[BUG] cuml/forest_inference_demo.ipynb notebook test failures #749
Description
Describe the bug
In RAPIDS 25.06 builds, the cuml/forest_inference_demo.ipynb notebook is failing like this:
...
File fil.pyx:693, in cuml.experimental.fil.fil.ForestInference.load()
TypeError: load() takes exactly 2 positional arguments (1 given)
...
NameError: name 'fil_model' is not defined
...
NameError: name 'fil_preds' is not defined
...
File fil.pyx:693, in cuml.experimental.fil.fil.ForestInference.load()
TypeError: load() takes exactly 2 positional arguments (1 given)
...
KeyError: 'fil_model'
...
NameError: name 'fil_inference_time' is not defined
full traceback (click me)
Testing cuml/forest_inference_demo.ipynb
TypeError ---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In[9], line 1
----> 1 fil_model = ForestInference.load(
2 filename=model_path,
3 algo='BATCH_TREE_REORG',
4 output_class=True,
5 threshold=0.50,
6 model_type='xgboost_ubj'
7 )
File fil.pyx:693, in cuml.experimental.fil.fil.ForestInference.load()
TypeError: load() takes exactly 2 positional arguments (1 given)
NameError ---------------------------------------------------------------------------
NameError Traceback (most recent call last)
File <timed exec>:2
NameError: name 'fil_model' is not defined
NameError ---------------------------------------------------------------------------
NameError Traceback (most recent call last)
Cell In[11], line 2
1 print("The shape of predictions obtained from xgboost : ", (trained_model_preds).shape)
----> 2 print("The shape of predictions obtained from FIL : ", (fil_preds).shape)
3 print("Are the predictions for xgboost and FIL the same : ", cupy.allclose(trained_model_preds, fil_preds))
NameError: name 'fil_preds' is not defined
TypeError ---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
File <timed eval>:1
File /opt/conda/lib/python3.10/site-packages/distributed/client.py:3192, in Client.run(self, function, workers, wait, nanny, on_error, *args, **kwargs)
3109 def run(
3110 self,
3111 function,
(...)
3117 **kwargs,
3118 ):
3119 """
3120 Run a function on all workers outside of task scheduling system
3121
(...)
3[190](https://github.com/rapidsai/docker/actions/runs/14575734472/job/40882194854?pr=748#step:9:191) >>> c.run(print_state, wait=False) # doctest: +SKIP
3191 """
-> 3192 return self.sync(
3193 self._run,
3194 function,
3195 *args,
3196 workers=workers,
3197 wait=wait,
3198 nanny=nanny,
3199 on_error=on_error,
3200 **kwargs,
3201 )
File /opt/conda/lib/python3.10/site-packages/distributed/utils.py:363, in SyncMethodMixin.sync(self, func, asynchronous, callback_timeout, *args, **kwargs)
361 return future
362 else:
--> 363 return sync(
364 self.loop, func, *args, callback_timeout=callback_timeout, **kwargs
365 )
File /opt/conda/lib/python3.10/site-packages/distributed/utils.py:439, in sync(loop, func, callback_timeout, *args, **kwargs)
436 wait(10)
438 if error is not None:
--> 439 raise error
440 else:
441 return result
File /opt/conda/lib/python3.10/site-packages/distributed/utils.py:413, in sync.<locals>.f()
411 awaitable = wait_for(awaitable, timeout)
412 future = asyncio.ensure_future(awaitable)
--> 413 result = yield future
414 except Exception as exception:
415 error = exception
File /opt/conda/lib/python3.10/site-packages/tornado/gen.py:766, in Runner.run(self)
764 try:
765 try:
--> 766 value = future.result()
767 except Exception as e:
768 # Save the exception for later. It's important that
769 # gen.throw() not be called inside this try/except block
770 # because that makes sys.exc_info behave unexpectedly.
771 exc: Optional[Exception] = e
File /opt/conda/lib/python3.10/site-packages/distributed/client.py:3097, in Client._run(self, function, nanny, workers, wait, on_error, *args, **kwargs)
3094 continue
3096 if on_error == "raise":
-> 3097 raise exc
3098 elif on_error == "return":
3099 results[key] = exc
Cell In[18], line 2, in worker_init()
1 def worker_init(dask_worker, model_file='xgb.model'):
----> 2 dask_worker.data["fil_model"] = ForestInference.load(
3 filename=model_file,
4 algo='BATCH_TREE_REORG',
5 output_class=True,
6 threshold=0.50,
7 model_type='xgboost_ubj'
8 )
File fil.pyx:693, in cuml.experimental.fil.fil.ForestInference.load()
TypeError: load() takes exactly 2 positional arguments (1 given)
KeyError ---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
Cell In[22], line 2
1 tic = time.perf_counter()
----> 2 distributed_predictions.compute()
3 toc = time.perf_counter()
5 fil_inference_time = toc-tic
File /opt/conda/lib/python3.10/site-packages/dask/dataframe/dask_expr/_collection.py:489, in FrameBase.compute(self, fuse, concatenate, **kwargs)
487 out = out.repartition(npartitions=1)
488 out = out.optimize(fuse=fuse)
--> 489 return DaskMethodsMixin.compute(out, **kwargs)
File /opt/conda/lib/python3.10/site-packages/dask/base.py:374, in DaskMethodsMixin.compute(self, **kwargs)
350 def compute(self, **kwargs):
351 """Compute this dask collection
352
353 This turns a lazy Dask collection into its in-memory equivalent.
(...)
372 dask.compute
373 """
--> 374 (result,) = compute(self, traverse=False, **kwargs)
375 return result
File /opt/conda/lib/python3.10/site-packages/dask_cuda/explicit_comms/dataframe/shuffle.py:665, in _patched_compute(traverse, optimize_graph, scheduler, get, *args, **kwargs)
662 return repack(results)
664 else:
--> 665 return _base_compute(
666 *args,
667 traverse=traverse,
668 optimize_graph=optimize_graph,
669 scheduler=scheduler,
670 get=get,
671 **kwargs,
672 )
File /opt/conda/lib/python3.10/site-packages/dask/base.py:662, in compute(traverse, optimize_graph, scheduler, get, *args, **kwargs)
659 postcomputes.append(x.__dask_postcompute__())
661 with shorten_traceback():
--> 662 results = schedule(dsk, keys, **kwargs)
664 return repack([f(r, *a) for r, (f, a) in zip(results, postcomputes)])
Cell In[20], line 3, in predict()
1 def predict(input_df):
2 worker = get_worker()
----> 3 return worker.data["fil_model"].predict(input_df)
File /opt/conda/lib/python3.10/site-packages/dask_cuda/device_host_file.py:273, in __getitem__()
[271](https://github.com/rapidsai/docker/actions/runs/14575734472/job/40882194854?pr=748#step:9:272) elif key in self.host_buffer:
[272](https://github.com/rapidsai/docker/actions/runs/14575734472/job/40882194854?pr=748#step:9:273) return self.host_buffer[key]
--> [273](https://github.com/rapidsai/docker/actions/runs/14575734472/job/40882194854?pr=748#step:9:274) raise KeyError(key)
KeyError: 'fil_model'
NameError ---------------------------------------------------------------------------
NameError Traceback (most recent call last)
Cell In[23], line 2
1 total_samples = len(df)
----> 2 print(f' {total_samples:,} inferences in {fil_inference_time:.5f} seconds'
3 f' -- {int(total_samples/fil_inference_time):,} inferences per second ')
NameError: name 'fil_inference_time' is not defined
build: https://github.com/rapidsai/docker/actions/runs/14575734472/job/40882194854?pr=748
Steps/Code to reproduce bug
See any recent CI workflow here running the notebook tests.
Expected behavior
All notebooks should pass testing.
Environment details (please complete the following information):
See recent CI jobs.
For example: https://github.com/rapidsai/docker/actions/runs/14538359125/job/40791268880?pr=747#step:9:516
logs showing conda installs (click me)
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#12 37.43 + libhwy 1.2.0 hf40a0c7_0 conda-forge 1MB
#12 37.43 + libjpeg-turbo 3.0.0 hd590300_1 conda-forge 619kB
#12 37.43 + libjxl 0.11.1 h7b0646d_1 conda-forge 2MB
#12 37.43 + libkml 1.3.0 hf539b9f_1021 conda-forge 402kB
#12 37.43 + libkvikio 25.6.0a17 cuda11_250418_9f143867 rapidsai-nightly 306kB
#12 37.43 + liblapack 3.9.0 31_h7ac8fdf_openblas conda-forge 17kB
#12 37.43 + libllvm14 14.0.6 hcd5def8_4 conda-forge 31MB
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#12 37.43 + libntlm 1.8 hb9d3cd8_0 conda-forge 33kB
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#12 37.43 + libprotobuf 5.29.3 h501fc15_0 conda-forge 3MB
#12 37.43 + libraft 25.6.0a25 cuda11_250418_8f79e34a rapidsai-nightly 3MB
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#12 37.43 + libraft-headers-only 25.6.0a25 cuda11_250418_8f79e34a rapidsai-nightly 2MB
#12 37.43 + librdkafka 2.8.0 h2e2c4f7_0 conda-forge 18MB
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#12 37.43 + librmm 25.6.0a25 cuda11_250418_c7a33143 rapidsai-nightly 1MB
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#12 37.43 + libthrift 0.21.0 h0e7cc3e_0 conda-forge 426kB
#12 37.43 + libtiff 4.7.0 hd9ff[511](https://github.com/rapidsai/docker/actions/runs/14538359125/job/40791268880?pr=747#step:9:517)_3 conda-forge 428kB
#12 37.43 + libucxx 0.44.0a11 cuda11_250418_7ebd1200 rapidsai-nightly 298kB
#12 37.43 + libudev1 257.4 hbe16f8c_1 conda-forge 144kB
#12 37.43 + libutf8proc 2.10.0 h4c51ac1_0 conda-forge 83kB
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#12 37.43 + libxcb 1.17.0 h8a09558_0 conda-forge 396kB
#12 37.43 + libxgboost 2.1.4 rapidsai_hb8415e6_4 rapidsai-nightly 100MB
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#12 37.43 + mistune 3.1.3 pyh29332c3_0 conda-forge 73kB
#12 37.43 + msgpack-python 1.1.0 py310h3788b33_0 conda-forge 98kB
#12 37.43 + multidict 6.4.3 py310h89163eb_0 conda-forge 80kB
#12 37.43 + multipledispatch 0.6.0 pyhd8ed1ab_1 conda-forge 17kB
#12 37.43 + munkres 1.1.4 pyh9f0ad1d_0 conda-forge 12kB
#12 37.43 + narwhals 1.35.0 pyh29332c3_0 conda-forge 205kB
#12 37.43 + nbclient 0.10.2 pyhd8ed1ab_0 conda-forge 28kB
#12 37.43 + nbconvert-core 7.16.6 pyh29332c3_0 conda-forge 201kB
#12 37.43 + nbformat 5.10.4 pyhd8ed1ab_1 conda-forge 101kB
#12 37.43 + nccl 2.26.2.1 h03a54cd_1 conda-forge 132MB
#12 37.43 + networkx 3.4.2 pyh267e887_2 conda-forge 1MB
#12 37.43 + nodejs 22.13.0 hf235a45_0 conda-forge 22MB
#12 37.43 + numba 0.60.0 py310h5dc88bb_0 conda-forge 4MB
#12 37.43 + numba-cuda 0.4.0 pyh267e887_0 conda-forge 332kB
#12 37.43 + numpy 2.0.2 py310hd6e36ab_1 conda-forge 8MB
#12 37.43 + nvcomp 4.2.0.11 hf3d1f9a_1 conda-forge 20MB
#12 37.43 + nvidia-ml-py 12.570.86 pyhd8ed1ab_0 conda-forge 44kB
#12 37.43 + nvtx 0.2.11 py310ha75aee5_0 conda-forge 96kB
#12 37.43 + nx-cugraph 25.6.0a14 py310_250418_9f6e317e rapidsai-nightly 208kB
#12 37.43 + openjpeg 2.5.3 h5fbd93e_0 conda-forge 343kB
#12 37.43 + orc 2.1.1 h17f744e_1 conda-forge 1MB
#12 37.43 + overrides 7.7.0 pyhd8ed1ab_1 conda-forge 30kB
#12 37.43 + pandas 2.2.3 py310h5eaa309_3 conda-forge 13MB
#12 37.43 + pandocfilters 1.5.0 pyhd8ed1ab_0 conda-forge 12kB
#12 37.43 + panel 1.6.2 pyhd8ed1ab_0 conda-forge 22MB
#12 37.43 + param 2.2.0 pyhd8ed1ab_0 conda-forge 105kB
#12 37.43 + parso 0.8.4 pyhd8ed1ab_1 conda-forge 75kB
#12 37.43 + partd 1.4.2 pyhd8ed1ab_0 conda-forge 21kB
#12 37.43 + pcre2 10.44 hba22ea6_2 conda-forge 952kB
#12 37.43 + pexpect 4.9.0 pyhd8ed1ab_1 conda-forge 54kB
#12 37.43 + pickleshare 0.7.5 pyhd8ed1ab_1004 conda-forge 12kB
#12 37.43 + pillow 11.1.0 py310h7e6dc6c_0 conda-forge 42MB
#12 37.43 + pkgutil-resolve-name 1.3.10 pyhd8ed1ab_2 conda-forge 11kB
#12 37.43 + polars 1.26.0 py310hc556931_0 conda-forge 27MB
#12 37.43 + proj 9.6.0 h0054346_1 conda-forge 3MB
#12 37.43 + prometheus-cpp 1.3.0 ha5d0236_0 conda-forge 200kB
#12 37.43 + prometheus_client 0.21.1 pyhd8ed1ab_0 conda-forge 49kB
#12 37.43 + prompt-toolkit 3.0.51 pyha770c72_0 conda-forge 272kB
#12 37.43 + propcache 0.3.1 py310h89163eb_0 conda-forge 54kB
#12 37.43 + psutil 7.0.0 py310ha75aee5_0 conda-forge 354kB
#12 37.43 + pthread-stubs 0.4 hb9d3cd8_1002 conda-forge 8kB
#12 37.43 + ptxcompiler 0.8.1 py310hda4ad70_4 conda-forge 8MB
#12 37.43 + ptyprocess 0.7.0 pyhd8ed1ab_1 conda-forge 19kB
#12 37.43 + pure_eval 0.2.3 pyhd8ed1ab_1 conda-forge 17kB
#12 37.43 + py-xgboost 2.1.4 rapidsai_pyh35aab83_4 rapidsai-nightly 137kB
#12 37.43 + pyarrow 19.0.1 py310hff[520](https://github.com/rapidsai/docker/actions/runs/14538359125/job/40791268880?pr=747#step:9:526)83_0 conda-forge 25kB
#12 37.43 + pyarrow-core 19.0.1 py310hac404ae_0_cpu conda-forge 5MB
#12 37.43 + pyct 0.5.0 pyhd8ed1ab_1 conda-forge 20kB
#12 37.43 + pygments 2.19.1 pyhd8ed1ab_0 conda-forge 889kB
#12 37.43 + pylibcudf 25.6.0a172 cuda11_py310_250418_19162047 rapidsai-nightly 4MB
#12 37.43 + pylibcugraph 25.6.0a43 cuda11_py310_250418_9e1211ac rapidsai-nightly 760kB
#12 37.43 + pylibraft 25.6.0a25 cuda11_py310_250418_8f79e34a rapidsai-nightly 256kB
#12 37.43 + pynvml 12.0.0 pyhd8ed1ab_0 conda-forge 26kB
#12 37.43 + pyogrio 0.10.0 py310h0aed7a2_1 conda-forge 610kB
#12 37.43 + pyparsing 3.2.3 pyhd8ed1ab_1 conda-forge 96kB
#12 37.43 + pyproj 3.7.1 py310h71d0299_1 conda-forge [536](https://github.com/rapidsai/docker/actions/runs/14538359125/job/40791268880?pr=747#step:9:542)kB
#12 37.43 + python-confluent-kafka 2.8.0 py310ha75aee5_0 conda-forge 272kB
#12 37.43 + python-dateutil 2.9.0.post0 pyhff2d567_1 conda-forge 223kB
#12 37.43 + python-fastjsonschema 2.21.1 pyhd8ed1ab_0 conda-forge 226kB
#12 37.43 + python-json-logger 2.0.7 pyhd8ed1ab_0 conda-forge 13kB
#12 37.43 + python-tzdata 2025.2 pyhd8ed1ab_0 conda-forge 144kB
#12 37.43 + pytz 2025.2 pyhd8ed1ab_0 conda-forge 189kB
#12 37.43 + pyviz_comms 3.0.4 pyhd8ed1ab_1 conda-forge 49kB
#12 37.43 + pywavelets 1.8.0 py310hf462985_0 conda-forge 4MB
#12 37.43 + pyyaml 6.0.2 py310h89163eb_2 conda-forge 183kB
#12 37.43 + pyzmq 26.4.0 py310h71f11fc_0 conda-forge 338kB
#12 37.43 + qhull 2020.2 h434a139_5 conda-forge 553kB
#12 37.43 + raft-dask 25.6.0a25 cuda11_py310_250418_8f79e34a rapidsai-nightly 227kB
#12 37.43 + rapids 25.06.00a cuda11_py310_250418_gc1098b4_0 rapidsai-nightly 6kB
#12 37.43 + rapids-dask-dependency 25.6.0a2 250411_f7b1ecd9 rapidsai-nightly 22kB
#12 37.43 + rapids-logger 0.1.11 h98325ef_0 rapidsai-nightly 161kB
#12 37.43 + rapids-xgboost 25.06.00a cuda11_py310_250418_gc1098b4_0 rapidsai-nightly 6kB
#12 37.43 + rav1e 0.6.6 he8a937b_2 conda-forge 15MB
#12 37.43 + rdma-core 56.1 h5888daf_1 conda-forge 1MB
#12 37.43 + re2 2024.07.02 h9925aae_3 conda-forge 27kB
#12 37.43 + referencing 0.36.2 pyh29332c3_0 conda-forge 52kB
#12 37.43 + rfc3339-validator 0.1.4 pyhd8ed1ab_1 conda-forge 10kB
#12 37.43 + rfc3986-validator 0.1.1 pyh9f0ad1d_0 conda-forge 8kB
#12 37.43 + rich 14.0.0 pyh29332c3_0 conda-forge 200kB
#12 37.43 + rmm 25.6.0a25 cuda11_py310_250418_c7a33143 rapidsai-nightly 469kB
#12 37.43 + rpds-py 0.24.0 py310hc1293b2_0 conda-forge 391kB
#12 37.43 + s2n 1.5.16 hba75a32_1 conda-forge 353kB
#12 37.43 + scikit-image 0.24.0 py310h5eaa309_3 conda-forge 11MB
#12 37.43 + scikit-learn 1.6.1 py310h27f47ee_0 conda-forge 9MB
#12 37.43 + scipy 1.15.2 py310h1d65ade_0 conda-forge 16MB
#12 37.43 + send2trash 1.8.3 pyh0d859eb_1 conda-forge 23kB
#12 37.43 + shapely 2.1.0 py310h247727d_0 conda-forge [544](https://github.com/rapidsai/docker/actions/runs/14538359125/job/40791268880?pr=747#step:9:550)kB
#12 37.43 + simpervisor 1.0.0 pyhd8ed1ab_1 conda-forge 14kB
#12 37.43 + six 1.17.0 pyhd8ed1ab_0 conda-forge 16kB
#12 37.43 + snappy 1.2.1 h8bd8927_1 conda-forge 43kB
#12 37.43 + sniffio 1.3.1 pyhd8ed1ab_1 conda-forge 15kB
#12 37.43 + sortedcontainers 2.4.0 pyhd8ed1ab_1 conda-forge 29kB
#12 37.43 + soupsieve 2.5 pyhd8ed1ab_1 conda-forge 37kB
#12 37.43 + sqlite 3.49.1 h9eae976_2 conda-forge 860kB
#12 37.43 + stack_data 0.6.3 pyhd8ed1ab_1 conda-forge 27kB
#12 37.43 + streamz 0.6.4 pyhd8ed1ab_1 conda-forge 68kB
#12 37.43 + svt-av1 3.0.2 h5888daf_0 conda-forge 3MB
#12 37.43 + sysroot_linux-64 2.17 h0157908_18 conda-forge 15MB
#12 37.43 + tblib 3.1.0 pyhd8ed1ab_0 conda-forge 18kB
#12 37.43 + terminado 0.18.1 pyh0d859eb_0 conda-forge 22kB
#12 37.43 + threadpoolctl 3.6.0 pyhecae5ae_0 conda-forge 24kB
#12 37.43 + tifffile 2025.3.30 pyhd8ed1ab_0 conda-forge 180kB
#12 37.43 + tinycss2 1.4.0 pyhd8ed1ab_0 conda-forge 28kB
#12 37.43 + toolz 1.0.0 pyhd8ed1ab_1 conda-forge 52kB
#12 37.43 + tornado 6.4.2 py310ha75aee5_0 conda-forge 650kB
#12 37.43 + traitlets 5.14.3 pyhd8ed1ab_1 conda-forge 110kB
#12 37.43 + treelite 4.4.1 py310hebdfe98_1 conda-forge 593kB
#12 37.43 + types-python-dateutil 2.9.0.20241206 pyhd8ed1ab_0 conda-forge 22kB
#12 37.43 + typing-extensions 4.13.2 h0e9735f_0 conda-forge 90kB
#12 37.43 + typing_extensions 4.13.2 pyh29332c3_0 conda-forge 52kB
#12 37.43 + typing_utils 0.1.0 pyhd8ed1ab_1 conda-forge 15kB
#12 37.43 + uc-micro-py 1.0.3 pyhd8ed1ab_1 conda-forge 11kB
#12 37.43 + ucx 1.18.0 hfd9a62f_3 conda-forge 7MB
#12 37.43 + ucx-py 0.44.0a4 py310_250418_ab4ee896 rapidsai-nightly 420kB
#12 37.43 + ucxx 0.44.0a11 cuda11_py3.10_250418_7ebd1200 rapidsai-nightly 482kB
#12 37.43 + unicodedata2 16.0.0 py310ha75aee5_0 conda-forge 405kB
#12 37.43 + uri-template 1.3.0 pyhd8ed1ab_1 conda-forge 24kB
#12 37.43 + uriparser 0.9.8 hac33072_0 conda-forge 48kB
#12 37.43 + wcwidth 0.2.13 pyhd8ed1ab_1 conda-forge 33kB
#12 37.43 + webcolors 24.11.1 pyhd8ed1ab_0 conda-forge 18kB
#12 37.43 + webencodings 0.5.1 pyhd8ed1ab_3 conda-forge 15kB
#12 37.43 + websocket-client 1.8.0 pyhd8ed1ab_1 conda-forge 47kB
#12 37.43 + xarray 2025.3.1 pyhd8ed1ab_0 conda-forge 854kB
#12 37.43 + xerces-c 3.2.5 h988505b_2 conda-forge 2MB
#12 37.43 + xgboost 2.1.4 rapidsai_pyh0e8b7e3_4 rapidsai-nightly 17kB
#12 37.43 + xorg-libxau 1.0.12 hb9d3cd8_0 conda-forge 15kB
#12 37.43 + xorg-libxdmcp 1.1.5 hb9d3cd8_0 conda-forge 20kB
#12 37.43 + xyzservices 2025.1.0 pyhd8ed1ab_0 conda-forge 49kB
#12 37.43 + yaml 0.2.5 h7f98852_2 conda-forge 89kB
#12 37.43 + yarl 1.20.0 py310h89163eb_0 conda-forge 145kB
#12 37.43 + zeromq 4.3.5 h3b0a872_7 conda-forge 335kB
#12 37.43 + zfp 1.0.1 h[588](https://github.com/rapidsai/docker/actions/runs/14538359125/job/40791268880?pr=747#step:9:594)8daf_2 conda-forge 279kB
#12 37.43 + zict 3.0.0 pyhd8ed1ab_1 conda-forge 36kB
#12 37.43 + zipp 3.21.0 pyhd8ed1ab_1 conda-forge 22kB
#12 37.43 + zlib 1.3.1 hb9d3cd8_2 conda-forge 92kB
#12 37.43 + zlib-ng 2.2.4 h7955e40_0 conda-forge 109kB
#12 37.43
#12 37.43 Upgrade:
#12 37.43 ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#12 37.43
#12 37.43 - libxml2 2.13.5 h0d44e9d_1 conda-forge 690kB
#12 37.43 + libxml2 2.13.7 h4bc477f_1 conda-forge 692kB
#12 37.43
#12 37.43 Summary:
#12 37.43
#12 37.43 Install: 355 packages
#12 37.43 Upgrade: 1 packages
#12 37.43
#12 37.43 Total download: 5GB
#12 37.43
#12 37.43 ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Additional context
I noticed in those logs that cuml 25.6.0a79 is getting installed... even though the latest version is 25.6.0a84 (https://anaconda.org/rapidsai-nightly/cuml). Maybe something somewhere in the RAPIDS stack is causing dependency conflicts that prevent the newer versions from being installed.