Adding notes about mouning oneAPI-samples scripts and notebooks (#115)

* Adding notes about mouning oneAPI-samples scripts and notebooks Signed-off-by: Abolfazl Shahbazi <abolfazl.shahbazi@intel.com> * Adding notes for exposing user's samples into the container Signed-off-by: Abolfazl Shahbazi <abolfazl.shahbazi@intel.com> * Update README.md formatting Signed-off-by: Abolfazl Shahbazi <abolfazl.shahbazi@intel.com> * updates per code review Signed-off-by: Abolfazl Shahbazi <abolfazl.shahbazi@intel.com> --------- Signed-off-by: Abolfazl Shahbazi <abolfazl.shahbazi@intel.com>
intel · Aug 18, 2023 · 49a396a · 49a396a
1 parent c588b61
commit 49a396a
Showing 1 changed file with 31 additions and 4 deletions.
diff --git a/preset/README.md b/preset/README.md
@@ -7,14 +7,33 @@ Intel® Web Configurator Preset Containers provides data scientists and develope
 | -----------------------------| ------------- | ------------- | ----------------- |
 | Data Analytics | Perform large scale data analysis | [Intel® Distribution For Python](https://www.intel.com/content/www/us/en/developer/tools/oneapi/distribution-for-python.html), [Modin](https://github.com/modin-project/modin), [Intel® Dataset Librarian](https://github.com/IntelAI/models/tree/master/datasets/dataset_api), [Intel® Data Connector](https://github.com/IntelAI/models/tree/master/datasets/cloud_data_connector) | `intel/data-analytics:2023.2-py3.9`<br />`intel/data-analytics:2023.2-py3.10` |
 | Classical ML | Train classical-ml models using scikit, modin and xgboost | [Intel® Distribution For Python](https://www.intel.com/content/www/us/en/developer/tools/oneapi/distribution-for-python.html), [Intel® extension for SciKit Learn](https://github.com/intel/scikit-learn-intelex), [XGBoost](https://github.com/dmlc/xgboost), [Modin](https://github.com/modin-project/modin), <br /> [Intel® Dataset Librarian](https://github.com/IntelAI/models/tree/master/datasets/dataset_api), [Intel® Data Connector](https://github.com/IntelAI/models/tree/master/datasets/cloud_data_connector) | `intel/classical-ml:2023.2-py3.9`<br />`intel/classical-ml:2023.2-py3.10` |
-| Deep Learning | Train large scale Deep Learning models with Tensorflow or PyTorch | [Intel® Distribution For Python](https://www.intel.com/content/www/us/en/developer/tools/oneapi/distribution-for-python.html), [PyTorch](https://pytorch.org/), [Tensorflow](https://www.tensorflow.org/),<br /> [Intel® Extension for PyTorch](https://github.com/intel/intel-extension-for-pytorch), [Intel® Extension for Tensorflow](https://github.com/intel/intel-extension-for-tensorflow),<br /> [Intel® Optimization for Horovod](https://github.com/intel/intel-optimization-for-horovod), [Intel® Dataset Librarian](https://github.com/IntelAI/models/tree/master/datasets/dataset_api), [Intel® Data Connector](https://github.com/IntelAI/models/tree/master/datasets/cloud_data_connector) | `intel/deep-learning:2023.2-py3.9`<br />`intel/deep-learning:2023.2-py3.10` |
+| Deep Learning | Train large scale Deep Learning models with Tensorflow or PyTorch | [Intel® Distribution For Python](https://www.intel.com/content/www/us/en/developer/tools/oneapi/distribution-for-python.html), [PyTorch](https://pytorch.org/), [Tensorflow](https://www.tensorflow.org/),<br /> [Intel® Extension for PyTorch](https://github.com/intel/intel-extension-for-pytorch), [Intel® Extension for Tensorflow](https://github.com/intel/intel-extension-for-tensorflow),<br /> [Intel® Optimization for Horovod](https://github.com/intel/intel-optimization-for-horovod), [Intel® Dataset Librarian](https://github.com/IntelAI/models/tree/master/datasets/dataset_api), [Intel® Data Connector](https://github.com/IntelAI/models/tree/master/datasets/cloud_data_connector), [Intel® Extension for DeepSpeed*](https://github.com/intel/intel-extension-for-deepspeed) | `intel/deep-learning:2023.2-py3.9`<br />`intel/deep-learning:2023.2-py3.10` |
 | Inference Optimization | Optimize Deep Learning models for inference<br /> using Intel® Neural Compressor | [Intel® Distribution For Python](https://www.intel.com/content/www/us/en/developer/tools/oneapi/distribution-for-python.html), [PyTorch](https://pytorch.org/), [Tensorflow](https://www.tensorflow.org/), <br /> [Intel® Extension for PyTorch](https://github.com/intel/intel-extension-for-pytorch), [Intel® Extension for Tensorflow](https://github.com/intel/intel-extension-for-tensorflow),<br /> [Intel® Neural Compressor](https://github.com/intel/neural-compressor), [Intel® Dataset Librarian](https://github.com/IntelAI/models/tree/master/datasets/dataset_api), [Intel® Data Connector](https://github.com/IntelAI/models/tree/master/datasets/cloud_data_connector) | `intel/inference-optimization:2023.2-py3.9`<br />`intel/inference-optimization:2023.2-py3.10` |
 
 The Deep Learning and Inference Optimization containers have separate conda environments for each framework: `torch` and `tensorflow`.
 
 ## Prerequisites
 Make sure [docker](https://docs.docker.com/engine/) is installed on the machine. Follow the [instruction here](https://docs.docker.com/engine/install/) to install docker engine on a host machine.
 
+## Clone the oneAPI Samples
+It is recommended to have [oneAPI samples](https://github.com/oneapi-src/oneAPI-samples) repository cloned prior to running these containers.
+For these containers, use the [Intel® AI Analytics Toolkit](https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics) examples.
+
+```bash
+git clone --single-branch --branch=2023.2_AIKit https://github.com/oneapi-src/oneAPI-samples.git
+cd oneAPI-samples/AI-and-Analytics
+```
+Since the container runs as non root user, it is advised to give correct permissions to files and folders under `AI-and-Analytics` folder.
+
+To not expose samples into the container, simply remove `-v ${PWD}:/home/dev/jupyter` from `docker run` commands below.
+
+In addition to the examples, any files in the working directory will be exposed in the container. For more information about exposing, see [Volumes](https://docs.docker.com/storage/volumes/).
+In order to do so, add the following option to any of the `docker run` commands below:
+```bash
+-v /absolute/path/to/examples/dir:/home/dev/jupyter/<my_samples>
+```
+Once the container is ready, your can find your examples under `my_samples` directory.
+
 ## Run Preset Container
 There are 2 modes to run thess containers:
 
@@ -29,7 +48,14 @@ This mode allows running the container in an interactive shell. This enables the
 Please note that certain applications use shared memory to share data between processes. But the default shared memory segment size is 64M for container, and is not enough for multithreaded applications(Ex. Modin). Docker recommends increasing shared memory size using --shm-size command line options provided at runtime to `docker run` command. Please, modify shared memory segment size in the following format according to need: --shm-size=256M or --shm-size=4G for example as a command line option like so: `docker run --shm-size=4G`.
 
 ```bash
-docker run -it --rm <image name> bash
+docker run -it --rm -v ${PWD}:/home/dev/jupyter <image name> bash
+```
+or if you plan to exapose your own samples directory into the container as well:
+```bash
+docker run -it --rm \
+    -v ${PWD}:/home/dev/jupyter \
+    -v <path_to_your_examples_directory>:/home/dev/jupyter/<my_samples> \
+    <image name> bash
 ```
 
 If you want to enable [Intel® Flex/Max GPU](https://www.intel.com/content/www/us/en/products/details/discrete-gpus/data-center-gpu.html) optimization, please use the following command. Please note that test for the `render` group sets the Group ID for the GPU device to enable GPU optimization inside the container.
@@ -41,6 +67,7 @@ test -z "$RENDER" || RENDER_GROUP="--group-add ${RENDER}"
 docker run -it --rm \
     ${RENDER_GROUP} \
     --device=/dev/dri \
+    -v ${PWD}:/home/dev/jupyter \
     -v /dev/dri/by-path:/dev/dri/by-path \
     <image name> bash
 ```
@@ -50,7 +77,7 @@ This mode launches a jupyterlab notebook server. The command below will start th
 
 ```bash
 export PORT=<port>
-docker run -it --rm -e PORT=$PORT -p $PORT:$PORT <image name>
+docker run -it --rm -e PORT=$PORT -p $PORT:$PORT -v ${PWD}:/home/dev/jupyter <image name>
 ```
 
 If you want to enable [Intel® Flex/Max GPU](https://www.intel.com/content/www/us/en/products/details/discrete-gpus/data-center-gpu.html) optimization, please use the following command. Please note that test for the `render` group sets the Group ID for the GPU device to enable GPU optimization inside the container.
@@ -63,6 +90,7 @@ test -z "$RENDER" || RENDER_GROUP="--group-add ${RENDER}"
 docker run -it --rm -e PORT=$PORT \
     ${RENDER_GROUP} \
     --device=/dev/dri \
+    -v ${PWD}:/home/dev/jupyter \
     -v /dev/dri/by-path:/dev/dri/by-path \
     -p $PORT:$PORT \
     <image name>
@@ -72,4 +100,3 @@ After running this command the terminal should display an output similar to disp
 
 ## Troubleshooting and Support
 If you face some issue in using the container you can find more information on how to troubleshoot [here](https://github.com/intel/ai-containers#troubleshooting). If you need more help feel free to submit an [issue](https://github.com/intel/ai-containers/issues).
-