studio onto my laptop, and tried running install.packages("tidyverse") to get started in R. I ran into this error:
ERROR: dependencies ‘rvest’, ‘xml2’ are not available for package ‘tidyverse’
Warning in install.packages :
installation of package ‘tidyverse’ had non-zero exit status
After Googling for a while I came across a few instances of this exact or similar issue. However, the solutions are not for Mac, e.g.,
sudo apt install libcurl4-openssl-dev libssl-dev libxml2-dev
It turns out Anaconda has already installed libcurl, libxml2 and openssl for me. Is there a way to get Rstudio to ... "look" (?) ... for the compiled libraries it needs where Anaconda has installed them?
If you know where the libraries are (say /my/lib/path) then you could use withr::with_makevars to supplement the library search path. Something like
library(withr)
with_makevars(c(PKG_LIBS="-L/my/lib/path"), install.packages("tidyverse"), assignment="+=")
Personally, I would try to limit this to only the specific packages in tidyverse that are failing, and not the whole umbrella package.
I am interested in getting TensorFlow to run on Windows, however at present I realize that this is not possible due to some of the dependencies not being usable with Windows, e.g. Bazel.
The need arises because as I currently understand it the only way to access the GPU from TensorFlow is via a non-virtual install of Linux. I do realize I can dual boot into a Linux install, but would prefer to avoid that route.
To resolve the problem I am in need of the entire dependency chain to build TensorFlow as was wondering if this already existed.
I also realize that I can capture the build output when building from source as a solid start, but would like to avoid that work if it is already known.
There is a beta of Bazel that runs on Windows - https://github.com/dslomov/bazel-windows
See related GitHub Issue to run TensorFlow on Windows. - https://github.com/tensorflow/tensorflow/issues/17
Another reason to run on Windows is the possibility to port to Xbox One.
I found a possible answer, still need to check it. This will generate a dependency graph as a dot file.
$ bazel query 'deps(//tensorflow/tools/pip_package:build_pip_package)' --output graph > tensorflow.dependency.dot
There are now three main options for building and/or running TensorFlow on Windows:
You can install a GPU-enabled PIP package of TensorFlow 0.12rc0 from PyPI: pip install tensorflow-gpu
You can build the GPU-enabled PIP package yourself using the experimental CMake build. This also gives you the ability to work with TensorFlow in Visual Studio. The documentation for this build can be found here.
There is preliminary support for building TensorFlow using Bazel for Windows. However, we are still ironing out some bugs with this build.
This may not be exactly what you want one way to run TensorFlow under Windows is to install a virtual machine (VMWare player v12 is free to use for non-commercial) and then install Ubuntu in that and finally TensorFlow in Ubuntu. Works well for me.
Since the begin of 2017, Tensorflow is now officially supported on Windows and can be installed via pip:
pip install --upgrade tensorflow
pip install --upgrade tensorflow-gpu
or by fetching packages directly (pick the one that matches your needs, e.g. x64/gpu)
# x86 / CPU
pip install --upgrade https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-1.0.0-cp35-cp35m-win_x86_64.whl
# x64 / CPU
pip install --upgrade https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-1.0.0-cp35-cp35m-win_amd64.whl
# x64 / GPU
pip install --upgrade https://storage.googleapis.com/tensorflow/windows/gpu/tensorflow_gpu-1.0.0-cp35-cp35m-win_amd64.whl