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100
docs/readthedocs/source/doc/Orca/Tutorial/k8s.md
View file

@ -2,7 +2,7 @@
This tutorial provides a step-by-step guide on how to run BigDL-Orca programs on Kubernetes (K8s) clusters, using a [PyTorch Fashin-MNIST program](https://github.com/intel-analytics/BigDL/tree/main/python/orca/tutorial/pytorch/FashionMNIST) as a working example.
The __Develop Node__ is the host machine where you launch the client container or create a Kubernetes Deployment. The **Client Container** is the created Docker container where you launch or submit your applications.
In this tutorial, the __Develop Node__ is the host machine where you launch the client container or create a Kubernetes Deployment. The **Client Container** is the created BigDL K8s Docker container where you launch or submit your applications.
---
## 1. Basic Concepts
@ -14,24 +14,23 @@ from bigdl.orca import init_orca_context
init_orca_context(cluster_mode, master, container_image,
cores, memory, num_nodes, driver_cores, driver_memory,
extra_python_lib, penv_archive, conf)
extra_python_lib, conf)
```
In `init_orca_context`, you may specify necessary runtime configurations for running the example on K8s, including:
* `cluster_mode`: one of `"k8s-client"`, `"k8s-cluster"` or `"spark-submit"` when you run on K8s clusters.
* `master`: a URL format to specify the master address of the K8s cluster.
* `container_image`: a string that specifies the name of Docker container image for executors. The Docker container image for BigDL is `intelanalytics/bigdl-k8s`.
* `cores`: an integer that specifies the number of cores for each executor (default to be `2`).
* `memory`: a string that specifies the memory for each executor (default to be `"2g"`).
* `num_nodes`: an integer that specifies the number of executors (default to be `1`).
* `driver_cores`: an integer that specifies the number of cores for the driver node (default to be `4`).
* `driver_memory`: a string that specifies the memory for the driver node (default to be `"2g"`).
* `extra_python_lib`: a string that specifies the path to extra Python packages, separated by comma (default to be `None`). `.py`, `.zip` or `.egg` files are supported.
* `penv_archive`: a string that specifies the path to a packed conda archive (default to be `None`).
* `container_image`: the name of Docker container image for K8s pods. The Docker container image for BigDL is `intelanalytics/bigdl-k8s`.
* `cores`: the number of cores for each executor (default to be `2`).
* `memory`: the memory for each executor (default to be `"2g"`).
* `num_nodes`: the number of executors (default to be `1`).
* `driver_cores`: the number of cores for the driver node (default to be `4`).
* `driver_memory`: the memory for the driver node (default to be `"2g"`).
* `extra_python_lib`: the path to extra Python packages, separated by comma (default to be `None`). `.py`, `.zip` or `.egg` files are supported.
* `conf`: a dictionary to append extra conf for Spark (default to be `None`).
__Note__:
* All arguments __except__ `cluster_mode` will be ignored when using [`spark-submit`](#use-spark-submit) or [`Kubernetes deployment`](#use-kubernetes-deployment) to submit and run Orca programs, in which case you are supposed to specify these configurations via the submit command or the YAML file.
* All arguments __except__ `cluster_mode` will be ignored when using [`spark-submit`](#use-spark-submit) or [`Kubernetes deployment`](#use-kubernetes-deployment) to submit and run Orca programs, in which case you are supposed to specify these configurations via the submit command.
After Orca programs finish, you should always call `stop_orca_context` at the end of the program to release resources and shutdown the underlying distributed runtime engine (such as Spark or Ray).
```python
@ -52,7 +51,7 @@ Please see more details in [K8s-Cluster](https://spark.apache.org/docs/latest/ru
For **k8s-client** mode, you can directly find the driver logs in the console.
For **k8s-cluster** mode, a `driver-pod-name` (`train-py-fc5bec85fca28cb3-driver` in the following log) will be returned when the application is completed.
For **k8s-cluster** mode, a `driver-pod-name` (`train-py-fc5bec85fca28cb3-driver` in the following log) will be returned when the application completes.
```
23-01-29 08:34:47 INFO LoggingPodStatusWatcherImpl:57 - Application status for spark-9341aa0ec6b249ad974676c696398b4e (phase: Succeeded)
23-01-29 08:34:47 INFO LoggingPodStatusWatcherImpl:57 - Container final statuses:
@ -80,7 +79,7 @@ kubectl logs <driver-pod-name>
kubectl describe pod <driver-pod-name>
```
* You may need to delete the driver pod manually after the application finished:
* You may need to delete the driver pod manually after the application finishes:
```bash
kubectl delete pod <driver-pod-name>
```
@ -89,7 +88,7 @@ kubectl delete pod <driver-pod-name>
### 1.3 Load Data from Volumes
When you are running programs on K8s, please load data from [Volumes](https://kubernetes.io/docs/concepts/storage/volumes/) accessible to all K8s pods. We use Network File Systems (NFS) with path `/bigdl/nfsdata` in this tutorial as an example. You are recommended to put your working directory in the Volume (NFS) as well.
To load data from Volumes, please set the corresponding Volume configurations for spark using `--conf` option in Spark scripts or specifying `conf` in `init_orca_context`. Here we list the configurations for using NFS as Volume.
To load data from Volumes, please set the corresponding Volume configurations for spark using `--conf` option in Spark scripts or specifying `conf` in `init_orca_context`. Here we list the configurations for using NFS as the Volume.
For **k8s-client** mode:
* `spark.kubernetes.executor.volumes.persistentVolumeClaim.nfsvolumeclaim.options.claimName`: specify the claim name of persistentVolumeClaim with volumnName `nfsvolumeclaim` to mount into executor pods.
@ -116,7 +115,7 @@ Sample conf for NFS in the Fashion-MNIST example provided by this tutorial is as
}
```
After mounting the Volume (NFS) into the BigDL container (see __[Section 2.2](#create-a-k8s-client-container)__ for more details), the Fashion-MNIST example could load data from NFS as local storage.
After mounting the Volume (NFS) into the pods, the Fashion-MNIST example could load data from NFS as local storage.
```python
import torch
@ -146,13 +145,13 @@ sudo docker pull intelanalytics/bigdl-k8s:latest
sudo docker pull intelanalytics/bigdl-k8s:2.2.0
```
In the docker container:
In the BigDL K8s Docker image:
- Spark is located at `/opt/spark`. Spark version is 3.1.3.
- BigDL is located at `/opt/bigdl-VERSION`. For the latest nightly build image, BigDL version would be `xxx-SNAPSHOT` (e.g. 2.3.0-SNAPSHOT).
---
## 3. Create BigDL K8s Container
Note that you can __skip__ this section if you want to run applications with [`Kubernetes deployment`](#use-kubernetes-deployment).
Note that you can __SKIP__ this section if you want to run applications with [`Kubernetes deployment`](#use-kubernetes-deployment).
You need to create a BigDL K8s client container only when you use [`python` command](#use-python-command) or [`spark-submit`](#use-spark-submit).
@ -165,8 +164,6 @@ sudo docker run -itd --net=host \
-v /etc/kubernetes:/etc/kubernetes \
-v /root/.kube:/root/.kube \
-v /path/to/nfsdata:/bigdl/nfsdata \
-e NOTEBOOK_PORT=12345 \
-e NOTEBOOK_TOKEN="your-token" \
-e http_proxy=http://your-proxy-host:your-proxy-port \
-e https_proxy=https://your-proxy-host:your-proxy-port \
-e RUNTIME_SPARK_MASTER=k8s://https://<k8s-apiserver-host>:<k8s-apiserver-port> \
@ -178,14 +175,12 @@ sudo docker run -itd --net=host \
```
In the script:
* **Please switch the tag according to the BigDL image you pull.**
* **Please switch the version tag according to the BigDL K8s Docker image you pull.**
* **Please make sure you are mounting the correct Volume path (e.g. NFS) into the container.**
* `--net=host`: use the host network stack for the Docker container.
* `-v /etc/kubernetes:/etc/kubernetes`: specify the path of Kubernetes configurations to mount into the Docker container.
* `-v /root/.kube:/root/.kube`: specify the path of Kubernetes installation to mount into the Docker container.
* `-v /path/to/nfsdata:/bigdl/nfsdata`: mount NFS path on the host into the container as the specified path (e.g. "/bigdl/nfsdata").
* `NOTEBOOK_PORT`: an integer that specifies the port number for the Notebook. This is not necessary if you don't use notebook.
* `NOTEBOOK_TOKEN`: a string that specifies the token for Notebook. This is not necessary if you don't use notebook.
* `-v /path/to/nfsdata:/bigdl/nfsdata`: mount NFS path on the host into the Docker container as the specified path (e.g. "/bigdl/nfsdata").
* `RUNTIME_SPARK_MASTER`: a URL format that specifies the Spark master: `k8s://https://<k8s-apiserver-host>:<k8s-apiserver-port>`.
* `RUNTIME_K8S_SERVICE_ACCOUNT`: a string that specifies the service account for the driver pod.
* `RUNTIME_K8S_SPARK_IMAGE`: the name of the BigDL K8s Docker image. Note that you need to change the version accordingly.
@ -202,7 +197,7 @@ Once the container is created, a `containerID` would be returned and with which
```bash
sudo docker exec -it <containerID> bash
```
In the remaining part of this tutorial, you are supposed to operate and run commands *__inside__* this __Client Container__.
In the remaining part of this tutorial, you are supposed to operate and run commands *__inside__* this __Client Container__ if you use [`python` command](#use-python-command) or [`spark-submit`](#use-spark-submit).
---
@ -211,9 +206,9 @@ In the launched BigDL K8s **Client Container** (if you use [`python` command](#u
- See [here](../Overview/install.md#install-anaconda) to install conda and prepare the Python environment.
- See [here](../Overview/install.md#to-install-orca-for-spark3) to install BigDL Orca in the created conda environment. *Note that if you use [`spark-submit`](#use-spark-submit) or [`Kubernetes deployment`](#use-kubernetes-deployment), please __skip__ this step and __DO NOT__ install BigDL Orca with pip install command in the conda environment.*
- See [here](../Overview/install.md#to-install-orca-for-spark3) to install BigDL Orca in the created conda environment. Note that if you use [`spark-submit`](#use-spark-submit) or [`Kubernetes deployment`](#use-kubernetes-deployment), please __SKIP__ this step and __DO NOT__ install BigDL Orca with pip install command in the conda environment.
- You should install all the other Python libraries that you need in your program in the conda environment as well. `torch` and `torchvision` are needed to run the Fashion-MNIST example we provide:
- You should install all the other Python libraries that you need in your program in the conda environment as well. `torch`, `torchvision` and `tqdm` are needed to run the Fashion-MNIST example we provide:
```bash
pip install torch torchvision tqdm
```
@ -221,19 +216,19 @@ pip install torch torchvision tqdm
---
## 5. Prepare Dataset
To run the Fashion-MNIST example provided by this tutorial on K8s, you should upload the dataset to a K8s Volume (e.g. NFS).
To run the Fashion-MNIST example provided by this tutorial on K8s, you should upload the dataset to the Volume (e.g. NFS) beforehand.
Please download the Fashion-MNIST dataset manually on your __Develop Node__ and put the data into the Volume. Note that PyTorch `FashionMNIST Dataset` requires unzipped files located in `FashionMNIST/raw/` under the dataset folder.
Please manually download the Fashion-MNIST dataset and put the data into the Volume. Note that PyTorch `FashionMNIST Dataset` requires unzipped files located in `FashionMNIST/raw/` under the dataset folder.
```bash
# PyTorch official dataset download link
git clone https://github.com/zalandoresearch/fashion-mnist.git
# Copy the dataset files to the folder FashionMNIST/raw in NFS
cp /path/to/fashion-mnist/data/fashion/* /bigdl/nfsdata/dataset/FashionMNIST/raw
cp /path/to/fashion-mnist/data/fashion/* /path/to/nfs/dataset/FashionMNIST/raw
# Extract FashionMNIST archives
gzip -d /bigdl/nfsdata/dataset/FashionMNIST/raw/*
gzip -d /path/to/nfs/dataset/FashionMNIST/raw/*
```
In the given example, you can specify the argument `--data_dir` to be the directory on NFS for the Fashion-MNIST dataset. The directory should contain `FashionMNIST/raw/train-images-idx3-ubyte` and `FashionMNIST/raw/t10k-images-idx3`.
@ -249,7 +244,7 @@ __Note:__ Please upload the extra Python dependency files to the Volume (e.g. NF
* When using [`python` command](#use-python-command), please specify `extra_python_lib` in `init_orca_context`.
```python
init_orca_context(..., extra_python_lib="/bigdl/nfsdata/model.py")
init_orca_context(..., extra_python_lib="/path/to/model.py")
```
For more details, please see [BigDL Python Dependencies](https://bigdl.readthedocs.io/en/latest/doc/Orca/Overview/orca-context.html#python-dependencies).
@ -258,7 +253,7 @@ For more details, please see [BigDL Python Dependencies](https://bigdl.readthedo
```bash
spark-submit
...
--py-files /bigdl/nfsdata/model.py
--py-files /path/to/model.py
...
```
For more details, please see [Spark Python Dependencies](https://spark.apache.org/docs/latest/submitting-applications.html).
@ -269,7 +264,6 @@ For more details, please see [Spark Python Dependencies](https://spark.apache.or
from model import model_creator, optimizer_creator
```
__Notes__:
If your program depends on a nested directory of Python files, you are recommended to follow the steps below to use a zipped package instead.
@ -287,7 +281,7 @@ If your program depends on a nested directory of Python files, you are recommend
---
## 7. Run Jobs on K8s
In the following part, we will illustrate three ways to submit and run BigDL Orca applications on K8s.
In the remaining part of this tutorial, we will illustrate three ways to submit and run BigDL Orca applications on K8s.
* Use `python` command
* Use `spark-submit`
@ -295,7 +289,7 @@ In the following part, we will illustrate three ways to submit and run BigDL Orc
You can choose one of them based on your preference or cluster settings.
We provide the running command for the [Fashion-MNIST example](https://github.com/intel-analytics/BigDL/blob/main/python/orca/tutorial/pytorch/FashionMNIST/) in the __Client Container__ in this section.
We provide the running command for the [Fashion-MNIST example](https://github.com/intel-analytics/BigDL/blob/main/python/orca/tutorial/pytorch/FashionMNIST/) in this section.
### 7.1 Use `python` command
This is the easiest and most recommended way to run BigDL Orca on K8s as a normal Python program.
@ -310,7 +304,7 @@ python train.py --cluster_mode k8s-client --data_dir /bigdl/nfsdata/dataset
#### 7.1.2 K8s-Cluster
Before running the example on `k8s-cluster` mode in the __Client Container__, you should:
Before running the example on k8s-cluster mode in the __Client Container__, you should:
1. Pack the current activate conda environment to an archive:
```bash
@ -357,9 +351,9 @@ If you prefer to use `spark-submit`, please follow the steps below in the __Clie
Some runtime configurations for Spark are as follows:
* `--master`: a URL format that specifies the Spark master: k8s://https://<k8s-apiserver-host>:<k8s-apiserver-port>.
* `--master`: a URL format that specifies the Spark master: `k8s://https://<k8s-apiserver-host>:<k8s-apiserver-port>`.
* `--name`: the name of the Spark application.
* `--conf spark.kubernetes.container.image`: the name of the BigDL K8s Docker image.
* `--conf spark.kubernetes.container.image`: the name of Docker container image for K8s pods. The Docker container image for BigDL is `intelanalytics/bigdl-k8s`.
* `--num-executors`: the number of executors.
* `--executor-cores`: the number of cores for each executor.
* `--total-executor-cores`: the total number of executor cores.
@ -376,7 +370,7 @@ Some runtime configurations for Spark are as follows:
#### 7.2.1 K8s Client
Submit and run the program for `k8s-client` mode following the `spark-submit` script below:
Submit and run the program for k8s-client mode following the `spark-submit` script below:
```bash
${SPARK_HOME}/bin/spark-submit \
--master ${RUNTIME_SPARK_MASTER} \
@ -411,7 +405,7 @@ In the `spark-submit` script:
#### 7.2.2 K8s Cluster
Before running the example on `k8s-cluster` mode in the __Client Container__, you should:
Before running the example on k8s-cluster mode in the __Client Container__, you should:
1. Upload the conda archive to NFS:
```bash
@ -426,7 +420,7 @@ Before running the example on `k8s-cluster` mode in the __Client Container__, yo
cp /path/to/model.py /bigdl/nfsdata
```
Submit and run the program for `k8s-cluster` mode following the `spark-submit` script below:
Submit and run the program for k8s-cluster mode following the `spark-submit` script below:
```bash
${SPARK_HOME}/bin/spark-submit \
--master ${RUNTIME_SPARK_MASTER} \
@ -498,11 +492,11 @@ We define a Kubernetes Deployment in a YAML file. Some fields of the YAML are ex
* `metadata`: a nested object filed that every deployment object must specify.
* `name`: a string that uniquely identifies this object and job. We use "orca-pytorch-job" in our example.
* `restartPolicy`: the restart policy for all containers within the pod. One of Always, OnFailure, Never. Default to Always.
* `restartPolicy`: the restart policy for all containers within the pod. One of Always, OnFailure, Never. Default to be Always.
* `containers`: a single application container to run within a pod.
* `name`: the name of the container. Each container in a pod will have a unique name.
* `image`: the name of the BigDL K8s Docker image. Note that you need to change the version accordingly.
* `imagePullPolicy`: the pull policy of the docker image. One of Always, Never and IfNotPresent. Defaults to Always if `:latest` tag is specified, or IfNotPresent otherwise.
* `imagePullPolicy`: the pull policy of the Docker image. One of Always, Never and IfNotPresent. Default to be Always if `latest` tag is specified, or IfNotPresent otherwise.
* `command`: the command for the containers to run in the pod.
* `args`: the arguments to submit the spark application in the pod. See more details in [`spark-submit`](#use-spark-submit).
* `securityContext`: the security options the container should be run with.
@ -517,8 +511,8 @@ We define a Kubernetes Deployment in a YAML file. Some fields of the YAML are ex
#### 7.3.1 K8s Client
BigDL has provided an example [orca-tutorial-k8s-client.yaml](https://github.com/intel-analytics/BigDL/blob/main/python/orca/tutorial/pytorch/docker/orca-tutorial-client.yaml)__ to directly run the Fashion-MNIST example for k8s-client mode.
Note that you need to change the configurations in the YAML file accordingly, including the version of the docker image, RUNTIME_SPARK_MASTER, BIGDL_VERSION and BIGDL_HOME.
BigDL has provided an example [orca-tutorial-k8s-client.yaml](https://github.com/intel-analytics/BigDL/blob/main/python/orca/tutorial/pytorch/docker/orca-tutorial-client.yaml) to directly run the Fashion-MNIST example for k8s-client mode.
Note that you need to change the configurations in the YAML file accordingly, including the version of the Docker image, RUNTIME_SPARK_MASTER, BIGDL_VERSION and BIGDL_HOME.
You need to uncompress the conda archive in NFS before submitting the job:
```bash
@ -527,8 +521,9 @@ mkdir environment
tar -xzvf environment.tar.gz --directory environment
```
orca-tutorial-k8s-client.yaml
```bash
# orca-tutorial-k8s-client.yaml
apiVersion: batch/v1
kind: Job
metadata:
@ -608,7 +603,7 @@ Submit the application using `kubectl`:
kubectl apply -f orca-tutorial-k8s-client.yaml
```
Note that you need to delete the job before re-submitting another one:
Note that you need to delete the job __BEFORE__ re-submitting another one:
```bash
kubectl delete job orca-pytorch-job
```
@ -630,11 +625,12 @@ kubectl delete job orca-pytorch-job
```
#### 7.3.2 K8s Cluster
BigDL has provided an example [orca-tutorial-k8s-cluster.yaml](https://github.com/intel-analytics/BigDL/blob/main/python/orca/tutorial/pytorch/docker/orca-tutorial-cluster.yaml)__ to run the Fashion-MNIST example for k8s-cluster mode.
Note that you need to change the configurations in the YAML file accordingly, including the version of the docker image, RUNTIME_SPARK_MASTER, BIGDL_VERSION and BIGDL_HOME.
BigDL has provided an example [orca-tutorial-k8s-cluster.yaml](https://github.com/intel-analytics/BigDL/blob/main/python/orca/tutorial/pytorch/docker/orca-tutorial-cluster.yaml) to run the Fashion-MNIST example for k8s-cluster mode.
Note that you need to change the configurations in the YAML file accordingly, including the version of the Docker image, RUNTIME_SPARK_MASTER, BIGDL_VERSION and BIGDL_HOME.
orca-tutorial-k8s-cluster.yaml
```bash
# orca-tutorial-k8s-cluster.yaml
apiVersion: batch/v1
kind: Job
metadata:
@ -651,12 +647,10 @@ spec:
imagePullPolicy: IfNotPresent
command: ["/bin/sh","-c"]
args: ["
export RUNTIME_DRIVER_HOST=$( hostname -I | awk '{print $1}' );
${SPARK_HOME}/bin/spark-submit \
--master ${RUNTIME_SPARK_MASTER} \
--name orca-k8s-cluster-tutorial \
--deploy-mode ${SPARK_MODE} \
--conf spark.driver.host=${RUNTIME_DRIVER_HOST} \
--conf spark.kubernetes.container.image=${RUNTIME_K8S_SPARK_IMAGE} \
--conf spark.kubernetes.authenticate.driver.serviceAccountName=${RUNTIME_K8S_SERVICE_ACCOUNT} \
--num-executors 2 \
@ -718,7 +712,7 @@ Submit the application using `kubectl`:
kubectl apply -f orca-tutorial-k8s-cluster.yaml
```
Note that you need to delete the job before re-submitting another one:
Note that you need to delete the job __BEFORE__ re-submitting another one:
```bash
kubectl delete job orca-pytorch-job
```

21
docs/readthedocs/source/doc/Orca/Tutorial/yarn.md
View file

@ -18,12 +18,12 @@ sc = init_orca_context(cluster_mode, cores, memory, num_nodes,
In `init_orca_context`, you may specify necessary runtime configurations for running the example on YARN, including:
* `cluster_mode`: one of `"yarn-client"`, `"yarn-cluster"`, `"bigdl-submit"` or `"spark-submit"` when you run on Hadoop/YARN clusters.
* `cores`: an integer that specifies the number of cores for each executor (default to be `2`).
* `memory`: a string that specifies the memory for each executor (default to be `"2g"`).
* `num_nodes`: an integer that specifies the number of executors (default to be `1`).
* `driver_cores`: an integer that specifies the number of cores for the driver node (default to be `4`).
* `driver_memory`: a string that specifies the memory for the driver node (default to be `"2g"`).
* `extra_python_lib`: a string that specifies the path to extra Python packages, separated by comma (default to be `None`). `.py`, `.zip` or `.egg` files are supported.
* `cores`: the number of cores for each executor (default to be `2`).
* `memory`: memory for each executor (default to be `"2g"`).
* `num_nodes`: the number of executors (default to be `1`).
* `driver_cores`: the number of cores for the driver node (default to be `4`).
* `driver_memory`: the memory for the driver node (default to be `"2g"`).
* `extra_python_lib`: the path to extra Python packages, separated by comma (default to be `None`). `.py`, `.zip` or `.egg` files are supported.
* `conf`: a dictionary to append extra conf for Spark (default to be `None`).
__Note__:
@ -111,9 +111,9 @@ __Note__:
### 2.2 Install Python Libraries
- See [here](../Overview/install.md#install-anaconda) to install conda and prepare the Python environment on the __Client Node__.
- See [here](../Overview/install.md#install-bigdl-orca) to install BigDL Orca in the created conda environment. *Note that if you use [`spark-submit`](#use-spark-submit), please __skip__ this step and __DO NOT__ install BigDL Orca with pip install command in the conda environment.*
- See [here](../Overview/install.md#install-bigdl-orca) to install BigDL Orca in the created conda environment. Note that if you use [`spark-submit`](#use-spark-submit), please __SKIP__ this step and __DO NOT__ install BigDL Orca with pip install command in the conda environment.
- You should install all the other Python libraries that you need in your program in the conda environment as well. `torch` and `torchvision` are needed to run the Fashion-MNIST example:
- You should install all the other Python libraries that you need in your program in the conda environment as well. `torch`, `torchvision` and `tqdm` are needed to run the Fashion-MNIST example:
```bash
pip install torch torchvision tqdm
```
@ -130,7 +130,7 @@ __Note__:
---
## 3. Prepare Dataset
To run the Fashion-MNIST example provided by this tutorial on YARN, you should upload the Fashion-MNIST dataset to a distributed storage (such as HDFS or S3).
To run the Fashion-MNIST example provided by this tutorial on YARN, you should upload the Fashion-MNIST dataset to a distributed storage (such as HDFS or S3) beforehand.
First, download the Fashion-MNIST dataset manually on your __Client Node__. Note that PyTorch `FashionMNIST Dataset` requires unzipped files located in `FashionMNIST/raw/` under the dataset folder.
```bash
@ -176,7 +176,6 @@ For more details, please see [Spark Python Dependencies](https://spark.apache.or
from model import model_creator, optimizer_creator
```
__Note__:
If your program depends on a nested directory of Python files, you are recommended to follow the steps below to use a zipped package instead.
@ -193,7 +192,7 @@ If your program depends on a nested directory of Python files, you are recommend
---
## 5. Run Jobs on YARN
In the following part, we will illustrate three ways to submit and run BigDL Orca applications on YARN.
In the remaining part of this tutorial, we will illustrate three ways to submit and run BigDL Orca applications on YARN.
* Use `python` command
* Use `bigdl-submit`