tfenv | Terraform version manager | Infrastructure Automation library

 by   tfutils Shell Version: v3.0.0 License: MIT

kandi X-RAY | tfenv Summary

kandi X-RAY | tfenv Summary

tfenv is a Shell library typically used in Devops, Infrastructure Automation, Terraform applications. tfenv has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. You can download it from GitHub.

Terraform version manager inspired by rbenv.
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              tfenv has a medium active ecosystem.
              It has 3831 star(s) with 395 fork(s). There are 44 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 57 open issues and 142 have been closed. On average issues are closed in 294 days. There are 12 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of tfenv is v3.0.0

            kandi-Quality Quality

              tfenv has no bugs reported.

            kandi-Security Security

              tfenv has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              tfenv is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              tfenv releases are available to install and integrate.
              Installation instructions, examples and code snippets are available.

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            tfenv Key Features

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            tfenv Examples and Code Snippets

            No Code Snippets are available at this moment for tfenv.

            Community Discussions

            QUESTION

            np.select with more than two pandas column
            Asked 2020-Sep-05 at 20:22

            I am trying to solve a pandas problem statement. The panda's data frame looks like this :

            ...

            ANSWER

            Answered 2020-Sep-05 at 20:22

            You can vectorize this with pandas masking, so that you are only doing the operations needed, but still have the advantages of vectorization. For brevity df is your dataframe:

            Source https://stackoverflow.com/questions/63756980

            QUESTION

            Tools to easily swap terraform versions
            Asked 2020-Jul-18 at 17:59

            I found the tools tfenv and tfswitch to easily and quickly change the terraform and terragrunt version - and I guess the tools work cause people use it, but for some reason they don't chagne the versions on my computer, after I run let's say

            ...

            ANSWER

            Answered 2020-Jul-18 at 17:59

            I had the same problem in the past, I solved it by removing the reference of Terraform from .bash_profile or .zshrc for zsh.

            Source https://stackoverflow.com/questions/62968710

            QUESTION

            Ansible not able to create symlink
            Asked 2019-Sep-10 at 13:20

            I'm trying to create a symlink and I'm not able to solve this error . Please suggest me a solution on how to solve this error

            Code: Creating a symlink for /usr/local/bin/terraform-env/bin/* in folder /usr/local/bin I tried with /usr/local/bin/ (with and without slash)

            ...

            ANSWER

            Answered 2019-Sep-10 at 13:20

            the directory /usr/local/bin/ is not empty, refusing to convert it

            You are trying to create the symlink directly on the existing directory rather than creating an entry inside that dir to support the symlink. The following corrected task should get you going:

            Source https://stackoverflow.com/questions/57871685

            QUESTION

            Keras gives an error when building a dummy model
            Asked 2019-Jun-19 at 10:01

            I am building the tiny yolo cnn model in Keras. I get a strange error. Anyone can help?

            Imports

            ...

            ANSWER

            Answered 2019-Jun-19 at 10:01

            Seems you forgot to put brackets in kernel size in your Conv2D layers. Making them have strides bigger than 1. Causing the tensors become too small for a (2, 2) pooling.

            Source https://stackoverflow.com/questions/56664751

            QUESTION

            Error in Sequence Generator: StopIteration: list index out of range
            Asked 2018-Jun-16 at 10:33

            I am using sequence generator in keras to parallelly fetch data from the disk but I am getting a very odd error.

            So, this is my code for the sequence generator

            ...

            ANSWER

            Answered 2018-Jun-16 at 10:33

            You are assuming that your data can perfectly be divided by the batch_size, this might not necessarily be the case as the last batch might be smaller than batch_size as your using a slice. Instead of having a fixed range, use the size of slice:

            Source https://stackoverflow.com/questions/50885601

            QUESTION

            How to find the origin of a TensorFlow NaN error on a Multi GPU system with NVIDIA Tesla P100 GPUs?
            Asked 2018-Jan-20 at 21:59
            Baseline situation

            I train on a GPU cluster with 8 NVIDIA Tesla P100 GPUs. The script code is based on the TensorFlow Tutorial Convolutional Neural Networks. As training data I create binary files based on the Cifar10 data set, which contain only 5000 of the 50,000 images. I use only one of these files per training session.

            Here are some basic data about the training:

            • Epoch: 100,000
            • Batch size: 128
            • Number of training data: 5,000
            • Init learning rate: 0.1
            • Learning rate decay factor: 0.1
            • Number of Epochs per decay 350.0
            • No exponential moving average
            • ...
            • If you need more information please comment

            The problem is, if I do the training with more than one GPU, I get a NaN error sooner or later. The loss value then explodes within a few epochs from ~0.4 to values above ~1.e+26 to infinity I think and then comes the NaN error.

            What have I done so far

            So far I have tried the following to identify the origin of the NaN bug, so that I can fix it.

            • I added tf.check_numerics() after almost every return of a tensor. (Error messages are below)
            • I added tf.add_check_numerics_ops(). The error messages are as incomprehensible to me as those of tf.check_numerics() :-)
            • I checked the input data for NaN, data are okay.
            • I have reduced the learning rate, so the error comes later.
              • Init learning rate: 0.01
              • Learning rate decay factor: 0.01
            • Run simpleP2P (Cuda Examples) and passed the test. (Output are below)
            • Modified the TensorFlow Tutorial Code only so far that the tf.train.string_input_producer() gets only 1 file instead of 5 files. filename_queue = tf.train.string_input_producer([/path/traindata.bin]) And set number of training data to 5,000.
            • To save the variables instead of cpu:0 (see TensorFlow Tutorial here and here) on gpu:0 and to train only with gpu: 1-7. But that was so slow it wasn't an option and I broke it off. (Maybe I did it wrong, too)
            • Only train with 4 GPUs, (0-3 or 4-7), but the NaN error came later.
            System
            • Linux kernel: 4.4.72-18.12-default x86_64
            • 8x NVIDIA Tesla P100-PCIE-16GB
            • Cuda 8.0 - V8.0.61
            • TenserFlow 1.4.1
            • Python3
            Some code and error messages

            tf.check_numerics() error message:

            ...

            ANSWER

            Answered 2018-Jan-20 at 21:59

            I found the reason for the NaN's error. In retrospect, I have to say, it's been in front of my nose the whole time.

            The short version:

            I used the tf.train.GradientDescentOptimizer() with tf.train.exponential_decay() for the optimization. Changing it to tf.train.AdamOptimizer() solve my problem.

            The long version:

            So it wasn't the GPU cluster, but the optimization algorithm. But I didn't immediately notice that, because if I used only one GPU on the GPU cluster, the total loss values were not infinite but if I used several GPUs, the loss value added up and then went into the infinite range. Only when I ran the script on my local machine for a very long time (using a NVIDIA GTX 770) did I get a NaN error. That's when I knew it had nothing to do with the NVIDIA Tesla P100. This GitHub issue caused me to get more involved with the tf.train.GradientDescentOptimizer(). Now it looks like that solved my problem.

            The TensorFlow tutorial Convolutional Neural Networks use tf.train.GradientDescentOptimizer() and i change now the code from:

            Source https://stackoverflow.com/questions/48347728

            QUESTION

            cannot activate virtualenv: No such file or directory
            Asked 2017-Sep-08 at 15:11

            I have problem with activating virtualenv. I'm working on the server and using SSH secure shell.

            My final goal is to activate virtualenv and run the latest version of tensorflow

            The following is the command lines:

            ...

            ANSWER

            Answered 2017-Sep-08 at 15:11

            Check if you have python 2 versions of pip and python (python-all & python-pip packages). Venv installs both v2 and v3 versions of python & pip (regardless of python version of venv).

            Source https://stackoverflow.com/questions/46117754

            QUESTION

            InvalidArgumentError: logits and labels must be same size: logits_size=[1,2] labels_size=[1,1]
            Asked 2017-Apr-09 at 09:47

            I've worked through a few other errors that've been thrown, but I've never seen this error before and even after doing some research I'm still not sure exactly what the issue is here or how to solve it.

            I'm guessing reshaping the data at some point is required, but what i don't get is why this is an issue, or what the sizes of [1,2] and [1,1] actually mean.

            The data input into the script is [128 x 128 x 128 ndarray, binary label]

            The code I'm using is:

            ...

            ANSWER

            Answered 2017-Apr-09 at 09:47

            After taking a closer look I found that the issue was that the output of the 3rd fully-connected layer was 2 classes, when the labels are binary for a single class. Changed code in last fully connected layer to account for the single class, and this error was solved.

            Source https://stackoverflow.com/questions/43274169

            QUESTION

            Shape mismatch with tf.placeholder
            Asked 2017-Apr-06 at 17:45

            I'm using 128 x 128 x 128 ndarrays as input in to a cnn using:

            ...

            ANSWER

            Answered 2017-Apr-06 at 12:59

            Your placeholder has rank 5 so you need to feed a 5-dimensional np array, but you reshaped to a 4-dimensional np array. So, use data = np.reshape(data, (1, 128, 128, 128, 1)) instead of data = np.reshape(data, (128, 128, 128, 1)) as pointed out in the comments.

            Essentially, None in the shape of a placeholder means that the size of this dimension is variable, but the dimension should still be there.

            Source https://stackoverflow.com/questions/43254849

            QUESTION

            tf.placeholder not reading rbg colour parameter
            Asked 2017-Apr-04 at 23:57

            I'm trying to feed a single 300 x 300 x 300 tensor into a placeholder in tensorflow, and although I'm using imgs = tf.placeholder(tf.float32, [1, 300, 300, 300, 3]) I'm getting the ValueError:

            ...

            ANSWER

            Answered 2017-Apr-04 at 23:57

            The tensor you try to feed has the shape of (1, 300, 300, 300), and your placeholder has the shape (1, 300, 300, 300, 3). Both must have the same shape, so change your placeholder to:

            Source https://stackoverflow.com/questions/43210158

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

            Vulnerabilities

            No vulnerabilities reported

            Install tfenv

            Install a specific version of Terraform. If no parameter is passed, the version to use is resolved automatically via .terraform-version files or TFENV_TERRAFORM_VERSION environment variable (TFENV_TERRAFORM_VERSION takes precedence), defaulting to 'latest' if none are found.
            x.y.z Semver 2.0.0 string specifying the exact version to install
            latest is a syntax to install latest version
            latest:<regex> is a syntax to install latest version matching regex (used by grep -e)
            min-required is a syntax to recursively scan your Terraform files to detect which version is minimally required. See required_version docs. Also see min-required section below.

            Support

            Currently tfenv supports the following OSes.
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