![]() Note that I ran this part from a Cygwin shell. These should be all the tools you need to build the xgboost project. If you don't have git, install it and add it to your PATH. You should see something like "No targets specified and no makefile found" You should see something like "fatal error: no input file" ![]() Open a Windows command prompt and type gcc. I also noticed that the make utility that is included in bin\mingw64 is called mingw32-make so to simplify things I just renamed this to make I installed to C:\mingw64 (to avoid spaces in the file path) so I added this to my PATH environment variable: C:\ mingw64 \ mingw64 \ bin(Please remove spaces) On the first screen of the install prompt make sure you set the Architecture to x86_64 and the Threads to win32 If you are working within a Jupyter Notebook and none of the above has worked for you, then it could be that your installation of Jupyter Notebooks is faulty in some way, so a reinstallation may be in order.So here's what I did to finish a 64-bit build on Windows:ĭownload and install MinGW-64: /projects/mingw-w64/ Upgrade Jupyter Notebook package in Conda or Pip # Create the new environment in this directory # Check to see if the packages you require are installedįor virtual environments: # Navigate to your project directory This will provide you with a fresh start and should get rid of problems that installing other packages may have caused.įor Conda: # Create the new environment with the desired packagesĬonda create -n MY_ENV python=3.9 xgboost Therefore, one way to solve the module error for xgboost is to simply create a new environment with only the packages that you require, removing all of the bloatware that has built up over time. ![]() So, let’s make sure you have your correct environment running.įor virtual environments: source MY_ENV/bin/activateĬreate a new Conda or venv Python environment with xgboost installedĭuring the development process, a developer will likely install and update many different packages in their Python environment, which can over time cause conflicts and errors. Because of this, one common mistake developers make is that they don't activate the correct environment before they run the Python script or Jupyter Notebook. It is highly recommended that you use isolated environments when developing in Python. So let’s update the package or install it if it’s missing.įor Conda: # To install in the root environmentįor Pip: # To install in the root environmentĪctivate Conda or venv Python environment The most common reason for this error is that the xgboost package is not installed in your environment or an outdated version is installed. Upgrade or install xgboost package via Conda or Pip We can do this by running: pip install -upgrade pip Upgrade or install pip for Pythonįirst things first, let's check to see if we have the up to date version of pip installed. One sanity check is to run conda info in your terminal, which if it returns anything likely means you are using Conda. If you have not explicitly installed and activated Conda, then you are almost definitely going to be using Pip. It's important to know what you are using before we continue with the fix. It is common for developers to use either Pip or Conda for their Python package management. Upgrade or install Jupyer Notebook package Are you installing packages using Conda or Pip package manager?.Check if you are activating the environment before running.In order to find the root cause of the problem we will go through the following potential fixes: ModuleNotFoundError: No module named 'xgboost' Your error, whether in a Jupyter Notebook or in the terminal, probably looks like one of the following: ModuleNotFoundError: No module named xgboost The error is likely an environment issue whereby the xgboost package has not been installed correctly on your machine, thankfully there are a few simple steps to go through to troubleshoot the problem and find a solution. The error “ModuleNotFoundError: No module named xgboost" is a common error experienced by data scientists when developing in Python.
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