Portainer vs. Pytorch

Many developers, especially lesser experienced developers, don't have a really good background in setting up containers from the command line. Portainer is invaluable to them. Giving a UI to them gives them much more confidence and allows them to learn properties and capabilities of containers under far less stress. On the flip side of this, giving then a UI on a production system can lead to chaos...never give junior developers access to production servers.

Incentivized

Independently Invited by TrustRadius

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info

They have created Pytorch Lightening on top of Pytorch to make the life of Data Scientists easy so that they can use complex models they need with just a few lines of code, so it's becoming popular. As compared to TensorFlow(Keras), where we can create custom neural networks by just adding layers, it's slightly complicated in Pytorch.

Incentivized

Independently Invited by TrustRadius

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info

• Separating server maintenance with application development, providing a clear user interface for developers who don't want to worry about the underlying server.
• RBAC for container deployment linked to a SAML IDP, not something particularly easy in a native Docker instance but point and shoot in Portainer, allowing the use of Azure / Okta etc to provide user access.
• Image management with multiple repositories is super clear and reduces incidents

Incentivized

Invited by the Vendor

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info

• flexibility
• Clean code, close to the algorithm.
• Fast
• Handles GPUs, multiple GPUs on a single machine, CPUs, and Mac.
• Versatile, can work efficiently on text/audio/image/tabular datasets.

Incentivized

Independently Invited by TrustRadius

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info

• When setting up static IPs for a new container, having the used ones for a specific network at hand would be cool or something like a mini IPAM of some sort...
• Using the developertools to see "oh, it's a 40x or 500" when something doesn't seem to load because the ui just states "Didn't work" is kinda annoying. expandable toasts or something would be nice.

Incentivized

Invited by the Vendor

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info

• Since pythonic if developing an app with pytorch as backend the response can be substantially slow and support is less compares to Tensorflow

Incentivized

Independently Invited by TrustRadius

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info

It is an excellent tool.

Accessibility for Non-Experts: even with some people having a bit longer on-boarding it is still very simple Quick setup is insanely useful, we can get it running in 10 seconds after installing docker Portainer has once again super clean UI and is very user friendly. Deployment/monitoring and management are super easy. I can tell just from a glance if something is out of date (watching at you Watchtower not doing your job for some reason)

Incentivized

Invited by the Vendor

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info

The big advantage of PyTorch is how close it is to the algorithm. Oftentimes, it is easier to read Pytorch code than a given paper directly. I particularly like the object-oriented approach in model definition; it makes things very clean and easy to teach to software engineers.

Incentivized

Independently Invited by TrustRadius

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info

One of their staff members jumped on a video call immediately with me and led me through the problem and solution during a quick session of screen sharing. In this day and age that is above and beyond, especially when it comes to software. It took approximately 5-10 minutes to diagnose and fix, including pleasantries!

It is really easy. Just follow the documentation.

Incentivized

Invited by the Vendor

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info

Portainer takes the cup in terms of usability and features. It is also more useful for smaller deployments, whereas Kubernetes in our opinion and experience, could probably be more suited to certain other use cases. Portainer is also a fresh feel among all the preexisting container management solutions and brings positivity and a new breeze in the industry.

Pytorch is very, very simple compared to TensorFlow. Simple to install, less dependency issues, and very small learning curve. TensorFlow is very much optimised for robust deployment but very complicated to train simple models and play around with the loss functions. It needs a lot of juggling around with the documentation. The research community also prefers PyTorch, so it becomes easy to find solutions to most of the problems. Keras is very simple and good for learning ML / DL. But when going deep into research or building some product that requires a lot of tweaks and experimentation, Keras is not suitable for that. May be good for proving some hypotheses but not good for rigorous experimentation with complex models.

Incentivized

Independently Invited by TrustRadius

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info

• Increased productivity: Portainer's user-friendly interface and streamlined container management can help increase the productivity of IT teams.
• Cost savings: By simplifying container management, Portainer can help reduce the time and resources required to manage container environments, potentially leading to cost savings.

Incentivized

Invited by the Vendor

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info

• The ability to make models as never before
• Being able to control the bias of models was not done before the arrival of Pytorch in our company

Incentivized

Independently Invited by TrustRadius

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info