From 203535bb134425ad5299e16c4cbbfad835b7bef6 Mon Sep 17 00:00:00 2001 From: Zach Nation Date: Fri, 8 Dec 2017 15:44:23 -0800 Subject: [PATCH] Clean up file drop * Restore README.md (delete README.txt) * Delete dummy.cpp and add to .gitignore (this gets created every build) * Remove stray .gitattributes --- .gitattributes | 4 -- .gitignore | 2 + dummy.cpp | 0 src/unity/python/README.md | 132 ++++++++++++++++++++++++++++++++++++ src/unity/python/README.txt | 12 ---- 5 files changed, 134 insertions(+), 16 deletions(-) delete mode 100644 .gitattributes delete mode 100644 dummy.cpp create mode 100644 src/unity/python/README.md delete mode 100644 src/unity/python/README.txt diff --git a/.gitattributes b/.gitattributes deleted file mode 100644 index 3c252d00d8..0000000000 --- a/.gitattributes +++ /dev/null @@ -1,4 +0,0 @@ -lfs/** filter=lfs diff=lfs merge=lfs -text -src/unity/python/turicreate/canvas/webapp/webapp.tar.gz filter=lfs diff=lfs merge=lfs -text -src/unity/python/turicreate/mxnet/mac/mxnet.tar.gz filter=lfs diff=lfs merge=lfs -text -src/unity/python/turicreate/mxnet/linux/mxnet.tar.gz filter=lfs diff=lfs merge=lfs -text diff --git a/.gitignore b/.gitignore index 6ef45c2cd0..7d569be297 100644 --- a/.gitignore +++ b/.gitignore @@ -92,3 +92,5 @@ deps/env deps/local deps/build doc/ + +/dummy.cpp diff --git a/dummy.cpp b/dummy.cpp deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/src/unity/python/README.md b/src/unity/python/README.md new file mode 100644 index 0000000000..b33a2f18e9 --- /dev/null +++ b/src/unity/python/README.md @@ -0,0 +1,132 @@ +Turi Create + +# Turi Create + +Turi Create simplifies the development of custom machine learning models. You +don't have to be a machine learning expert to add recommendations, object +detection, image classification, image similarity or activity classification to +your app. + +* **Easy-to-use:** Focus on tasks instead of algorithms +* **Visual:** Built-in, streaming visualizations to explore your data +* **Flexible:** Supports text, images, audio, video and sensor data +* **Fast and Scalable:** Work with large datasets on a single machine +* **Ready To Deploy:** Export models to Core ML for use in iOS, macOS, watchOS, and tvOS apps + +Example: Image classifier with a few lines of code +-------------------------------------------------- + +If you want your app to recognize specific objects in images, you can build your own model with just a few lines of code: + +```python +import turicreate as tc + +# Load data +data = tc.SFrame('photoLabel.sframe') + +# Create a model +model = tc.image_classifier.create(data, target='photoLabel') + +# Make predictions +predictions = model.predict(data) + +# Export to Core ML +model.export_coreml('MyClassifier.mlmodel') +``` + +It's easy to use the resulting model in an [iOS application](https://developer.apple.com/documentation/vision/classifying_images_with_vision_and_core_ml): + +

Turi Create

+ +With Turi Create, you can can tackle a number of common scenarios: +* [Recommender systems](https://apple.github.io/turicreate/docs/userguide/recommender/introduction.html) +* [Image classification](https://apple.github.io/turicreate/docs/userguide/image_classifier/introduction.html) +* [Image similarity](https://apple.github.io/turicreate/docs/userguide/image_similarity/introduction.html) +* [Object detection](https://apple.github.io/turicreate/docs/userguide/object_detection/introduction.html) +* [Activity classifier](https://apple.github.io/turicreate/docs/userguide/activity_classifier/introduction.html) +* [Text classifier](https://apple.github.io/turicreate/docs/userguide/text_classifier/introduction.html) + +You can also work with essential machine learning models, organized into algorithm-based toolkits: +* [Classifiers](https://apple.github.io/turicreate/docs/userguide/supervised-learning/classifier.html) +* [Regression](https://apple.github.io/turicreate/docs/userguide/supervised-learning/regression.html) +* [Graph analytics](https://apple.github.io/turicreate/docs/userguide/graph_analytics/intro.html) +* [Clustering](https://apple.github.io/turicreate/docs/userguide/clustering/intro.html) +* [Nearest Neighbors](https://apple.github.io/turicreate/docs/userguide/nearest_neighbors/nearest_neighbors.html) +* [Topic models](https://apple.github.io/turicreate/docs/userguide/text/intro.html) + +System Requirements +------------------- + +* Python 2.7 (Python 3.5+ support coming soon) +* x86\_64 architecture +* macOS 10.11+, Linux with glibc 2.12+ (including WSL on Windows 10) + +Installation +------------ + +For detailed instructions for different varieties of Linux see [LINUX\_INSTALL.md](https://github.com/apple/turicreate/LINUX_INSTALL.md). +For common installation issues see [INSTALL\_ISSUES.md](https://github.com/apple/turicreate/INSTALL_ISSUES.md). + +We recommend using virtualenv to use, install, or build Turi Create. +Be sure to install virtualenv using your system pip. + +```shell +pip install virtualenv +``` + +The method for installing *Turi Create* follows the +[standard python package installation steps](https://packaging.python.org/installing/). +To create a Python virtual environment called `venv` follow these steps: + +```shell +# Create a Python virtual environment +cd ~ +virtualenv venv +``` + +To activate your new virtual environment and install `Turi Create` in this environment, follow these steps: +```shell +# Active your virtual environment +source ~/venv/bin/activate + +# Install Turi Create in the new virtual environment, pythonenv +(venv) pip install -U turicreate +``` + +Documentation +------------- + +The package [User Guide](https://apple.github.io/turicreate/docs/userguide) and [API Docs](https://apple.github.io/turicreate/docs/api) contain +more details on how to use Turi Create. + +GPU Support +----------- + +By default, `turicreate` takes a dependency on the default installation of +`mxnet`. To enable GPU support after installation of the `turicreate` package, +please perform the following steps: + + * Install CUDA 8.0 ([instructions](http://docs.nvidia.com/cuda/cuda-installation-guide-linux/)) + * Install cuDNN 5 for CUDA 8.0 ([instructions](https://developer.nvidia.com/cudnn)) + +Make sure to add the CUDA library path to your `LD_LIBRARY_PATH` environment +variable. In the typical case, this means adding the following line to your +`~/.bashrc` file: + +```shell +export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH +``` + +If you installed the cuDNN files into a separate directory, make sure to +separately add it as well. Next step is to uninstall `mxnet` and install the +CUDA-enabled `mxnet-cu80` package: + +``` +(pythonenv) pip uninstall -y mxnet +(pythonenv) pip install mxnet-cu80==0.11.0 +``` + +Make sure you install the same version of MXNet as the one `turicreate` depends +on (currently `0.11.0`). If you have trouble setting up the GPU, the [MXNet +installation instructions](https://mxnet.incubator.apache.org/get_started/install.html) may +offer additional help. diff --git a/src/unity/python/README.txt b/src/unity/python/README.txt deleted file mode 100644 index c3e83ea624..0000000000 --- a/src/unity/python/README.txt +++ /dev/null @@ -1,12 +0,0 @@ -License -======= -See the attached LICENSE.txt file to see the license under which Turi Create is distributed. - -References -========== -Documentation and source code available at: -https://github.com/apple/turicreate - -Contributors -============ -Turi Create Team