- Linux GPU: Python 2 ( build history ) / Python 3.4 ( build history ) / Python 3.5 ( build history )
- Latest commit 55b0159 Jan 1, 2017 yifeif committed on GitHub Merge pull request #6588 from terrytangyuan/run_config_flag
- TensorFlow is an open source software library for numerical computation using data flow graphs.
- Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them.
- TensorFlow also includes TensorBoard, a data visualization toolkit.
tensorflow – Computation using data flow graphs for scalable machine learning
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- If you are deploying a model to a cloud environment, you want to know that your model can execute on the hardware available to it, without unpredictable interactions with other code that may access the same hardware.
- For example, the Udacity tutorials and the RNN tutorial using Penn TreeBank data to build a language model are very illustrative, thanks to their simplicity.
- For me, holding mental context for a new framework and model I’m building to solve a hard problem is already pretty taxing, so it can be really helpful to inspect a totally different representation of a model; the TensorBoard graph visualization is great for this.
- But good programmers know it is much harder to write code that humans will use, versus code that a machine can compile and execute.
- We appreciate their strategy of integrating new features and tests first so early adopters can try things before they are documented.
A survey of six months rapid evolution (+ tips/hacks and code to fix the ugly stuff) from Dan Kuster, one of indico’s deep learning researchers.
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