gradient-cli | The command line interface for Gradient - https | Machine Learning library

by Paperspace Python Version: v2.0.2 License: ISC

X-Ray Key Features Code Snippets Community Discussions(3)Vulnerabilities Install Support

kandi X-RAY | gradient-cli Summary

gradient-cli is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow, Docker applications. gradient-cli 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.

The Gradient CLI follows a standard [command] [--options] syntax.

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Security

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Support

gradient-cli has a low active ecosystem.

It has 48 star(s) with 13 fork(s). There are 15 watchers for this library.

It had no major release in the last 12 months.

There are 0 open issues and 21 have been closed. On average issues are closed in 92 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of gradient-cli is v2.0.2

Quality

gradient-cli has 0 bugs and 0 code smells.

Security

gradient-cli has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

gradient-cli code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

gradient-cli is licensed under the ISC License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

gradient-cli releases are available to install and integrate.

Build file is available. You can build the component from source.

Installation instructions are not available. Examples and code snippets are available.

It has 68122 lines of code, 1676 functions and 217 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed gradient-cli and discovered the below as its top functions. This is intended to give you an instant insight into gradient-cli implemented functionality, and help decide if they suit your requirements.

Generic PUT operation
Returns the full path of the given url
Concatenate filenames
Make a POST request
Creates a new machine
Adds tags to an entity
Merge existing tags
List all machines
Builds a repository object
De - signed files
Updates an existing machine
Start a new notebook
Creates a new model
Read the value from the config file
Update a deployment
Wait for a new version to be used
Yield logs from a notebook
Lists notebook logs
Create a new repository
Generate options template
Returns usage for model deployment
Creates a new notebook
Get a deployment by id
List deployments
Execute one or more files
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gradient-cli Key Features

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gradient-cli Examples and Code Snippets

No Code Snippets are available at this moment for gradient-cli.

Community Discussions

Trending Discussions on gradient-cli

Abnormal increase in loss after 75 epochs (Using MSE and Binary Crossentropy)

training loss is nan in keras LSTM

How to scale a gradient norm in Keras

QUESTION

Abnormal increase in loss after 75 epochs (Using MSE and Binary Crossentropy)

Asked 2021-Sep-26 at 10:55

I have trained a tensorflow.keras model over the night and was suprised about the training process (please see the attached picture). Can anyone tell me, what can produce such an effect during training? I have trained with mse (right) and one other loss displayed (binary crossentropy). I have trained an autoencoder with 'normal' samples. The validation samples are 'anomaly' samples.

If you need more information, please let me know.

Edit: I might found the reason, but I am not sure: I have features as input-data, which do not have values strictly in [0,1], actually I have nearly all values in [0,1] but a few values a little bit bigger than 1. As I am training with the MSE, I thought this should not be a problem, but as reference I also use the binary crossentropy loss (needs values in [0,1]). This might cause some iritation to the training. I am using:

...

ANSWER

Answered 2021-Sep-26 at 10:49

I found a solution:

-I reinstalled python completely and changed the learning rate to a smaller value (I think the learning rate was the main factor) and since then, no loss explosition occured anymore (trained now several times).

Source https://stackoverflow.com/questions/69240829

QUESTION

training loss is nan in keras LSTM

Asked 2021-Mar-17 at 05:53

I have tun this code in google colab with GPU to create a multilayer LSTM. It is for time series prediction.

...

ANSWER

Answered 2021-Mar-17 at 05:53

I'm more familiar with working with PyTorch than Keras. However there are still a couple of things I would recommend doing:

Check your data. Ensure that there are no missing or null values in the data that you pass into your model. This is is the most likely culprit. A single null value will cause the loss to be NaN.
You could try lowering the learning rate (0.001 or something even smaller) and/or removing gradient clipping. I've actually had gradient contributing be the cause of NaN loss before.
Try scaling your data (though unscaled data will usually cause infinite losses rather than NaN loses). Use StandardScaler or one of the other scalers in sklearn.

If all that fails then I'd try to just pass some very simple dummy data into the model and see if the problem persists. Then you will know if it is a code problem or a data problem. Hope this helps and feel free to ask questions if you have them.

Source https://stackoverflow.com/questions/66667550

QUESTION

How to scale a gradient norm in Keras

Asked 2020-Jan-06 at 17:27

In the pseudocode for MuZero, they do the following:

...

ANSWER

Answered 2020-Jan-06 at 17:27

You can use the MaxNorm constraint presented here.

It's very simple and straightforward. Import it from keras.constraints import MaxNorm

If you want to apply it to weights, when you define a Keras layer, you use kernel_constraint = MaxNorm(max_value=2, axis=0) (read the page for details on axis)

You can also use bias_constraint = ...

If you want to apply it to any other tensor, you can simply call it with a tensor:

Source https://stackoverflow.com/questions/59616311

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

Vulnerabilities

No vulnerabilities reported

Install gradient-cli

You can download it from GitHub.
You can use gradient-cli 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.