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Copy file name to clipboardExpand all lines: README.md
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@@ -22,12 +22,12 @@ If you the first step above doesn't work for your use case, [open an issue](http
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## Opening a pull request
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1. Update the *Dockerfile*
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1. For changes specific to the GPU image, update the [gpu.Dockerfile](gpu.Dockerfile).
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2. Otherwise, update the [Dockerfile](Dockerfile).
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2. Follow the instructions below to build a new image.
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3. Add tests for your new package. See this [example](https://github.com/Kaggle/docker-python/blob/master/tests/test_fastai.py).
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4. Follow the instructions below to test the new image.
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5. Open a PR on this repo and you are all set!
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1. For changes specific to the GPU image, update the [gpu.Dockerfile](gpu.Dockerfile).
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1. Otherwise, update the [Dockerfile](Dockerfile).
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1. Follow the instructions below to build a new image.
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1. Add tests for your new package. See this [example](https://github.com/Kaggle/docker-python/blob/master/tests/test_fastai.py).
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1. Follow the instructions below to test the new image.
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1. Open a PR on this repo and you are all set!
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## Building a new image
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*`--gpu` to test the GPU image.
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## Tensorflow custom pre-built wheels
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## Tensorflow custom pre-built wheel
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We are building Tensorflow from sources mainly for:
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A Tensorflow custom pre-built wheel is used mainly for:
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* Better performance. When building from sources, we can leverage CPU specific optimizations
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* Tensorflow with GPU support must be built from sources
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* Faster build time: Building tensorflow from sources takes ~1h. Keeping this process outside the main build allows faster iterations when working on our Dockerfiles.
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Building Tensorflow from sources:
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* Increase performance: When building from sources, we can leverage CPU specific optimizations
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* Is required: Tensorflow with GPU support must be built from sources
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The [Dockerfile](tensorflow-whl/Dockerfile) and the [instructions](tensorflow-whl/README.md) can be found in the [tensorflow-whl folder/](tensorflow/).
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