e8948a2d9d
a node cannot be placed on a device, specifically when the cause is due to colocation constraints. For example, node colocation can cause a group of nodes to be colocated with each other in an unsatisfiable way. For example, if we have three ops, A, B, C, where A supports GPU and CPU, B supports only GPU, and C supports only CPU, a colocation group has no satisfiable assignment. In these cases, the cause is not just the lack of a kernel for the op that failed to place, but possibly due the set of ops in the colocation group. This change adds additional logging to the error message that lists the op types and their supported devices, so a user can figure out which combinations of ops are problematic. Fixes #2508 in that the error message should be clearer now. Change: 123330848
Linux CPU |
Linux GPU PIP |
Mac OS CPU |
Android |
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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. This flexible architecture lets you deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device without rewriting code. TensorFlow also includes TensorBoard, a data visualization toolkit.
TensorFlow was originally developed by researchers and engineers working on the Google Brain team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research. The system is general enough to be applicable in a wide variety of other domains, as well.
If you'd like to contribute to TensorFlow, be sure to review the contribution guidelines.
We use GitHub issues for tracking requests and bugs, but please see Community for general questions and discussion.
Installation
See Download and Setup for instructions on how to install our release binaries or how to build from source.
People who are a little bit adventurous can also try our nightly binaries:
- Linux CPU only: Python 2 (build history) / Python 3 (build history)
- Linux GPU: Python 2 (build history) / Python 3 (build history)
- Mac CPU only: Python 2 (build history) / Python 3 (build history)
- Android (build history)
Try your first TensorFlow program
$ python
>>> import tensorflow as tf
>>> hello = tf.constant('Hello, TensorFlow!')
>>> sess = tf.Session()
>>> sess.run(hello)
Hello, TensorFlow!
>>> a = tf.constant(10)
>>> b = tf.constant(32)
>>> sess.run(a+b)
42
>>>
##For more information
- TensorFlow website
- TensorFlow whitepaper
- [TensorFlow MOOC on Udacity] (https://www.udacity.com/course/deep-learning--ud730)
The TensorFlow community has created amazing things with TensorFlow, please see the resources section of tensorflow.org for an incomplete list.