The process can take a couple of minutes. In this step, Anaconda checks all the package dependencies and shows detailed information about the installation in a separate window.Ĭonfirm the installation by clicking Apply. Find the intelpython3_core and intelpython3_full packages in the list and mark them for installation.Ĭlick Apply to continue. Anaconda Navigator offers all suitable packages with descriptions and versions in the grid. Select All in the package box and input “intel” in the search area, as shown below. Now, we’re ready to install the required packages in our DL environment.įirst, let’s install the Intel Distribution for Python. The Anaconda Navigator application creates and activates the environment. We use intel_dl_env.Ĭonfirm the creation of the new environment in the window. Click Create and input the name of the environment in the Create new environment text box. Then click Update channels to finish the operation. Click the Channels button, and add the new channel, intel, in the opened window. Jupyter* Notebook web application to develop, execute, and test Python scriptsįirst, start your installed Anaconda Navigator application and go to the Environments tab.Īs we use Intel-optimized packages in the environment, let’s first add the new distribution channel - the Intel channel. Intel® Optimization for TensorFlow* to build neural networks and to fit and test DL models.Pillow library for loading and processing images.Intel® Distribution for Python for use as a kernel of the environment.We use the following components to build the environment: This environment includes tools to handle the usual tasks in DL applications for CV, such as working with images, creating neural networks, training, and testing neural network models. In this article, we build a DL environment for computer vision (CV). We’ll use the Anaconda Navigator desktop application to build the DL environment.īefore we begin, ensure that you download Anaconda and set it up on your machine. In this article, we’ll explore how to create a DL environment with optimized Intel packages. Anaconda makes it easy to install and update these packages without having to manage installations and dependencies, helping keep your DL environments up to date and optimized for modern processor architectures. Intel develops software optimized for high-speed, high-performance DL packages, and its Python and TensorFlow products come with integrated optimizations from the oneMKL and oneDNN libraries. To support the field and those working within it, Intel and Anaconda have collaborated to provide continuously improved and optimized tools for everyday and advanced DL tasks. Millions of students, developers, and researchers rely on their computers’ efficient DL ecosystems to stay at the forefront of the industry. Deep learning (DL) is a swiftly advancing field, with a rapid increase in model complexity along with the software ecosystem that attempts to address it.
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