Monday, August 17, 2020

Differences between Conda and Pip installation. Installing TensorFlow 2.1.0 using Conda on Windows 10 And 'Hello World' program. 



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'
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