What kind of targets is crystal used on? Strictly x86_64 linux? EDIT: Took a little digging but I found it [1] (and yes it's primarily x86_64 linux+macOS) [1] https://github.com/crystal-lang/crystal/wiki/Platform-Suppor...
Windows will probably be done in a year, but it's hard to say. Hardest part is finding people who want to work on windows support xD
No kidding. (I wasn't complaining btw nor was I even focused on OS support, I was curious about which arches people use it on).
What are the popular use cases - either ones driving current development or ones where it's best suited?
We have a bit of a problem like what you mention. Our backend/app is in Java but the DS/ML team generally works in python. The ML team basically doesn't ship production code. Here are the artifacts we produce: 1. For new models we often build a demo endpoints/glue code written in python/flask that can be compared against the prod output in dev/psup. 2. Deep learning models (much of what I do personally): saved in TF…
BentoML( https://github.com/bentoml/BentoML ) may help you with the process of building endpoints with both Deep learning models and logistic regression/tree models, and it automatically helps you to containerize the API server into docker image that's ready for production deployment. It also provides OpenAPI spec for your API endpoint, which allows you to generate API client in Java, for your backend/app teams.
FYI. One way to automatically generate API clients is to use OpenAPI Generator (https://github.com/OpenAPITools/openapi-generator), which is free, open-source and supports 30+ programming languages.
Disclosure: I'm the top contributor to the project.
BentoML( https://github.com/bentoml/BentoML ) may help you with the process of building endpoints with both Deep learning models and logistic regression/tree models, and it automatically helps you to containerize the API server into docker image that's ready for production deployment. It also provides OpenAPI spec for your API endpoint, which allows you to generate API client in Java, for your backend/app teams.
FYI. One way to automatically generate API clients is to use OpenAPI Generator ( https://github.com/OpenAPITools/openapi-generator ), which is free, open-source and supports 30+ programming languages. Disclosure: I'm the top contributor to the project.
So we've been using swagger, is there some comparison between OpenAPITools and swagger code gen?
Hi Aaron,
I was having similar issues. My main problem was integration of different programming languages and tools under same roof.
So I started with my own ML platform. Currently C# is supported, but there are other ones in the roadmap (Python, R, Nodejs...)
You can check it here : https://github.com/Zenodys/ZenDevTool
What didn't work: Shipping pickled models to other teams. Deploying Sagemaker endpoints (too costly). Requiring editing of config files to deploy endpoints. What did work: Shipping http endpoints. Deriving api documentation from model docstrings. Deploying lambdas (less costly than Sagemaker endpoints). Writing a ~150 line python script to pickle the model, save a requirements.txt, some api metadata, and test input/o…
Custom Unreal Engine simulator, simulating agents with NVidia Physx and publishing sensors through GStreamer. GStreamer has sinks and sources for ROS, and tensorflow elements for inferencing. We package this all into NVidia Docker for scalable simulations. Setup is similar for training and inference. The core framework is a streaming engine with stream combinators that enable reasoning about spatio-temporal data stre…
This sounds pretty advanced! What lab do you work in?
Custom Unreal Engine simulator, simulating agents with NVidia Physx and publishing sensors through GStreamer. GStreamer has sinks and sources for ROS, and tensorflow elements for inferencing. We package this all into NVidia Docker for scalable simulations. Setup is similar for training and inference. The core framework is a streaming engine with stream combinators that enable reasoning about spatio-temporal data stre…
How well does the training in the simulator transfer over to real world environments? Do the models require fine tuning on real life data, or do they immediately work? I would imagine they would get confused by things like wheel/tracks slipping a bit on the ground versus in the Unreal simulation.
Traditional simulations don’t translate well. Usually one has to choose a fidelity level; controls, navigation, exploration, mission, etc… We are having more success with models that can tolerate variable dynamics. Simulations don't need to be realistic, only consistent. We then reinforce models with an infinite number of simulation universes and rules, such that reality is just another sample. We don’t have the resources for end-to-end training, and our initial tests did not yield good results. But chaining smaller specific-purpose networks within traditional control and planning systems is looking really good.
Custom Unreal Engine simulator, simulating agents with NVidia Physx and publishing sensors through GStreamer. GStreamer has sinks and sources for ROS, and tensorflow elements for inferencing. We package this all into NVidia Docker for scalable simulations. Setup is similar for training and inference. The core framework is a streaming engine with stream combinators that enable reasoning about spatio-temporal data stre…
This is really cool, what are you training?
Quadcopters, railway vehicles and perching devices.