I have built a SparkML collaborative filtering algorithm that I want to train and deploy on Sagemaker. What is the best way to achieve this other than BYOC?
Also, I want to understand how distributed training works in Sagemaker if we go with the BYOC route.
I have tried to look for good resources on this, but documentation is pretty sparse on distributed aspect. You can provide instance_count in your Estimator but how is it used in BYOC scenario? Do we have to handle it in the training scripts, code ? Any example of doing that with SparkML?
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Context: When using SageMaker distributed training: Let’s say when training a network I do not provide any distribution parameter (keep it to default), but provide 2 instances for the instance_count value in the estimator (could be any deep learning based estimator, e.g., PyTorch).
In this scenario would there be any distributed training taking place? If so, what strategy is used by default?
NOTE: I could see both instances’ GPUs are actively used but wondering what sort of distributed training take place by default ?
If you're using custom code (custom Docker, custom code in Framework container) The answer is NO. Unless you are writing distributed code (Horovod, PyTorch DDP, MPI...), SageMaker will not distribute things for you. It will launch the same Docker or Python code N times, once per instance. Consider SageMaker Training API like a whiteboard, that can create multiple connected and configured machines for you. But the code is still yours to write. SageMaker Distributed Training Libraries can make distributed code much easier to write though.
If you're using a built-in algorithm, the answer is it depends. Some SageMaker built-in algorithms natively are multi-machine, like SM XGBoost or SM Random Cut Forest.
We’re training a big Temporal Fusion Transformer using PyTorch.
We’re looking into using Distributed Training and accelerate training jobs with SageMaker.
Does anyone have any examples of this? Any pattern you can recommend?
Although there is no direct example for the above mentioned model, you should be able to follow the below documentation for PL
https://docs.aws.amazon.com/sagemaker/latest/dg/data-parallel-modify-sdp-pt-lightning.html
Refer below example for a full example of using SageMaker DDP and Pytorch Lightning.
https://github.com/aws-samples/sagemaker-distributed-training-workshop/blob/main/1_data_parallel/PyTorch%20Lightning%20on%20SageMaker.ipynb
I'm currently running a quick Machine Learning proof of concept on AWS with SageMaker, and I've come across two libraries: sagemaker and sagemaker_pyspark. I would like to work with distributed data. My questions are:
Is using sagemaker the equivalent of running a training job without taking advantage of the distributed computing capabilities of AWS? I assume it is, if not, why have they implemented sagemaker_pyspark? Based on this assumption, I do not understand what it would offer regarding using scikit-learn on a SageMaker notebook (in terms of computing capabilities).
Is it normal for something like model = xgboost_estimator.fit(training_data) to take 4 minutes to run with sagemaker_pyspark for a small set of test data? I see that what it does below is to train the model and also create an Endpoint to be able to offer its predictive services, and I assume that this endpoint is deployed on an EC2 instance that is created and started at the moment. Correct me if I'm wrong. I assume this from how the estimator is defined:
from sagemaker import get_execution_role
from sagemaker_pyspark.algorithms import XGBoostSageMakerEstimator
xgboost_estimator = XGBoostSageMakerEstimator (
trainingInstanceType = "ml.m4.xlarge",
trainingInstanceCount = 1,
endpointInstanceType = "ml.m4.xlarge",
endpointInitialInstanceCount = 1,
sagemakerRole = IAMRole(get_execution_role())
)
xgboost_estimator.setNumRound(1)
If so, is there a way to reuse the same endpoint with different training jobs so that I don't have to wait for a new endpoint to be created each time?
Does sagemaker_pyspark support custom algorithms? Or does it only allow you to use the predefined ones in the library?
Do you know if sagemaker_pyspark can perform hyperparameter optimization? From what I see, sagemaker offers the HyperparameterTuner class, but I can't find anything like it in sagemaker_pyspark. I suppose it is a more recent library and there is still a lot of functionality to implement.
I am a bit confused about the concept of entry_point and container/image_name (both possible input arguments for the Estimator object from the sagemaker library): can you deploy models with and without containers? why would you use model containers? Do you always need to define the model externally with the entry_point script? It is also confusing that the class AlgorithmEstimator allows the input argument algorithm_arn; I see there are three different ways of passing a model as input, why? which one is better?
I see the sagemaker library offers SageMaker Pipelines, which seem to be very handy for deploying properly structured ML workflows. However, I don't think this is available with sagemaker_pyspark, so in that case, I would rather create my workflows with a combination of Step Functions (to orchestrate the entire thing), Glue processes (for ETL, preprocessing and feature/target engineering) and SageMaker processes using sagemaker_pyspark.
I also found out that sagemaker has the sagemaker.sparkml.model.SparkMLModel object. What is the difference between this and what sagemaker_pyspark offers?
sagemaker is the SageMaker Python SDK. It calls SageMaker-related AWS service APIs on your behalf. You don't need to use it, but it can make life easier
Is using sagemaker the equivalent of running a training job without taking advantage of the distributed computing capabilities of AWS? I assume it is, if not, why have they implemented sagemaker_pyspark?
No. You can run distributed training jobs using sagemaker (see instance_count parameter)
sagemaker_pyspark facilitates calling SageMaker-related AWS service APIs from Spark. Use it if you want to use SageMaker services from Spark
Is it normal for something like model = xgboost_estimator.fit(training_data) to take 4 minutes to run with sagemaker_pyspark for a small set of test data?
Yes, it takes a few minutes for an EC2 instance to spin-up. Use Local Mode if you want to iterate more quickly locally. Note: Local Mode won't work with SageMaker built-in algorithms, but you can prototype with (non AWS) XGBoost/SciKit-Learn
Does sagemaker_pyspark support custom algorithms? Or does it only allow you to use the predefined ones in the library?
Yes, but you'd probably want to extend SageMakerEstimator. Here you can provide the trainingImage URI
Do you know if sagemaker_pyspark can perform hyperparameter optimization?
It does not appear so. It'd probably be easier just to do this from SageMaker itself though
can you deploy models with and without containers?
You can certainly host your own models any way you want. But if you want to use SageMaker model inference hosting, then containers are required
why would you use model containers?
Do you always need to define the model externally with the entry_point script?
The whole Docker thing makes bundling dependencies easier, and also makes things language/runtime-neutral. SageMaker doesn't care if your algorithm is in Python or Java or Fortran. But it needs to know how to "run" it, so you tell it a working directory and a command to run. This is the entry point
It is also confusing that the class AlgorithmEstimator allows the input argument algorithm_arn; I see there are three different ways of passing a model as input, why? which one is better?
Please clarify which "three" you are referring to
6 is not a question, so no answer required :)
What is the difference between this and what sagemaker_pyspark offers?
sagemaker_pyspark lets you call SageMaker services from Spark, whereas SparkML Serving lets you use Spark ML services from SageMaker
I am aware that it is better to use aws Rekognition for this. However, it does not seem to work well when I tried it out with the images I have (which are sort of like small containers with labels on them). The text comes out misspelled and fragmented.
I am new to ML and sagemaker. From what I have seen, the use cases seem to be for prediction and image classification. I could not find one on training a model for detecting text in an image. Is it possible to to do it with Sagemaker? I would appreciate it if someone pointed me in the right direction.
The different services will all provide different levels of abstraction for Optical Character Recognition (OCR) depending on what parts of the pipeline you are most comfortable with working with, and what you prefer to have abstracted.
Here are a few options:
Rekognition will provide out of the box OCR with the DetectText feature. However, it seems you will need to perform some sort of pre-processing on your images in your current case in order to get better results. This can be done through any method of your choice (Lambda, EC2, etc).
SageMaker is a tool that will enable you to easily train and deploy your own models (of any type). You have two primary options with SageMaker:
Do-it-yourself option: If you're looking to go the route of labeling your own data, gathering a sizable training set, and training your own OCR model, this is possible by training and deploying your own model via SageMaker.
Existing OCR algorithm: There are many algorithms out there that all have different potential tradeoffs for OCR. One example would be Tesseract. Using this, you can more closely couple your pre-processing step to the text detection.
Amazon Textract (In preview) is a purpose-built dedicated OCR service that may offer better performance depending on what your images look like and the settings you choose.
I would personally recommend looking into pre-processing for OCR to see if it improves Rekognition accuracy before moving onto the other options. Even if it doesn't improve Rekognition's accuracy, it will still be valuable for most of the other options!
I have a csv file of 500GB and a mysql database of 1.5 TB of data and I want to run aws sagemaker classification and regression algorithm and random forest on it.
Can aws sagemaker support it? can model be read and trained in batches or chunks? any example for it
Amazon SageMaker is designed for such scales and it is possible to use it to train on very large datasets. To take advantage of the scalability of the service you should consider a few modifications to your current practices, mainly around distributed training.
If you want to use distributed training to allow much faster training (“100 hours of a single instance cost exactly the same as 1 hour of 100 instances, just 100 times faster”), more scalable (“if you have 10 times more data, you just add 10 times more instances and everything just works”) and more reliable, as each instance is only handling a small part of the datasets or the model, and doesn’t go out of disk or memory space.
It is not obvious how to implement the ML algorithm in a distributed way that is still efficient and accurate. Amazon SageMaker has modern implementations of classic ML algorithms such as Linear Learner, K-means, PCA, XGBoost etc. that are supporting distributed training, that can scale to such dataset sizes. From some benchmarking these implementations can be 10 times faster compared to other distributed training implementations such as Spark MLLib. You can see some examples in this notebook: https://github.com/awslabs/amazon-sagemaker-workshop/blob/master/notebooks/video-game-sales-xgboost.ipynb
The other aspect of the scale is the data file(s). The data shouldn’t be in a single file as it limits the ability to distribute the data across the cluster that you are using for your distributed training. With SageMaker you can decide how to use the data files from Amazon S3. It can be in a fully replicated mode, where all the data is copied to all the workers, but it can also be sharded by key, that distributed the data across the workers, and can speed up the training even further. You can see some examples in this notebook: https://github.com/awslabs/amazon-sagemaker-examples/tree/master/advanced_functionality/data_distribution_types
Amazon Sagemaker is built to help you scale your training activities. With large datasets, you might consider two main aspects:
The way data are stored and accessed,
The actual training parallelism.
Data storage: S3 is the most cost-effective way to store your data for training. To get faster startup and training times, you can consider the followings:
If your data is are already stored on Amazon S3, you might want first to consider leveraging the Pipe mode with built-in algorithms or bringing your own. But Pipe mode is not suitable all the time, for example, if your algorithm needs to backtrack or skip ahead within an epoch (the underlying FIFO cannot support lseek() operations) or if it is not easy to parse your training dataset from a streaming source.
In those cases, you may want to leverage Amazon FSx for Lustre and Amazon EFS file systems. If your training data is already in an Amazon EFS, I recommend using it as a data source; otherwise, choose Amazon FSx for Lustre.
Training Parallelism: With large datasets, it is likely you'll want to train on different GPUs. In that case, consider the followings:
If your training is already Horovod ready, you can do it with Amazon SageMaker (notebook).
In December, AWS has released managed data parallelism, which simplifies parallel training over multiple GPUs. As of today, it is available for TensorFlow and PyTorch.
(bonus) Cost Optimisation: Do not forget to leverage Managed Spot training to save up to 90% of the compute costs.
You will find other examples on the Amazon SageMaker Distributed Training documentation page
You can use SageMaker for large scale Machine Learning tasks! It's designed for that. I developed this open source project https://github.com/Kenza-AI/sagify (sagify), it's a CLI tool that can help you train and deploy your Machine Learning/Deep Learning models on SageMaker in a very easy way. I managed to train and deploy all of my ML models whatever library I was using (Keras, Tensorflow, scikit-learn, LightFM, etc)