TensorFlow is a Python library for high-performance numerical calculations that allows users to create sophisticated deep learning and machine learning applications. There are a number of methods that can be used to install TensorFlow, such as using pip to install the wheels available on PyPI. Installing TensorFlow using conda packages offers a number of benefits, including a complete package management system, wider platform support, a more streamlined GPU experience, and better CPU performance. These packages are available via the Anaconda Repository, and installing them is as easy as running “conda install tensorflow” or “conda install tensorflow-gpu” from a command line interface. One key benefit of installing TensorFlow using conda rather than pip is a result of the conda package management system. When TensorFlow is installed using conda, conda installs all the necessary and compatible dependencies for the packages as well. This is done automatically; users do not need to install any additional software via system packages managers or other means. Like other packages in the Anaconda repository, TensorFlow is supported on a number of platforms. TensorFlow conda packages are available for Windows, Linux, and macOS. The Linux packages for the 1.10.0 release support a number of Linux distributions including older distributions such as CentOS 6. This is a further benefit of the conda packages: in spite of being labeled as manylinux1-compatible (works on many versions of linux), the wheels available on PyPI support only a minimum of Ubuntu 16.04, which is much newer than many enterprise Linux installations. The conda TensorFlow packages are also designed for better performance on CPUs through the use of the Intel® Math Kernel Library for Deep Neural Networks (Intel® MKL-DNN). Starting with version 1.9.0, the conda TensorFlow packages are built using the Intel® MKL-DNN library, which demonstrates considerable performance improvements. For example, Figure 1 compares the performance of training and inference on two different image classification models using TensorFlow installed using conda verses the same version installed using pip. The performance of the conda installed version is over eight times the speed of the pip installed package in many of the benchmarks.Figure 1: Training performance of TensorFlow on a number of common deep learning models using synthetic data. Benchmarks were performed on an Intel® Xeon® Gold 6130. Interested in trying out these TensorFlow packages? After installing Anaconda or Miniconda, create a new conda environment containing TensorFlow and activate it $ conda create -n tensorflow_env tensorflow $ conda activate tensorflow_env Or for the GPU version $ conda create -n tensorflow_gpuenv tensorflow-gpu $ conda activate tensorflow_gpuenv TensorFlow is now installed and ready for use. For those new to TensorFlow, the tutorials offer a great place to get started. Ref: Anaconda Blog Implementation (base) C:\Users\ashish>conda env list # conda environments: # base * D:\programfiles\Anaconda3 py38 D:\programfiles\Anaconda3\envs\py38 (base) C:\Users\ashish>pip install tensorflow== ERROR: Could not find a version that satisfies the requirement tensorflow== (from versions: 1.13.0rc1, 1.13.0rc2, 1.13.1, 1.13.2, 1.14.0rc0, 1.14.0rc1, 1.14.0, 1.15.0rc0, 1.15.0rc1, 1.15.0rc2, 1.15.0rc3, 1.15.0, 1.15.2, 1.15.3, 2.0.0a0, 2.0.0b0, 2.0.0b1, 2.0.0rc0, 2.0.0rc1, 2.0.0rc2, 2.0.0, 2.0.1, 2.0.2, 2.1.0rc0, 2.1.0rc1, 2.1.0rc2, 2.1.0, 2.1.1, 2.2.0rc0, 2.2.0rc1, 2.2.0rc2, 2.2.0rc3, 2.2.0rc4, 2.2.0, 2.3.0rc0, 2.3.0rc1, 2.3.0rc2, 2.3.0) ERROR: No matching distribution found for tensorflow== (base) C:\Users\ashish>conda create -n tf Collecting package metadata (current_repodata.json): done Solving environment: done ==> WARNING: A newer version of conda exists. <== current version: 4.8.3 latest version: 4.8.4 Please update conda by running $ conda update -n base -c defaults conda ## Package Plan ## environment location: D:\programfiles\Anaconda3\envs\tf Proceed ([y]/n)? y Preparing transaction: done Verifying transaction: done Executing transaction: done # # To activate this environment, use # # $ conda activate tf # # To deactivate an active environment, use # # $ conda deactivate (base) C:\Users\ashish>conda activate tf (tf) C:\Users\ashish>pip install tensorflow==2.0.1 'pip' is not recognized as an internal or external command, operable program or batch file. (tf) C:\Users\ashish>conda install tensorflow==2.0.1 Collecting package metadata (current_repodata.json): done Solving environment: failed with initial frozen solve. Retrying with flexible solve. Collecting package metadata (repodata.json): done Solving environment: failed with initial frozen solve. Retrying with flexible solve. PackagesNotFoundError: The following packages are not available from current channels: - tensorflow==2.0.1 Current channels: - https://repo.anaconda.com/pkgs/main/win-64 - https://repo.anaconda.com/pkgs/main/noarch - https://repo.anaconda.com/pkgs/r/win-64 - https://repo.anaconda.com/pkgs/r/noarch - https://repo.anaconda.com/pkgs/msys2/win-64 - https://repo.anaconda.com/pkgs/msys2/noarch To search for alternate channels that may provide the conda package you're looking for, navigate to https://anaconda.org and use the search bar at the top of the page. (tf) C:\Users\ashish>conda deactivate (base) C:\Users\ashish>conda remove --name tf --all Remove all packages in environment D:\programfiles\Anaconda3\envs\tf: No packages found in D:\programfiles\Anaconda3\envs\tf. Continuing environment removal (base) C:\Users\ashish>conda env list # conda environments: # base * D:\programfiles\Anaconda3 py38 D:\programfiles\Anaconda3\envs\py38 (base) C:\Users\ashish>conda create -n tf tensorflow Collecting package metadata (current_repodata.json): done Solving environment: failed with repodata from current_repodata.json, will retry with next repodata source. Collecting package metadata (repodata.json): done Solving environment: done ==> WARNING: A newer version of conda exists. <== current version: 4.8.3 latest version: 4.8.4 Please update conda by running $ conda update -n base -c defaults conda ## Package Plan ## environment location: D:\programfiles\Anaconda3\envs\tf added / updated specs: - tensorflow The following packages will be downloaded: package | build ---------------------------|----------------- _tflow_select-2.2.0 | eigen 3 KB absl-py-0.9.0 | py37_0 168 KB astor-0.8.1 | py37_0 47 KB blinker-1.4 | py37_0 22 KB brotlipy-0.7.0 |py37he774522_1000 336 KB ca-certificates-2020.6.24 | 0 125 KB cachetools-4.1.1 | py_0 12 KB certifi-2020.6.20 | py37_0 156 KB click-7.1.2 | py_0 71 KB cryptography-2.9.2 | py37h7a1dbc1_0 523 KB gast-0.2.2 | py37_0 155 KB google-auth-1.20.1 | py_0 55 KB google-auth-oauthlib-0.4.1 | py_2 20 KB google-pasta-0.2.0 | py_0 46 KB grpcio-1.27.2 | py37h351948d_0 1.2 MB idna-2.10 | py_0 50 KB importlib-metadata-1.7.0 | py37_0 52 KB keras-applications-1.0.8 | py_1 29 KB keras-preprocessing-1.1.0 | py_1 37 KB libprotobuf-3.12.4 | h200bbdf_0 1.8 MB markdown-3.2.2 | py37_0 136 KB mkl_fft-1.1.0 | py37h45dec08_0 116 KB mkl_random-1.1.1 | py37h47e9c7a_0 233 KB numpy-1.19.1 | py37h5510c5b_0 22 KB numpy-base-1.19.1 | py37ha3acd2a_0 3.8 MB oauthlib-3.1.0 | py_0 91 KB openssl-1.1.1g | he774522_1 4.8 MB opt_einsum-3.1.0 | py_0 54 KB pip-20.2.2 | py37_0 1.7 MB protobuf-3.12.4 | py37ha925a31_0 555 KB pyasn1-0.4.8 | py_0 57 KB pyasn1-modules-0.2.7 | py_0 68 KB pycparser-2.20 | py_2 94 KB pyjwt-1.7.1 | py37_0 49 KB pyopenssl-19.1.0 | py_1 48 KB pysocks-1.7.1 | py37_1 28 KB python-3.7.7 | h81c818b_4 14.3 MB requests-2.24.0 | py_0 56 KB requests-oauthlib-1.3.0 | py_0 23 KB rsa-4.6 | py_0 26 KB scipy-1.5.0 | py37h9439919_0 11.8 MB setuptools-49.6.0 | py37_0 771 KB sqlite-3.32.3 | h2a8f88b_0 802 KB tensorboard-2.2.1 | pyh532a8cf_0 2.4 MB tensorboard-plugin-wit-1.6.0| py_0 630 KB tensorflow-2.1.0 |eigen_py37hd727fc0_0 4 KB tensorflow-base-2.1.0 |eigen_py37h49b2757_0 35.4 MB tensorflow-estimator-2.1.0 | pyhd54b08b_0 251 KB termcolor-1.1.0 | py37_1 8 KB urllib3-1.25.10 | py_0 98 KB vs2015_runtime-14.16.27012 | hf0eaf9b_3 1.2 MB werkzeug-0.16.1 | py_0 258 KB wrapt-1.12.1 | py37he774522_1 49 KB zipp-3.1.0 | py_0 13 KB ------------------------------------------------------------ Total: 84.8 MB The following NEW packages will be INSTALLED: _tflow_select pkgs/main/win-64::_tflow_select-2.2.0-eigen absl-py pkgs/main/win-64::absl-py-0.9.0-py37_0 astor pkgs/main/win-64::astor-0.8.1-py37_0 blas pkgs/main/win-64::blas-1.0-mkl blinker pkgs/main/win-64::blinker-1.4-py37_0 brotlipy pkgs/main/win-64::brotlipy-0.7.0-py37he774522_1000 ca-certificates pkgs/main/win-64::ca-certificates-2020.6.24-0 cachetools pkgs/main/noarch::cachetools-4.1.1-py_0 certifi pkgs/main/win-64::certifi-2020.6.20-py37_0 cffi pkgs/main/win-64::cffi-1.14.0-py37h7a1dbc1_0 chardet pkgs/main/win-64::chardet-3.0.4-py37_1003 click pkgs/main/noarch::click-7.1.2-py_0 cryptography pkgs/main/win-64::cryptography-2.9.2-py37h7a1dbc1_0 gast pkgs/main/win-64::gast-0.2.2-py37_0 google-auth pkgs/main/noarch::google-auth-1.20.1-py_0 google-auth-oauth~ pkgs/main/noarch::google-auth-oauthlib-0.4.1-py_2 google-pasta pkgs/main/noarch::google-pasta-0.2.0-py_0 grpcio pkgs/main/win-64::grpcio-1.27.2-py37h351948d_0 h5py pkgs/main/win-64::h5py-2.10.0-py37h5e291fa_0 hdf5 pkgs/main/win-64::hdf5-1.10.4-h7ebc959_0 icc_rt pkgs/main/win-64::icc_rt-2019.0.0-h0cc432a_1 idna pkgs/main/noarch::idna-2.10-py_0 importlib-metadata pkgs/main/win-64::importlib-metadata-1.7.0-py37_0 intel-openmp pkgs/main/win-64::intel-openmp-2020.1-216 keras-applications pkgs/main/noarch::keras-applications-1.0.8-py_1 keras-preprocessi~ pkgs/main/noarch::keras-preprocessing-1.1.0-py_1 libprotobuf pkgs/main/win-64::libprotobuf-3.12.4-h200bbdf_0 markdown pkgs/main/win-64::markdown-3.2.2-py37_0 mkl pkgs/main/win-64::mkl-2020.1-216 mkl-service pkgs/main/win-64::mkl-service-2.3.0-py37hb782905_0 mkl_fft pkgs/main/win-64::mkl_fft-1.1.0-py37h45dec08_0 mkl_random pkgs/main/win-64::mkl_random-1.1.1-py37h47e9c7a_0 numpy pkgs/main/win-64::numpy-1.19.1-py37h5510c5b_0 numpy-base pkgs/main/win-64::numpy-base-1.19.1-py37ha3acd2a_0 oauthlib pkgs/main/noarch::oauthlib-3.1.0-py_0 openssl pkgs/main/win-64::openssl-1.1.1g-he774522_1 opt_einsum pkgs/main/noarch::opt_einsum-3.1.0-py_0 pip pkgs/main/win-64::pip-20.2.2-py37_0 protobuf pkgs/main/win-64::protobuf-3.12.4-py37ha925a31_0 pyasn1 pkgs/main/noarch::pyasn1-0.4.8-py_0 pyasn1-modules pkgs/main/noarch::pyasn1-modules-0.2.7-py_0 pycparser pkgs/main/noarch::pycparser-2.20-py_2 pyjwt pkgs/main/win-64::pyjwt-1.7.1-py37_0 pyopenssl pkgs/main/noarch::pyopenssl-19.1.0-py_1 pyreadline pkgs/main/win-64::pyreadline-2.1-py37_1 pysocks pkgs/main/win-64::pysocks-1.7.1-py37_1 python pkgs/main/win-64::python-3.7.7-h81c818b_4 requests pkgs/main/noarch::requests-2.24.0-py_0 requests-oauthlib pkgs/main/noarch::requests-oauthlib-1.3.0-py_0 rsa pkgs/main/noarch::rsa-4.6-py_0 scipy pkgs/main/win-64::scipy-1.5.0-py37h9439919_0 setuptools pkgs/main/win-64::setuptools-49.6.0-py37_0 six pkgs/main/noarch::six-1.15.0-py_0 sqlite pkgs/main/win-64::sqlite-3.32.3-h2a8f88b_0 tensorboard pkgs/main/noarch::tensorboard-2.2.1-pyh532a8cf_0 tensorboard-plugi~ pkgs/main/noarch::tensorboard-plugin-wit-1.6.0-py_0 tensorflow pkgs/main/win-64::tensorflow-2.1.0-eigen_py37hd727fc0_0 tensorflow-base pkgs/main/win-64::tensorflow-base-2.1.0-eigen_py37h49b2757_0 tensorflow-estima~ pkgs/main/noarch::tensorflow-estimator-2.1.0-pyhd54b08b_0 termcolor pkgs/main/win-64::termcolor-1.1.0-py37_1 urllib3 pkgs/main/noarch::urllib3-1.25.10-py_0 vc pkgs/main/win-64::vc-14.1-h0510ff6_4 vs2015_runtime pkgs/main/win-64::vs2015_runtime-14.16.27012-hf0eaf9b_3 werkzeug pkgs/main/noarch::werkzeug-0.16.1-py_0 wheel pkgs/main/win-64::wheel-0.34.2-py37_0 win_inet_pton pkgs/main/win-64::win_inet_pton-1.1.0-py37_0 wincertstore pkgs/main/win-64::wincertstore-0.2-py37_0 wrapt pkgs/main/win-64::wrapt-1.12.1-py37he774522_1 zipp pkgs/main/noarch::zipp-3.1.0-py_0 zlib pkgs/main/win-64::zlib-1.2.11-h62dcd97_4 Proceed ([y]/n)? y ... Preparing transaction: done Verifying transaction: done Executing transaction: done # # To activate this environment, use # # $ conda activate tf # # To deactivate an active environment, use # # $ conda deactivate (base) C:\Users\ashish>conda activate tf (tf) C:\Users\ashish>pip show tensorflow Name: tensorflow Version: 2.1.0 Summary: TensorFlow is an open source machine learning framework for everyone. Home-page: https://www.tensorflow.org/ Author: Google Inc. Author-email: packages@tensorflow.org License: Apache 2.0 Location: d:\programfiles\anaconda3\envs\tf\lib\site-packages Requires: opt-einsum, six, gast, keras-preprocessing, wrapt, numpy, tensorboard, keras-applications, absl-py, google-pasta, termcolor, wheel, protobuf, scipy, grpcio, tensorflow-estimator, astor Required-by: In TensorFlow 1.X, we would have written some code like this to check TensorFlow installation: import tensorflow as tf hello = tf.constant('Hello, TensorFlow!') sess = tf.Session() print(sess.run(hello)) Out: b'Hello, TensorFlow!' Ref: StackOverflow This won't work in TensorFlow 2.X. With TensorFlow 2.X, you would get the error message: "AttributeError: module 'tensorflow' has no attribute 'session'" for line "sess = tf.Session()". The TF2.x "hello world" program would be like this: import tensorflow as tf msg = tf.constant('Hello, TensorFlow!') tf.print(msg) Out: Hello, TensorFlow! (tf) C:\Users\ashish>python Python 3.7.7 (default, May 6 2020, 11:45:54) [MSC v.1916 64 bit (AMD64)] :: Anaconda, Inc. on win32 Type "help", "copyright", "credits" or "license" for more information. >>> import tensorflow as tf >>> tf.__version__ '2.1.0' >>> hello = tf.constant('Hello, TensorFlow!') 2020-08-17 12:59:21.446424: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2 >>> hello [tf.Tensor: shape=(), dtype=string, numpy=b'Hello, TensorFlow!'] >>> print(hello) tf.Tensor(b'Hello, TensorFlow!', shape=(), dtype=string) >>> msg = tf.constant('Hello, TensorFlow!') >>> tf.print(msg) Hello, TensorFlow! >>> >>> tf.print(hello) Hello, TensorFlow! Reason for this is: TF2 runs Eager Execution by default, thus removing the need for Sessions. If you want to run static graphs, the more proper way is to use tf.function() in TF2. While Session can still be accessed via tf.compat.v1.Session() in TF2, I would discourage using it. References: % TensorFlow.Org % StackOverflow: AttributeError: module 'tensorflow' has no attribute 'session'
Monday, August 17, 2020
Differences between Conda and Pip installation. Installing TensorFlow 2.1.0 using Conda on Windows 10 And 'Hello World' program.
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Hi, Ashish, a great job. But how to access the list of books under the heading "Dated: October 2017"? Plz help.
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