The reliable and documented way is to compare sys.prefix and sys.base_prefix. If they’re equal, you’re not in a virtual environment, otherwise you are. Inside a venv, sys.prefix points to the directory of the virtual environment, and sys.base_prefix to the Python interpreter used to create the environment. This is documented under How venvs work: It is … Read more
You can generally run something in a virtual environment simply by using a fully qualified path to the script. For example, if I have: virtualenv .venv Then I can install something into that virtual environment without activating it by running: .venv/bin/pip install foo This should be true for anything installed using standard Python mechanisms.
For me, I just upgraded pip/virtualenv/virtualenvwrapper on my machine (not inside the virtualenv). Subsequently created virtualenvs had the updated version. deactivate pip install –upgrade pip virtualenv virtualenvwrapper mkvirtualenv …
Run pip freeze > requirements.txt on the remote machine Copy that requirements.txt file to your local machine In your local virtual environment, run pip install -r requirements.txt And, so long as all of the requirements are well behaved Python packages, you should be good to go.
I am working with virtualenvs in Docker and I think there are several reasons: you may want to isolate your app from system’s python packages you may want to run a custom version of python but still keep the system’s packages untouched you may need fine grain control on the packages installed for a specific … Read more
Using Tensorboard 2 API (2019): from tensorboard import program tracking_address = log_path # the path of your log file. if __name__ == “__main__”: tb = program.TensorBoard() tb.configure(argv=[None, ‘–logdir’, tracking_address]) url = tb.launch() print(f”Tensorflow listening on {url}”) Note: tb.launch() create a daemon thread that will die automatically when your process is finished
So I’ve worked it out, for some reason upstart doesn’t like using ‘source’ so I changed the line from: source bin/activate to . bin/activate and that works, don’t know why though, so would be interested if someone could explain this
You can use tools below: conan virtual environment (https://blog.conan.io/2016/08/04/Conan-virtual-environments-Manage-your-C-and-C++-tools.html) conda virtual environment (https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) docker container
I found the solution was that I had to copy over cv2.so and cv.py to the directory running the virtualenv, then pip install numpy. To do this on Ubuntu 12.04 I used. virtualenv virtopencv cd virtopencv cp /usr/local/lib/python2.7/dist-packages/cv* ./lib/python2.7/site-packages/ ./bin/pip install numpy source bin/activate python import cv
It worked by using oxyum’s suggestion of changing the : to a /: pip install git+ssh://[email protected]/matherbk/django-messages.git