so in case you dont have access to internet, then you can use conda pack.Ĭonda-pack is a command line tool that archives a conda environment, All other previous methods require internet connection. If you plan on getting an exact copy of your current environment and then move it to another machine with the same platform and OS, without redownloading all packages again from Internet (good for offline machines/behind firewalls). # as long as some version of Python is already installed on the machine. # Note that this command can also be run without activating the environment # Cleanup prefixes from in the active environment. # libraries will work fine, but things that require prefix cleanups # Use Python without activating or fixing the prefixes. # Pack environment located at an explicit path into my_Īnd to restore it on the other machine(s): # Unpack environment into directory `my_env` With this knowledge, you’ll be well-equipped to tackle any machine learning project using TensorFlow on Anaconda.Conda-forge: conda install -c conda-forge conda-packīacking up: # Pack environment my_env into my_ Remember to activate your environment before installing TensorFlow and to test your installation to make sure everything is working correctly. By following these steps, you can quickly and easily set up a new environment and install any version of TensorFlow you need for your machine learning projects. In this tutorial, we’ve shown you how to install TensorFlow with a specific version on Anaconda. If everything is installed correctly, you should see the version number of TensorFlow printed to the console. Save the file as “test_tf.py” and run it from the command line using the following command: python test_tf.py Open a new Python file and add the following code: import tensorflow as tf To test that TensorFlow is installed correctly, we can run a quick script that loads TensorFlow and prints its version number. If you want to install a different version of TensorFlow, simply replace “2.4.1” with the version you want to install. This will install TensorFlow 2.4.1 and all of its dependencies. Once your environment is activated, you can use the following command to install TensorFlow: conda install tensorflow=2.4.1 To install TensorFlow 2.4.1, for example, open the Anaconda Prompt (Windows) or Terminal (Mac/Linux) and activate your new environment by running the following command: conda activate tf_env To install a specific version of TensorFlow, we’ll use the “conda” package manager, which is included with Anaconda. Now that we have a new environment, we can install TensorFlow. Then click the “Create” button to create your new environment. In the “Create new environment” dialog box, enter a name for your new environment (e.g., “tf_env”) and choose the Python version you want to use (e.g., Python 3.8). Then click the “Create” button at the bottom of the screen. To create a new environment, open the Anaconda Navigator and click on the “Environments” tab. An environment is a self-contained workspace where we can install specific packages and their dependencies without affecting the rest of our system. Now that we have Anaconda installed, we need to create a new environment to install TensorFlow. When the installation is complete, open the Anaconda Navigator. Once you’ve downloaded the installer, run it and follow the on-screen instructions to install Anaconda on your system. You can download the latest version of Anaconda from the official website. Anaconda is a distribution of the Python programming language that includes many popular packages used in data science and machine learning. Step 1: Install Anacondaīefore we can install TensorFlow, we need to install Anaconda. But with these step-by-step instructions, you’ll be able to install any version of TensorFlow on Anaconda quickly and easily. Installing TensorFlow on Anaconda can be a bit tricky, especially if you want to install a specific version. In this tutorial, we’ll show you how to install TensorFlow with a specific version on Anaconda, a popular Python distribution for scientific computing. It’s an open-source software library that’s used for dataflow and differentiable programming and is widely used in machine learning applications such as neural networks. If you’re a data scientist or machine learning engineer, you’re probably familiar with TensorFlow.
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