training | training courses and example code | Microservice library

 by   cloud-native-java Java Version: Current License: Apache-2.0

kandi X-RAY | training Summary

kandi X-RAY | training Summary

training is a Java library typically used in Architecture, Microservice, MongoDB, Spring Boot, Spring applications. training has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

This repository contains various training materials that accompany the sections and chapters in O'Reilly's Cloud Native Java: Building Resilient Systems with Spring Boot, Spring Cloud, and Cloud Foundry. To find more information on the book, please visit

            kandi-support Support

              training has a low active ecosystem.
              It has 32 star(s) with 25 fork(s). There are 9 watchers for this library.
              It had no major release in the last 6 months.
              There are 1 open issues and 0 have been closed. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of training is current.

            kandi-Quality Quality

              training has 0 bugs and 0 code smells.

            kandi-Security Security

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

            kandi-License License

              training 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

              training releases are not available. You will need to build from source code and install.
              Build file is available. You can build the component from source.
              Installation instructions are available. Examples and code snippets are not available.
              training saves you 2579 person hours of effort in developing the same functionality from scratch.
              It has 5603 lines of code, 248 functions and 159 files.
              It has low code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed training and discovered the below as its top functions. This is intended to give you an instant insight into training implemented functionality, and help decide if they suit your requirements.
            • Applies authorization to your HttpSecurityFilter
            • The REST template bean .
            • Gets profile .
            • Any resource limiter that should be used for routing requests .
            • Configure the client details .
            • Runs all reservations .
            • Registers a new user .
            • Returns a String representation of this User object .
            • Returns the set of reservations .
            • Lookup a user by username .
            Get all kandi verified functions for this library.

            training Key Features

            No Key Features are available at this moment for training.

            training Examples and Code Snippets

            No Code Snippets are available at this moment for training.

            Community Discussions


            Group and create three new columns by condition [Low, Hit, High]
            Asked 2022-Feb-10 at 16:22

            I have a large dataset (~5 Mio rows) with results from a Machine Learning training. Now I want to check to see if the results hit the "target range" or not. Lets say this range contains all values between -0.25 and +0.25. If it's inside this range, it's a Hit, if it's below Low and on the other side High.

            I now would create this three columns Hit, Low, High and calculate for each row which condition applies and put a 1 into this col, the other two would become 0. After that I would group the values and sum them up. But I suspect there must be a better and faster way, such as calculate it directly while grouping. I'm happy for any idea.

            Data ...


            Answered 2022-Feb-10 at 16:13

            You could use cut to define the groups and pivot_table to reshape:



            Saving model on Tensorflow 2.7.0 with data augmentation layer
            Asked 2022-Feb-04 at 17:25

            I am getting an error when trying to save a model with data augmentation layers with Tensorflow version 2.7.0.

            Here is the code of data augmentation:



            Answered 2022-Feb-04 at 17:25

            This seems to be a bug in Tensorflow 2.7 when using combined with the parameter save_format="tf", which is set by default. The layers RandomFlip, RandomRotation, RandomZoom, and RandomContrast are causing the problems, since they are not serializable. Interestingly, the Rescaling layer can be saved without any problems. A workaround would be to simply save your model with the older Keras H5 format"test", save_format='h5'):



            logistic regression and GridSearchCV using python sklearn
            Asked 2021-Dec-10 at 14:14

            I am trying code from this page. I ran up to the part LR (tf-idf) and got the similar results

            After that I decided to try GridSearchCV. My questions below:




            Answered 2021-Dec-09 at 23:12

            You end up with the error with precision because some of your penalization is too strong for this model, if you check the results, you get 0 for f1 score when C = 0.001 and C = 0.01



            AssertionError: Tried to export a function which references untracked resource
            Asked 2021-Sep-07 at 11:23

            I wrote a unit-test in order to safe a model after noticing that I am not able to do so (anymore) during training.



            Answered 2021-Sep-06 at 13:25

            Your issue is not related to 'transformer_transducer/transducer_encoder/inputs_embedding/ convolution_stack/conv2d/kernel:0'.
            The error code tells you this element is referring to a non trackable element. It seems the non-trackable object is not directly assigned to an attribute of this conv2d/kernel:0.

            To solve your issue, we need to localize Tensor("77040:0", shape=(), dtype=resource) from this error code:



            android react-native avoiding "Background location access not declared" in Google approval process
            Asked 2021-May-25 at 14:40

            Okay, so Google is telling us "Background location access not declared" and not letting us publish our app. We have no use for background location, so we're trying to elimiate it completely.

            Of course my manifest doesn't have it:



            Answered 2021-Mar-12 at 11:42

            I had this issue a few weeks ago, what a pain! In my case I had one dependency that was requiring background location without me noticing. Secondly, I had a wrong permission declaration on Google Play so my builds kept being rejected.

            1. Find the evil dependency

            To do this I used the Merged Manifest inspector in Android Studio. This shows you what your manifest looks like after all project dependencies have been taken into account. Find ACCESS_BACKGROUND_LOCATION and double click on it, this will bring you to the actual manifest where it's requested. Scroll to the top of this file and the should help you identify what it is. In my case the permission was requested by an old dependency I didn't use anymore so I just uninstalled it.

            Note: if you're often working on different branches, make sure you have the correct dependencies installed and make a clean build before checking the merged manifest:



            Why is this tensorflow training taking so long?
            Asked 2021-May-13 at 12:42

            I'm learning DRL with the book Deep Reinforcement Learning in Action. In chapter 3, they present the simple game Gridworld (instructions here, in the rules section) with the corresponding code in PyTorch.

            I've experimented with the code and it takes less than 3 minutes to train the network with 89% of wins (won 89 of 100 games after training).

            As an exercise, I have migrated the code to tensorflow. All the code is here.

            The problem is that with my tensorflow port it takes near 2 hours to train the network with a win rate of 84%. Both versions are using the only CPU to train (I don't have GPU)

            Training loss figures seem correct and also the rate of a win (we have to take into consideration that the game is random and can have impossible states). The problem is the performance of the overall process.

            I'm doing something terribly wrong, but what?

            The main differences are in the training loop, in torch is this:



            Answered 2021-May-13 at 12:42
            Why is TensorFlow slow

            TensorFlow has 2 execution modes: eager execution, and graph mode. TensorFlow default behavior, since version 2, is to default to eager execution. Eager execution is great as it enables you to write code close to how you would write standard python. It's easier to write, and it's easier to debug. Unfortunately, it's really not as fast as graph mode.

            So the idea is, once the function is prototyped in eager mode, to make TensorFlow execute it in graph mode. For that you can use tf.function. tf.function compiles a callable into a TensorFlow graph. Once the function is compiled into a graph, the performance gain is usually quite important. The recommended approach when developing in TensorFlow is the following:

            • Debug in eager mode, then decorate with @tf.function.
            • Don't rely on Python side effects like object mutation or list appends.
            • tf.function works best with TensorFlow ops; NumPy and Python calls are converted to constants.

            I would add: think about the critical parts of your program, and which ones should be converted first into graph mode. It's usually the parts where you call a model to get a result. It's where you will see the best improvements.

            You can find more information in the following guides:

            Applying tf.function to your code

            So, there are at least two things you can change in your code to make it run quite faster:

            1. The first one is to not use model.predict on a small amount of data. The function is made to work on a huge dataset or on a generator. (See this comment on Github). Instead, you should call the model directly, and for performance enhancement, you can wrap the call to the model in a tf.function.

            Model.predict is a top-level API designed for batch-predicting outside of any loops, with the fully-features of the Keras APIs.

            1. The second one is to make your training step a separate function, and to decorate that function with @tf.function.

            So, I would declare the following things before your training loop:



            I got the warning "UserWarning: One or more of the test scores are non-finite" when revising a toy scikit-learn gridsearchCV example
            Asked 2021-May-05 at 17:04

            I have the following code which works normally but got a



            Answered 2021-May-01 at 13:10

            Remove roc_auc if it is multi class. They do not play well together. Use default scoring or choose something else.



            How to load a pre-trained PyTorch model?
            Asked 2021-Apr-22 at 12:51

            I'm following this guide on saving and loading checkpoints. However, something is not right. My model would train and the parameters would correctly update during the training phase. However, there seem to be a problem when I load the checkpoints. That is, the parameters are not being updated anymore.

            My model:



            Answered 2021-Apr-22 at 12:51

            The way you are loading your data is not the recommended way to load your parameters because you're overwriting the graph connections (or something along those lines...). You even save the model state_dict, so why not use it!

            I changed the load function to:



            How to build a Tensorflow model with more than one input?
            Asked 2021-Apr-21 at 01:20

            I want to create a Tensorflow neural network model using the Functional API, but I'm not sure how to separate the input into two. I wanted to do something like: given an input, its first half goes to the first part of the neural network, its second half goes to the second part, and each input is passed through the layers until they concatenate, go through another layer and finally reach the output. I thought of something like the snippet of code below, along with a quick sketch.



            Answered 2021-Apr-20 at 20:07

            There is some issue with your code. I will try to answer the main issue here and discard some side questions such as whether you should use Dropout or BatchNormalization layers in your model or not because that's totally out of the scope of your main question and also irrelevant.

            If you try to build your model, using m = define_model(), I'm pretty sure you will encounter the following error:



            Gradient Accumulation with Custom in TF.Keras?
            Asked 2021-Mar-10 at 09:47

            Please add a minimum comment on your thoughts so that I can improve my query. Thanks. :)

            I'm trying to train a tf.keras model with Gradient Accumulation (GA). But I don't want to use it in the custom training loop (like) but customizing the .fit() method by overriding the train_step.Is it possible? How to accomplish this? The reason is if we want to get the benefit of keras built-in functionality like fit, callbacks, we don't want to use the custom training loop but at the same time if we want to override train_step for some reason (like GA or else) we can customize the fit method and still get the leverage of using those built-in functions.

            And also, I know the pros of using GA but what are the major cons of using it? Why it's not come as a default but an optional feature with the framework?



            Answered 2021-Mar-10 at 09:47

            Thanks to @Mr.For Example for his convenient answer.

            Usually, I also observed that using Gradient Accumulation, won't speed up training since we are doing n_gradients times forward pass and compute all the gradients. But it will speed up the convergence of our model. And I found that using the mixed_precision technique here can be really helpful here. Details here.


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


            No vulnerabilities reported

            Install training

            If you're looking for the source code and materials for O'Reilly's Live Training: Building Microservices with Spring Boot, Spring Cloud, and Cloud Foundry, then look no further!. First, clone this repository, and then navigate to the microservices-online-training module for instructions on getting started with the example projects from the 2-day live online training. If you've somehow found this repository but are looking for the live training videos, please visit Safari to sign-up for upcoming live training courses for Cloud Native Java.


            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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