Datalab currently seems to be running 0.6.0. I wanted to update to version 0.8.0
I did:
!pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.8.0-cp27-none-linux_x86_64
I got:
SSLError: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed (_ssl.c:581)
Storing debug log for failure in /root/.pip/pip.log
How can I fix this?
It is not recommended to update packages which are installed in Datalab by default. This is to ensure that you do not break a working Datalab environment.
Please try one of the following solutions:
If you deployed Datalab using https://datalab.cloud.google.com/ , visit the Datalab GitHub Issues page and submit an issue to have a new version of datalab published. In the Datalab source code on github, tensorflow is at version 0.8.0)
If you have installed Datalab locally, or on GCE, then simply rebuild the Datalab image to get tensorflow 0.8.0 . See the Datalab Getting Started Wiki page for more information.
If you want to temporarily install a newer version into your existing environment for testing purposes (although this isn't recommended) , then you could try installing tensorflow with the no dependencies option (--no-deps) in order to reduce the chance of breaking the working datalab environment.
%%bash
wget https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.8.0-cp27-none-linux_x86_64.whl && pip install --ignore-installed --no-deps tensorflow-0.8.0-cp27-none-linux_x86_64.whl
After running the above command, I can see tensorflow is at version 0.8.0.
>> import tensorflow
>> tensorflow.__version__
'0.8.0'
>>!pip show tensorflow
---
---
Metadata-Version: 2.0
Name: tensorflow
Version: 0.8.0
Summary: TensorFlow helps the tensors flow
Home-page: http://tensorflow.org/
Author: Google Inc.
Author-email: opensource#google.com
Installer: pip
License: Apache 2.0
Location: /usr/local/lib/python2.7/dist-packages
Requires: six, protobuf, wheel, numpy
Please keep an eye out for any anomalies now that you have updated a package used by datalab. For example, certain sample notebooks may not work. Also, please note that this setup may not be supported. For example, you may encounter an issue which is directly related to updating a package used by datalab. In that case, the solution may be to revert the updated package and see if that resolves your issue.
Related
i followed the instructions given by the official tf documentation, but i just cannot resolve the various problems encountered.
Did anyone have the experience installing tff on m1 mac and can show me your overall process?
conda create -n federated python=3.8
conda activate federated
pip install --upgrade tensorflow_federated
everything seems to be fine according to the terminal output, however,
after
import tensorflow_federated as tff
i got a RunTimeError:
RuntimeError: This version of jaxlib was built using AVX instructions, which your CPU and/or operating system do not support. You may be able work around this issue by building jaxlib from source.
how to resolve this?
When I start "Running Train a GPT-2 (or GPT Neo) Text-Generating Model w/ GPU on Colab" in my Colab, following error comes up:
ERROR: tensorflow 2.5.0 has requirement tensorboard~=2.5, but you'll
have tensorboard 2.4.1 which is incompatible. ERROR: pytorch-lightning
1.3.8 has requirement PyYAML<=5.4.1,>=5.1, but you'll have pyyaml 3.13 which is incompatible.
What to do? Is it because of my Mac, or do I need to upgrade my Colab account would that help?
The problem comes from the default packages installed in the Colab environment. I does not depend on the platform you are using to access Colab or on the type of your subscription.
You have to upgrade the Python packages using pip.
In general you can run shell commands like pip in Colab prepending a ! character,
so in your case the following lines should be sufficient to fix the problem
!pip install tensorboard==2.5
!pip install pyyaml==5.4.1
If you need to run more shell commands, you can use more user-friedly methods (see the answers to this question).
I recently installed python with the version 3.8.3 and upgraded pip to 20.1.1. According to enter link description here, conda install -c conda-forge tensorflow should work. However, I get this result
Solving environment: failed with repodata from current_repodata.json, will retry with next repodata source.
Collecting package metadata (repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.
Solving environment: -
Found conflicts! Looking for incompatible packages.
This can take several minutes. Press CTRL-C to abort.
failed
UnsatisfiableError: The following specifications were found
to be incompatible with the existing python installation in your environment:
Specifications:
- tensorflow -> python[version='3.5.*|3.6.*|>=3.5,<3.6.0a0|>=3.6,<3.7.0a0|>=3.7,<3.8.0a0|3.7.*']
Your python: python=3.8
If python is on the left-most side of the chain, that's the version you've asked for.
When python appears to the right, that indicates that the thing on the left is somehow
not available for the python version you are constrained to. Note that conda will not
change your python version to a different minor version unless you explicitly specify
that.
since I use
(base) C:\Users\ivan>python --version
Python 3.8.3
(base) C:\Users\ivan>pip --version
pip 20.1.1 from C:\Users\ivan\anaconda3\lib\site-packages\pip (python 3.8)
I wonder if it is possible to solve this issue without downgrading. For users of anaconda 2020.07, python 3.8 is used by default. Downgrading it will break anaconda.
People have reported problems using tensorflow with python 3.8, it is best to use 3.7. You are incorrect about breaking Anaconda. Here is what to do.
In Anaconda home page click on environments. At the bottom left of the page click on create. A window will appear. Give the new environment a name (say python3.7). In the drop down menu select 3.7. Now a new environment is created using python 3.7. Now in the conda terminal type conda activate python3.7. Then use conda to install tensorflow. It will install version 2.1.1, the cuda toolkit version 10.1.243 and cudnn version 7.6.5. Note conda can only install tensorflow up to version 2.1.1. If you want tensorflow 2.2 install it with pip using pip install tensorflow ==2.2.0. after you have installed 2.1. The cuda toolkit and cudnn work with version 2.2. Now use pip or conda to install any other packages you need in your python3.7 environment and you should be good to go!
I'm a newbie when it comes to AWS and Tensorflow and I've been learning about CNNs over the last week via Udacity's Machine Learning course.
Now I've a need to use an AWS instance of a GPU. I launched a p2.xlarge instance of Deep Learning AMI with Source Code (CUDA 8, Ubuntu) (that's what they recommended)
But now, it seems that tensorflow is not using the GPU at all. It's still training using the CPU. I did some searching and I found some answers to this problem and none of them seemed to work.
When I run the Jupyter notebook, it still uses the CPU
What do I do to get it to run on the GPU and not the CPU?
The problem of tensorflow not detecting GPU can possibly be due to one of the following reasons.
Only the tensorflow CPU version is installed in the system.
Both tensorflow CPU and GPU versions are installed in the system, but the Python environment is preferring CPU version over GPU version.
Before proceeding to solve the issue, we assume that the installed environment is an AWS Deep Learning AMI having CUDA 8.0 and tensorflow version 1.4.1 installed. This assumption is derived from the discussion in comments.
To solve the problem, we proceed as follows:
Check the installed version of tensorflow by executing the following command from the OS terminal.
pip freeze | grep tensorflow
If only the CPU version is installed, then remove it and install the GPU version by executing the following commands.
pip uninstall tensorflow
pip install tensorflow-gpu==1.4.1
If both CPU and GPU versions are installed, then remove both of them, and install the GPU version only.
pip uninstall tensorflow
pip uninstall tensorflow-gpu
pip install tensorflow-gpu==1.4.1
At this point, if all the dependencies of tensorflow are installed correctly, tensorflow GPU version should work fine. A common error at this stage (as encountered by OP) is the missing cuDNN library which can result in following error while importing tensorflow into a python module
ImportError: libcudnn.so.6: cannot open shared object file: No such
file or directory
It can be fixed by installing the correct version of NVIDIA's cuDNN library. Tensorflow version 1.4.1 depends upon cuDNN version 6.0 and CUDA 8, so we download the corresponding version from cuDNN archive page (Download Link). We have to login to the NVIDIA developer account to be able to download the file, therefore it is not possible to download it using command line tools such as wget or curl. A possible solution is to download the file on host system and use scp to copy it onto AWS.
Once copied to AWS, extract the file using the following command:
tar -xzvf cudnn-8.0-linux-x64-v6.0.tgz
The extracted directory should have structure similar to the CUDA toolkit installation directory. Assuming that CUDA toolkit is installed in the directory /usr/local/cuda, we can install cuDNN by copying the files from the downloaded archive into corresponding folders of CUDA Toolkit installation directory followed by linker update command ldconfig as follows:
cp cuda/include/* /usr/local/cuda/include
cp cuda/lib64/* /usr/local/cuda/lib64
ldconfig
After this, we should be able to import tensorflow GPU version into our python modules.
A few considerations:
If we are using Python3, pip should be replaced with pip3.
Depending upon user privileges, the commands pip, cp and ldconfig may require to be run as sudo.
Jupyter Notebook kernel dies when importing tensorflow 1.5.0
I have read a lot of posts relating to this but they have all had higher version numbers of tensorflow and have solved it by downgrading to 1.5.0. I also had higher version number and followed the advice to downgrade but I still have the problem.
Does anyone know what to try next?
pip install h5py==2.8.0
worked for me
When trying using the command prompt I got an error message not related to the tensorflow issue (I think);
"Warning! HDF5 library version mismatched error"
The key information from that message body was "Headers are 1.10.1, library is 1.10.2" so I downgraded hdf5 library by "conda install -c anaconda hdf5=1.10.1" and now the error message is gone and the kernel does not die when importing tensorflow.
I got similar problems, any tensorflow or tensorflow related packages (e.g. keras) made my kernel to die when loading, from any interface (jupyter, spyder, console....)
For those having this kind of problems, try running python from the console with verbose mode (python -v) then import tensorflow and look for errors.
I spot errors related to h5py, similar to the reply of #DBSE. I just upgraded the h5py package then everything was solved !
If you are using a conda environment, then the easiest method for fixing this issue is to just create a new environment and install tensorflow with just a single command. I had the same issue, I have tried a lot on most of the version of python and tensorflow. But at the last I have successfully configured it with just a single steps.
Run this command for installing GPU version
conda create --name tf_gpu tensorflow-gpu
The above line of code will automatically install that version of python and tf which is comaptible with your GPU or CPU.
For CPU, Run this command
conda create --name tf_env tensorflow
Both of these command work 100 % with my system for GPU and CPU access and will download the latest version which are compatible with system. It will resolved/fixed "Illegal Instruction (code dumps)" error.
pip install h5py==3.1.0
This is the most updated version which worked for me.
Try using import numpy before Keras and Tensorflow.