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This guide will walk you through the most common features of the Deep-Water framework. At startup, you need to provide the connection info of a SAMBA network storage that will be used as a shared storage between the server and the workers. Edit the following environment variables in the script:. The script needs the Dockerfile that can be found in the DWF-server directory, so start the script from that folder. After the script executed successfully, you have to wait for the database to get ready. You can check it by visiting. When you see that Kibana loaded successfully, you can start using the framework, which can be accessed at. Now you have a running instance of the master node of DWF; to have your learn tasks processed you have to start at least one worker node. Run it with the following command:. When the client connects to the server, it receives a unique ID, as you can see it in the clients' console log. This hash code identifies the worker, and is also visible in the Workers view. There you can check the connected workers and their statuses. To run multiple clients from the same machine, you have to copy the DWF-client directory for each worker instances, otherwise they will conflict. Nonetheless, in the typical scenario only one worker runs in a single machine and you can add multiple machines with running workers to the same server. Having the server and at least one worker running, you have a fully functional DWF framework. The input data for the machine learning part of the framework has to be in a csv formatted file, which contains only numeric values as data and a header row. Therefore, we have to use a dataset that is in the required format or we can implement a new feature assembler plugin to process the data source easily. However, implementing a custom feature assembler is out of the scope for this tutorial. First, download the. To convert the downloaded file to a proper csv file, replace all spaces with commas in it e. The last row in the csv file may contain a few empty commas after the process, if that is the case, delete this empty row:. Add a header row to the file, and name the columns anything you like. Here is an example:. Save the file and move it to the network storage Samba share that you previously set up in the starting script. Open localhost in a browser, then select New Experiment from the menu at the top of the page. Give your experiment a name and fill the Markdown field with some description of it. Then click Submit. As we mentioned above, the learning phase of the tasks accepts csv formatted files with only numeric values as data. Here comes the option that you can implement your own feature assembler into play. If you have lots of files in the same format, like images, text documents or data with nominal features, you might want to implement a custom feature assembling method, which can process and convert your data into the required format automatically. Now we will use the built-in Manual file input with our previously 'preprocessed' file. To do so, click on add feature assembling config. Then select Manual File Input as the strategy, which means that the training data will be produced by simply loading it from the selected csv file. If you extend the functionality of the framework, you will find your own feature assembling methods here. Browse the file you prepared previously, and fill in the Label column field with the column name that you want to predict the class labels in the csv file. Because the tutorial dataset consists of only a few hundreds of rows, using deep neural networks would have a limited use, thus we are going to employ some other algorithms in this tutorial. Here you can add a learning strategy with parameters or parameter sets. The framework provides you some default values to the required parameters for each algorithm, but you can change them any time. As our first algorithm, select Random Forest Classifier from the strategy selector dropdown menu. In the current state of the Experiment you can add or remove configurations. To run the tasks you have to finalize the setup. Once you done that, no modifications are allowed. Your tasks are generated, which means all the possible feature assembling and learning combinations are prepared and they can be run now. You can select what task you want to run first by clicking on the Play button next to the selected task, or you can start all task at once with the Run all button. Note that the server will assign these task to running workers and if you have only one of them, the execution of tasks will be sequential. Once the worker finishes the processing of a task, its results will be saved in the framework's database. You can compare the results of multiple tasks by clicking on the Summary link. Here you can see the results of all learning algorithms with achieved performance measures, making it easy to compare them. Clicking on the name of a specific task will navigate you to the Task Details page, where you can see the details of the learning tasks, i. Skip to content. You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. You switched accounts on another tab or window. Dismiss alert. Notifications You must be signed in to change notification settings Fork 3 Star 7. Latest commit. History History. File metadata and controls Preview. Getting Started. Running the server. Wait for the database starting up. Running the worker. Preparing the dataset. Download the data file. Reformat the data file. Use the framework. Create an Experiment. Configure a feature assembling method. Configure a learning algorithm. Generate tasks. Run tasks. Check the results.

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