For the first time I'v installed tensorflow with conda installation. Then I actually work with a seq2seq model. After that I have again installed the tensorflow with the pip installation. But now the libraries are very different. All the old scripts are misplaced etc. Why is that ? Why I didn't face this when I was working with coda instillation
It has been claimed that Tensorflow installed with Conda performs a lot faster than a Pip installation, for example:
https://towardsdatascience.com/stop-installing-tensorflow-using-pip-for-performance-sake-5854f9d9eb0c
Conda also installs all of the package dependencies automatically, which Pip does not, as far as I'm aware.
https://www.anaconda.com/blog/developer-blog/tensorflow-in-anaconda/
Pip and conda install to two different locations. You should try to stick to one or the other. I would recommend uninstalling the conda version and sticking to pip but it's up to you how to proceed.
Update 01-02-2019: It seems that conda is now the faster and preferred way to install tensorflow. Note this may change again in the future.
Related
While I am in a conda environment, the 'conda list' and 'pip freeze' show different number of libraries. For example, 'tensorflow-gpu' is listed in 'pip freeze', but not in 'conda list'. If I want to use tensorflow-gpu in this environment, should I run pip install tensorflow-gpu to install it again, or not necessary?
I think when you are using the conda environment. The conda list is going to show all the general packages that shared by the same conda environment. And the reason why 'tensorflow-gpu' is listed in 'pip freeze', but not in 'conda list', is because you used pip install to installed 'tensorflow-gpu'(could be you or the IDE). In this case, 'tensorflow-gpu' is only exists under this python project I believe. Actually, there is an official document about this topic.
Issues may arise when using pip and conda together. When combining
conda and pip, it is best to use an isolated conda environment. Only
after conda has been used to install as many packages as possible
should pip be used to install any remaining software. If modifications
are needed to the environment, it is best to create a new environment
rather than running conda after pip. When appropriate, conda and pip
requirements should be stored in text files.
Use pip only after conda Install as many requirements as possible with
conda then use pip.
Pip should be run with --upgrade-strategy only-if-needed (the
default).
Do not use pip with the --user argument, avoid all users installs.
And here is the link.
previously, I installed the tensorflow 1.13 in my machine.
There are some projects depending on different version of tensorflow and I do not want to mixed up different version of tensowflow.
So I just tried create a env called tf2.0 and used pip to install tensorflow 2.0.0b1 in that specific virtual environment.
However, after I ran 'pip install tensorflow-gpu==2.0.0b1` in that "tf2.0" conda environment, I found that it takes effect globally, which mean I have to use tensorflow-gpu 2.0.0b1 even when that virtual env "tf2.0" disactivated.
I wish I could use tensorflow 1.13 when virtual env is deactivated.
It's hard to troubleshoot the described conditions without more details (exact commands run, showing PATH before and after and post activation, etc.). Nevertheless, you can try switching to following the most recent recommendations for mixing Conda and Pip. Namely, avoid installing things ad hoc, which is prone to using the wrong pip and clobbering packages, but instead define a YAML file and always create the whole env in one go.
As a minimal example:
my_env.yaml
name: my_env
channels:
- defaults
dependencies:
- python
- pip
- pip:
- tensorflow-gpu==2.0.0b1
which can be created with conda env create -f my_env.yaml. Typically, it is best to include everything possible in the "non-pip" section of dependencies.
It is mostly that you used a wrong pip. To make sure you are using correct pip, it is usually a good practice to do
python -m pip install —user PACKAGE_NAME
Given that you have conda, pip should be the last resort.
Conda channel conda-forge most likely has the latest package version you are looking for.
conda install -c conda-forge PACKAGE_NAME
If you have to use pip, make sure you are in an environment and that environment has its own pip.
conda create -n test python=3.7
conda activate test
python -m pip install PACKAGE_NAME
From your described problem, I can guess that your environment is not activated in which you are trying to install the tensorflow2.0
Please make sure to activate the environment after making it.
so after creating the environment do this-
conda activate tf2.0
make sure you see this
(tf2.0) C:\Users\XYZ>
And then you install your tensorflow.
I don't have sudo access to the remote pc where cuda is already installed. Now, I have to install tensorflow-gpu on that system. Please give me the step by step guide to install it without sudo.
Operating System : Ubuntu 18.04
I had to do this before. Basically, I installed miniconda (you can also use anaconda, same thing and installation works without sudo), and installed everything using conda.
Create my environment and activate it:
conda create --name myenv python=3.6.8
conda actiavate myenv
Install the CUDA things and Tensorflow
conda install cudatoolkit=9.0 cudnn=7.1.2 tensorflow-gpu
Depending on your system, you may need to change version numbers.
Not sure how familiar you are with conda - it is basically a package-manager/repository and environment manager like pip/venv with the addition that it can handle non-python things as well (such as cudnn for example). As a note - if a package is not availabe through conda, you can still use pip as a fallback.
Untested with pip
I previously tried to do it without conda and using pip (I ended up failing due to some version conflicts, got frustrated with the process and moved to conda). It gets a little more complicated since you need to manually install it. So first, download cudnn from nvidia and unpack it anywhere you want. Then, you need to add it to the LD_LIBRARY_PATH:
export LD_LIBRARY_PATH=/path/to/cuda/lib64:/path/to/cudnn/lib64/:${LD_ LIBRARY_PATH}
I installed Tensorflow for GPU using: pip install tensorflow-gpu
But when I tried the same for Keras pip install keras-gpu, it pulled me an error: could not find the version that satisfies the requirements.
Adding to the answer below which is the correct answer in terms of recommending to use Anaconda package manager, but out of date in that there is now a keras-gpu package on Anaconda Cloud.
So once you have Anaconda installed, you simply need to create a new environment where you want to install keras-gpu and execute the command:
conda install -c anaconda keras-gpu
This will install Keras along with both tensorflow and tensorflow-gpu libraries as the backend. (There is also no need to install separately the CUDA runtime and cudnn libraries as they are also included in the package - tested on Windows 10 and working).
There is not any keras-gpu package [UPDATE: now there is, see other answer above]; Keras is a wrapper around some backends, including Tensorflow, and these backends may come in different versions, such as tensorflow and tensorflow-gpu. But this does not hold for Keras itself, which should be installed simply with
pip install keras
independently of whatever backend is used (see the PyPi docs).
Additionally, and since you have tagged the question as anaconda, too, be informed that it is generally not advisable to mix your package managers (i.e pip with conda), and you may be better off installing Keras from the Anaconda cloud with
conda install -c conda-forge keras
Finally, you may be also interested to know that recent versions of Tensorflow include Keras as a subpackage, so you can use it without any additional installation; see https://www.tensorflow.org/guide/keras
For installing tensorflow-gpu from Anaconda cloud, you should use
conda install -c anaconda tensorflow-gpu
before installing Keras. Be sure you do it in a different virtual environment, or after having uninstalled other versions (i.e. pip-installed ones), as there have been reported problems otherwise.
Adding to the above two answers, ensure your TensorFlow/Keras environment is using Python 3.6. Keras/TensorFlow doesn't work very well with Python 3.7, as of May 10, 2019.
I tried to use Keras/TensorFlow with Python 3.7 and I ended up having to reinstall Anaconda, since it sort of broke my Anaconda Prompt.
To install tensorflow-gpu with particular cuda version 9.0, use:
conda install tensorflow-gpu cudatoolkit==9.0 -c anaconda
Similarly for keras-gpu
I installed tensorflow using pip earlier (CPU only version). It was showing me some warnings whenever I used to work with it. Now I tried to install the GPU version. Now when I import tensorflow, it gives a bunch of errors.
So I wanted to know if there was a way to completely delete tensorflow from Ubuntu and then reinstall it later?
pip packages can be uninstalled pip uninstall tensorflow. Make sure you are uninstalling it from the environment of concern (if applicable).The pip output will inform you of it's successful removal. This should be enough to remove tensorflow from your system. You can then pip install tensorflow --ignore-installed to reinstall. The --ignore-install flag should overwrite tensorflow if you're concerned it was not successfully removed.
Please note that if you tried to install tensorflow GPU version that you will need to uninstall using tensorflow-gpu isntead of tensorflow. Same for re-installing.
Regarding your other issues, I suggest you open a fresh question for your errors. There's a good chance you weren't receiving errors but just the standard info messages from Tensorflow-GPU.