tensorpack | A Neural Net Training Interface on TensorFlow , with focus | Machine Learning library

 by   tensorpack Python Version: 0.11 License: Apache-2.0

kandi X-RAY | tensorpack Summary

kandi X-RAY | tensorpack Summary

tensorpack is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Keras applications. tensorpack has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. However tensorpack has 3 bugs. You can install using 'pip install tensorpack' or download it from GitHub, PyPI.

Tensorpack is a neural network training interface based on TensorFlow v1.

            kandi-support Support

              tensorpack has a medium active ecosystem.
              It has 6274 star(s) with 1851 fork(s). There are 201 watchers for this library.
              It had no major release in the last 12 months.
              There are 9 open issues and 1343 have been closed. On average issues are closed in 185 days. There are 1 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of tensorpack is 0.11

            kandi-Quality Quality

              tensorpack has 3 bugs (1 blocker, 0 critical, 2 major, 0 minor) and 417 code smells.

            kandi-Security Security

              tensorpack has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              tensorpack code analysis shows 0 unresolved vulnerabilities.
              There are 21 security hotspots that need review.

            kandi-License License

              tensorpack is licensed under the Apache-2.0 License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              tensorpack releases are available to install and integrate.
              Deployable package is available in PyPI.
              Build file is available. You can build the component from source.
              Installation instructions are not available. Examples and code snippets are available.
              tensorpack saves you 12500 person hours of effort in developing the same functionality from scratch.
              It has 25185 lines of code, 2345 functions and 240 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed tensorpack and discovered the below as its top functions. This is intended to give you an instant insight into tensorpack implemented functionality, and help decide if they suit your requirements.
            • Creates a 2D convolution .
            • Creates a convolution layer .
            • Basic batch normalization
            • Convert weights to a layer .
            • Wrap a layer .
            • finalize the data structures
            • Resize image .
            • Collect environment information .
            • Setup Keras trainer .
            • Finds the full path of the library .
            Get all kandi verified functions for this library.

            tensorpack Key Features

            No Key Features are available at this moment for tensorpack.

            tensorpack Examples and Code Snippets

            tensorpack - imagenet utils-Image Net Models
            Pythondot img1Lines of Code : 304dot img1License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            # File: imagenet_utils.py
            import multiprocessing
            import numpy as np
            import os
            from abc import abstractmethod
            import cv2
            import tqdm
            from tensorpack import tfv1 as tf
            from tensorpack import ModelDesc
            from tensorpack.dataflow import (
            tensorpack - flownet models
            Pythondot img2Lines of Code : 271dot img2License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            #! /usr/bin/env python
            # -*- coding: utf-8 -*-
            # Author: Patrick Wieschollek 
            import tensorflow as tf
            from tensorpack import ModelDesc, argscope, enable_argscope_for_module
            # FlowNet2 follows the convention   
            tensorpack - generalized rcnn
            Pythondot img3Lines of Code : 253dot img3License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            # -*- coding: utf-8 -*-
            # File:
            from tensorpack import tfv1 as tf
            from tensorpack import ModelDesc
            from tensorpack.models import GlobalAvgPooling, l2_regularizer, regularize_cost
            from tensorpack.tfutils import optimizer
            from tensorpack.tfutils.summa  

            Community Discussions


            CalledProcessError: Command '['convert', '--version']' returned non-zero exit status 1
            Asked 2020-Sep-24 at 21:01


            Answered 2020-Sep-24 at 21:01

            matplotlib.animation requires ffmpeg for saving movies and ImageMagick for saving animated gifs.

            See https://matplotlib.org/users/installing.html#install-requirements

            Install them with your system package manager:

            Source https://stackoverflow.com/questions/64053840


            File disappeared after git rebase attempt
            Asked 2020-Mar-06 at 08:29

            I tried to do git rebase, and in the process lost a file. Not a very important file, but I'd like to understand what happened.

            I wanted to merge branch 'master' into branch 'learning' using rebase command.

            Here is a summary of my actions:

            Did git rebase master, got a conflict, resolved it, tried to continue rebase, failed again with more conflicts, added/commited an untracked file, failed rebase again, tried rebase --skip, failed, aborted rebase, finally did git merge, which was successful.

            The untracked file (test.py) I committed during rebase is now gone.

            Here is the detailed record of my actions:



            Answered 2020-Mar-06 at 08:29

            First, that does not merge master into learning: a git rebase master will replay learning on top of master.

            Second, if at any point you added/committed test.py, you can find it back in the reflog. See "Query git reflog for all commits to a specific file".

            Source https://stackoverflow.com/questions/49060742


            pip search finds tensorflow, but pip install does not
            Asked 2020-Jan-23 at 06:55

            I am trying to build a Django app that would use Keras models to make recommendations. Right now I'm trying to use one custom container that would hold both Django and Keras. Here's the Dockerfile I've written.



            Answered 2019-Jan-02 at 22:56

            It looks like tensorflow only publishes wheels (and only up to 3.6), and Alpine linux is not manylinux1-compatible due to its use of musl instead of glibc. Because of this, pip cannot find a suitable installation candidate and fails. Your best options are probably to build from source or change your base image.

            Source https://stackoverflow.com/questions/54014076


            importing tensorpack logger error
            Asked 2018-May-15 at 18:05

            importing logger of tensorpack gives following error:



            Answered 2018-Jan-24 at 12:10

            there is lines in the source tensorpack/tensorpack/dataflow/parallel. although I dont know what's the purpose of those lines, but I know I missed the pyarrow package, pip install pyarrow solved the problem

            Source https://stackoverflow.com/questions/48421061


            TF wrappers: Performance of Keras vs Tensorpack
            Asked 2018-Mar-05 at 07:55

            I've a question regarding the training performance of Keras vs other TF Wrappers like Tensorpack.

            During a Q&A session, the author of Keras stated that the package comes with no performance cost:

            Keras provides a simpler, quicker way to build and train models in TensorFlow, at no performance cost since the models are still being run by the same TensorFlow engine.

            On the other hand though, the Tensorpack github claims that it's 2 times faster than Keras, even with a tiny CNN:

            Tensorpack trainer is almost always faster than feed_dict based wrappers. Even on a tiny CNN example, the training runs 2x faster than the equivalent Keras code.

            So now I wonder the following: Who is right? A factor of two would be very significant for me in training.



            Answered 2018-Mar-05 at 07:55

            I'm the author of tensorpack. The reasons for major performance improvements are summarized in the Input Pipeline page in the documentation: http://tensorpack.readthedocs.io/en/latest/tutorial/input-source.html These arguments are for single GPU training case. For multi GPU training we have more performance improvements to match Google's benchmark code.

            The answer by @jdehesa is not right, we never use TFRecord to read data (although it is capable of doing so, I don't see much advantage). You can read everything by pure Python and get this speed.

            Also, the 2x speedup might be a bit outdated, I know Keras has been making improvements on reducing framework overhead.

            Source https://stackoverflow.com/questions/45411755


            How to set hyperparameter (learning_rate) schedule in TensorFlow?
            Asked 2017-Dec-20 at 01:10

            What is the way to schedule hyper-parameters in TensorFlow?

            Namely, for the sake of reproducibility I would like to implement a ResNet (you name one) using suggested learning rate schedule {0: 0.1, 1: 1., 100: 0.01, 150: 0.001}, or enable the weight decay only after first few initial epoch.

            For example, tensorpack provides an optionas follows:



            Answered 2017-Dec-20 at 01:10

            Ok, it wasn't that hard

            Source https://stackoverflow.com/questions/47896698


            Failing installation of tensorpack
            Asked 2017-Apr-03 at 12:04

            I'm trying to install tensorpack on Ubuntu 16.04 LTS.



            Answered 2017-Apr-03 at 12:04

            Tensorpack worked for me, but the per-requisite were

            1. Using a Virtual Environment

              virtualenv tensorpack

            2. Secondly updating pip

              pip install --upgrade pip

            3. Lastly not using "sudo"

              pip install tensorpack

            Source https://stackoverflow.com/questions/43183392

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install tensorpack

            You can install using 'pip install tensorpack' or download it from GitHub, PyPI.
            You can use tensorpack like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.


            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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            pip install tensorpack

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            gh repo clone tensorpack/tensorpack

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