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Apache SINGA is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.
Deep Learning Software
Apache SINGA 1.1.0 [MD5] [KEYS] Release Notes 1.1.0 New features and major updates, Create Docker images (CPU and GPU versions) Create Amazon AMI for SINGA (CPU version) Integrate with Jenkins for automatically generating Wheel and Debian packages (for installation), and updating the website. Enhance the FeedFowardNet, e.g., multiple inputs and verbose mode for debugging Add Concat and Slice layers Extend CrossEntropyLoss to accept instance with multiple labels Add image_tool.py with image augmentation methods Support model loading and saving via the Snapshot API Compile SINGA source on Windows Compile mandatory dependent libraries together with SINGA code Enable Java binding (basic) for SINGA Add version ID in checkpointing files Add Rafiki toolkit for providing RESTFul APIs Add examples pretrained from Caffe, including GoogleNet
Small (<50 employees), Medium (50 to 1000 Enterprise (>1001 employees)
Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision making process have stabilized in a manner consistent with other successful ASF projects.
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Apache SINGA is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision making process have stabilized in a manner consistent with other successful ASF projects. While incubation status is not necessarily a reflection of the completeness or stability of the code, it does indicate that the project has yet to be fully endorsed by the ASF.SINGA’s software stack includes three major components, namely, core, IO and model. Figure 1 illustrates these components together with the hardware. The core component provides memory management and tensor operations; IO has classes for reading (and writing) data from (to) disk and network; The model component provides data structures and algorithms for machine learning models, e.g., layers for neural network models, optimizers/initializer/metric/loss for general machine learning models. Tensor and Device are two core abstractions in SINGA. Tensor class represents a multi-dimensional array, which stores model variables and provides linear algebra operations for machine learning algorithms, including matrix multiplication and random functions. Each tensor instance (i.e. a tensor) is allocated on a Device instance. Each Device instance (i.e. a device) is created against one hardware device, e.g. a GPU card or a CPU core. Devices manage the memory of tensors and execute tensor operations on its execution units, e.g. CPU threads or CUDA streams. With the Tensor abstraction, SINGA would be able to run a wide range of models, including deep learning models and other traditional machine learning models.
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