Comments (7)
n.b.: We'd probably want to having staging.coiledhq.com use the pre-release in its default software environment, which will probably require changes to https://github.com/coiled/software-environments/.
from benchmarks.
It would be very helpful for us to have pre-releases of coiled-runtime available, either cut directly from dask main or a hand selected more recent version.
Sounds good. I figured this would be something we'd want to do at some point.
We could use this internally at coiled on our staging deployment as well to test things before promoting it to a full release
Just curious, how much activity does the staging deployment get? For example, I know there's an integration test suite -- does that run on staging?
from benchmarks.
The integ tests run on both staging and prod.
It's a lot of launching clusters with various options. No serious computations. (Closest thing is we run the taxi quickstart example.)
Currently they fail a lot. The most common reason is that we are using coiled-runtime
as the software env, which doesn't include this: dask/distributed#5982.
from benchmarks.
Thanks for the additional info @dchudz
I'll suggest we add a new GHA workflow which runs on a cron and publishes a nightly version of coiled-runtime
that uses the nightly of dask
and distributed
conda packages that are published on the dask
conda channel https://anaconda.org/dask. I suspect we can also reuse some of the logic around how those dask
and distributed
nightlies are generated here for publishing a nightly coiled-runtime
.
from benchmarks.
Sounds great, I think. After that someone could shift our GHA's in https://github.com/coiled/software-environments/ to use the nightly for staging.
In the meantime I think @shughes-uk is going to hack the default environment on staging to use latest.
from benchmarks.
In the meantime I think @shughes-uk is going to hack the default environment on staging to use latest.
Just checking in -- was a temporary hack possible? I'm thinking about prioritization and wanted to get a sense for how critical getting a pre-release process out is
from benchmarks.
@jrbourbeau yes I hacked it in. My highest priority ask now is just getting a new version of dask/distributed out and into coiled runtime, the stability difference is huge with the very minor plugin tweak!
from benchmarks.
Related Issues (20)
- [TPC-H] PySpark workloads are vulnerable to undetected worker loss HOT 9
- ⚠️ CI failed ⚠️ - Regression - TPC-H queries 12, 14, 19 HOT 2
- ⚠️ CI failed ⚠️ - ServerError - Coiled credit balance errors HOT 1
- ⚠️ CI failed ⚠️ - Regression - test_adjacent_groups [1-128MiB-p2p-disk] Duration HOT 1
- ⚠️ CI failed ⚠️ - stability/test_deadlock.py::test_repeated_merge_spill HOT 1
- Set index regression HOT 3
- ⚠️ CI failed ⚠️ - test_join_big_small / test_set_index duration regressions HOT 1
- ⚠️ CI failed ⚠️ - regressions: dataframe_cow_chain - prepreocess - q6 - q8 - set_index, write_wide_data HOT 3
- ⚠️ CI failed ⚠️ - test_basic_sum[slow-square] TimeoutError HOT 1
- ⚠️ CI failed ⚠️ - Regression: test_spilling HOT 1
- ⚠️ CI failed ⚠️ HOT 1
- optuna is failing HOT 1
- DuckDB fails with OutOfMemoryException HOT 1
- How to create a dev environment to run tpch benchmarks? HOT 4
- Difficulty generating local data HOT 3
- tpc-h query operations aren't aligned across backends HOT 1
- Add datafusion, chdb HOT 2
- Is duckdb out-of-core processing properly enabled? HOT 1
- Fair dataframe API vs API vs SQL benchmarking. HOT 7
- Make TPC-H data publicly available HOT 2
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from benchmarks.