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I haven't tried it yet, but based on their doc it seems like after setting the host in the juice.cfg, you basically just need to run `juicify [application path]`: https://github.com/Juice-Labs/Juice-Labs/wiki/Juice-for-Wind...
Inference servers essentially turn a model running on CPU and/or GPU hardware into a microservice.
Many of them support the kserve API standard[0] that supports everything from model loading/unloading to (of course) inference requests across models, versions, frameworks, etc.
So in the case of Triton[1] you can have any number of different TensorFlow/torch/tensorrt/onnx/etc models, versions, and variants. You can have one or more Triton instances running on hardware with access to local GPUs (for this example). Then you can put standard REST and or grpc load balancers (or whatever you want) in front of them, hit them via another API, whatever.
Now all your applications need to do to perform inference is do an HTTP POST (or use a client[2]) for model input, Triton runs it on a GPU (or CPU if you want), and you get back whatever the model output is.
Not a sales pitch for Triton but it (like some others) can also do things like dynamic batching with QoS parameters, automated model profiling and performance optimization[3], really granular control over resources, response caching, python middleware for application/biz logic, accelerated media processing with Nvidia DALI, all kinds of stuff.
[0] - https://github.com/kserve/kserve
[1] - https://github.com/triton-inference-server/server
[2] - https://github.com/triton-inference-server/client
[3] - https://github.com/triton-inference-server/model_analyzer
Inference servers essentially turn a model running on CPU and/or GPU hardware into a microservice.
Many of them support the kserve API standard[0] that supports everything from model loading/unloading to (of course) inference requests across models, versions, frameworks, etc.
So in the case of Triton[1] you can have any number of different TensorFlow/torch/tensorrt/onnx/etc models, versions, and variants. You can have one or more Triton instances running on hardware with access to local GPUs (for this example). Then you can put standard REST and or grpc load balancers (or whatever you want) in front of them, hit them via another API, whatever.
Now all your applications need to do to perform inference is do an HTTP POST (or use a client[2]) for model input, Triton runs it on a GPU (or CPU if you want), and you get back whatever the model output is.
Not a sales pitch for Triton but it (like some others) can also do things like dynamic batching with QoS parameters, automated model profiling and performance optimization[3], really granular control over resources, response caching, python middleware for application/biz logic, accelerated media processing with Nvidia DALI, all kinds of stuff.
[0] - https://github.com/kserve/kserve
[1] - https://github.com/triton-inference-server/server
[2] - https://github.com/triton-inference-server/client
[3] - https://github.com/triton-inference-server/model_analyzer
Inference servers essentially turn a model running on CPU and/or GPU hardware into a microservice.
Many of them support the kserve API standard[0] that supports everything from model loading/unloading to (of course) inference requests across models, versions, frameworks, etc.
So in the case of Triton[1] you can have any number of different TensorFlow/torch/tensorrt/onnx/etc models, versions, and variants. You can have one or more Triton instances running on hardware with access to local GPUs (for this example). Then you can put standard REST and or grpc load balancers (or whatever you want) in front of them, hit them via another API, whatever.
Now all your applications need to do to perform inference is do an HTTP POST (or use a client[2]) for model input, Triton runs it on a GPU (or CPU if you want), and you get back whatever the model output is.
Not a sales pitch for Triton but it (like some others) can also do things like dynamic batching with QoS parameters, automated model profiling and performance optimization[3], really granular control over resources, response caching, python middleware for application/biz logic, accelerated media processing with Nvidia DALI, all kinds of stuff.
[0] - https://github.com/kserve/kserve
[1] - https://github.com/triton-inference-server/server
[2] - https://github.com/triton-inference-server/client
[3] - https://github.com/triton-inference-server/model_analyzer
Inference servers essentially turn a model running on CPU and/or GPU hardware into a microservice.
Many of them support the kserve API standard[0] that supports everything from model loading/unloading to (of course) inference requests across models, versions, frameworks, etc.
So in the case of Triton[1] you can have any number of different TensorFlow/torch/tensorrt/onnx/etc models, versions, and variants. You can have one or more Triton instances running on hardware with access to local GPUs (for this example). Then you can put standard REST and or grpc load balancers (or whatever you want) in front of them, hit them via another API, whatever.
Now all your applications need to do to perform inference is do an HTTP POST (or use a client[2]) for model input, Triton runs it on a GPU (or CPU if you want), and you get back whatever the model output is.
Not a sales pitch for Triton but it (like some others) can also do things like dynamic batching with QoS parameters, automated model profiling and performance optimization[3], really granular control over resources, response caching, python middleware for application/biz logic, accelerated media processing with Nvidia DALI, all kinds of stuff.
[0] - https://github.com/kserve/kserve
[1] - https://github.com/triton-inference-server/server
[2] - https://github.com/triton-inference-server/client
[3] - https://github.com/triton-inference-server/model_analyzer