Skip to main content

CondaValueError: Value error: invalid package specification

Recently I was trying to create Conda Environment and wanted to install Tensorflow but i have faced some issue , so i have done some research and done trouble shooting related to that . Here am going to share how to trouble shoot if you are getting Conda Value error while creating Conda environment and install tensorflow
.


  1. Open Anaconda Prompt (as administrator if it was installed for all users)
  2. Run conda update conda
  3. Run the installer again
Make sure all pkg are updated:





Launch the console from Anaconda Navigator and
conda create -n mypython python=3.6.8

After Installing Conda environment please active the conda now : conda activate mypython
once conda environment has been activated kindly install tensorflow 2.0 by using this command pip install tensorflow==2.0.0




once Tensorflow has been successfully install kindly run the command : pip show tensorflow
Try to Run Comman PIP Install Jupyter lab and after installing launch the console and open Jupyter notebook
Happy Learning...!!

Comments

  1. Thanks Anand its really help and guide me solve very similar issue.

    ReplyDelete

Post a Comment

Popular posts from this blog

Math Skills required for Data Science Aspirants

The knowledge of this essential math is particularly important for newcomers arriving at data science from other professions, Specially whosoever wanted to transit their career in to Data Science field (Aspirant). Because mathematics is backbone of Data science , you must have knowledge to deal with data, behind any algorithm mathematics plays an important role. Here am going to iclude some of the topics which is Important if you dont have maths background.  1. Statistics and Probability 2. Calculus (Multivariable) 3. Linear Algebra 4.  Methods for Optimization 5. Numerical Analysis 1. Statistics and Probability Statistics and Probability is used for visualization of features, data preprocessing, feature transformation, data imputation, dimensionality reduction, feature engineering, model evaluation, etc. Here are the topics you need to be familiar with: Mean, Median, Mode, Standard deviation/variance, Correlation coefficient and the covariance matrix, Probability distribution...