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

Deep Learning Interview Questions - Part 1

Q1. What do you mean by Deep Learning?  Deep Learning  is nothing but a paradigm of machine learning which has shown incredible promise in recent years. This is because of the fact that Deep Learning shows a great analogy with the functioning of the human brain. Q2. What is the difference between machine learning and deep learning? Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed. Machine learning can be categorised in the following three categories. Supervised machine learning, Unsupervised machine learning, Reinforcement learning Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks. Q3. What, in your opinion, is the reason for the popularity of Deep Learning in recent times? Now although Deep Learning has been around for many years, the major breakthroughs from these te...

How to deal with missing values in data cleaning

The data you inherit for analysis will come from multiple sources and would have been pulled adhoc. So this data will not be immediately ready for you to run any kind of model on. One of the most common issues you will have to deal with is missing values in the dataset. There are many reasons why values might be missing - intentional, user did not fill up, online forms broken, accidentally deleted, legacy issues etc.  Either way you will need to fix this problem. There are 3 ways to do this - either you will ignore the missing values, delete the missing value rows or fill the missing values with an approximation. Its easiest to just drop the missing observations but you need to very careful before you do that, because the absence of a value might actually be conveying some information about the data pattern. If you decide to drop missing values : df_no_missing = df.dropna() will drop any rows with any value missing. Even if some values are available in a row it will still get dropp...

Mathematics & Statistics for Data Science

Converting raw and quantitative data into organized and informative information needs a lot of brain power and understanding. It is true that everyone can’t be Aryabhatta but, you can be hardworking, focused and dedicated. So, it is time to show your dedication and hard work for learning maths and statistics for data science. Mathematics and Statistics are two of the most important concepts of Data Science. Data Science revolves around these two fields and draws their concepts to operate on the data. Today, we will explore the various concepts that build up data science and their practical usages in this field.Data Science has become a trending technology in the world today. In order to learn data science, you must reinforce your knowledge of mathematics and statistics. So let’s first explore how much maths is required for data science – Math for Data Science Mathematics has created an impact on every discipline. The magnitude of the usage of mathematics varies according to the ...