Turi Create simplifies the development of custom machine learning models.

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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

With Turi Create, you can accomplish many common ML tasks:

ML Task Description
Recommender Personalize choices for users
Image Classification Label images
Drawing Classification Recognize Pencil/Touch Drawings and Gestures
Sound Classification Classify sounds
Object Detection Recognize objects within images
One Shot Object Detection Recognize 2D objects within images using a single example
Style Transfer Stylize images
Activity Classification Detect an activity using sensors
Image Similarity Find similar images
Classifiers Predict a label
Regression Predict numeric values
Clustering Group similar datapoints together
Text Classifier Analyze sentiment of messages

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:

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:

Turi Create

Supported Platforms

Turi Create supports:

  • macOS 10.12+
  • Linux (with glibc 2.10+)
  • Windows 10 (via WSL)

System Requirements

Turi Create requires:

  • Python 2.7, 3.5, 3.6, 3.7, 3.8
  • x86_64 architecture
  • At least 4 GB of RAM

Installation

For detailed instructions for different varieties of Linux see LINUX_INSTALL.md. For common installation issues see INSTALL_ISSUES.md.

We recommend using virtualenv to use, install, or build Turi Create.

pip install virtualenv

The method for installing Turi Create follows the standard python package installation steps. To create and activate a Python virtual environment called venv follow these steps:

# Create a Python virtual environment
cd ~
virtualenv venv

# Activate your virtual environment
source ~/venv/bin/activate

Alternatively, if you are using Anaconda, you may use its virtual environment:

conda create -n virtual_environment_name anaconda
conda activate virtual_environment_name

To install Turi Create within your virtual environment:

(venv) pip install -U turicreate

Documentation

The package User Guide and API Docs contain more details on how to use Turi Create.

GPU Support

Turi Create does not require a GPU, but certain models can be accelerated 9-13x by utilizing a GPU.

Linux macOS 10.13+ macOS 10.14+ discrete GPUs, macOS 10.15+ integrated GPUs
Activity Classification Image Classification Activity Classification
Drawing Classification Image Similarity Object Detection
Image Classification Sound Classification One Shot Object Detection
Image Similarity Style Transfer
Object Detection
One Shot Object Detection
Sound Classification
Style Transfer

macOS GPU support is automatic. For Linux GPU support, see LinuxGPU.md.

Building From Source

If you want to build Turi Create from source, see BUILD.md.

Contributing

Prior to contributing, please review CONTRIBUTING.md and do not provide any contributions unless you agree with the terms and conditions set forth in CONTRIBUTING.md.

We want the Turi Create community to be as welcoming and inclusive as possible, and have adopted a Code of Conduct that we expect all community members, including contributors, to read and observe.

Comments
  • Different behavior between Model and Core ML model

    Different behavior between Model and Core ML model

    I successfully trained a Object Detection model (default TC YOLO) with TC b3 and exported in CoreML format. My model has 8000 iterations and 0.8 final loss.

    I then validate it with some images using TC and bounding box drawing util and it recognizes them better that I expected!

    I then downloaded the sample project for recognizing objects in live capture presented by @znation during the WWDC and replaced the model in the project with my new model.

    What it's weird is that the object are no longer recognized. Is NOT a problem of VNDetectedObjectObservation because they are correctly returned, but it seems that the classing and the bounding box does not represent the detected object correctly (different class and wrong bounding box). I used iOS 12 beta 9 and Xcode 10 beta 6 as developing environment, with an iPad Pro 2017 (or 2016, I don't remember).

    From my first test seems this could be a rotation issue, but I don't know if that is the real issue and eventually how to fix it.

    Does anybody faced a similar issue or can eventually help me with that?

  • Mac hardware dependent Failed assertion in object detection model running in MacOS

    Mac hardware dependent Failed assertion in object detection model running in MacOS

    Hi,

    I am getting the following error when I run an object detection model based on the cats and dogs example in a command-line MacOS project in Xcode:

    validateComputeFunctionArguments:852: failed assertion Compute Function(TARR_elementwise_mul_f16_pack4): The pixel format (MTLPixelFormatRGBA32Float) of the texture (name:<null>) bound at index 2 is incompatible with the data type (MTLDataTypeHalf) of the texture parameter (src_b [[texture(0)]]). MTLPixelFormatRGBA32Float is compatible with the data type(s) (
        float
    ).
    (lldb) 
    

    What's especially unusual is that this error only appears on my iMac (Retina 5K, 27-inch, 2017) and not on my mid 2010 Macbook Pro. The code actually works perfectly on the mid 2010 Macbook Pro as expected. I also noticed that the following message always appears when I run a turicreate model on the 2017 iMac and not on the mid 2010 Macbook Pro:

    2017-12-29 19:22:36.740697+0100 CmdSand[29721:7166209] VPA info: plugin is INTEL.
    VPA info: plugin is INTEL, AVD_id = 1080020, AVD_api.Create:0x125636cfa
    2017-12-29 19:22:36.747578+0100 CmdSand[29721:7166209] AVD info: codecHALEnableHEVCDecoder = 1
    

    Even on the 2017 iMac, everything works fine afterwards with an image classifier model, but with the object detection model I get the error message above. Unfortunately, I cannot find any useful documentation about the MTLPixelFormat formats to troubleshot. It seems to me, that the HEVC capability of the new iMac is the cause of this problem. Is there any way to disable this within Xcode as a workaround?

    I am using the following code that was mostly derived from the cats and dogs example:

    let semaphore = DispatchSemaphore(value: 2)
    
    func output_handler2(request: VNRequest, error: Error?) {
        let results = request.results as! [VNCoreMLFeatureValueObservation]
        
        let coordinates = results[0].featureValue.multiArrayValue!
        let confidence = results[1].featureValue.multiArrayValue!
        
        let confidenceThreshold = 0.25
        var unorderedPredictions = [Prediction]()
        let numBoundingBoxes = confidence.shape[0].intValue
        let numClasses = confidence.shape[1].intValue
        let confidencePointer = UnsafeMutablePointer<Double>(OpaquePointer(confidence.dataPointer))
        let coordinatesPointer = UnsafeMutablePointer<Double>(OpaquePointer(coordinates.dataPointer))
        for b in 0..<numBoundingBoxes {
            var maxConfidence = 0.0
            var maxIndex = 0
            for c in 0..<numClasses {
                let conf = confidencePointer[b * numClasses + c]
                if conf > maxConfidence {
                    maxConfidence = conf
                    maxIndex = c
                }
            }
            if maxConfidence > confidenceThreshold {
                let x = coordinatesPointer[b * 4]
                let y = coordinatesPointer[b * 4 + 1]
                let w = coordinatesPointer[b * 4 + 2]
                let h = coordinatesPointer[b * 4 + 3]
                
                let rect = CGRect(x: CGFloat(x - w/2), y: CGFloat(y - h/2),
                                  width: CGFloat(w), height: CGFloat(h))
                
                let prediction = Prediction(labelIndex: maxIndex,
                                            confidence: Float(maxConfidence),
                                            boundingBox: rect)
                unorderedPredictions.append(prediction)
            }
        }
        semaphore.signal()
    }
    
    let model_features = try VNCoreMLModel(for: detect_features().model)
    
    let request_features = VNCoreMLRequest(model: model_features, completionHandler: output_handler2)
    
    let link_str = "https://media-cdn.tripadvisor.com/media/photo-s/01/62/9d/5b/addo-national-park.jpg"
    var imageURL = URL(string: link_str)
    let inputImage = CIImage(contentsOf: imageURL!)
    if(inputImage != nil)
    {
        let object_image = inputImage!
        let handler_features = VNImageRequestHandler(ciImage: inputImage!)
        try handler_features.perform([request_features])
        semaphore.wait()
    }
    

    Best wishes,

    Leif

  • pip install -U turicreate Showing Error (using python3 and windows 10)

    pip install -U turicreate Showing Error (using python3 and windows 10)

    Collecting turicreate Using cached turicreate-6.0.tar.gz (1.9 kB) Building wheels for collected packages: turicreate Building wheel for turicreate (setup.py) ... error ERROR: Command errored out with exit status 1: command: 'C:\Users\Hridoy\Anaconda3\envs\virtual_environment_name\python.exe' -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\Users\Hridoy\AppData\Local\Temp\pip-install-yglwdhwi\turicreate\setup.py'"'"'; file='"'"'C:\Users\Hridoy\AppData\Local\Temp\pip-install-yglwdhwi\turicreate\setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(file);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, file, '"'"'exec'"'"'))' bdist_wheel -d 'C:\Users\Hridoy\AppData\Local\Temp\pip-wheel-yqhedwiv' cwd: C:\Users\Hridoy\AppData\Local\Temp\pip-install-yglwdhwi\turicreate
    Complete output (31 lines): running bdist_wheel running build installing to build\bdist.win-amd64\wheel running install

          ==================================================================================
          TURICREATE ERROR
    
          If you see this message, pip install did not find an available binary package
          for your system.
    
          Supported Platforms:
              * macOS 10.12+ x86_64.
              * Linux x86_64 (including WSL on Windows 10).
    
          Support Python Versions:
              * 2.7
              * 3.5
              * 3.6
              * 3.7
    
    
          Another possible cause of this error is an outdated pip version. Try:
              `pip install -U pip`
    
          ==================================================================================
    

    ERROR: Failed building wheel for turicreate Running setup.py clean for turicreate Failed to build turicreate Installing collected packages: turicreate Running setup.py install for turicreate ... error ERROR: Command errored out with exit status 1: command: 'C:\Users\Hridoy\Anaconda3\envs\virtual_environment_name\python.exe' -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\Users\Hridoy\AppData\Local\Temp\pip-install-yglwdhwi\turicreate\setup.py'"'"'; file='"'"'C:\Users\Hridoy\AppData\Local\Temp\pip-install-yglwdhwi\turicreate\setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(file);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, file, '"'"'exec'"'"'))' install --record 'C:\Users\Hridoy\AppData\Local\Temp\pip-record-d_6jd0af\install-record.txt' --single-version-externally-managed --compile --install-headers 'C:\Users\Hridoy\Anaconda3\envs\virtual_environment_name\Include\turicreate' cwd: C:\Users\Hridoy\AppData\Local\Temp\pip-install-yglwdhwi\turicreate
    Complete output (28 lines): running install

            ==================================================================================
            TURICREATE ERROR
    
            If you see this message, pip install did not find an available binary package
            for your system.
    
            Supported Platforms:
                * macOS 10.12+ x86_64.
                * Linux x86_64 (including WSL on Windows 10).
    
            Support Python Versions:
                * 2.7
                * 3.5
                * 3.6
                * 3.7
    
    
            Another possible cause of this error is an outdated pip version. Try:
                `pip install -U pip`
    
            ==================================================================================
    
    
    
    ----------------------------------------
    

    ERROR: Command errored out with exit status 1: 'C:\Users\Hridoy\Anaconda3\envs\virtual_environment_name\python.exe' -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\Users\Hridoy\AppData\Local\Temp\pip-install-yglwdhwi\turicreate\setup.py'"'"'; file='"'"'C:\Users\Hridoy\AppData\Local\Temp\pip-install-yglwdhwi\turicreate\setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(file);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, file, '"'"'exec'"'"'))' install --record 'C:\Users\Hridoy\AppData\Local\Temp\pip-record-d_6jd0af\install-record.txt' --single-version-externally-managed --compile --install-headers 'C:\Users\Hridoy\Anaconda3\envs\virtual_environment_name\Include\turicreate' Check the logs for full command output.

  • tc.object_detector.create doesn't create VNRecognizedObjectObservation ?

    tc.object_detector.create doesn't create VNRecognizedObjectObservation ?

    I'm using the boiler-plate code: import turicreate as tc

    Load the data

    data = tc.SFrame('annotations.sframe')

    # Make a train-test split
    train_data, test_data = data.random_split(0.8)
    
    # Create a model
    model = tc.object_detector.create(train_data, feature='image', max_iterations=100, model='darknet-yolo')
    
    # Save predictions to an SArray
    predictions = model.predict(test_data)
    			
    # Evaluate the model and save the results into a dictionary
    metrics = model.evaluate(test_data)
    
    # Save the model for later use in Turi Create
    model.save('mymodel.model')
    
    # Export for use in Core ML
    model.export_coreml('MyCustomObjectDetector.mlmodel')
    

    to train a model and the out-of-the-box project: https://developer.apple.com/documentation/vision/recognizing_objects_in_live_capture

    to try and have my objects detected in live capture. It seems like the code produces a classification object and not a VNRecognizedObjectObservation, since no box is being drawn on the mobile screen.

    By looking at the info on the .mlmodel, I can see that the outputs arrays of confidence and coordinates are multiarray of (Double 2535x1) and (Double 2535 x 4) respectively as opposed to (Double 0 x 0) for the ObjectDetector model that comes with the project.

    I'm using Version: 4.0

  • tc.config.set_num_gpus fails on Ubuntu 16.0.4 with multiple GPUs

    tc.config.set_num_gpus fails on Ubuntu 16.0.4 with multiple GPUs

    Hello

    Successfully running Turi Create 5.0b1 on Ubuntu with GPU training and putting Core ML models. Very cool.

    Our system has 5 GPUs - 2x 1080s and 3x 1070s. Our Turi Create script sets tc.config.set_num_gpus(-1) but we fail to ever see any GPU other than the first in use.

    Is this a known issue? is this subject to hardware config?

    System: Ubuntu 16.0.4

    NVidia Drivers 390.48, Cuda 9.0,

    Topology:

    GPU0	GPU1	GPU2	GPU3	GPU4	CPU Affinity
    

    GPU0 X PHB PHB PHB PHB 0-3 GPU1 PHB X PHB PHB PHB 0-3 GPU2 PHB PHB X PHB PHB 0-3 GPU3 PHB PHB PHB X PHB 0-3 GPU4 PHB PHB PHB PHB X 0-3

    +-----------------------------------------------------------------------------+ | NVIDIA-SMI 390.48 Driver Version: 390.48 | |-------------------------------+----------------------+----------------------+ | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | |===============================+======================+======================| | 0 GeForce GTX 1080 Off | 00000000:01:00.0 On | N/A | |100% 39C P2 58W / 180W | 1067MiB / 8116MiB | 37% Default | +-------------------------------+----------------------+----------------------+ | 1 GeForce GTX 1080 Off | 00000000:02:00.0 Off | N/A | |100% 36C P8 16W / 180W | 786MiB / 8119MiB | 0% Default | +-------------------------------+----------------------+----------------------+ | 2 GeForce GTX 1070 Off | 00000000:04:00.0 Off | N/A | |100% 30C P8 19W / 230W | 734MiB / 8119MiB | 0% Default | +-------------------------------+----------------------+----------------------+ | 3 GeForce GTX 1070 Off | 00000000:05:00.0 Off | N/A | |100% 32C P8 20W / 230W | 640MiB / 8119MiB | 0% Default | +-------------------------------+----------------------+----------------------+ | 4 GeForce GTX 1070 Off | 00000000:09:00.0 Off | N/A | |100% 29C P8 20W / 230W | 546MiB / 8119MiB | 0% Default | +-------------------------------+----------------------+----------------------+

  • ERROR: pip's dependency resolver does not currently take into account all the packages that are installed

    ERROR: pip's dependency resolver does not currently take into account all the packages that are installed

    ubuntu 16.04, virtualenv, python3.7 Following the instructions of gpu, after installed the turicreate, execute these two cmds: (venv) pip uninstall -y tensorflow (venv) pip install tensorflow-gpu

    Then it shows: Requirement already satisfied: zipp>=0.5 in ./lib/python3.7/site-packages (from importlib-metadata->markdown>=2.6.8->tensorboard~=2.4->tensorflow-gpu) (3.4.0) Installing collected packages: tensorboard-plugin-wit, numpy, grpcio, tensorflow-estimator, tensorboard, gast, flatbuffers, astunparse, tensorflow-gpu Attempting uninstall: numpy Found existing installation: numpy 1.18.5 Uninstalling numpy-1.18.5: Successfully uninstalled numpy-1.18.5 Attempting uninstall: grpcio Found existing installation: grpcio 1.35.0 Uninstalling grpcio-1.35.0: Successfully uninstalled grpcio-1.35.0 Attempting uninstall: tensorflow-estimator Found existing installation: tensorflow-estimator 2.0.1 Uninstalling tensorflow-estimator-2.0.1: Successfully uninstalled tensorflow-estimator-2.0.1 Attempting uninstall: tensorboard Found existing installation: tensorboard 2.0.2 Uninstalling tensorboard-2.0.2: Successfully uninstalled tensorboard-2.0.2 Attempting uninstall: gast Found existing installation: gast 0.2.2 Uninstalling gast-0.2.2: Successfully uninstalled gast-0.2.2 ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts. turicreate 6.4.1 requires tensorflow<2.1.0,>=2.0.0, which is not installed. Successfully installed astunparse-1.6.3 flatbuffers-1.12 gast-0.3.3 grpcio-1.32.0 numpy-1.19.5 tensorboard-2.4.1 tensorboard-plugin-wit-1.8.0 tensorflow-estimator-2.4.0 tensorflow-gpu-2.4.1

    Is this installation OK?

  • unable to read from/write to a non public S3 bucket

    unable to read from/write to a non public S3 bucket

    It seems not possible to use method turicreate.aws.set_credentials() described in documentation https://apple.github.io/turicreate/docs/api/generated/turicreate.SFrame.html

    There is no aws object within turicreate. It seems also not possible to use environment variables, either when set in the shell environment, or before calling the load_sframe method.

    This is the code i'm using to get an SFrame in S3. It works well with the sframe library, but fails with turicreate

    def getSFrame(s3SFramePath): import os import turicreate as tc global S3_ACCESS_KEY, S3_SECRET_KEY os.environ['AWS_ACCESS_KEY_ID'] = S3_ACCESS_KEY os.environ['AWS_SECRET_ACCESS_KEY'] = S3_SECRET_KEY return tc.load_sframe(s3SFramePath)

  • Turi create memory error-> MemoryError: std::bad_alloc  in GPU & CPU machine

    Turi create memory error-> MemoryError: std::bad_alloc in GPU & CPU machine

    I have trained turi create model in CPU machine for 4k dataset and got memory error

    Total dataset size : 103.7 MB, Single image size in KB only .

    Machine configuration : image

    I have referred memory error already posted in https://github.com/apple/turicreate/issues/267 and updated batch_size parameter as 32 and still got same issue

    Memory error : image

    Modified batch size changes : image

  • Trying to install

    Trying to install

    Which way would you recommend installing turicreate preferably the easiest way. Tried using Jupiter Notebook like in the WWDC 2018 video, but I was having trouble importing turicreate into the environment. It always said that my python is too new and that I need v.3.6 bu ti have 3.7. does anyone know how to downgrade python using anaconda?

  • Kernel dies when trying to train object detection model with radeon 560 gpu

    Kernel dies when trying to train object detection model with radeon 560 gpu

    I have a 2018 Mac book pro with a radeon 560 gpu. I can train a object detection model with cpu using turicreate and use my radeon gpu for training other models using "plaid", but every time I try to train using turicreate and it chooses my AMD Radeon 560 gpu my kernel dies before training starts and I get this message:

    /BuildRoot/Library/Caches/com.apple.xbs/Sources/MetalPerformanceShaders/MetalPerformanceShaders-121.1.1/MPSCore/Utility/MPSLibrary.mm:218: failed assertion `MPSKernel MTLComputePipelineStateCache unable to load function cnnConv_Update_32x64. Compiler encountered an internal error: (null)

    I'm on turicreate 5.2.

    Thanks

  • Error when trying to 'import turicreate' in Google Cloud Datalab

    Error when trying to 'import turicreate' in Google Cloud Datalab


    ImportError Traceback (most recent call last) in () ----> 1 import turicreate

    /usr/local/envs/py3env/lib/python3.5/site-packages/turicreate/init.py`

    in () 17 from turicreate.version_info import version 18 ---> 19 from turicreate.data_structures.sgraph import Vertex, Edge 20 from turicreate.data_structures.sgraph import SGraph 21 from turicreate.data_structures.sarray import SArray

    /usr/local/envs/py3env/lib/python3.5/site-packages/turicreate/data_structures/init.py in () 16 17 from . import image ---> 18 from . import sframe 19 from . import sarray 20 from . import sgraph

    /usr/local/envs/py3env/lib/python3.5/site-packages/turicreate/data_structures/sframe.py in () 14 from future import division as _ 15 from future import absolute_import as _ ---> 16 from ..connect import main as glconnect 17 from ..cython.cy_flexible_type import infer_type_of_list 18 from ..cython.context import debug_trace as cython_context

    /usr/local/envs/py3env/lib/python3.5/site-packages/turicreate/connect/main.py in () 11 from future import absolute_import as _ 12 ---> 13 from ..cython.cy_unity import UnityGlobalProxy 14 from ..cython.cy_server import EmbeddedServer 15

    ImportError: libblas.so.3: cannot open shared object file: No such file or directory


    Above is the output of when I try import turicreate. I have trained a few models in Jupyter Notebook using turicreate on my local machine. But, Google Cloud Datalab is basically a cloud version of Jupyter Notebook, and I can't figure out how install 'libblas' as was suggested in #191

  • Is the image similarity example broken for CoreML?

    Is the image similarity example broken for CoreML?

    Hi,

    I've tried several times to get the turi create image similarity examples to work. Whenever I run the coreML in a Swift app, I get no results back. First time was with my own set of images, second time I downloaded the Caltech images used in the example – still no luck.

    I'm wondering if CoreML has changed since the tutorial/docs were written? Perhaps in a way that has broken things? Is anyone able to successfully export a basic image similarity model to coreML and get it to return results in a Swift/iOS app?

    Thanks!

  • Install turicreate on Google colab

    Install turicreate on Google colab

    Has anyone managed to install turicreate in colab. I have the following dependencies error:

    ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
    xarray-einstats 0.2.2 requires numpy>=1.21, but you have numpy 1.18.5 which is incompatible.
    tensorflow-probability 0.16.0 requires gast>=0.3.2, but you have gast 0.2.2 which is incompatible.
    tables 3.7.0 requires numpy>=1.19.0, but you have numpy 1.18.5 which is incompatible.
    librosa 0.8.1 requires resampy>=0.2.2, but you have resampy 0.2.1 which is incompatible.
    jaxlib 0.3.14+cuda11.cudnn805 requires numpy>=1.19, but you have numpy 1.18.5 which is incompatible.
    jax 0.3.14 requires numpy>=1.19, but you have numpy 1.18.5 which is incompatible.
    datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.
    albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.
    

    nvcc version es :

    nvcc: NVIDIA (R) Cuda compiler driver
    Copyright (c) 2005-2020 NVIDIA Corporation
    Built on Mon_Oct_12_20:09:46_PDT_2020
    Cuda compilation tools, release 11.1, V11.1.105
    Build cuda_11.1.TC455_06.29190527_0
    

    Any advice to be able to use it on colab ?

  • Kernel Died when export_coreml

    Kernel Died when export_coreml

    I have trouble exporting the simple model I created for classifying images of my and my cats, the code runs well through saving the model, but every time I try to export the model by "model.export_coreml('me-mimi.mlmodel')" it fails and the kernel stop (error message screenshot below). Does anyone knows why and what I can do to fix it? thanks!

    image

    import turicreate as tc import os

    data = tc.image_analysis.load_images('mimi_me', with_path = True) data['label'] = data['path'].apply(lambda path:'me' if '/me' in path else 'mimi') data.save('me-mimi.sframe') data = tc.SFrame('me-mimi.sframe/') train_set, test_set = data.random_split(0.8) model = tc.image_classifier.create(train_set, target='label') predictions = model.evaluate(test_set) model.save('me-mimi.model') model.export_coreml('me-mimi.mlmodel')

  • export_coreml operation fails using Rosetta in MacBook Pro M1 Max

    export_coreml operation fails using Rosetta in MacBook Pro M1 Max

    I have experienced an issue when I tried to export the model in coreml format getting the next error:

    illegal hardware instruction /usr/local/opt/[email protected]/bin/python3

    I'm using rosetta to execute the code, everything seems to work as expected less the coreml exportation.

    Thanks

  • ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts. datasette 0.59.1 requires httpx>=0.20, but you have httpx 0.19.0 which is incompatible. flake8 4.0.1 requires importlib-metadata<4.3; python_version <

    ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts. datasette 0.59.1 requires httpx>=0.20, but you have httpx 0.19.0 which is incompatible. flake8 4.0.1 requires importlib-metadata<4.3; python_version < "3.8", but you have importlib-metadata 4.8.3 which is incompatible.

    This is the current error I'm getting and I"m trying to create a dashboard on skills network with Plotly, any suggestions on how to resolve this so I can complete my dashboard?

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A flexible, high-performance serving system for machine learning models

XGBoost Serving This is a fork of TensorFlow Serving, extended with the support for XGBoost, alphaFM and alphaFM_softmax frameworks. For more informat

Aug 1, 2022
A library for creating Artificial Neural Networks, for use in Machine Learning and Deep Learning algorithms.
A library for creating Artificial Neural Networks, for use in Machine Learning and Deep Learning algorithms.

iNeural A library for creating Artificial Neural Networks, for use in Machine Learning and Deep Learning algorithms. What is a Neural Network? Work on

Apr 5, 2022
Deploying Deep Learning Models in C++: BERT Language Model
 Deploying Deep Learning Models in C++: BERT Language Model

This repository show the code to deploy a deep learning model serialized and running in C++ backend.

Mar 24, 2022
tutorial on how to train deep learning models with c++ and dlib.

Dlib Deep Learning tutorial on how to train deep learning models with c++ and dlib. usage git clone https://github.com/davisking/dlib.git mkdir build

Dec 21, 2021
Triton - a language and compiler for writing highly efficient custom Deep-Learning primitives.
Triton - a language and compiler for writing highly efficient custom Deep-Learning primitives.

Triton - a language and compiler for writing highly efficient custom Deep-Learning primitives.

Aug 12, 2022
Deep Learning in C Programming Language. Provides an easy way to create and train ANNs.
Deep Learning in C Programming Language. Provides an easy way to create and train ANNs.

cDNN is a Deep Learning Library written in C Programming Language. cDNN provides functions that can be used to create Artificial Neural Networks (ANN)

Apr 18, 2022
An Open Source Machine Learning Framework for Everyone
An Open Source Machine Learning Framework for Everyone

Documentation TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, a

Aug 13, 2022
Distributed machine learning platform

Veles Distributed platform for rapid Deep learning application development Consists of: Platform - https://github.com/Samsung/veles Znicz Plugin - Neu

Jul 25, 2022
An open source machine learning library for performing regression tasks using RVM technique.

Introduction neonrvm is an open source machine learning library for performing regression tasks using RVM technique. It is written in C programming la

May 31, 2022
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

Website | Documentation | Tutorials | Installation | Release Notes CatBoost is a machine learning method based on gradient boosting over decision tree

Aug 12, 2022
A lightweight C++ machine learning library for embedded electronics and robotics.

Fido Fido is an lightweight, highly modular C++ machine learning library for embedded electronics and robotics. Fido is especially suited for robotic

Jun 25, 2022
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

Light Gradient Boosting Machine LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed a

Aug 10, 2022
High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.
High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.

What is xLearn? xLearn is a high performance, easy-to-use, and scalable machine learning package that contains linear model (LR), factorization machin

Aug 1, 2022
Feature Store for Machine Learning
Feature Store for Machine Learning

Overview Feast (Feature Store) is an operational data system for managing and serving machine learning features to models in production. Please see ou

Aug 11, 2022
Machine Learning Platform for Kubernetes
Machine Learning Platform for Kubernetes

Reproduce, Automate, Scale your data science. Welcome to Polyaxon, a platform for building, training, and monitoring large scale deep learning applica

Aug 4, 2022
In-situ data analyses and machine learning with OpenFOAM and Python

PythonFOAM: In-situ data analyses with OpenFOAM and Python Using Python modules for in-situ data analytics with OpenFOAM 8. NOTE that this is NOT PyFO

Aug 5, 2022
CNStream is a streaming framework for building Cambricon machine learning pipelines
CNStream is a streaming framework for building Cambricon  machine learning pipelines

CNStream is a streaming framework for building Cambricon machine learning pipelines

Aug 4, 2022
SecMML: Secure MPC(multi-party computation) Machine Learning Framework
SecMML: Secure MPC(multi-party computation) Machine Learning Framework

SecMML 介绍 SecMML是FudanMPL(Multi-Party Computation + Machine Learning)的一个分支,是用于训练机器学习模型的高效可扩展的安全多方计算(MPC)框架,基于BGW协议实现。此框架可以应用到三个及以上参与方联合训练的场景中。目前,SecMM

Jul 14, 2022