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Production infrastructure for machine learning at scale
#### Description When deploying a new API, we only validate against the scheduled workloads on the cluster. We're not taking into consideration the situation where all of the existing APIs...
### Description https://eksctl.io/usage/cloudwatch-cluster-logging/ Consider using a retention policy on the log groups ### Motivation * Expose more logs * Remove the misleading message regarding cloudwatch logging during `cluster up`
#### Description When creating a cluster with node group names that have more than a few characters, then ARN names of the corresponding node groups are getting truncated. Is this...
#### Description Assume we have these node groups: * A CPU node group with a live instance. This can fit 10 iris-classifier replicas (which only request CPU). Max_instances is set...
#### Description The status of a Job can take up to 60 seconds to reflect the actual status of a Job because the all job statuses are updated together in...
### Version 0.25 ### Stack traces ```text ValueError: sleep length must be non-negative File "threading.py", line 864, in run self._target(*self._args, **self._kwargs) File "batch.py", line 86, in renew_message_visibility time.sleep((cur_time + interval)...
### Version 0.25 ### Stack traces ```text ClientError: An error occurred (InvalidParameterValue) when calling the DeleteMessage operation: Value for parameter ReceiptHandle is invalid. Reason: The receipt handle has... File "batch.py",...
### Description AsyncAPI currently only supports strings, JSON, and binary payloads. Multipart/form-data is a very common payload type and could be particularly useful for this API type. ### Motivation This...
### Description Allow different levels of access for different CLI users. To start, it could be just two levels: list/get or list/get + deploy/delete. #### Proposal - [ ] use...
"expected_json_schema" Also consider adding tests that the GPU version of text-generator is faster than the CPU version, and/or log regex validation
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Production infrastructure for machine learning at scale