|
|
|
|
|
|
by jiayq84
987 days ago
|
|
|
It's not only about "building a docker" but also maintaining multiple models, multiple environments and a lot of users. Imagine there is a group of engineers each needing to deploy their own models: one needs tensorflow 1.x, one needs tensorflow 2.x, one needs pytorch and one needs a very strange combination of dependencies. Trust me, things get complex very easily: https://github.com/leptonai/examples/blob/main/advanced/whis... I definitely agree that for a fixed use case, building a docker once and for all is probably the simplest and best approach. However, it quickly gets more complex and out of hand. Also the basic plan is free for independent developers. You don't need to pay more than as if you were using EC2 instances, but with the platform convenience - we definitely hope it's worth it! |
|
|