To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. 11:50:29.264401: I tensorflow/core/platform/cpu_feature_:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX AVX2 First, remove the old NVIDIA GPG sign key and update/upgrade libraries: sudo sudo apt-key del 7fa2af80. To install them, execute the below steps attentively. 11:50:29.257000: I tensorflow/stream_executor/cuda/cuda_:176] hostname: DESKTOP-LKK3I7H After those versions are installed from the Windows side, WSL2 expects the same versions. 11:50:29.247104: I tensorflow/stream_executor/cuda/cuda_:169] retrieving CUDA diagnostic information for host: DESKTOP-LKK3I7H 11:50:29.234980: W tensorflow/stream_executor/cuda/cuda_:269] failed call to cuInit: UNKNOWN ERROR (303) 11:50:29.225694: W tensorflow/stream_executor/platform/default/dso_:64] Could not load dynamic library 'nvcuda.dll' dlerror: nvcuda.dll not found 11:50:25.622571: I tensorflow/stream_executor/cuda/cudart_:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine. 11:50:25.612224: W tensorflow/stream_executor/platform/default/dso_:64] Could not load dynamic library 'cudart64_110.dll' dlerror: cudart64_110.dll not found If you getting tensor in output, than latest TensorFlow version is installed successfully. Python -c "import tensorflow as tf print(tf.reduce_sum(tf.random.normal()))" 11:48:40.033709: I tensorflow/stream_executor/cuda/cudart_:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine. 11:48:40.023881: W tensorflow/stream_executor/platform/default/dso_:64] Could not load dynamic library 'cudart64_110.dll' dlerror: cudart64_110.dll not found Note: you might be prompted a bit different to this, it doesn’t matter just hit Enter, Anaconda will do the best for you.Python -c "import tensorflow as tf print(tf._version_)" conda create -n yolov3_tf2 python=3.7Īfter that, you will be prompted something like this, just type ‘ y‘ and then hit the Enter. Just type or copy the following command to your Anaconda prompt and hit Enter. Open Anaconda prompt, and create a new environment called yolov3_tf2 ( I gave this name because it relates to my next article about the implementation of YOLOv3 in TensorFlow 2.0). Now, we’re going to create our first environment, but be sure that you’ve installed Anaconda on your computer. (Note: For more details on how to use Anaconda, you can visit the Anaconda user guide here). I will let you explore it, but anyhow, if you have any problem, you can simply post a comment in the comment section and I will try to do my best for you. Go ahead and choose the appropriate version, follow the instructions and install it. Instead, the default Python used by your programs will be the one that comes with Anaconda. In case you have already installed Python on your computer, don’t worry, it won’t ruin anything. If you need, you can easily install Python 2.7 versions later. I suggest you choose the Python version 3.7 64-bit installer if you have a 64-bit machine, otherwise choose the 32-bit installer, instead. Installing AnacondaĪnaconda is available for Windows, Mac OS X, and Linux, you can find the installation file in the anaconda official site. If we want to use a different Python version or package libraries, just create a different environment and play around without any risk of crashing the system library. This can save time and energy for other things.Īnaconda can be used across different platforms, Windows, macOS, and Linux. So, we no need to worry about the system library or anything like that. Installing Anaconda meaning installing Python with some commonly used libraries such as Numpy, Pandas, Scrip, and Matplotlib.įor a Python developer or a data science researcher, using Anaconda has a lot of advantages, such as independently installing/updating packages without ruining the system. It comes with many useful built-in third-party libraries. What is Anaconda and why I recommend it?Īnaconda is a Python-based data processing built for data science. Here, I’m going to show you how to install TensorFlow 2.0 in Anaconda. That’s way, TensorFlow 2.0 is more friendly than the older version 1.x.įor those of you who don’t have prior experience with this topic, this post is special for you. We can now easily debug TensorFlow’s variables and print their values just like in the standard Python. With eager execution by default and tight integration with Keras, now TensorFlow 2.0 makes the development of machine learning applications much easier than before. In September last year, 2019, Google finally announced the availability of the final release of TensorFlow 2.0. It has been used in many different fields of applications including handwritten digit classification, image recognition, object detection, word embeddings, and natural language processing (NLP). TensorFlow is still one of the popular Deep learning frameworks.
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