simple-sagemaker 0.9.17

A *simpler* and *cheaper* way to distribute python (training) code on machines of your choice in the (AWS) cloud.

Simple Sagemaker

A simpler and cheaper way to distribute work (python/shell/training) work on machines of your choice in the (AWS) cloud.

Note: this (initial) work is still in progress. Only SageMaker's PyTorch and TensorFlow frameworks are currently supported. But, these frameworks are enough to distribute any type of work, including shell commands, just without the specific customization.

Requirements

1. Python 3.6+
2. An AWS account + region and credentials configured for boto3, as explained on the Boto3 docs
3. (Optional) The Docker Engine, to be able to customize a docker image
4. (Optional) The Docker Compose, for local testing

Getting started

To install Simple Sagemaker

pip install simple-sagemaker

Then, to get the shell command cat /proc/cpuinfo && nvidia-smi run on a single ml.p3.2xlarge instance, run the following ssm command (documentation of the ssm CLI is given below):

ssm shell -p simple-sagemaker-example-cli-shell -t shell-task -o ./output --cmd_line "cat /proc/cpuinfo && nvidia-smi"

Output including the logs with script stdout is downloaded to ./output.

$ cat ./output/logs/logs0
processor: 0
model name: Intel(R) Xeon(R) CPU E5-2686 v4 @ 2.30GHz
cpu cores: 4
....
processor: 2
....
processor: 7
....
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 440.33.01    Driver Version: 440.33.01    CUDA Version: 10.2     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  Tesla V100-SXM2...  On   | 00000000:00:1E.0 Off |                    0 |
| N/A   46C    P0    27W / 300W |      0MiB / 16160MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+
....

Similarly, to run the following worker1.py on two ml.p3.2xlarge spot instances

import torch

for i in range(torch.cuda.device_count()):
    print(f"-***- Device {i}: {torch.cuda.get_device_properties(i)}")

Just run the below ssm command:

ssm run -p simple-sagemaker-example-cli -t task1 -e worker1.py -o ./output/example1 --it ml.p3.2xlarge --ic 2

The output is saved to ./output/example1, logs to ./output/example1/logs/logs0 and ./output/example1/logs/logs1:

$ cat ./output/example1/logs/logs0
...
-***- Device 0: _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', major=7, minor=0, total_memory=16160MB, multi_processor_count=80)
...

It's recommended to review the fully featured advanced example, as a demonstration of most features.

More examples (below)

CLI based examples:

• Passing command line arguments
• Task state and output
• Providing input data
• Chaining tasks
• Configuring the docker image
• Defining code dependencies

API based example:

• Single file example

Motivation

Well, I couldn't find a simple and cheap way to run my existing code on cloud. And, I wanted to try having my own OS project including documentation and a full automated testing and publishing piepline, so here it is :) Please refer to this blog post.

Background

Simple Sagemaker is a thin wrapper around SageMaker's training jobs, that makes distribution of work (python/shell) on any supported instance type very simple.

The distribution solution is composed of two parts, one on each side: a runner on the client machine that manages the distribution process, and a worker which is the code being distributed on the cloud.

• The runner is the main part of this package, can mostly be controlled by using the ssm command line interface (CLI), or be fully customized by using the python API.
• The worker is basically the work (shell/python code) being distributed. Python code may be adapted to use a small task_toolkit library (that is automatically injected to the worker) for getting the environment configuration (WorkerConfig, see below), i.e. input/output/state paths, running parameters, and to mark a job as completed. Shell command can access the same parameters on the command line, and completion is determined by the exit code (i.e. 0 is a success) etc.

The runner is used to configure tasks and projects:

• A task is a logical step that runs on a defined input and provide output. It's defined by providing a local code path, entrypoint, and a list of additional local dependencies
• A SageMaker job is a task instance, i.e. a single job is created each time a task is executed
  • State is maintained between consecutive execution of the same task (see more below)
  • Task can be marked as completed, to avoid re-running it next time (unless enforced otherwise)
• A project is a series of related tasks, with possible dependencies
  • The output of a completed task can be consumed as input by a consecutive task

Main features

1. "Simpler" - Except for holding an AWS account credentials, no other pre-configuration nor knowledge is assumed (well, almost :). Behind the scenes you get:
   • Jobs IAM role creation, including policies for accessing needed S3 buckets
   • Building and uploading a customized docker image to AWS (ECS service)
   • Synchronizing local source code / input data to a S3 bucket
   • Downloading the results from S3
   • ...
2. "Cheaper" - "pay only for what you use", and save up to 90% of the cost with spot instances, which got used by default!
3. Abstraction of how data is maintained on AWS (S3 service)
   • No need to mess with S3 paths, the data is automatically
   • State is automatically maintained between consecutive execution of jobs that belongs to the same task
4. A simple way to define how data flows between tasks of the same project, e.g. how the first task's outputs is used as an input for a second task
5. (Almost) no code changes are to the existing code - the API is mostly wrapped by a command line interface (named ssm) to control the execution (a.k.a implement the runner, see below)
   • In most cases it's only about 1 line for getting the environment configuration (e.g. input/output/state paths and running parameters, see below) and passing it on to the original code
6. Easy customization of the docker image (based on a pre-built one)
7. The rest of the SageMaker advantages, which (mostly) behaves "normally" as defined by AWS, e.g.
   • (Amazon SageMaker Developer Guide)[https://docs.aws.amazon.com/sagemaker/latest/dg/whatis.html]
   • (Amazon SageMaker Python SDK @ Read the Docs)[https://sagemaker.readthedocs.io/en/stable/index.html]

High level flow diagram

Worker environment

The worker entry point (entry_point parameter), directory (source_dir for python code, dir_files for shell script), along with all dependencies (dependencies parameter) are getting copied to a single directory (/opt/ml/code) on each instance, and the entry point is then executed. On top of the above, for python code tasks, the task_toolkit library is also added as a dependency in this folder.

Configuration

The worker can access the environment configuration parameters in two ways:

1. The environment variables, e.g. SM_NUM_CPUS represents the number of CPUs.
2. Using the worker_lib library: initialize a WorkerConfig instance, worker_config = worker_lib.WorkerConfig(), and then all params can be accessible from the worker_config variable, e.g. worker_lib.num_cpus is the number of CPUs.

The complete list of configuration parameters:

Description	Environment variable	worker_config field name	Example
The name of the current running job	SAGEMAKER_JOB_NAME	job_name	'task1-2020-09-23-17-12-46-0JNcrR6H'
Input channels:
Names of the input channels	SM_CHANNELS	channels	['data']
The data input channel	SM_CHANNEL_DATA	channel_data	'/opt/ml/input/data/data'
Path where the input model (given by model_uri parameter) is located	SM_CHANNEL_MODEL	channel_model	'/opt/ml/input/data/model'
Generally - path where the channel [ChannelName] is located	SM_CHANNEL_[ChannelName]	channel_[ChannelName]	'/opt/ml/input/data/[ChannelName]'
Additional command line parameters / hyperparameters	SM_HPS	hps	{'arg': 'hello world!', 'task': 1, 'worker': 1}
State:
The root path of where state should be stored	SSM_STATE	state	'/state'
The instance specific state path	SSM_INSTANCE_STATE	instance_state	'/state/algo-1'
Output:
The path where output data should be stored	SM_OUTPUT_DATA_DIR	output_data_dir	'/opt/ml/output/data'
Path where model output should be stored	SM_MODEL_DIR	model_dir	'/opt/ml/model'
System:
The number of available CPUs on this instance	SM_NUM_CPUS	num_cpus	2
The number of available GPUs instance	SM_NUM_GPUS	num_gpus	1
Name of the current host	SM_CURRENT_HOST	current_host	'algo-1'
Names of all other hosts that are running on this job	SM_HOSTS	hosts	['algo-1', 'algo-2']
The name of the network interface	SM_NETWORK_INTERFACE_NAME	network_interface_name	'eth0'

State

State is maintained between executions of the same task, i.e. between jobs that belongs to the same task. The local path is available in worker_config.state. When running multiple instances, the state data is merged into a single directory (post execution). To avoid collisions, set the init_multi_instance_state parameter of WorkerConfig constructor to True (the default behavior), which initializes a per instance sub directory, and keep it in worker_config.instance_state. On top of that, WorkerConfig provides an additional important API to mark the task as completed: worker_config.markCompleted(). If all instances of a job marked it as completed, the task is assumed to be completed by that job, which allows:

1. To skip it next time (unless enforced otherwise e.g. by using --force_running or if the state is cleared using clean_state)
2. To use its output as input for other tasks (see below: "Chaining tasks")

Output

On top of the state, there're 3 main other output mechanisms:

1. Logs - any output written to standard output / error
2. Output data - any data in worker_config.output_data_dir is compressed into a output.tar.gz. Only the main instance output data is kept.
3. Model - any data in worker_config.model_dir is compressed into a model.tar.gz. As data from all instance is merged, be carful with collisions.

Data maintenance on S3

All data, including input, code, state and output, is maintained on S3. The bucket to use can be defined, or the default one is used. The files and directories structure is as follows:

[Bucket name]/[Project name]/[Task name]
|-- state
|-- input
|-- [Job name]
|   |-- output
|   |   |-- model.tar.gz
|   |   `-- output.tar.gz
|   `-- source/sourcedir.tar.gz
|-- [Job name 2]
|        ...

• state - the task state, shared between all jobs, i.e. task executions
• input - the task input, shared as well
• [Job name] - a per job specific folder
  • model.tar.gz - model output data, merged from all instances
  • output.tar.gz - the main instance output data (other outputs are ignored)
  • sourcedir.tar.gz - source code and dependencies
• [Job name 2] - another execution of the same task

Local mode

SageMaker offers partial "local mode" support in order to test locally. The basic mode runs just the docker locally, while keep using S3 for input/output, and there's the local_code mode that does everything locally. To use the basic mode with Simple Sagemaker, local or local_gpu as instance type and local_mode = True for SageMakerProject constructor (this is done automatically with ssm CLI). Notes:

• Local mode doesn't support all features, e.g. state isn't supported. More notes and exclusions can be seen on the documentation
• local_code mode isn't currently supported by Simple Sagemaker

Distributed training

Sagemaker's PyTorch and TensorFlow pre-built images has extra customization for distributed training. Make sure to configure framework, framework_version and py_version to use the image that matches your needs (the full list is here). For TensorFlow you'll need to use the distribution parameters. For more details on the built in support see:

• PyTorch - Distributed PyTorch Training
• TensorFlow - Distributed TensorFlow Training.

CLI

The ssm CLI supports 3 commands:

• run - to run a python based task
• shell - to run a shell based task
• data - to manage (download/clear state) the data of an existing task

$ ssm -h
usage: ssm run [-h] --project_name PROJECT_NAME --task_name TASK_NAME
               [--bucket_name BUCKET_NAME] [--source_dir SOURCE_DIR]
               --entry_point ENTRY_POINT
               [--dependencies DEPENDENCIES [DEPENDENCIES ...]]
               [--instance_type INSTANCE_TYPE]
               [--instance_count INSTANCE_COUNT] [--volume_size VOLUME_SIZE]
               [--no_spot] [--use_spot_instances]
               [--max_wait_mins MAX_WAIT_MINS] [--max_run_mins MAX_RUN_MINS]
               [--aws_repo_name AWS_REPO_NAME] [--repo_name REPO_NAME]
               [--image_tag IMAGE_TAG]
               [--docker_file_path_or_content DOCKER_FILE_PATH_OR_CONTENT]
               [--framework {pytorch,tensorflow}]
               [--framework_version FRAMEWORK_VERSION]
               [--py_version PY_VERSION]
               [--input_path INPUT_PATH [INPUT_PATH ...]]
               [--model_uri MODEL_URI] [--input_s3 INPUT_S3 [INPUT_S3 ...]]
               [--input_task INPUT_TASK [INPUT_TASK ...]] [--force_running]
               [--distribution DISTRIBUTION] [--clean_state] [--keep_state]
               [--metric_definitions name regexp] [--enable_sagemaker_metrics]
               [--tag key value] [--output_path OUTPUT_PATH]
               [--download_state] [--download_model] [--download_output]

optional arguments:
  -h, --help            show this help message and exit
  --project_name PROJECT_NAME, -p PROJECT_NAME
                        Project name.
  --task_name TASK_NAME, -t TASK_NAME
                        Task name.

Code:
  --source_dir SOURCE_DIR, -s SOURCE_DIR
                        Path (absolute, relative or an S3 URI) to a directory
                        with any other source code dependencies aside from the
                        entry point file. If source_dir is an S3 URI, it must
                        point to a tar.gz file. Structure within this
                        directory are preserved when running on Amazon
                        SageMaker.
  --entry_point ENTRY_POINT, -e ENTRY_POINT
                        Path (absolute or relative) to the local Python source
                        file which should be executed as the entry point. If
                        source_dir is specified, then entry_point must point
                        to a file located at the root of source_dir.
  --dependencies DEPENDENCIES [DEPENDENCIES ...], -d DEPENDENCIES [DEPENDENCIES ...]
                        A list of paths to directories (absolute or relative)
                        with any additional libraries that will be exported to
                        the container The library folders will be copied to
                        SageMaker in the same folder where the entrypoint is
                        copied.

Instance:
  --instance_type INSTANCE_TYPE, --it INSTANCE_TYPE
                        Type of EC2 instance to use.
  --instance_count INSTANCE_COUNT, --ic INSTANCE_COUNT
                        Number of EC2 instances to use.
  --volume_size VOLUME_SIZE, -v VOLUME_SIZE
                        Size in GB of the EBS volume to use for storing input
                        data. Must be large enough to store input data.
  --no_spot             Use on demand instances
  --use_spot_instances  Specifies whether to use SageMaker Managed Spot
                        instances.
  --max_wait_mins MAX_WAIT_MINS
                        Timeout in minutes waiting for spot instances. After
                        this amount of time Amazon SageMaker will stop waiting
                        for Spot instances to become available. If 0 is
                        specified and spot instances are used, its set to
                        max_run_mins
  --max_run_mins MAX_RUN_MINS
                        Timeout in minutes for running. After this amount of
                        time Amazon SageMaker terminates the job regardless of
                        its current status.

Image:
  --aws_repo_name AWS_REPO_NAME, --ar AWS_REPO_NAME
                        Name of ECS repository.
  --repo_name REPO_NAME, --rn REPO_NAME
                        Name of local repository.
  --image_tag IMAGE_TAG
                        Image tag.
  --docker_file_path_or_content DOCKER_FILE_PATH_OR_CONTENT, --df DOCKER_FILE_PATH_OR_CONTENT
                        Path to a directory containing the DockerFile. The
                        base image should be set to `__BASE_IMAGE__` within
                        the Dockerfile, and is automatically replaced with the
                        correct base image.
  --framework {pytorch,tensorflow}, -f {pytorch,tensorflow}
                        The framework to use, see https://github.com/aws/deep-
                        learning-containers/blob/master/available_images.md
  --framework_version FRAMEWORK_VERSION, --fv FRAMEWORK_VERSION
                        The framework version
  --py_version PY_VERSION, --pv PY_VERSION
                        The python version

Running:
  --force_running       Force running the task even if it's already completed.
  --distribution DISTRIBUTION
                        Tensorflows distribution policy, see https://sagemake
                        r.readthedocs.io/en/stable/frameworks/tensorflow/using
                        _tf.html#distributed-training.
  --tag key value       Tag to be attached to the jobs executed for this task.

I/O:
  --bucket_name BUCKET_NAME, -b BUCKET_NAME
                        S3 bucket name (a default one is used if not given).
  --input_path INPUT_PATH [INPUT_PATH ...], -i INPUT_PATH [INPUT_PATH ...]
                        INPUT: PATH [distribution] Local/s3 path for the input
                        data. If a local path is given, it will be synced to
                        the task folder on the selected S3 bucket before
                        launching the task.
  --model_uri MODEL_URI
                        URI where a pre-trained model is stored, either
                        locally or in S3. If specified, the estimator will
                        create a channel pointing to the model so the training
                        job can download it. This model can be a
                        ‘model.tar.gz’ from a previous training job, or other
                        artifacts coming from a different source.
  --input_s3 INPUT_S3 [INPUT_S3 ...], --iis INPUT_S3 [INPUT_S3 ...]
                        INPUT_S3: INPUT_NAME S3_URI [distribution] Additional
                        S3 input sources (a few can be given).
  --input_task INPUT_TASK [INPUT_TASK ...], --iit INPUT_TASK [INPUT_TASK ...]
                        INPUTTASK: INPUT_NAME TASK_NAME TYPE [distribution]
                        Use an output of a completed task in the same project
                        as an input source (a few can be given). Type should
                        be one of ['state', 'model', 'source', 'output'].
  --clean_state, --cs   Clear the task state before running it. The task will
                        be running again even if it was already completed
                        before.
  --keep_state, --ks    Keep the current task state. If the task is already
                        completed, its current output will be taken without
                        running it again.
  --metric_definitions name regexp, --md name regexp
                        Name and regexp for a metric definition, a few can be
                        given. See https://docs.aws.amazon.com/sagemaker/lates
                        t/dg/training-metrics.html.
  --enable_sagemaker_metrics, -m
                        Enables SageMaker Metrics Time Series. See https://doc
                        s.aws.amazon.com/sagemaker/latest/dg/training-
                        metrics.html.

Download:
  --output_path OUTPUT_PATH, -o OUTPUT_PATH
                        Local path to download the outputs to.
  --download_state      Download the state once task is finished
  --download_model      Download the model once task is finished
  --download_output     Download the output once task is finished

Running a shell based task is very similar, except for source_dir and entry_point which are replaced by dir_files and cmd_line, respectively. Run ssm shell -h for more details.

To manage the data of an existing command:

$ ssm data -h 

usage: ssm data [-h] --project_name PROJECT_NAME --task_name TASK_NAME
                [--bucket_name BUCKET_NAME] [--output_path OUTPUT_PATH]
                [--download_state] [--download_model] [--download_output]

optional arguments:
  -h, --help            show this help message and exit
  --project_name PROJECT_NAME, -p PROJECT_NAME
                        Project name.
  --task_name TASK_NAME, -t TASK_NAME
                        Task name.
  --bucket_name BUCKET_NAME, -b BUCKET_NAME
                        S3 bucket name (a default one is used if not given).
  --output_path OUTPUT_PATH, -o OUTPUT_PATH
                        Local path to download the outputs to.
  --download_state      Download the state once task is finished
  --download_model      Download the model once task is finished
  --download_output     Download the output once task is finished

A fully featured advanced example

And now to a real advanced and fully featured version, yet simple to implement. In order to exemplify most of the possible features, the following files are used in CLI Example 6_1:

.
|-- Dockerfile
|-- code
|   |-- internal_dependency
|   |   `-- lib2.py
|   |-- requirements.txt
|   `-- worker6.py
|-- data
|   |-- sample_data1.txt
|   `-- sample_data2.txt
`-- external_dependency
    `-- lib1.py

• Dockerfile - the dockerfile specifying how to extend the pre-built image

  # __BASE_IMAGE__ is automatically replaced with the correct base image
  FROM __BASE_IMAGE__ 
  RUN pip3 install pandas==0.25.3 scikit-learn==0.21.3
  

• code - the source code folder
  • internal_dependency - a dependency that is part of the source code folder
  • requirements.txt - pip requirements file lists needed packages to be installed before running the worker

    transformers==3.0.2
    

  • worker6.py - the worker code
• data - input data files
• external_dependency - additional code dependency

The code is then launched a few time by run.sh, to demonstrate different features:

# Example 6_1 - a complete example part 1. 
#   - Uses local data folder as input, that is distributed among instances (--i, ShardedByS3Key)
#   - Uses a public s3 bucket as an additional input (--iis)
#   - Builds a custom docker image (--df, --repo_name, --aws_repo_name)
#   - Hyperparameter task_type
#   - 2 instance (--ic)
#   - Use an on-demand instance (--no_spot)
ssm run -p simple-sagemaker-example-cli -t task6-1 -s $BASEDIR/example6/code -e worker6.py \
    -i $BASEDIR/example6/data ShardedByS3Key \
    --iis persons s3://awsglue-datasets/examples/us-legislators/all/persons.json \
    --df $BASEDIR/example6 --repo_name "task6_repo" --aws_repo_name "task6_repo" --no_spot \
    --ic 2 --task_type 1 -o $1/example6_1

# Example 6_2 - a complete example part 2.
#   - Uses outputs from part 1 (--iit)
#   - Uses additional local code dependencies (-d)
#   - Uses the tensorflow framework as pre-built image (-f)
#   - Tags the jobs (--tag)
#   - Defines sagemaker metrics (-m, --md)
ssm run -p simple-sagemaker-example-cli -t task6-2 -s $BASEDIR/example6/code -e worker6.py \
    -d $BASEDIR/example6/external_dependency --iit task_6_1_model task6-1 model \
    --iit task_6_1_state task6-1 state ShardedByS3Key \
    -f tensorflow -m --md "Score" "Score=(.*?);" --tag "MyTag" "MyValue" \
    --ic 2 --task_type 2 -o $1/example6_2 &

# Running task6_1 again
#   - A completed task isn't executed again, but the current output is used instead. 
#       --ks (keep state, the default) is used to keep the current state
ssm run -p simple-sagemaker-example-cli -t task6-1 -s $BASEDIR/example6/code -e worker6.py \
    -i $BASEDIR/example6/data ShardedByS3Key \
    --iis persons s3://awsglue-datasets/examples/us-legislators/all/persons.json \
    --df $BASEDIR/example6 --repo_name "task6_repo" --aws_repo_name "task6_repo" \
    --ic 2 --task_type 1 -o $1/example6_1 > $1/example6_1_2_stdout --ks &


wait # wait for all processes

worker6.py contains the following:

    logger.info("Score=10;")
    time.sleep(60)  # sleep to be able to see the two scores
    logger.info("Score=20;")

This get captured by the "Score=(.*?);" regular expression in the ssm command above, then the metrics graphs can be viewed on the AWS console:

More information can be found here.

Feel free to dive more into the files of this example. Specifically, note how the same worker code is used for the two parts, and the task_type hyperparameter is used to distinguish between the two.

More examples

CLI based examples:

• A fully featured advanced example
• Passing command line arguments
• Task state and output
• Providing input data
• Chaining tasks
• Configuring the docker image
• Defining code dependencies

API based example:

• Single file example

Passing command line arguments

Any extra argument passed to the command line in assumed to be an additional command line argument / hyperparameter, and is accessible for the worker by the hps dictionary within the environment configuration. For example, see the following worker code worker2.py:

from worker_toolkit import worker_lib

worker_config = worker_lib.WorkerConfig(False)
print("-***-", worker_config.hps["msg"])

Runner CLI:

ssm run -p simple-sagemaker-example-cli -t task2 -e worker2.py --msg "Hello, world!" -o ./output/example2

Output from the log file

Invoking script with the following command:

/opt/conda/bin/python worker2.py --msg Hello, world!

Hello, world!

Task state and output

A complete example can be seen in worker3.py:

import os

from worker_toolkit import worker_lib

worker_config = worker_lib.WorkerConfig(False)

open(os.path.join(worker_config.output_data_dir, "output_data_dir"), "wt").write(
    "output_data_dir file"
)
open(os.path.join(worker_config.model_dir, "model_dir"), "wt").write("model_dir file")
open(os.path.join(worker_config.state, "state_dir"), "wt").write("state_dir file")

# Mark the tasks as completed, to allow other tasks to use its output, 
# and to avoid re-running it (unless enforced)
worker_config.markCompleted()

Runner CLI:

ssm run -p simple-sagemaker-example-cli -t task3 -e worker3.py -o ./output/example3

Output from the log file

Invoking script with the following command:

/opt/conda/bin/python worker2.py --msg Hello, world!

Hello, world!

Providing input data

A Job can be configured to get a few data channels:

• A single local path can be used with the -i/--input_path argument. This path is synchronized to the task directory on the S3 bucket before running the task. On the worker side the data is accessible in worker_config.channel_data
• Additional S3 paths (many) can be set as well. Each input source is provided with --iis [name] [S3 URI], and is accessible by the worker with worker_config.channel_[name] when [name] is the same one as was provided on the command line.
• Setting an output of a another task on the same project, see below "Chaining tasks"

Assuming a local data folder containing a single sample_data.txt file, a complete example can be seen in worker4.py:

import logging
import subprocess
import sys

from worker_toolkit import worker_lib

logger = logging.getLogger(__name__)


def listDir(path):
    logger.info(f"*** START listing files in {path}")
    logger.info(
        subprocess.run(
            ["ls", "-la", "-R", path], stdout=subprocess.PIPE, universal_newlines=True
        ).stdout
    )
    logger.info(f"*** END file listing {path}")


if __name__ == "__main__":
    logging.basicConfig(stream=sys.stdout)
    worker_config = worker_lib.WorkerConfig(False)
    listDir(worker_config.channel_data)
    listDir(worker_config.channel_bucket)

Running command:

ssm run -p simple-sagemaker-example-cli -t task4 -e worker4.py -i ./data \
    --iis bucket s3://awsglue-datasets/examples/us-legislators/all/persons.json -o ./output/example4

Output from the log file

...
INFO:__main__:*** START listing files in /opt/ml/input/data/data
INFO:__main__:/opt/ml/input/data/data:
total 12
drwxr-xr-x 2 root root 4096 Sep 14 21:51 .
drwxr-xr-x 4 root root 4096 Sep 14 21:51 ..
-rw-r--r-- 1 root root   19 Sep 14 21:51 sample_data.txt

INFO:__main__:*** END file listing /opt/ml/input/data/data
INFO:__main__:*** START listing files in /opt/ml/input/data/bucket
INFO:__main__:/opt/ml/input/data/bucket:
total 7796
drwxr-xr-x 2 root root    4096 Sep 14 21:51 .
drwxr-xr-x 4 root root    4096 Sep 14 21:51 ..
-rw-r--r-- 1 root root 7973806 Sep 14 21:51 persons.json

INFO:__main__:*** END file listing /opt/ml/input/data/bucket
...

Chaining tasks

The output of a completed task on the same project can be used as an input to another task, by using the --iit [name] [task name] [output type] command line parameter, where:

• [name] - is the name of the input source, accessible by the worker with worker_config.channel_[name]
• [task name] - the name of the task whose output is used as input
• [output type] - the task output type, one of "model", "output", "state"

Using the model output of task3 and the same worker4.py code, we can now run:

ssm run -p simple-sagemaker-example-cli -t task5 -e worker4.py --iit bucket task3 model -o ./output/example5

And get the following output from in the log file:

INFO:__main__:*** START listing files in 
INFO:__main__:
INFO:__main__:*** END file listing 
INFO:__main__:*** START listing files in /opt/ml/input/data/bucket
INFO:__main__:/opt/ml/input/data/bucket:
total 12
drwxr-xr-x 2 root root 4096 Sep 14 21:55 .
drwxr-xr-x 3 root root 4096 Sep 14 21:55 ..
-rw-r--r-- 1 root root  128 Sep 14 21:55 model.tar.gz

INFO:__main__:*** END file listing /opt/ml/input/data/bucket

Configuring the docker image

The image used to run a task can either be selected from a pre-built ones or extended with additional Dockerfile commands. The framework, framework_version and py_version CLI parameters are used to define the pre-built image, then if a path to a directory containing the Dockerfile is given by docker_file_path_or_content, it used along with aws_repo_name, repo_name and image_tag to build and push an image to ECS, and then set it as the used image. The base image should be set to __BASE_IMAGE__ within the Dockerfile, and is automatically replaced with the correct base image (according to the provided parameters above) before building it. The API parameter for the Dockerfile path is named docker_file_path_or_content and allows to provide the content of the Dockerfile, e.g.

dockerFileContent = """
# __BASE_IMAGE__ is automatically replaced with the correct base image
FROM __BASE_IMAGE__
RUN pip3 install pandas==1.1 scikit-learn==0.21.3
"""

Sample usages:

1. CLI Example 6_1- a CLI example launched by run.sh
2. single file example - API with Dockerfile content
3. single task example - API with Dockerfile path

Defining code dependencies

Additional local code dependencies can be specified with the dependencies CLI/API parameters. These dependencies are packed along with the source code, and are extracted on the root code folder in run time.

Sample usages:

1. CLI Example 6_2- a CLI example launched by run.sh
2. single task example - API

Single file example

A single file example can be found in the examples directory. First, define the runner:

dockerFileContent = """
# __BASE_IMAGE__ is automatically replaced with the correct base image
FROM __BASE_IMAGE__
RUN pip3 install pandas==1.1 scikit-learn==0.21.3
"""
file_path = Path(__file__).parent


def runner(project_name="simple-sagemaker-sf", prefix="", postfix="", output_path=None):
    from simple_sagemaker.sm_project import SageMakerProject

    sm_project = SageMakerProject(prefix + project_name + postfix)
    # define the code parameters
    sm_project.setDefaultCodeParams(
        source_dir=None, entry_point=__file__, dependencies=[]
    )
    # define the instance parameters
    sm_project.setDefaultInstanceParams(instance_count=2, max_run_mins=15)
    # docker image
    sm_project.setDefaultImageParams(
        aws_repo_name="task_repo",
        repo_name="task_repo",
        image_tag="latest",
        docker_file_path_or_content=dockerFileContent,
    )
    image_uri = sm_project.buildOrGetImage(
        instance_type=sm_project.defaultInstanceParams.instance_type
    )

    # *** Task 1 - process input data
    task1_name = "task1"
    # set the input data
    input_data_path = file_path / "data"
    # run the task
    sm_project.runTask(
        task1_name,
        image_uri,
        input_distribution="ShardedByS3Key",  # distribute the input files among the workers
        hyperparameters={"worker": 1, "arg": "hello world!", "task": 1},
        input_data_path=str(input_data_path) if input_data_path.is_dir() else None,
        clean_state=True,  # clean the current state, also forces re-running
    )
    # download the results
    if not output_path:
        output_path = file_path / "output"
    shutil.rmtree(output_path, ignore_errors=True)
    sm_project.downloadResults(task1_name, Path(output_path) / "output1")

An additional task that depends on the previous one can now be scheduled as well:

    # *** Task 2 - process the results of Task 1
    task2_name = "task2"
    # set the input
    additional_inputs = {
        "task2_data": sm_project.getInputConfig(task1_name, "model"),
        "task2_data_dist": sm_project.getInputConfig(
            task1_name, "model", distribution="ShardedByS3Key"
        ),
    }
    # run the task
    sm_project.runTask(
        task2_name,
        image_uri,
        hyperparameters={"worker": 1, "arg": "hello world!", "task": 2},
        clean_state=True,  # clean the current state, also forces re-running
        additional_inputs=additional_inputs,
    )
    # download the results
    sm_project.downloadResults(task2_name, Path(output_path) / "output2")

    return sm_project

Then, the worker code (note: the same function is used for the two different tasks, depending on the task hyperparameter):

def worker():
    from worker_toolkit import worker_lib

    logger.info("Starting worker...")
    # parse the arguments
    worker_config = worker_lib.WorkerConfig()

    logger.info(f"Hyperparams: {worker_config.hps}")
    logger.info(f"Input data files: {list(Path(worker_config.channel_data).rglob('*'))}")
    logger.info(f"State files: { list(Path(worker_config.state).rglob('*'))}")

    if int(worker_config.hps["task"]) == 1:
        # update the state per running instance
        open(
            f"{worker_config.instance_state}/state_{worker_config.current_host}", "wt"
        ).write("state")
        # write to the model output directory
        for file in Path(worker_config.channel_data).rglob("*"):
            if file.is_file():
                relp = file.relative_to(worker_config.channel_data)
                path = Path(worker_config.model_dir) / (
                    str(relp) + "_proc_by_" + worker_config.current_host
                )
                path.write_text(
                    file.read_text() + " processed by " + worker_config.current_host
                )
        open(
            f"{worker_config.model_dir}/output_{worker_config.current_host}", "wt"
        ).write("output")
    elif int(worker_config.hps["task"]) == 2:
        logger.info(
            f"Input task2_data: {list(Path(worker_config.channel_task2_data).rglob('*'))}"
        )
        logger.info(
            f"Input task2_data_dist: {list(Path(worker_config.channel_task2_data_dist).rglob('*'))}"
        )

    # mark the task as completed
    worker_config.markCompleted()
    logger.info("finished!")

To pack everything in a single file, we use the command line argument --worker (as defined in the runner function) to distinguish between runner and worker runs

import logging
import shutil
import sys
from pathlib import Path

logger = logging.getLogger(__name__)

...

def main():
    logging.basicConfig(stream=sys.stdout, level=logging.INFO)
    if "--worker" in sys.argv:
        worker()
    else:
        runner()


if __name__ == "__main__":
    main()

Running the file, with a sibling directory named data with a sample file as on the example, prduces the following outputs for Task 1:

INFO:__main__:Hyperparams: {'arg': 'hello world!', 'task': 1, 'worker': 1}
INFO:__main__:Input data files: [PosixPath('/opt/ml/input/data/data/sample_data1.txt')]
INFO:__main__:State files: [PosixPath('/state/algo-1')]
INFO:worker_toolkit.worker_lib:Marking instance algo-1 completion
INFO:worker_toolkit.worker_lib:Creating instance specific state dir
INFO:__main__:finished!

INFO:__main__:Hyperparams: {'arg': 'hello world!', 'task': 1, 'worker': 1}
INFO:__main__:Input data files: [PosixPath('/opt/ml/input/data/data/sample_data2.txt')]
INFO:__main__:State files: [PosixPath('/state/algo-2')]
INFO:worker_toolkit.worker_lib:Marking instance algo-2 completion
INFO:worker_toolkit.worker_lib:Creating instance specific state dir
INFO:__main__:finished!

And the following for Task 2:

INFO:__main__:Hyperparams: {'arg': 'hello world!', 'task': 2, 'worker': 1}
INFO:__main__:Input data files: [PosixPath('worker_toolkit'), PosixPath('example.py'), PosixPath('worker_toolkit/worker_lib.py'), PosixPath('worker_toolkit/__pycache__'), PosixPath('worker_toolkit/__init__.py'), PosixPath('worker_toolkit/__pycache__/__init__.cpython-38.pyc'), PosixPath('worker_toolkit/__pycache__/worker_lib.cpython-38.pyc')]
INFO:__main__:State files: [PosixPath('/state/algo-1')]
INFO:__main__:Input task2_data: [PosixPath('/opt/ml/input/data/task2_data/model.tar.gz')]
INFO:__main__:Input task2_data_dist: [PosixPath('/opt/ml/input/data/task2_data_dist/model.tar.gz')]
INFO:worker_toolkit.worker_lib:Marking instance algo-1 completion
INFO:worker_toolkit.worker_lib:Creating instance specific state dir

INFO:__main__:Hyperparams: {'arg': 'hello world!', 'task': 1, 'worker': 1}
INFO:__main__:Input data files: [PosixPath('/opt/ml/input/data/data/sample_data2.txt')]
INFO:__main__:State files: [PosixPath('/state/algo-2')]
INFO:worker_toolkit.worker_lib:Marking instance algo-2 completion
INFO:worker_toolkit.worker_lib:Creating instance specific state dir
INFO:__main__:finished!

As mentioned, the complete code can be found in this directory,

Development

Pushing a code change

1. Develop ...
2. Format & lint

tox -e cf
tox -e lint

3. Cleanup

tox -e clean

3. Test

tox

4. Generate & test coverage

tox -e report

5. [Optionally] - bump the version string on /src/simple_sagemaker/init to allow the release of a new version
6. Push your code to a development branch
   • Every push is tested for linting + some
7. Create a pull request to the master branch
   • Every master push is fully tested
8. If the tests succeed, the new version is published to PyPi

Open issues

1. S3_sync doesn't delete remote files if deleted locally + optimization
2. Handling spot instance / timeout termination / signals
3. Full documentation of the APIs (Readme / Read the docs + CLI?)
4. Add support for additional SageMaker features:
   • Built in algorithms
   • More frameworks
   • Experiments
   • Debugger
   • Automatic Tuning