Comments (3)
Still the same error in nightly GPU: https://github.com/recommenders-team/recommenders/actions/runs/8372395934/job/22923293913
2024-03-21 09:53:25.709498: W external/local_tsl/tsl/framework/bfc_allocator.cc:497] ********____***_____*_____*****************___*____*____***************************************xxxxx
2024-03-21 09:53:25.709518: W tensorflow/core/framework/op_kernel.cc:1839] OP_REQUIRES failed at conv_grad_input_ops.cc:345 : RESOURCE_EXHAUSTED: OOM when allocating tensor with shape[10000,1,10,300] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
_ test_slirec_quickstart_functional[recommenders/models/deeprec/config/sli_rec.yaml-tests/resources/deeprec/slirec-10-400-expected_values0-42] _
notebooks = ***'als_deep_dive': '/mnt/azureml/cr/j/b321b89c582248be83f964f9e412bd91/exe/wd/examples/02_model_collaborative_filtering...rk_movielens': '/mnt/azureml/cr/j/b321b89c582248be83f964f9e412bd91/exe/wd/examples/06_benchmarks/movielens.ipynb', ...***
output_notebook = 'output.ipynb', kernel_name = 'python3'
yaml_file = 'recommenders/models/deeprec/config/sli_rec.yaml'
data_path = 'tests/resources/deeprec/slirec', epochs = 10, batch_size = 400
expected_values = ***'auc': 0.7183***, seed = 42
@pytest.mark.gpu
@pytest.mark.notebooks
@pytest.mark.parametrize(
"yaml_file, data_path, epochs, batch_size, expected_values, seed",
[
(
"recommenders/models/deeprec/config/sli_rec.yaml",
os.path.join("tests", "resources", "deeprec", "slirec"),
10,
400,
***
"auc": 0.7183
***, # Don't do logloss check as SLi-Rec uses ranking loss, not a point-wise loss
42,
)
],
)
def test_slirec_quickstart_functional(
notebooks,
output_notebook,
kernel_name,
yaml_file,
data_path,
epochs,
batch_size,
expected_values,
seed,
):
notebook_path = notebooks["slirec_quickstart"]
params = ***
"yaml_file": yaml_file,
"data_path": data_path,
"EPOCHS": epochs,
"BATCH_SIZE": batch_size,
"RANDOM_SEED": seed,
***
> execute_notebook(
notebook_path, output_notebook, kernel_name=kernel_name, parameters=params
)
from recommenders.
Could you provide more info about the GPU size used for nightly build? @miguelgfierro
from recommenders.
T4: https://learn.microsoft.com/en-us/azure/virtual-machines/nct4-v3-series
from recommenders.
Related Issues (20)
- [ASK] MIND test dataset doesn't work for run_eval HOT 3
- [BUG] using NextItNet in main/examples/00_quick_start/sequential_recsys_amazondataset.ipynb HOT 5
- [FEATURE] Replace SASRec TF with PyTorch version HOT 4
- [BUG] np.float and np.int deprecated and removed
- [ASK] Discuss whether we should replace LightFM with the UniRec FM version HOT 1
- [BUG] Can't download xdeepfmresources.zip HOT 1
- [BUG] Test breaking due to error with Movielens host HOT 3
- [BUG] Cornac BiVAE test failing due to csc_matrix attribute error HOT 3
- [BUG] Remove scipy<=1.13.1
- [ASK] The Training using a GPU is very slow for NCF model
- Should I use jupyter notebook or vs code for this project [ASK] HOT 1
- [BUG] Unable to access MIND dataset due to public access restriction on the storage account HOT 6
- [BUG] Warnings from pytest
- [FEATURE] On pymanopt HOT 10
- [ASK] Confusion between top_k and by_threshold relevancy_method in python_evaluation.py HOT 2
- Is the 'mrr_score' implementation correct? HOT 1
- [BUG] Test failing Service invocation timed out HOT 15
- [BUG] No detailed information in testing error HOT 1
- [BUG] Review new MIND tests HOT 1
- [BUG] Ranking Evaluation Metrics Exceed 1 with "by_threshold" Relevancy Method HOT 4
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