{"_id":"563fc7651594380d009c1a66","__v":5,"project":"5503ea178c5e913700362c70","user":"5503e897e508a017002013bd","category":{"_id":"563fc7641594380d009c1a5d","project":"5503ea178c5e913700362c70","__v":2,"version":"563fc7631594380d009c1a5c","pages":["563fc7651594380d009c1a66","563fc7651594380d009c1a67","563fc7651594380d009c1a68","5654ff14648fc80d00bd09c2"],"sync":{"url":"","isSync":false},"reference":false,"createdAt":"2015-03-14T07:58:16.556Z","from_sync":false,"order":0,"slug":"general","title":"General"},"version":{"_id":"563fc7631594380d009c1a5c","project":"5503ea178c5e913700362c70","__v":2,"createdAt":"2015-11-08T22:06:27.279Z","releaseDate":"2015-11-08T22:06:27.278Z","categories":["563fc7641594380d009c1a5d","563fc7641594380d009c1a5e","563fc7641594380d009c1a5f","5654ff257b89070d00f96386"],"is_deprecated":false,"is_hidden":false,"is_beta":true,"is_stable":true,"codename":"","version_clean":"0.0.9","version":"0.0.9"},"updates":["55ea286bc584fb2300291e0d"],"next":{"pages":[],"description":""},"createdAt":"2015-03-14T07:58:17.255Z","link_external":false,"link_url":"","githubsync":"","sync_unique":"","hidden":false,"api":{"results":{"codes":[]},"settings":"","auth":"required","params":[],"url":""},"isReference":false,"order":0,"body":"[block:api-header]\n{\n  \"type\": \"basic\",\n  \"title\": \"What is OpenDeep?\"\n}\n[/block]\n[OpenDeep](http://www.opendeep.org) is a general purpose commercial and research grade [deep learning](http://markus.com/deep-learning-101) library for Python built from the ground up in [Theano](http://deeplearning.net/software/theano/index.html) that brings unprecedented flexibility for both industry data scientists and cutting-edge researchers.\n\nUse OpenDeep to:\n\n * Quickly prototype complex networks through a focus on **complete modularity** and containers similar to Torch.\n * Configure and train existing **state-of-the-art** models.\n * Write **your own models** from scratch in Theano and plug into OpenDeep for easy training and dataset integration.\n * Use **visualization** and debugging tools to see exactly what is happening with your neural net architecture.\n * Plug into your existing **Numpy/Scipy/Pandas/Scikit-learn** pipeline.\n * Run on the **CPU** or **GPU**.\n\n**This library is currently undergoing rapid development and is in its alpha stages.** \n[block:api-header]\n{\n  \"type\": \"basic\",\n  \"title\": \"Installation\"\n}\n[/block]\n#Dependencies\n\n* **[Theano](http://deeplearning.net/software/theano/index.html)**: Theano and its dependencies are required to use OpenDeep. You need to install the bleeding-edge version, which has [installation instructions here](http://deeplearning.net/software/theano/install.html#bleeding-edge-install-instructions). \n\n  * For GPU integration with Theano, you also need the latest **[CUDA drivers](https://developer.nvidia.com/cuda-toolkit)**. Here are [instructions for setting up Theano for the GPU](http://deeplearning.net/software/theano/tutorial/using_gpu.html). If you prefer to use a server on Amazon Web Services, here are instructions for setting up an [EC2 server with Theano](http://markus.com/install-theano-on-aws).\n\n  * **[CuDNN](https://developer.nvidia.com/cuDNN)** (optional): for a fast convolutional net support from Nvidia. You will want to move the files to Theano's directory like the instructions say here: [Theano cuDNN integration](http://deeplearning.net/software/theano/library/sandbox/cuda/dnn.html).\n\n* **[Pillow (PIL)](https://pillow.readthedocs.org/installation.html)**: image manipulation functionality.\n\n* **[PyYAML](http://pyyaml.org/)** (optional): used for YAML parsing of config files.\n\n* **[Bokeh](http://bokeh.pydata.org/en/latest/)** (optional): if you want live charting/plotting of values during training or testing. Make sure you can use the bokeh-server command.\n\n* **[NLTK](http://www.nltk.org/)** (optional): if you want NLP functions like word tokenization.\n\n\n\n#Install from source\nBecause OpenDeep is still in alpha, you have to install via setup.py. Here are the steps to install:\n\n1) Navigate to your desired installation directory and download the github repository like so:\n[block:code]\n{\n  \"codes\": [\n    {\n      \"code\": \"git clone https://github.com/vitruvianscience/opendeep.git\",\n      \"language\": \"shell\"\n    }\n  ]\n}\n[/block]\n2) Navigate to the top-level folder (should be named OpenDeep and contain the file setup.py) and run setup.py with develop mode like so:\n[block:code]\n{\n  \"codes\": [\n    {\n      \"code\": \"cd opendeep\\npython setup.py develop\",\n      \"language\": \"shell\"\n    }\n  ]\n}\n[/block]\nUsing `python setup.py develop` instead of the normal `python setup.py install` allows you to update the repository files by pulling from git and have the whole package update! No need to reinstall.\n\nThat's it! Now you should be able to import opendeep into python modules.\n\nCheck out some Tutorials in the navigation bar on the left. \n\nTo learn how to use existing models, check out [Tutorial: First Steps](doc:tutorial-first-steps).","excerpt":"This page will help you get started with OpenDeep. You'll be up and running in a jiffy!","slug":"getting-started","type":"basic","title":"Installation/Getting Started"}

Installation/Getting Started

This page will help you get started with OpenDeep. You'll be up and running in a jiffy!

[block:api-header] { "type": "basic", "title": "What is OpenDeep?" } [/block] [OpenDeep](http://www.opendeep.org) is a general purpose commercial and research grade [deep learning](http://markus.com/deep-learning-101) library for Python built from the ground up in [Theano](http://deeplearning.net/software/theano/index.html) that brings unprecedented flexibility for both industry data scientists and cutting-edge researchers. Use OpenDeep to: * Quickly prototype complex networks through a focus on **complete modularity** and containers similar to Torch. * Configure and train existing **state-of-the-art** models. * Write **your own models** from scratch in Theano and plug into OpenDeep for easy training and dataset integration. * Use **visualization** and debugging tools to see exactly what is happening with your neural net architecture. * Plug into your existing **Numpy/Scipy/Pandas/Scikit-learn** pipeline. * Run on the **CPU** or **GPU**. **This library is currently undergoing rapid development and is in its alpha stages.** [block:api-header] { "type": "basic", "title": "Installation" } [/block] #Dependencies * **[Theano](http://deeplearning.net/software/theano/index.html)**: Theano and its dependencies are required to use OpenDeep. You need to install the bleeding-edge version, which has [installation instructions here](http://deeplearning.net/software/theano/install.html#bleeding-edge-install-instructions). * For GPU integration with Theano, you also need the latest **[CUDA drivers](https://developer.nvidia.com/cuda-toolkit)**. Here are [instructions for setting up Theano for the GPU](http://deeplearning.net/software/theano/tutorial/using_gpu.html). If you prefer to use a server on Amazon Web Services, here are instructions for setting up an [EC2 server with Theano](http://markus.com/install-theano-on-aws). * **[CuDNN](https://developer.nvidia.com/cuDNN)** (optional): for a fast convolutional net support from Nvidia. You will want to move the files to Theano's directory like the instructions say here: [Theano cuDNN integration](http://deeplearning.net/software/theano/library/sandbox/cuda/dnn.html). * **[Pillow (PIL)](https://pillow.readthedocs.org/installation.html)**: image manipulation functionality. * **[PyYAML](http://pyyaml.org/)** (optional): used for YAML parsing of config files. * **[Bokeh](http://bokeh.pydata.org/en/latest/)** (optional): if you want live charting/plotting of values during training or testing. Make sure you can use the bokeh-server command. * **[NLTK](http://www.nltk.org/)** (optional): if you want NLP functions like word tokenization. #Install from source Because OpenDeep is still in alpha, you have to install via setup.py. Here are the steps to install: 1) Navigate to your desired installation directory and download the github repository like so: [block:code] { "codes": [ { "code": "git clone https://github.com/vitruvianscience/opendeep.git", "language": "shell" } ] } [/block] 2) Navigate to the top-level folder (should be named OpenDeep and contain the file setup.py) and run setup.py with develop mode like so: [block:code] { "codes": [ { "code": "cd opendeep\npython setup.py develop", "language": "shell" } ] } [/block] Using `python setup.py develop` instead of the normal `python setup.py install` allows you to update the repository files by pulling from git and have the whole package update! No need to reinstall. That's it! Now you should be able to import opendeep into python modules. Check out some Tutorials in the navigation bar on the left. To learn how to use existing models, check out [Tutorial: First Steps](doc:tutorial-first-steps).