macbot | A simple way to configure your mac | TLS library

by echohack Shell Version: Current License: Apache-2.0

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kandi X-RAY | macbot Summary

macbot is a Shell library typically used in Security, TLS applications. macbot has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

Macbot is a shell script to automatically configure your Mac with sane and secure settings. It's free to use, Apache 2.0 licensed.

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macbot has a low active ecosystem.

It has 115 star(s) with 31 fork(s). There are 4 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 1 have been closed. On average issues are closed in 209 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of macbot is current.

Quality

macbot has no bugs reported.

Security

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

License

macbot 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.

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macbot releases are not available. You will need to build from source code and install.

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macbot Key Features

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macbot Examples and Code Snippets

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Community Discussions

Trending Discussions on macbot

Trouble Using tf.data API, TFRecordDataset and Serialization

QUESTION

Trouble Using tf.data API, TFRecordDataset and Serialization

Asked 2018-Apr-03 at 13:53

I have a large dataset of numpy integers which I want to analyze with a GPU. The dataset is too large to fit into main memory on the GPU so I am trying to serialize them into a TFRecord and then use the API to stream the record for processing. The below code is example code: it wants to create some fake data, serialize it into the TFRecord object, then using a TF session read the data back into memory, parsing with the map() function. My original data is non-homogenous in terms of the dimensions of the numpy arrays, though each is a 3D array with 10 as the length of the first axis. I recreated the hetorogeneity using random numbers when I made the fake data. The idea is to store the size of each image as I serialize the data, and I can use that to restore each array to its original size. It is most definitely not working for whatever reason. Here is the code:

...

ANSWER

Answered 2018-Apr-03 at 13:46

It seems like you are writing the results of np.random.rand as they are. However, this returns float64 values. On the other hand you are telling Tensorflow to interpret the bytes as float32. This is a mismatch -- and would explain why there are twice as many numbers as you expect (since there are twice as many bytes!).

Try using files.append(np.random.rand(10,i_vals[x],j_vals[x]).astype(np.float32)) instead. Using float32 is recommended for CUDA. You need to be careful with this in general: By default numpy uses float64 (but int32) in most places.

Source https://stackoverflow.com/questions/49631109

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

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You can download it from GitHub.

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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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