Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

fixed "cannot allocate memory" exception by specifying max_processes #910

Merged
merged 1 commit into from
Mar 19, 2021
Merged

fixed "cannot allocate memory" exception by specifying max_processes #910

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
5 changes: 4 additions & 1 deletion haystack/retriever/dense.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -270,6 +270,7 @@ def train(self,
train_filename: str,
dev_filename: str = None,
test_filename: str = None,
max_processes: int = 128,
dev_split: float = 0,
batch_size: int = 2,
embed_title: bool = True,
Expand All @@ -295,6 +296,8 @@ def train(self,
:param train_filename: training filename
:param dev_filename: development set filename, file to be used by model in eval step of training
:param test_filename: test set filename, file to be used by model in test step after training
:param max_processes: the maximum number of processes to spawn in the multiprocessing.Pool used in DataSilo.
It can be set to 1 to disable the use of multiprocessing or make debugging easier.
:param dev_split: The proportion of the train set that will sliced. Only works if dev_filename is set to None
:param batch_size: total number of samples in 1 batch of data
:param embed_title: whether to concatenate passage title with each passage. The default setting in official DPR embeds passage title with the corresponding passage
Expand Down Expand Up @@ -333,7 +336,7 @@ def train(self,

self.model.connect_heads_with_processor(self.processor.tasks, require_labels=True)

data_silo = DataSilo(processor=self.processor, batch_size=batch_size, distributed=False)
data_silo = DataSilo(processor=self.processor, batch_size=batch_size, distributed=False, max_processes=max_processes)

# 5. Create an optimizer
self.model, optimizer, lr_schedule = initialize_optimizer(
Expand Down