Thingsboard VS pytorch-lightning

Until recently I was still using TB v3.2.1, now I've set about updating the installation to the latest version. I proceeded as described at https://github.com/thingsboard/thingsboard/tree/master/docker:

You might find something more attuned to that use case to be more helpful out of the box (like thingsboard.io) but if you're committed to django, set up an API endpoint to receive json updates ('events') sent from the arduino.

Introduction：As a 100% open source IoT platform that can be hosted as a SaaS or PaaS solution, Thingsboard can provide device management, data collection, processing and visualization for your IoT project. The standard protocols that provide device connectivity such as MQTT, CoAP, and HTTP are all available on ThingsBoard. In addition, it supports cloud and local deployment and provides more than 30 customizable components that allow you to build end-user custom dashboards for most IoT cases. GitHub: https://github.com/thingsboardThingsboard: http://thingsboard.io/ Features:

Then host a ThingsBoard server and use the HTTP API to push data from the device. You can send alerts with Pushover.

**Source** [thingsboard (a foreign iot platform)](https://thingsboard.io/)

ThingsBoard is an open-source IoT platform for data collection, processing, visualization and device management. It supports device connectivity via protocols, such as MQTT, CoAP and HTTP, and supports both cloud and private deployments. Deliver, monitor and control your IoT entities in a secure way using rich server-side APIs that define the relationships between your devices, assets, customers, or any other entities. Collect and store telemetry data in a scalable and fault-tolerant manner, visualize your data with built-in or custom widgets and flexible dashboards, and share the Dashboard interface with your customers.

The company I work for uses https://thingsboard.io and the team from Ukraine that built it are awesome and the company is still responding even though they are going through this hell of a war.

def on_validation_end(self, trainer: Trainer, pl_module: LightningModule) -> None: # Trainer calls `on_validation_end` for sanity check. Therefore, it is necessary to avoid # calling `trial.report` multiple times at epoch 0. For more details, see # https://github.com/PyTorchLightning/pytorch-lightning/issues/1391. if trainer.sanity_checking: return

Under the hood, the ModelWrapper object will create a ML model based on the config (so far, an XGBoost model and a PyTorch Lightning model). Each of those will have a wrapper that conducts training and evaluation (since from my understanding of Lightning, Trainers are required to be outside of the class). In lack of a better name, I call these wrappers Fitters. For uniformity, I thought about adding a common interface IFitter, which is inherited by all model wrappers as outlined below.

Join their Slack to ask the community questions and check out the GitHub here.

Pytorch lightning benchmarks against pytorch on every PR (benchmarks to make sure that it is mot slower.

There is already a ton of momentum around automating ML workflows. I would suggest you contribute to a preexisting project like, for instance, PyTorch Lightning or fast.ai.

Is the problem the sheer number of options, or the fact that they are all together in one place? Would it be better if they were organized into the different trainer entrypoints (fit, validate, ...)? If that is the case, there was an RFC proposing this which you might find interesting, feel free to drop by and comment on the issue: https://github.com/PyTorchLightning/pytorch-lightning/issues/10444

I wanted to make a quick performance comparison between the GPU (Tesla K80) and TPU (v2-8) available in Google Colab with PyTorch. To do so quickly, I used an MNIST example from pytorch-lightning that trains a simple CNN.

We've noticed GPU 0 on our 3 GPU system is sometimes idle (which would explain performance differences). However its unclear to us why that may be. Similar to this issue

If you want a good example for reference, take a look at Pytorch Lightning's readme (https://github.com/PyTorchLightning/pytorch-lightning) It answers the 3 questions of "what is this", "why should I care", and "how do i use it" almost instantly