krsh | A declarative KubeFlow Management Tool | MLOps library

 by   riiid Python Version: 1.0.0a0 License: Apache-2.0

kandi X-RAY | krsh Summary

kandi X-RAY | krsh Summary

krsh is a Python library typically used in Data Preparation, MLOps applications. krsh has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install krsh' or download it from GitHub, PyPI.

:lollipop: KRSH is Alpha version, so many bugs can be reported. If you find a bug, please write an Issue and grow the project together!. A declarative Kubeflow Management Tool inspired by Terraform. KRSH (pronounce, krush) is a tool that allows you to declaratively manage Kubeflow's pipelines. By managing Kubeflow Pipeline through KRSH, developers can reduce the cost of managing Pipeline Versions and deploy pipelines much faster than ever before. KRSH was greatly inspired by Terraform's behavior through Write, Plan, and Apply Cycle and its declarative management of resources. Also, since KRSH provides the KRSH Project Boilerplate through the krsh create command, the developer who develops the pipeline no longer needs to worry about which project structure to choose to manage the Kubeflow Pipeline.

            kandi-support Support

              krsh has a low active ecosystem.
              It has 79 star(s) with 1 fork(s). There are 10 watchers for this library.
              It had no major release in the last 12 months.
              krsh has no issues reported. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of krsh is 1.0.0a0

            kandi-Quality Quality

              krsh has 0 bugs and 0 code smells.

            kandi-Security Security

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

            kandi-License License

              krsh 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

              krsh releases are not available. You will need to build from source code and install.
              Deployable package is available in PyPI.
              Build file is available. You can build the component from source.
              Installation instructions, examples and code snippets are available.
              It has 750 lines of code, 75 functions and 37 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed krsh and discovered the below as its top functions. This is intended to give you an instant insight into krsh implemented functionality, and help decide if they suit your requirements.
            • Check planned pipelines
            • Calculate planned pipelines
            • Calculate the planned plan between two Pipelines
            • Parse local Pipelines
            • Apply commands to the root
            • Applies a multiprocessing pipeline
            • Apply pipeline
            • Convert planned pipelines to a list
            • Configure the command line interface
            • Configure project configuration
            • Prompts the user for an icon
            • Create a new project
            • Create project directory
            • Create a project directory
            • Create a new component
            • Create a component
            • Echo an OK message
            • Create a new pipeline
            • Create a pipeline
            • Echo the change diff
            • Echo an icon
            • Generate a unique identifier for the pipeline
            • Compile the given pipeline
            • Echo a text message
            • Echo the given name and namespace
            • Show the destroy diff message
            Get all kandi verified functions for this library.

            krsh Key Features

            No Key Features are available at this moment for krsh.

            krsh Examples and Code Snippets

            No Code Snippets are available at this moment for krsh.

            Community Discussions


            Prepare for Binary Masks used for the image segmentation
            Asked 2022-Mar-30 at 08:33

            I am trying to prepare the masks for image segmentation with Pytorch. I have three questions about data preparation.

            1. What is the appropriate data format to save the binary mask in general? PNG? JPEG?

            2. Is the mask size needed to be set square such as (224x224), not a rectangle such as (224x448)?

            3. Is the mask value fixed when the size is converted from rectangle to square?

            For example, the original mask image size is (600x900), which is binary [0,1]. However, when I applied



            Answered 2022-Mar-30 at 08:33
            1. PNG, because it is lossless by design.
            2. It depends. More convenient is to use standard resolution, (224x224), I would start with that.
            3. Use resize without interpolation transforms.Resize((300, 300), interpolation=InterpolationMode.NEAREST)



            Yolov5 object detection training
            Asked 2022-Mar-25 at 04:06

            Please i need you help concerning my yolov5 training process for object detection!

            I try to train my object detection model yolov5 for detecting small object ( scratch). For labelling my images i used roboflow, where i applied some data augmentation and some pre-processing that roboflow offers as a services. when i finish the pre-processing step and the data augmentation roboflow gives the choice for different output format, in my case it is yolov5 pytorch, and roboflow does everything for me splitting the data into training validation and test. Hence, Everything was set up as it should be for my data preparation and i got at the end the folder with data.yaml and the images with its labels, in data.yaml i put the path of my training and validation sets as i saw in the GitHub tutorial for yolov5. I followed the steps very carefully tought.

            The problem is when the training start i get nan in the obj and box column as you can see in the picture bellow, that i don't know the reason why, can someone relate to that or give me any clue to find the solution please, it's my first project in computer vision.

            This is what i get when the training process starts

            This the last message error when the training finish

            I think the problem comes maybe from here but i don't know how to fix it, i used the code of yolov5 team as it's in the tuto

            The training continue without any problem but the map and precision remains 0 all the process !!

            Ps : Here is the link of tuto i followed :



            Answered 2021-Dec-04 at 09:38

            Running my code in colab worked successfully and the resulats were good. I think that the problem was in my personnel laptop environment maybe the version of pytorch i was using '1.10.0+cu113', or something else ! If you have any advices to set up my environnement for yolov5 properly i would be happy to take from you guys. many Thanks again to @alexheat



            scatter plot color bar does not look right
            Asked 2022-Mar-24 at 22:20

            I have written my code to create a scatter plot with a color bar on the right. But the color bar does not look right, in the sense that the color is too light to be mapped to the actual color used in the plot. I am not sure what is missing or wrong here. But I am hoping to get something similar to what's shown here: (about in the middle of the page)



            Answered 2022-Mar-24 at 22:20

            The colorbar uses the given alpha=.3. In the scatterplot, many dots with the same color are superimposed, causing them to look brighter than a single dot.

            One way to tackle this, is to create a ScalarMappable object to be used by the colorbar, taking the colormap and the norm of the scatter plot (but not its alpha). Note that simply changing the alpha of the scatter object (scatter.set_alpha(1)) would also change the plot itself.



            using DelimitedFiles with title of the header
            Asked 2022-Mar-09 at 19:26

            When importing a .csv file, is there any way to read the data from the title of the header? Consider the .csv file in the following:

            I mean, instead of start_node = round.(Int64, data[:,1]) is there another way to say "start_node" is the one in the .csv file that its header is "start node i"



            Answered 2022-Mar-09 at 19:08

            The most natural way is to use CSV along with the DataFrames package.

            Consider file:



            Pytorch : different behaviours in GAN training with different, but conceptually equivalent, code
            Asked 2022-Feb-16 at 13:43

            I'm trying to implement a simple GAN in Pytorch. The following training code works:



            Answered 2022-Feb-16 at 13:43
            Why do we different results?

            Supplying inputs in either the same batch, or separate batches, can make a difference if the model includes dependencies between different elements of the batch. By far the most common source in current deep learning models is batch normalization. As you mentioned, the discriminator does include batchnorm, so this is likely the reason for different behaviors. Here is an example. Using single numbers and a batch size of 4:



            Is there a way to query a csv file in Karate?
            Asked 2022-Feb-02 at 03:20

            I am looking for a similar functionality like Fillo Excel API where we can do CRUD operations in an excel file using query like statements.

            A select statement in a csv file is a great addition to the framework to provide more flexibility in test data driven approach testing.

            Sample scenario: A test case that needs to have multiple data preparation of inserting records to database.

            Instead of putting all test data in 1 row or 1 cell like this and do a string split before processing.



            Answered 2022-Feb-02 at 03:20

            There's no need. Karate can transform a CSV file into a JSON array in one line:



            Multi Processing with sqlalchemy
            Asked 2022-Feb-01 at 22:50

            I have a python script that handles data transactions through sqlalchemy using:



            Answered 2022-Jan-31 at 06:48

            This is an interesting situation. It seems that maybe you can sidestep some of the manual process/thread handling and utilize something like multiprocessing's Pool. I made an example based on some other data initializing code I had. This delegates creating test data and inserting it for each of 10 "devices" to a pool of 3 processes. One caveat that seems necessary is to dispose of the engine before it is shared across fork(), ie. before the Pool tasks are created, this is mentioned here: engine-disposal



            Trouble changing imputer strategy in scikit-learn pipeline
            Asked 2022-Jan-27 at 05:26

            I am trying to use GridSearchCV to select the best imputer strategy but I am having trouble doing that.

            First, I have a data preparation pipeline for numerical and categorical columns-



            Answered 2022-Jan-27 at 05:26

            The way you specify the parameter is via a dictionary that maps the name of the estimator/transformer and name of the parameter you want to change to the parameters you want to try. If you have a pipeline or a pipeline of pipelines, the name is the names of all its parents combined with a double underscore. So for your case, it looks like



            Does tensorflow re-initialize weights when training in a for loop?
            Asked 2022-Jan-20 at 15:12

            I'm training a model within a for loop, because...I can. I know there are alternative like tf.Dataset API with generators to stream data from disk, but my question is on the specific case of a loop.

            Does TF re-initialize weights of the model at the beginning of each loop ? Or does the initialization only occurs the first time the model is instantiated ?

            EDIT :



            Answered 2022-Jan-20 at 15:06

            Weights are initialized when the layers are defined (before fit). It does not re-initialize weights afterward - even if you call fit multiple times.

            To show this is the case, I plotted the decision boundary at regular training epochs (by calling fit and then predict):



            How to force Pytest to execute the only function in parametrize?
            Asked 2022-Jan-20 at 14:32

            I have 2 tests. I want to run the only one:



            Answered 2022-Jan-20 at 14:32

            It looks like that only something like that can resolved the issue.


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


            No vulnerabilities reported

            Install krsh

            You can install using 'pip install krsh' or download it from GitHub, PyPI.
            You can use krsh 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.


            We welcome any form of contribution. If you're new to KRSH, read You can submit any PR that can improve project, report bugs in project, or submit an Issue to request that you add new features.
            Find more information at:

            Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items

            Find more libraries
          • PyPI

            pip install krsh

          • CLONE
          • HTTPS


          • CLI

            gh repo clone riiid/krsh

          • sshUrl


          • Stay Updated

            Subscribe to our newsletter for trending solutions and developer bootcamps

            Agree to Sign up and Terms & Conditions

            Share this Page

            share link