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HOW TO RUN JUPYTER NOTEBOOK ON WINDOWS FOR FILE OF 2 GB INSTALL
HOW TO RUN JUPYTER NOTEBOOK ON WINDOWS FOR FILE OF 2 GB SOFTWARE
HOW TO RUN JUPYTER NOTEBOOK ON WINDOWS FOR FILE OF 2 GB CODE

This can take almost 1.1 GB of the hard disk. Note: the new environment is automatically created in the current users for Windows devices. yml file with the given code: conda env create -f hello-tf.yml It will automatically open the notepad then, the user can edit the file from there.

HOW TO RUN JUPYTER NOTEBOOK ON WINDOWS FOR FILE OF 2 GB CODE

Users have to paste the given code in the terminal to edit the file because the yml file is ready to edit or macOS users can use the vim for editing the yml file. Step 4: Edit the yml file For macOS devices: The file name for the windows device is: 'echo.>hello-tf.yml.' For macOS devices:įor the macOS users, the file name will appear in the anaconda3: 'touch hello-tf.yml.' At last, copy and then paste the code in the terminal. The user has to create the yml file in the new working directory, and after the process installs the complete dependencies, the user has to run TensorFlow. Step 3: Create the yml file for the device: It is essential to wrap the PATH as the 'PATH' because this can turn the working directory to PATH.įor Windows user (make sure that the folder placed before Anaconda3): cd C:\Users\Admin\Anaconda3 Users can change the path to cd PATH in case the path on the text editor does not match the working directory.

HOW TO RUN JUPYTER NOTEBOOK ON WINDOWS FOR FILE OF 2 GB INSTALL

In the macOS devices, the latest folder will be shown right before the $, and the terminal will install the libraries in the working directory. For macOS:įor the macOS, the default working directory will be Users/USERNAME, path anaconda3, and the working directory are entirely identical. The user has to decide an appropriate directory in which the user wants to create a.yml file, and it will be located in the Anaconda. Step 2: Set and select the working directory

HOW TO RUN JUPYTER NOTEBOOK ON WINDOWS FOR FILE OF 2 GB SOFTWARE

There is a fast way to install libraries and software to write a.yml file. The user has to create a new folder in the Anaconda, which contains Jupyter, TensorFlow, and ipython.

For Windowsįor the windows, the user can use the Anaconda type and Prompt: C:\>where Anacondaįor the macOS device, the user can use the type and terminal: Users can create a new anaconda environment, which includes the essential libraries.

Install TensorFlow (it is only for Windows user) Locating the Anacondaįor the installation of Anaconda, the user has to locate a path for it.

yml file to install TensorFlow, and the process is: There are two versions of TensorFlow available for the Windows user, and these versions are:ĭownload the Anaconda version 4.3.1 for the required system, and this version is essential for Python 3.6. It can provide faster training by training algorithms. It means that the process of computations can be divided according to the devices to enhance the speed of the training with the help of parallelization. Installing TensorFlowīefore starting the process of installation, let's learn that TensorFlow supports multiple GPUs and CPUs.

TensorFlow is used for the video detection system, which means it is helpful for motion detection or real-time threat detection systems.

It is used for analyzing the data and separating the meaningful states of it, and the best example of this use case is online streaming platforms (Netflix, YouTube), it uses statistical data to provide the best user experiences.

TensorFlow helps to improve the text-based recognition technology, and the best example is Google that supports various languages and the capability to transfer the text from one language to another.

Voice and sound recognition process can be done through the TensorFlow, and it is also used for sentiment analysis or flaw detection.

This technology is used for automatic image recognition systems because it consists of algorithms to identify shapes for modeling purposes.

Most importantly, it supports production prediction at scale with exact models that are used for the training.įigure 1 TensorFlow logo Five main use cases of TensorFlow TensorFlow can run and train deep neural networks for image recognition, for handwritten digit classification, recurrent neural networks, word embeddings, and sequence-to-sequence models for natural language processing, PDE (partial differential equation) and machine translation. In simple words, TensorFlow is an open-source library for numerical computation, which is used to enhance the convenience and ease in machine learning, and it is an entirely Python friendly library.

Step 3: Create the yml file for the device:.

Step 2: Set and select the working directory.

Step 1: Install Anaconda in the device:.