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Caffe

Updated

Welcome to hosting your Caffe model on Algorithmia! This guide is designed as an introduction to hosting a Caffe model and publishing an algorithm even if you’ve never used Algorithmia before.

Prerequisites

Maybe you’ve got a great idea or have tirelessly worked on a project in your spare time and you want it to be useful to others. Before you get started hosting your model on Algorithmia there are a few things you’ll want to do first:

Train your model.

You have a model that labels images. You’ve discovered the features you want to include, you’ve trained your model and validated it. You’re happy with your results and have saved your model files so you can upload it to Algorithmia.

Create a Data Collection

Here you’ll want to create a data collection to host your Caffe model.

  • To use the Data API, log into your Algorithmia account and create a data collection via the Data Collections page.

  • Click on “Add Collection” under the “My Collections” section on your data collections page.

  • After you create your collection you can set the read and write access on your data collection. For more information check out: Data Collection Types

Create a data collection

Upload your Model into a Collection

Next, upload your model files to your newly created data collection.

  • Load model by clicking box “Drop files here to upload”

  • Note the path to your files:

    • data://username/collections_name/file_name.prototxt.txt,
    • data://username/collections_name/file_name.caffemodel

Create a data collection

Create your Algorithm

Creating your algorithm is easy!

  • To add an algorithm, simply click “Add Algorithm” from the user profile icon.
  • Name your algorithm, select the language, choose permissions and make the code either open or closed source.

Note: There is also a checkbox for ‘Standard Execution Environment’ or ‘Advanced GPU’. For deep learning models you will want to check ‘Advanced GPU’.

Create your algorithm

Set your Dependencies

Now is the time to set your dependencies that your model relies on.

  • Click on the “Dependencies” button at the top right of the UI and list your packages under the required ones already listed and click “Save Dependencies” on the bottom right corner.

Please note that you will need to use the protobuf==3.0.0b2.post1 package to be able to run a caffe algorithm.

Set your dependencies

Load your Model

Now you’ll want to run your model which will be called by the apply() function. Our recommendation is to preload your model in a separate function before apply(). The reasoning behind this is because when your model is first loaded it can take some time to load depending on the file size. However, with all subsequent calls only the apply() function gets called which will be much faster since your model is already loaded!

Here is an example for loading your model based on the Classifying MNIST digits based on Caffe’s tutorial.

import Algorithmia
import numpy as np
import caffe

caffe.set_mode_cpu()

client = Algorithmia.client()

def initialize_model():
    """
    Load caffe.Net model with layers
    """

    # Load model files from user collections
    model_uri = "data://user_name/caffe_demo/lenet.prototxt.txt"
    pretrained_uri = "data://user_name/caffe_demo/lenet_iter_10000.caffemodel"

    model_file = client.file(model_uri).getFile().name
    pretrained_file = client.file(pretrained_uri).getFile().name

    # Create net and load weights
    net = caffe.Net(model_file, pretrained_file, caffe.TEST, raw_scale=1, image_dims=(28, 28))

    return net

# Gets called once
net = initialize_model()

def apply(input):
    """
    Input is an image file

    Input examples:
    Data Sources via https://algorithmia.com/data, or http(s) URLs using the
    Smart Image Downloader.
    """

    client = Algorithmia.client()
    imgDataPath = client.algo("util/SmartImageDownloader").pipe(input).result["savePath"][0]
    imgAbsPath = client.file(imgDataPath).getFile().name

    image = caffe.io.load_image(imgAbsPath, color=False)
    out = net.forward_all(data=np.asarray([image]))
    probability_vector = out['prob'][0].argmax(axis=0)
    print(predicted_vector)
    prediction = net.predict([resized_image])

    return prediction

If you are authoring an algorithm, avoid using the ‘.my’ pseudonym in the source code. When the algorithm is executed, ‘.my’ will be interpreted as the user name of the user who called the algorithm, rather than the author’s user name.

Publish your Algorithm

Last is publishing your algorithm. The best part of hosting your model on Algorithmia is that users can access it via an API that takes only a few lines of code to use! Here is what you can set when publishing your algorithm:

  • Set version permissions to public or private use

  • Set it to royalty free or set to per-call royalty

  • Set access permissions to have full access to the internet and ability to call other algorithms

If you want to have a better idea of how a finished caffe algorithm looks like, check out: CaffeNet

For more information and detailed steps: creating and publishing your algorithm

Publish your algorithm

That’s it for hosting your Caffe model on Algorithmia!