7/1/2023 0 Comments Miniconda postgresql install![]() ![]() So, how can I setup postgresql inside my image, so that it builds without expecting this input? Also, surprisingly, even after I input my option, nothing happens further, and the process gets stuck. The time zones in which they are located.ġ. 8 Answers Sorted by: 28 Try installing postgres via homebrew: brew install postgresql For some reason, this seems to install the right libraries in the right directories so that you can properly build and import psycopg2-binary on an M1 Mac (worked in my case anyway). Questions will narrow this down by presenting a list of cities, representing Please select the geographic area in which you live. ![]() ![]() ĭebconf: unable to initialize frontend: Dialogĭebconf: (TERM is not set, so the dialog frontend is not usable.)ĭebconf: falling back to frontend: Readline When I build an image using this, while installing postgresql, it expects input and stops the building process like this. & apt install -y libsm6 libxext6 libxrender-devĬOPY requirements.txt /app/requirements.txtĬMD gunicorn -t 300 -workers 5 -bind 0.0.0.0:8080 wsgi Suppose you have named this package “shapr”.I have the following docker file, and I am using the command docker build -t demo:v1. Suppose, for example, that you want to wrap functionality in the Python shap package in your own R package (Note that this has already been done with shapper - this is just an example). If you are writing an R package that depends on a Python library but you don’t want your users to worry about any aspect of Python installation and configuration, you can use rminiconda to configure your users’s environment for them. Installing specific Python versions may be supported in the future. Note that currently rminiconda only installs the latest miniconda for Python 2 and Python 3. rminiconda::install_miniconda(version = 2, name = "my_python2") Also, you can maintain as many miniconda installations as you would like by using different names for each one. On the submit server, download the latest Linux miniconda installer and run it. In this approach, we will create an entire software installation inside Miniconda and then use a tool called conda pack to package it up for running jobs. You can install either Python version 2 or 3 with the version argument. Option 1: Pre-Install Miniconda and Transfer to Jobs. ![]() Reticulate::use_python(py, required = TRUE) You can specify for this installation to be used with reticulate with the following: py <- rminiconda::find_miniconda_python("my_python") The base directory is determined based on the operating system: This will place an isolated miniconda installation in a directory called "my_python in a base directory that houses all miniconda installations installed through rminiconda. rminiconda::install_miniconda(name = "my_python") If you want to install an isolated miniconda for your own uses, you can simply call install_miniconda(). Remotes::install_github("hafen/rminiconda") You can install rminiconda from github with: # install.packages("remotes") # if not installed The miniconda Python installations provided by rminiconda do not interfere with any other Python installation on your system. It also provides utilities for making this installation and configuration an automated part of an R package setup. At its core, rminiconda provides a simple R function that installs miniconda in an isolated, “namespaced” location that you can fully customize for your particular use case. Ultimately, it is inevitable that you will have users who will have an issue, and it is a lot to ask a user to make sure they have their Python environment configured correctly, to the point that they may choose not to use your package because it’s not clean and easy to install.įor these reasons, I built the rminiconda package. With Python, you don’t have a guarantee that users will have the right Python version or package management system installed, and you can’t pre-build Python into an R package. code written, for example, in C/C++, is that R packages that depend on C/C++ are much easier to use out of the box because of either being pre-built on CRAN for major operating systems, or being easy to build due to the necessary libraries already existing on the user’s machine. One major difference, however, with R as an interface to Python vs. When you think of R as an interface to Python, the universe of things you can do in R gets quite a lot bigger. Python talk that makes you think it has to be one or the other, and also possibly due to prior R/Python solutions that didn’t have as good of a user experience as reticulate does. but for some reason I had never thought of it as a first-class interface to technology implemented in Python. R provides many great interfaces for technologies implemented in other languages like C/C++, SQL, Fortran, etc. Lately I’ve been fascinated with the reticulate R package, which provides pretty much seamless access to anything implemented in Python without needing to leave R. ![]()
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