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…rol slots and control slots must only be linked to control slots
…ervanaSystems/ngraph-tf into shrestha/Update_Readme_Option3
…ervanaSystems/ngraph-tf into shrestha/Update_Readme_Option3
…ervanaSystems/ngraph-tf into shrestha/Update_Readme_Option3
…ervanaSystems/ngraph-tf into shrestha/Update_Readme_Option3
…ervanaSystems/ngraph-tf into shrestha/Update_Readme_Option3
…ervanaSystems/ngraph-tf into shrestha/Update_Readme_Option3
…ervanaSystems/ngraph-tf into shrestha/Update_Readme_Option3
…ervanaSystems/ngraph-tf into shrestha/Update_Readme_Option3
…ervanaSystems/ngraph-tf into shrestha/Update_Readme_Option3
shresthamalik
changed the title
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| # Intel® nGraph™ Compiler and runtime for TensorFlow* | |||
| # nGraph-Tensorflow : Intel® nGraph™ Compiler and Runtime for TensorFlow | |||
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nGraph-Tensorflow is the name of this too: https://github.com/NervanaSystems/ngraph-tensorflow
But I suppose it is clear from the context which is which.
@avijit-nervana ?
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That repo will likely be retired at some point so I'm not sure it matters too much, but I think nGraph-TensorFlow is still a little confusing. To me it kind of sounds like a fork of TensorFlow, which is not what ngraph-tf is.
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Yes, it would be nice to have a name for the repo. Earlier, we were referencing it as 'ngraph-tensorflow bridge'. Bridge is more of an internal usage, so we removed it. Our current version, does not explicitly call out a name but at many places we reference 'ngraph-tf bridge'. Requires more brainstorming.
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@sayantan-nervana That repo is now private - so won't matter much. I agree with @shresthamalik that it needs more brainstorming. For now I would recommend not introducing this.
avijit-nervana
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avijit-nervana
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I am wondering how to deal with changes in master vs. a specific release. The documentation is a bit different. Let's discuss.
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| # Intel® nGraph™ Compiler and runtime for TensorFlow* | |||
| # nGraph-Tensorflow : Intel® nGraph™ Compiler and Runtime for TensorFlow | |||
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@sayantan-nervana That repo is now private - so won't matter much. I agree with @shresthamalik that it needs more brainstorming. For now I would recommend not introducing this.
| customize and deploy Deep Learning inference models that will "just work" with | ||
| a variety of nGraph-enabled backends: CPU, GPU, and custom silicon like the | ||
| [Intel® Nervana™ NNP](https://itpeernetwork.intel.com/inteldcisummit-artificial-intelligence/). | ||
| nGraph-Tensorflow (ngraph-tf) enables TensorFlow to run with [Intel® nGraph™](https://github.com/NervanaSystems/ngraph), compiler and |
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I would recommend keeping this as is.
README.md
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| [tensorflow] repo to your machine. | ||
| 1. Install [bazel](#install-bazel-for-building-tensorflow-source) and other TensorFlow dependencies. Now, clone the source of the [tensorflow] repo to your machine. | ||
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| :warning: You need the following version of TensorFlow: `v1.11.0-rc2` |
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Please update this to point to v1.11.0
README.md
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| @@ -88,18 +95,7 @@ The installation prerequisites are the same as described in the TensorFlow | |||
| git status | |||
| HEAD detached at v1.11.0-rc2 | |||
| 1. Create and activate a [python virtual environment](#create-a-python-virtual-environment) | ||
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| 2. Install TensorFlow v1.11.0-rc2. Note that this is a pre-release so you need | ||
| to use the following steps to install this: |
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Now we are at 1.11, so may need to change all instances of 1.11-rc2 to 1.11
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| 7. Next, build and install nGraph bridge. | ||
| 7. Next, build and install nGraph-tf. |
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step 6: Now clone the ngraph-tfrepo one level above -- in the *parent* directory of thetensorflow repo cloned in step 1
this requirement is probably no longer there.
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Updated the Readme: