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

Jump to bottom

Fixed typos #197

Merged
merged 1 commit into from
Jan 29, 2018
Merged

Fixed typos #197

Show file tree
Hide file tree
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
2 changes: 1 addition & 1 deletion docs/source/tutorial/step1_communicators_optimizers.rst
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -99,7 +99,7 @@ Copying datasets

In the MNIST example, the rank 0 process reads the entire portion of
the dataset and scatters it to other processes. In some applications,
such as the ImageNet ChainerMN exmaple, however, only the pathes to
such as the ImageNet ChainerMN example, however, only the pathes to
each data file are scattered and each process reads the actual data
files. In such cases, all datasets must be readable on all computing
nodes in the same location. You don't need to worry about this if you
Expand Down
3 changes: 1 addition & 2 deletions docs/source/tutorial/step2_datasets_evaluators.rst
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -49,8 +49,7 @@ from a standard evaluator by using method ``create_multi_node_evaluator``.
It behaves exactly the same as the given original evaluator
except that it reports the average of results over all workers.

The following line from the original MNIST example adds an evalautor extension to the trainer::

The following line from the original MNIST example adds an evaluator extension to the trainer::
trainer.extend(extensions.Evaluator(test_iter, model, device=args.gpu))

To create and use a multi-node evaluator, we modify that part as follows::
Expand Down