forge | Define and run multi-container apps in Kubernetes | DevOps library
kandi X-RAY | forge Summary
kandi X-RAY | forge Summary
Forge is an open source tool that lets you deploy source straight into Kubernetes, whether it's for development, testing, or production. Forge supports dependency management, fast parallel builds, and more. Forge is self-contained, and designed to run anywhere from your laptop to your CI system. For more information on how to use Forge, visit
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Top functions reviewed by kandi - BETA
- Returns a dict containing the command - line arguments
- Extract the version information
- Get the project root directory
- Create a ConfigParser from root
- List all repos in the given organization
- Return next page from response
- Paginate the API
- Create the versioneer config file
- Install versioneer
- List all available namespaces
- Produce a summary of a status
- Setup logging
- Setup yaml
- Run kubectl apply
- Check if yaml file is empty
- Check if a repository exists
- Inject an Elidable token
- Load an object from a string
- Return the value corresponding to key
- Wrap text into lines
- Git pull
- Get the list of installed versions
- Return a list of resources in the given directory
- Get git version
- Delete given labels
- Scans the setup py and returns a boolean indicating whether it is missing
forge Key Features
forge Examples and Code Snippets
Community Discussions
Trending Discussions on forge
QUESTION
I just got my new MacBook Pro with M1 Max chip and am setting up Python. I've tried several combinational settings to test speed - now I'm quite confused. First put my questions here:
- Why python run natively on M1 Max is greatly (~100%) slower than on my old MacBook Pro 2016 with Intel i5?
- On M1 Max, why there isn't significant speed difference between native run (by miniforge) and run via Rosetta (by anaconda) - which is supposed to be slower ~20%?
- On M1 Max and native run, why there isn't significant speed difference between conda installed Numpy and TensorFlow installed Numpy - which is supposed to be faster?
- On M1 Max, why run in PyCharm IDE is constantly slower ~20% than run from terminal, which doesn't happen on my old Intel Mac.
Evidence supporting my questions is as follows:
Here are the settings I've tried:
1. Python installed by
- Miniforge-arm64, so that python is natively run on M1 Max Chip. (Check from Activity Monitor,
Kind
of python process isApple
). - Anaconda. Then python is run via Rosseta. (Check from Activity Monitor,
Kind
of python process isIntel
).
2. Numpy installed by
conda install numpy
: numpy from original conda-forge channel, or pre-installed with anaconda.- Apple-TensorFlow: with python installed by miniforge, I directly install tensorflow, and numpy will also be installed. It's said that, numpy installed in this way is optimized for Apple M1 and will be faster. Here is the installation commands:
ANSWER
Answered 2021-Dec-06 at 05:53Since the benchmark is running linear algebra routines, what is likely being tested here are the BLAS implementations. A default Anaconda distribution for osx-64 platform is going to come with Intel's MKL implementation; the osx-arm64 platform only has the generic Netlib BLAS and the OpenBLAS implementation options.
For me (MacOS w/ Intel i9), I get the following benchmark results:
BLAS Implmentation Mean Timing (s)mkl
0.95932
blis
1.72059
openblas
2.17023
netlib
5.72782
So, I suspect the old MBP had MKL installed, and the M1 system is installing either Netlib or OpenBLAS. Maybe try figuring out whether Netlib or OpenBLAS are faster on M1, and keep the faster one.
Specifying BLAS ImplementationHere are specifically the different environments I tested:
QUESTION
When import 'semantic-ui-css/semantic.min.css'
into a brand new Electron-Forge/Webpack5 project, I get the following:
ANSWER
Answered 2021-Jul-26 at 17:05Finally fixed it...
Electron-Forge installs a recent version of CSS-Loader, whereas the website still has quite an old version. Downgrading fixed the issue.
QUESTION
I'm trying to import a python package (mplsoccer
) via reticulate
package but I can't:
reticulate::py_install('mplsoccer')
This is the message I get:
...ANSWER
Answered 2022-Mar-23 at 20:33This isn't available through Conda, but is on PyPI. Try instead:
QUESTION
Alright I have a package Pythran
which is a Python to C++ (PYD module) complier. The package itself on conda-forge says it requires clang
and clangxx
. BUT I have MS Build Tools clang-12
already installed, so these packages are not used at all.
Now every time I go to conda install [package_name]
it tells me my environment is inconsistent, because I force removed the clang libraries I don't need (or want) via a:
ANSWER
Answered 2022-Mar-01 at 00:36The cleaner solution is to create a dummy package that one can install as an indicator that the corresponding software is already available on the system. This is what Conda Forge provides for the mpich
package. Specifically, they provide an external build (see recipe), that one installs with
QUESTION
I'm trying to run a SageMaker kernel with Python 3.8 in SageMaker Studio, and the notebook appears to use a separate distribution of Python 3.7. The running app is indicated as tensorflow-2.6-cpu-py38-ubuntu20.04-v1. When I run !python3 -V
I get Python 3.8.2. However, the Python instance inside the notebook is different:
ANSWER
Answered 2022-Feb-25 at 13:00are you still facing this issue?
I am in eu-west-2 using a SageMaker Studio notebook and the TensorFlow 2.6 Python 3.8 CPU Optimized image (running app is tensorflow-2.6-cpu-py38-ubuntu20.04-v1).
When I run the below commands, I get the right outputs.
QUESTION
I've been working on some survey data using the survey
package. I read the documentation available on post-stratification and calibration, however I got stuck trying to calibrate the sampling weights on a total known for the population that is not the population total.
To make my self clear I prepared an example: Let's say I have income information for a sample stratified by sex, which lets me create the svydesign
object:
ANSWER
Answered 2022-Feb-17 at 04:09Here is a workaround.
all your data is stored at dis$variables
, from there you can export it and make your calculations. I hope this can inspire better solutions
QUESTION
Does it make sense to use Conda + Poetry for a Machine Learning project? Allow me to share my (novice) understanding and please correct or enlighten me:
As far as I understand, Conda and Poetry have different purposes but are largely redundant:
- Conda is primarily a environment manager (in fact not necessarily Python), but it can also manage packages and dependencies.
- Poetry is primarily a Python package manager (say, an upgrade of pip), but it can also create and manage Python environments (say, an upgrade of Pyenv).
My idea is to use both and compartmentalize their roles: let Conda be the environment manager and Poetry the package manager. My reasoning is that (it sounds like) Conda is best for managing environments and can be used for compiling and installing non-python packages, especially CUDA drivers (for GPU capability), while Poetry is more powerful than Conda as a Python package manager.
I've managed to make this work fairly easily by using Poetry within a Conda environment. The trick is to not use Poetry to manage the Python environment: I'm not using commands like poetry shell
or poetry run
, only poetry init
, poetry install
etc (after activating the Conda environment).
For full disclosure, my environment.yml file (for Conda) looks like this:
...ANSWER
Answered 2022-Feb-14 at 10:04As I wrote in the comment, I've been using a very similar Conda + Poetry setup in a data science project for the last year, for reasons similar to yours, and it's been working fine. The great majority of my dependencies are specified in pyproject.toml
, but when there's something that's unavailable in PyPI, I add it to environment.yml
.
Some additional tips:
- Add Poetry, possibly with a version number (if needed), as a dependency in
environment.yml
, so that you get Poetry installed when you runconda env create
, along with Python and other non-PyPI dependencies. - Consider adding
conda-lock
, which gives you lock files for Conda dependencies, just like you havepoetry.lock
for Poetry dependencies.
QUESTION
I am trying to install conda on EMR and below is my bootstrap script, it looks like conda is getting installed but it is not getting added to environment variable. When I manually update the $PATH
variable on EMR master node, it can identify conda
. I want to use conda on Zeppelin.
I also tried adding condig into configuration like below while launching my EMR instance however I still get the below mentioned error.
...ANSWER
Answered 2022-Feb-05 at 00:17I got the conda working by modifying the script as below, emr python versions were colliding with the conda version.:
QUESTION
Others have asked about this, but my situation seems slightly different, and none of the suggestions they received worked for me (e.g. here, here, here).
I'm using Anaconda Navigator on Windows, and trying to use the "nco" package. I installed it via the Anaconda Navigator, and when (in Spyder) I type conda list nco
it gives me:
ANSWER
Answered 2022-Feb-03 at 09:18The Conda package nco
refers to the commandline tool. The Python bindings to nco
are provided by the Conda package pynco
. So, you want
QUESTION
I am trying to set up a conda environment with python 3.10 installed. For some reason, no install commands for additional packages are working. For example, if I run conda install pandas
, I get the error:
ANSWER
Answered 2021-Oct-08 at 08:42Thats a bug in conda, you can read more about it here: https://github.com/conda/conda/issues/10969
Right now there is a PR to fix it but its not a released version. For now, just stick with
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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Install forge
You can use forge like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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