mltrace | grained lineage and tracing for machine learning pipelines | MLOps library

 by   loglabs Python Version: 0.17 License: Apache-2.0

kandi X-RAY | mltrace Summary

kandi X-RAY | mltrace Summary

mltrace is a Python library typically used in Data Preparation, MLOps applications. mltrace 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 mltrace' or download it from GitHub, PyPI.

mltrace is a lightweight, open-source Python tool to get "bolt-on" observability in ML pipelines. It offers the following:.

            kandi-support Support

              mltrace has a low active ecosystem.
              It has 369 star(s) with 23 fork(s). There are 5 watchers for this library.
              It had no major release in the last 12 months.
              There are 28 open issues and 102 have been closed. On average issues are closed in 69 days. There are 2 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of mltrace is 0.17

            kandi-Quality Quality

              mltrace has no bugs reported.

            kandi-Security Security

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

            kandi-License License

              mltrace 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

              mltrace 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, examples and code snippets are available.

            Top functions reviewed by kandi - BETA

            kandi has reviewed mltrace and discovered the below as its top functions. This is intended to give you an instant insight into mltrace implemented functionality, and help decide if they suit your requirements.
            • Log a single component run
            • List of properties
            • Creates a new component
            • Get attribute from descriptor
            • Get a list of running runs
            • Print the history of the component
            • Get a list of component runs
            • Unflag an output
            • Unflag an output_id
            • Get IOPPO pointer
            • List extracted labels
            • List tags
            • Add an output
            • Adds input to the parser
            • Print web traces
            • Add notes to a component run
            • Retrieve a label
            • Compute a metric
            • Retrieve information about a label
            • Builds a list of components
            • Clean a csv file
            • Run migrations
            • Convert components to ClientRun
            • Check for flagged output
            • Get information about a component run
            • Display a list of components
            Get all kandi verified functions for this library.

            mltrace Key Features

            No Key Features are available at this moment for mltrace.

            mltrace Examples and Code Snippets

            No Code Snippets are available at this moment for mltrace.

            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 mltrace

            You should have Docker installed on your machine. To get started, you will need to do 3 things:. If you are interested in learning about specific mltrace concepts, please read this page in the official docs.
            Set up the database and Flask server
            Run some pipelines with logging
            Launch the UI
            We use Postgres-backed SQLAlchemy. Assuming you have Docker installed, you can run the following commands from the root directory after cloning the most recent release:. And then to tear down the containers, you can run docker-compose down. Bring down the volumes as well, if you've made changes to DB schema using docker-compose down --volumes.


            Anyone is welcome to contribute, and your contribution is greatly appreciated! Feel free to either create issues or pull requests to address issues.
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            pip install mltrace

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            gh repo clone loglabs/mltrace

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