Is a cloned Conda environment similar to a Python Virtual Environment?
conda create --clone arcgispro-py3 --name arcgispro-py3_clone
Or are there any benefits to create a Visual Environment for this cloned environment?
I think I understand what you're asking. "virtual environment" when it comes to python usually refers to python environments created using virtualenv specifically. You could consider conda environments "virtual environments" as well, but that just gets confusing to refer to them that way and people don't do that. Say "conda environment".
Conda is its own environment and package manager, it can be used in place of virtualenv in a lot of cases. As with most things with overlapping (and largely incompatible) use, there are pros and cons to using one over the other. Since I don't know your use case, I don't know which might be right for you.
You shouldn't create a virtual environment from your conda environment. That would probably just cause a mess down the road. Either use a system python to create a virtualenv or use conda to create a conda environment, I don't recommend mixing them.
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I have created an environment using conda and set up tensorflow along with a few other packages. How do I clone this environment such that it is a direct replica of the environment itself?
I plan to clone the environment (call it base) twice: Once for testing packages and again for production. In total I would have the base environment, the test version, and production version. The goal being I can test out packages in "test" and if they work, add them to production. If they really do work as expected, add them to "base". With that being said, I am not savy with the command line and get tripped up easily with naming conventions. Any explicit answers would be very helpful.
Hi Jack i think the best way would be to use this command:
conda create --name cloned_env --clone original_env
cloned_env --> is the new conda environment
original_env --> is the new conda environment we are cloning
Another smart way to do this would be by creating a requirements.txt that contains all the packages with their version from the environment you want to clone.
And then use that file to install the packages in the new enviroment.
I have been working with base environment in anaconda till now and had installed all the required python modules/libraries. When I tried to install tensorflow-cpu, I came to know that I have to create another environment to install it from official documentation of anaconda. Now I have created the new environment named 'tf' to install tensorflow-cpu. Since I can activate only one environment at a time, I don't want want to install all the python modules/libraries again to this new tf environment as it will consume space in memory.
Is there any way, I can use all the modules of base environment to tf environment or vice versa?
Please help in this context!
It's definitely not a good idea too mix envs. They were invented to separate dependencies for independent projects. Trying to mix them is a "wrong" way of using envs.
Also, you don't have to create a new env when installing tf as the docs say, just install it where you want it to be, everything will work just fine. The docs recommend you to create one because it's not a good idea to install it into base env.
You have an env named tf now. Activate it and install all dependencies you need, then use it in your project.
Do not pollute your base env with packages, this one is not supposed to be used for development, rather for conda itself. Create a new env when you start a new project
I'm using anaconda for manage my Python's environments and I want to create an env with a python executable file as interpreter. I didn't find similar question on other topic.
To be more precise, I don't want to create an env like this :
conda create --name my_env python=3.6.9
I want to create an env with a pre-installed python interpreter, tell to anaconda where to find the python executable file in my machine (in my case /usr/bin/python3) and use it as the interpreter for the env. Is it possible?
The only way I can imagine accomplishing this would be to make your own build of the python package. Perhaps having a look at how Conda Forge does this might be informative, though you can likely do a very trimmed down version depending on where you expect to run it.
Otherwise, no, not possible, AFAIK.
I am going through the painful process of learning how to manage packages/ different (virtual) environments in Python/Anaconda. I was told that Anaconda is basically a python installation with all the packages I need (e.g. numpy, scipy, sci-kit learn etc).
However, when I create a new environment, none of these packages is readily available. I cannot import them when using PyCharm with the newly created environment. When I check the Pycharm project interpreter, or the anaconda navigator environments tab, It seems that indeed none of these packages are installed in my new environments. Why is this? It doesn't make sense to me to provide all these packages, but then not make them ready for use when creating new environments. Do I have to install all these packages manually in new env's or am I missing something?
Kindest regards, and thanks in advance.
The reason the default python environment doesn't come with numpy is because maybe you don't want numpy in the environment. Imagine writing an API (or general software package) where your users may or may not have access to numpy. You might want to run tests to make sure your software fails gracefully or has a pure python fallback if numpy is not installed on your user's machine. Conda environments provide this (insanely useful) benefit. Of course, the package in question doesn't have to be numpy. There are some more esoteric packages where this type of testing is useful.
Furthermore, you can create a conda environment with numpy pre-installed, or any other package you want pre-installed (just add them to the end of the conda create command):
conda create --name my-env-name numpy
Anaconda comes with available packages such as numpy, scipy, and sci-kit learn, but if you want to use them within your environment, you must:
1) Create the environment:
conda create --name new_env
2) Activate the environment:
source activate new_env
3) Install the desired package using conda install
conda install numpy
If you'd like to create a new environment that includes installations of all available Anaconda packages, see create anaconda python environment with all packages. You can include anaconda in the list of packages to install in the environment, which is a 'meta-package' meaning 'all the packages that come with the Anaconda installation'.
I don't know about "conda" environments but in general virtual environments are used to provide you a "unique" environment. This might include different packages, different environment variables etc.
The whole point of making a new virtual environment is to have a separate place where you can install all the binaries ( and other resources ) required for your project. If you have some pre-installed binaries in the environment, doesn't it defeat the purpose of creating one in the first place?
The fact that you can create multiple environments helps you to separate binaries that might be needed by one and not by the other.
For instance, if you are creating a project which requires numpy:1.1 but you have numpy:2.1 installed , then you have to change it. So basically, by not installing any other packages, they are not making assumptions about your project's requirements.
You can check the packages you have in your environment with the command:
conda list
If packages are not listed you just have to add it, with the command:
conda install numpy
I am a new user to conda environment and was setting up to use TensorFlow , on Windows.
I came across a command -
source activate IntroToTensorFlow.
I understood IntroToTensorFlow is an environment we are creating, but does it mean we need to create this environment every time?. I am using jupyter notebook, so if I shutdown the kernel will the environment get deactivated?
And if I restart my PC, should I activate the environment everytime ?
Conda is a package manager that installs and manages (usually) Python libraries and (sometimes) non-Python packages. A conda environment is a sort of virtualenv virtual environment; its typical use case is to have a Python interpreter (any version) along with your choice of compatible Python libraries (any version).
The following example might most likely pertain to you. Suppose you have downloaded the implementation of a very nice paper implemented in TF and you want to try it out. But the authors implemented that when Tensorflow was just growing. The APIs have changed now, and so is the required CUDA version. You want to work ideally on the latest TF. Now, what do you do? An easy way to just try out this implementation is to create a different conda environment with the libraries needed for that implementation, run that in this environment, and perhaps if you like it, you might consider upgrading the TF APIs and use it in your code.
The conda environments are also pretty simple in its construction. If you installed conda using Anaconda and default options, you will have your environments in ~/anaconda3/envs. The environments are nothing but directories here, each having various configurations of Python interpreter and libraries of your choice. (So when you shutdown your PC/Jupyter, the environments will of course persist.) At the time of usage, you just switch between the environments to suit your needs. That is, when you source activate an environment, you will be allowed to use the Python interpreter and installed libraries from that environment. Note if you source deactivate or start a new terminal session, you will still be using the root environment.
Besides, Jupyter notebook, if setup with this plugin, will allow you to have nice integration with conda environments and you wouldn't even need to source activate everytime you want to switch. You can choose between the various settings (or conda environments), which are interpreted as different kernels in the notebook. So it would as simple as choosing some environment using a drop-down.
source activate IntroToTensorFlow does not create an environment, it simply activates an environment that has already been created. To create that environment (with tensorflow installed), use conda create -n IntroToTensorFlow tensorflow.
You do not need to create the environment every time, but you do need to activate it every time in order to use the packages installed in it. This is done using source activate IntroToTensorFlow
If you shutdown a kernel, the environment does not get deactivated automatically. To do so, you have to explicitly say source deactivate, or activate a separate environment using source activate xxx, replacing xxx with whatever environment name you want (that you have created previously).
When restarting your PC, (or starting a new session at the command line), you have to manually activate your desired environment to use it. Otherwise, by default, it will be running in your root environment. So, if you've only installed tensorflow in IntroToTensorFlow environment, you have to use source activate IntroToTensorFlow every time in order to use it.
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