Currently, all buildkite runs report per default. Instead, we only want to report when running scheduled builds or when specifically overriding this behavior.
Infra errors are tackled with concurrency groups. Thus we can disable old mitigation methods like automatic infra retry for now.
We keep the script as it does other logic (e.g. checkout local test branch) and infra retry can be enabled via env variable if needed.
This PR improves broken k8s tests.
Use exponential backoff on the unstable HTTP path (getting job status sometimes has broken connection from the server. I couldn't really find the relevant logs to figure out why this is happening, unfortunately).
Fix benchmark tests resource leak check. The existing one was broken because the job submission uses 0.001 node IP resource, which means the cluster_resources can never be the same as available resources. I fixed the issue by not checking node IP resources
K8s infra doesn't support instances < 8 CPUs. I used m5.2xlarge instead of xlarge. It will increase the cost a bit, but it wouldn't be very big.
Fixes potential error if function not found in azure sdk when deploying ray cluster on azure
Adds additional python package needed to deploy ray cluster on azure in docs
Co-authored-by: Scott Graham <scgraham@microsoft.com>
Of all smoke test arguments, frequency is the only required one, so we should check for it. Additionally, not all fields should be able to be overwritten (e.g. legacy or name), so we enforce this as well.
It's really annoying to deal with parameter/argument conflicts. This is even frustrating when we merge code from the community to Ant's internal code base with hundreds of conflicts caused by parameters/arguments.
In this PR, I updated the clang-format style to make parameters/arguments stay on different lines if they can't fit into a single line.
There are several benefits:
* Conflict resolving is easier.
* Less potential human mistakes when resolving conflicts.
* Git history and Git blame are more straightforward.
* Better readability.
* Align with the new Python format style.
* Fix the normal task resources at GCS
* Fix comments
* Leave a TODO
* Bring back a UT
* consider object memory
* Fix
Co-authored-by: Chong-Li <lc300133@antgroup.com>
Previously, placement group had suboptimal bin-packing resulting in unexpected placement group stalls for users.
The root cause is lack of implementation for sorting of pg bundles by resource priority and size.
This PR implements a naive priority mechanism for bundles that can be improved upon (and even config by user in the future) in the GCS resource scheduler.
The behaviour is to schedule: "GPU" first, custom resources in int64_t order next, and finally, memory and then "CPU" last.
Run benchmark tests on k8s as well.
Note that until k8s cluster stability is confirmed, we will run the same tests twice at AWS and k8s. Once all benchmark tests look stable, we will start full migration
These changes add Dataset Read API support for (1) specifying custom block metadata provider callbacks, and (2) skipping path expansion. When paired with a custom block metadata provider that maintains an in-memory cache of BlockMetadata for each input file path, these changes reduced average S3-based dataset read times for production [Redshift Manifests](https://docs.aws.amazon.com/redshift/latest/dg/loading-data-files-using-manifest.html) stored in Amazon's internal data catalog by over 90%. A simple ParquetDatasource benchmark reading 144MM records across 100 ~70MiB (on-disk) Parquet files stored in S3 showed an ~75% reduction in read latency (from 4.62 seconds to 1.18 seconds on 2 r5n.8xlarge EC2 nodes).
E.g. long running tests run on small clusters (often 8 CPUs) but block other jobs for a long time. We should thus add more granularity to the concurrency groups.
Additionally, limits have been slightly adjusted to make more sense (e.g. 8 GPUs are now small-gpu, 9+ GPUs large-gpu, instead of 7 for small-gpu and 8 for large-gpu).
The `schema_to_deployment()` function preserve unset fields with unexpected default argument types. This change excludes unset fields in that function and also changes the dictionaries' default values to empty dicts.
Buffering writes to AWS S3 is highly recommended to maximize throughput. Reducing the number of remote I/O requests can make spilling to remote storages as effective as spilling locally.
In a test where 512GB of objects were created and spilled, varying just the buffer size while spilling to a S3 bucket resulted in the following runtimes.
Buffer Size | Runtime (s)
-- | --
Default | 3221.865916
256KB | 1758.885839
1MB | 748.226089
10MB | 526.406466
100MB | 494.830513
Based on these results, a default buffer size of 1MB has been added. This is the minimum buffer size used by AWS Kinesis Firehose, a streaming service for S3. On systems with larger availability, it is good to configure a larger buffer size.
For processes that reach the throughput limits provided by S3, we can remove that bottleneck by supporting more prefixes/buckets. These impacts are less noticeable as the performance gains from using a large buffer prevent us from reaching a bottleneck. The following runtimes were achieved by spilling 512GB with a 1MB buffer and varying prefixes.
Prefixes | Runtime (s)
-- | --
1 | 748.226089
3 | 527.658646
10 | 516.010742
Together these changes enable faster large-scale object spilling.
Co-authored-by: Ubuntu <ubuntu@ip-172-31-54-240.us-west-2.compute.internal>
Next move of #19220. This pr replace unordered_map to flat_hash_map in most GCS code and some util & common modules.
The placement group part, which exposes user interfaces in Java/Python, is exclusive as it's a little bit complicated.
The follow-up PRs would be migrating in core worker, placement group and others.
This PR is part of resource reporting refactoring. In this PR ray syncer is moved from gcs_resource_manager to gcs_placement_group_scheduler. With this one, gcs_resource_manager is totally decoupled from resource broadcasting.
Mainly the following things:
- This PR deletes the proto cache on RuntimeEnv, ensuring that the user's modification of RuntimeEnv can take effect in the Proto message.
- validate whole runtime env when serialize runtime_env.
- overload method `__setitem__` to parse and validate field when it has to modify.
Separate out the conversion of pandas dataframe to torch tensor in a utility function so that the same logic can be used in other places in Ray ML (for example during inference).
