diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index e25df21625..137f18bcc6 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -186,7 +186,7 @@ By making a contribution to this project, I certify that: Once the changes are ready, push your local branch to the remote, e.g. `git push --set-upstream origin `, and [open a pull request](https://github.com/Project-MONAI/tutorials/pulls). When the pull request is opened, the MONAI repository has a set of GitHub actions that will run checks on the changes. -Please check more [details in the guidelines](#ci-test-passing-guide) for how to pass the tests. +Please check more [details in the guidelines](#cicd-test-passing-guide) for how to pass the tests. In addition, the team will perform diligent code reviews following this [set of guidelines](#common-recommendations-for-the-review) to reduce the amount of work for users to run the tutorials. diff --git a/bundle/introducing_config/README.md b/bundle/introducing_config/README.md index 31479a8140..3ae7cde30c 100644 --- a/bundle/introducing_config/README.md +++ b/bundle/introducing_config/README.md @@ -155,5 +155,5 @@ plt.show() - Running customized Python components (made available on the `PYTHONPATH`). - Overriding the component in `example.yaml` using, for example, `--id=new_value` in the command line. - Multiple configuration files and cross-file references. -- Replacing in terms of plain texts instead of Python objects ([tutorial](../get_started.md)). +- Replacing in terms of plain texts instead of Python objects ([tutorial](../further_features.md)). - The debugging mode to investigate the intermediate variables and results. diff --git a/deepgrow/ignite/README.md b/deepgrow/ignite/README.md index d90b90aa1e..599308f53a 100644 --- a/deepgrow/ignite/README.md +++ b/deepgrow/ignite/README.md @@ -143,7 +143,7 @@ This notebook helps to run any pre-transforms before running inference over a De It also helps to run post-transforms to get the final label mask. -#### [Deepgrow Stats](./handler.py) +#### Deepgrow Stats It contains basic ignite handler to capture region/organ-wise statistics, save snapshots, outputs while running train/validation over a dataset that has multi-label mask. By-default the handler is added as part of training/validation steps. diff --git a/deployment/Triton/README.md b/deployment/Triton/README.md index e5801f05a8..aadd9b9be6 100644 --- a/deployment/Triton/README.md +++ b/deployment/Triton/README.md @@ -53,7 +53,7 @@ This demo and description borrows heavily from the [Triton Python Backend](https Get The Demo Source Code ------------------------------- -Pull down the demo repository and start with the [Quick Start] (#quick-start) guide. +Pull down the demo repository and start with the [Quick Start](#quick-start) guide. ``` $ git clone https://github.com/Project-MONAI/tutorials.git @@ -69,7 +69,7 @@ any C++ code. We will use this to demonstrate implementing MONAI code inside Tri * [Quick Start](#quick-start) * [Examples](#examples) * [Usage](#usage) -* [Model Config File](#model-config-file) +* [Model Config File](#model-config) * [Error Hanldling](#error-handling) @@ -106,7 +106,6 @@ Alternatively, the user can just run the shell script provided the previous step $ ./mednist_client_run.sh ``` The expected result is variety of classification results for body images and local inference times. -``` ## Examples: The example demonstrates running a Triton Python Backend on a single image classification problem. @@ -130,14 +129,14 @@ COPY models/monai_covid/1/model.py /models/monai_covid/1 ENTRYPOINT [ "tritonserver", "--model-repository=/models"] ``` -Note: The Triton service expects a certain directory structure discussed in [Model Config File](#model-config-file) to load the model definitions. +Note: The Triton service expects a certain directory structure discussed in [Model Config File](#model-config) to load the model definitions. 2. Next, the container with the Triton Service runs as a service (in background or separate terminal for demo). In this example, the ports used by the Triton Service are set to `8000` for client communications. ```Dockerfile: demo_app_image_name="monai_triton:demo" docker run --shm-size=128G --rm -p 127.0.0.1:8000:8000 -p 127.0.0.1:8001:8001 -p 127.0.0.1:8090:8002 ${demo_app_image_name} ``` -3. See [Model Config File](#model-config-file) to see the expected file structure for Triton. +3. See [Model Config File](#model-config) to see the expected file structure for Triton. - Modify the models/monai_prostrate/1/model.py file to satisfy any model configuration requirements while keeping the required components in the model definition. See the * [Usage](#usage) section for background. - In the models/monai_prostrate/1/config.pbtxt file configure the number of GPUs and which ones are used. e.g. Using two available GPUs and two parallel versions of the model per GPU diff --git a/full_gpu_inference_pipeline/README.md b/full_gpu_inference_pipeline/README.md index 4292aabdc4..82a9096c86 100644 --- a/full_gpu_inference_pipeline/README.md +++ b/full_gpu_inference_pipeline/README.md @@ -11,7 +11,7 @@ * [Benchmark](#benchmark) + [Understanding the benchmark output](#understanding-the-benchmark-output) + [HTTP vs. gRPC vs. shared memory](#http-vs-grpc-vs-shared-memory) - + [Pre/Post-processing on GPU vs. CPU](#pre-post-processing-on-gpu-vs-cpu) + + [Pre/Post-processing on GPU vs. CPU](#prepost-processing-on-gpu-vs-cpu) ## Overview @@ -57,7 +57,7 @@ Triton environment can be quickly setup by running a Triton docker container: docker run --gpus=1 -it --name='triton_monai' --ipc=host -p18100:8000 -p18101:8001 -p18102:8002 --shm-size=1g -v /yourfolderpath:/triton_monai nvcr.io/nvidia/tritonserver:21.12-py3 ``` Please note that when starting the docker container, --ipc=host should be set, so that shared memory can be used to do the data transmission between server and client. Also you should allocate a relatively large shared memory using --shm-size option, because starting from 21.04 release, Python backend uses shared memory to connect user's code to Triton. -### Setup python execution environment +### Setup python execution environment Since we will use MONAI transforms in Triton python backend, we should set up the python execution environment in Triton container by following the instructions in [Triton python backend repository](https://github.com/triton-inference-server/python_backend#using-custom-python-execution-environments). For the installation steps of MONAI, you can refer to [monai install](https://docs.monai.io/en/latest/installation.html "monai install"). Below are the steps used to setup the proper environments for this experiment: Install the software packages below: @@ -140,7 +140,7 @@ From the result, we can come to a conclusion that using shared memory will great ![](../figures/full_gpu_inference_figure1.png) -### Pre/Post-processing on GPU vs. CPU  +### Pre/Post-processing on GPU vs. CPU After doing pre and post-processing on GPU, we can get a 12x speedup for the full pipeline. ![](../figures/full_gpu_inference_figure2.png) diff --git a/nnunet/README.md b/nnunet/README.md index 0183797e09..d2cffd6dd5 100644 --- a/nnunet/README.md +++ b/nnunet/README.md @@ -52,7 +52,7 @@ The installation instruction is described [here](docs/install.md). The user needs to provide a data list (".json" file) for the new task and data root. In general, a valid data list needs to follow the format of the ones in [Medical Segmentation Decathlon (MSD)](https://drive.google.com/drive/folders/1HqEgzS8BV2c7xYNrZdEAnrHk7osJJ--2). -In [this tutorial](../auto3dseg/notebooks/msd_datalist_generator.ipynb), we provided example steps to download the [MSD Spleen dataset](http://medicaldecathlon.com) and prepare a datalist. +In [this tutorial](../auto3dseg/notebooks/msd_crossval_datalist_generator.ipynb), we provided example steps to download the [MSD Spleen dataset](http://medicaldecathlon.com) and prepare a datalist. Below we assume the dataset is downloaded to `/workspace/data/Task09_Spleen` and the datalist is in the current directory. ### Run with Minimal Input using ```nnUNetV2Runner``` diff --git a/reconstruction/MRI_reconstruction/unet_demo/README.md b/reconstruction/MRI_reconstruction/unet_demo/README.md index 526f7f0316..f34c0e9ade 100644 --- a/reconstruction/MRI_reconstruction/unet_demo/README.md +++ b/reconstruction/MRI_reconstruction/unet_demo/README.md @@ -19,7 +19,7 @@ This folder contains code to train and validate a U-Net for accelerated MRI reco * [Inference](#Inference) -* [Acknowledgment](#Acknowledgement) +* [Acknowledgment](#acknowledgment) *** diff --git a/reconstruction/MRI_reconstruction/varnet_demo/README.md b/reconstruction/MRI_reconstruction/varnet_demo/README.md index f21b43bda5..89ec05b164 100644 --- a/reconstruction/MRI_reconstruction/varnet_demo/README.md +++ b/reconstruction/MRI_reconstruction/varnet_demo/README.md @@ -19,7 +19,7 @@ This folder contains code to train and validate an e2e-VarNet ([https://arxiv.or * [Inference](#Inference) -* [Acknowledgment](#Acknowledgement) +* [Acknowledgment](#acknowledgment) ***