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I was able to use learning_phase but only in symbolic tensor mode (graph) mode:

So, at first we need to disable eager mode (this must be done right after importing tensorflow):

This function were removed in TensorFlow version 2.6. According to the keras in rstudio reference

update to

The same thing occurred to me yesterday when I used Colab. A possible reason may be that the version of opencv-python(4.1.2.30) does not match opencv-python-headless(4.5.5.62). Or the latest version 4.5.5 may have something wrong...

I uninstalled opencv-python-headless==4.5.5.62 and installed 4.1.2.30 and it fixed.

After contacting Apple Developer Technical Support (DTS) regarding this issue, they provided me with the following guidance.

Regarding "insufficient usage data available" for energy logs accessible via the Xcode organizer:

DTS indicated that they do not publish the thresholds for active users and usage logs would be expected to be present if you have more that a few thousand active users consistently on each version of your app. If your app meets this criteria and still does not show energy logs, DTS recommended opening a bug report with them.

Regarding how to collect energy log data for your app:

DTS recommended using MetricKit to get daily metric payloads. Payloads are delivered to your app every 24 hours and it is then possible to consume them and send them off device.

The instantiation of this is vey basic and can be as simple as:

All rows need the same data size, of course some values can be empty in csv.

I would propose to take a look in the RateLimiter direction. Maybe it does not work as expected, depending on the number of requests your application does over time. From the Javadoc for Ratelimiter: "It is important to note that the number of permits requested never affects the throttling of the request itself ... but it affects the throttling of the next request. I.e., if an expensive task arrives at an idle RateLimiter, it will be granted immediately, but it is the next request that will experience extra throttling, thus paying for the cost of the expensive task." Also helpful might be this discussion: github or github

I could imaginge there is some throttling adding up or other effect in the RateLimiter, i would try to play around with it and make sure this thing really works the way you want. Alternatively, consider using Spring @Scheduled to read from your queue. You might want to spice it up using embedded JMS for further goodies (message persistence etc).

There are many agents capable of saving metrics collected in k8s to remote Prometheus server outside the cluster, example Prometheus itself now support agent mode, exporter from Opentelemetry, or using managed Prometheus etc.

Unfortunately I don't know if any way to use that nice glue syntax with anything that's not on the left side of a :=. That's there the magic happens. You can get something to work if you take care of the explicity conversion to sumbol your self and do the string building manually. It's not pretty, but this works

This seems to be a bug in Tensorflow 2.7 when using model.save combined with the parameter save_format="tf", which is set by default. The layers RandomFlip, RandomRotation, RandomZoom, and RandomContrast are causing the problems, since they are not serializable. Interestingly, the Rescaling layer can be saved without any problems. A workaround would be to simply save your model with the older Keras H5 format model.save("test", save_format='h5'):