Skip to content

Commit b6e5ebd

Browse files
nv-kkudrynskishakandrew
nv-kkudrynski
authored and
shakandrew
committed
Typos and small fixes
1 parent ef98b2c commit b6e5ebd

6 files changed

Lines changed: 6 additions & 7 deletions

File tree

‎PyTorch/Segmentation/MaskRCNN/pytorch/docker/Dockerfile‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -11,7 +11,7 @@ RUN apt-get update -y \
1111
&& apt-get install -y libglib2.0-0 libsm6 libxext6 libxrender-dev
1212

1313
# Install Miniconda
14-
RUN curl -so /miniconda.sh https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh \
14+
RUN curl -Lso /miniconda.sh https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh \
1515
&& chmod +x /miniconda.sh \
1616
&& /miniconda.sh -b -p /miniconda \
1717
&& rm /miniconda.sh

‎PyTorch/Segmentation/MaskRCNN/pytorch/docker/docker-jupyter/Dockerfile‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -10,7 +10,7 @@ RUN apt-get update -y \
1010
&& apt-get install -y apt-utils git curl ca-certificates bzip2 cmake tree htop bmon iotop g++
1111

1212
# Install Miniconda
13-
RUN curl -so /miniconda.sh https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh \
13+
RUN curl -Lso /miniconda.sh https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh \
1414
&& chmod +x /miniconda.sh \
1515
&& /miniconda.sh -b -p /miniconda \
1616
&& rm /miniconda.sh

‎PyTorch/SpeechRecognition/Jasper/triton/README.md‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -235,7 +235,7 @@ bash triton/scripts/run_server.sh
235235
To run in the foreground interactively, for debugging purposes, run:
236236

237237
```bash
238-
DAEMON="--detach=false" bash trinton/scripts/run_server.sh
238+
DAEMON="--detach=false" bash triton/scripts/run_server.sh
239239
```
240240

241241
The script mounts and loads models at `$PWD/triton/deploy/model_repo` to the server with all visible GPUs. In order to selectively choose the devices, set `NVIDIA_VISIBLE_DEVICES`.

‎TensorFlow/Detection/SSD/Dockerfile‎

Lines changed: 0 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -29,5 +29,4 @@ ENV PYTHONPATH="/workdir/models/research/:/workdir/models/research/slim/:$PYTHON
2929

3030
COPY examples/ examples
3131
COPY configs/ configs/
32-
COPY qa/ qa/
3332
COPY download_all.sh download_all.sh

‎TensorFlow2/LanguageModeling/BERT/README.md‎

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -65,7 +65,7 @@ Other publicly available implementations of BERT include:
6565
[gluon-nlp](https://github.com/dmlc/gluon-nlp/tree/master/scripts/bert)
6666
[Google's official implementation](https://github.com/google-research/bert)
6767

68-
This model is trained with mixed precision using Tensor Cores on Volta, Turing, and the NVIDIA Ampere GPU architectures. Therefore, researchers can get results upto 4x faster than training without Tensor Cores, while experiencing the benefits of mixed precision training. This model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time.
68+
This model is trained with mixed precision using Tensor Cores on Volta, Turing, and the NVIDIA Ampere GPU architectures. Therefore, researchers can get results up to 4x faster than training without Tensor Cores, while experiencing the benefits of mixed precision training. This model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time.
6969

7070
### Model architecture
7171

@@ -76,7 +76,7 @@ BERT's model architecture is a multi-layer bidirectional transformer encoder. Ba
7676
|BERTBASE |12 encoder| 768| 12|4 x 768|512|110M|
7777
|BERTLARGE|24 encoder|1024| 16|4 x 1024|512|330M|
7878

79-
BERT training consists of two steps, pre-training the language model in an unsupervised fashion on vast amounts of unannotated datasets, and then using this pre-trained model for fine-tuning for various NLP tasks, such as question and answer, sentence classification, or sentiment analysis. Fine-tuning typically adds an extra layer or two for the specific task and further trains the model using a task-specific annotated dataset, starting from the pre-trained backbone weights. The end-to-end process in depicted in the following image:
79+
BERT training consists of two steps, pre-training the language model in an unsupervised fashion on vast amounts of unannotated datasets, and then using this pre-trained model for fine-tuning for various NLP tasks, such as question and answer, sentence classification, or sentiment analysis. Fine-tuning typically adds an extra layer or two for the specific task and further trains the model using a task-specific annotated dataset, starting from the pre-trained backbone weights. The end-to-end process is depicted in the following image:
8080

8181
![](data/images/bert_pipeline.png?raw=true)
8282

‎TensorFlow2/Segmentation/MaskRCNN/scripts/train.py‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -82,7 +82,7 @@ class CustomFormatter(argparse.ArgumentDefaultsHelpFormatter, argparse.RawTextHe
8282
cmd_train += ' ' + ' '.join(remainder)
8383
cmd_eval += ' ' + ' '.join(remainder)
8484
if flags.gpus is not None:
85-
cmd_train = f'CUDA_VISIBLE_DEVICES={",".join(map(str, range(flags.gpus)))} ' + cmd
85+
cmd_train = f'CUDA_VISIBLE_DEVICES={",".join(map(str, range(flags.gpus)))} ' + cmd_train
8686

8787
# print command
8888
line = '-' * shutil.get_terminal_size()[0]

0 commit comments

Comments
 (0)