But, increasingly, Mitchell's pretensions have shaped her appraisal of her own gifts. Not that this awkwardness can't be occasionally successful: on Hejira, she clung so resolutely to even the stray flat notes that the impression was an attractive one of stubbornness and strength. Since Blue, Mitchell has demonstrated an increasing fondness for formats that don't suit her. The best that can be said for Don Juan's Reckless Daughter is that it is an instructive failure. But, always, the unpredictable caliber of her work has been as exciting as it is frustrating. She has dabbled with jazz and African tribal music, ventured deep inside herself and fled far away. From that happy juncture, she has moved on to more graceful and sober self-scrutiny ( For the Roses and Court and Spark), to dramatic musical experimentation mixed with failed social commentary ( The Hissing of Summer Lawns), to ever-more-seductive singing ( Miles of Aisles) and to rambling, hypnotic flights of fancy ( Hejira). He has a Masters of Science in Computer Engineering from UC Davis, and a Music Technology degree from Foothill College.In retrospect, Blue turns out to have been the album that displayed Joni Mitchell at her most buoyant and comfortable - with herself, with the nature of her talents, and with the conventions of pop songwriting. Prior to starting Audoir, he worked as an engineer in Silicon Valley startups. His research involves the use of deep learning to create music. Wayne Cheng is an A.I., machine learning, and generative deep learning developer at Audoir, LLC. To keep all your software up to date, use this command : conda update -all dso_:44] Successfully opened dynamic library libcudnn.so.7 dso_:44] Successfully opened dynamic library libcusparse.so.10.0 dso_:44] Successfully opened dynamic library libcusolver.so.10.0 dso_:44] Successfully opened dynamic library libcurand.so.10.0 dso_:44] Successfully opened dynamic library libcufft.so.10.0 dso_:44] Successfully opened dynamic library libcublas.so.10.0 dso_:44] Successfully opened dynamic library libcudart.so.10.0 gpu_:1618] Found device 0 with properties: This command displays the GPU, and whether the Cuda libraries were successfully opened. Type the following into the Python shell : tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(log_device_placement=True)) If there are no errors, Tensorflow has installed correctly. Then type the following into the Python shell : import tensorflow as tf To check whether Tensorflow was installed correctly, open a Python shell by typing the following into the terminal : python Step 3 : Check Installation of Tensorflow and Detection of GPU Note : using “conda install -c anaconda keras-gpu” will install an older version of Keras, which is why the installation steps are broken up into two steps After the Anaconda installation is finished, refresh the terminal environment by typing the following into the terminal : source ~/.bashrcįirst, install Tensorflow with Conda, by typing the following into the terminal : conda install -c anaconda tensorflow-gpuĪfter the Tensorflow installation is finished, install Keras by typing the following into the terminal : conda install -c conda-forge keras When prompted by the installer, choose the recommended choices (accept the default install location, initialize Anaconda3 by running conda init). Using these instructions, Anaconda can be installed with the following steps :ġ) Install the prerequisite packages by typing the following into the terminal : apt-get install libgl1-mesa-glx libegl1-mesa libxrandr2 libxrandr2 libxss1 libxcursor1 libxcomposite1 libasound2 libxi6 libxtst6Ģ) Download Anaconda to the Downloads folder.ģ) After completing the download, check the integrity of the installer by typing the following into the terminal : sha256sum ~/Downloads/Anaconda3-2019.10-Linux-x86_64.shĤ) The hash key should match the key found on this page, or : 46d762284d252e51cd58a8ca6c8adc9da2eadc82c342927b2f66ed011d1d8b53ĥ) If the hash keys match, install Anaconda by typing the following into the terminal : bash ~/Downloads/Anaconda3-2019.10-Linux-x86_64.sh Conda is the better choice because it is better at resolving dependency issues that can occur when installing a new package. Keras and Tensorflow can be installed with either the Pip or Conda (Anaconda) package managers. This guide describes the process of installing Keras and Tensorflow on Ubuntu 18.04.
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