I am considering using the AWS with the machine learning AMI for training some deep networks that are to slow for my hardware setup.
However I see at the moment two possible major issues that might make this option less interesting or even impossible.
The training data is not in csv format, but images in nifti format. In the AWS description, it is stated that the data has to be in .csv.
Additionally, the FAQ states that trained models cannot be extracted. Which means that all sub-sequential inference and testing has to be made depending on instances in the AWS?
Are both of these issues real?
Yes, I assume you can use only csv format for training data:
http://docs.aws.amazon.com/machine-learning/latest/dg/step-1-download-edit-and-upload-data.html
AWS Machine Learning Datasources
and finally Data from other products can usually be exported into CSV files in Amazon S3, making it accessible to Amazon Machine Learning
It seems that csv is the only format so far, I found it a bit frustrating myself...
And yes, as Machine Learning FAQ indicate:
Q: Can I export my models out of Amazon Machine Learning?
A: No.
So, so far, no way to save your model...
You can probably create a C5.large (compute optimized) instance and install all the Python libraries needed for your machine learning projects. Then use scikit-learn feature to save your model.
If C5.large is not going to be enough you can easily scale it up, just use EBS storage for this instance.
I hope this verification helps
Related
I have the task of optimizing search on the website. The search should be for pictures and for text by text query. I have already developed, trained, tested and selected a machine learning model that transforms images and text into a feature vector (Python, based on OpenAI CLIP). This feature vector will be transferred to Elastic Search. Elastic Search will be configured by another specialist.
The model will be used first to determine the feature vector on all existing images and texts, and then be used whenever new content is added or existing content is changed.
There is a lot of existing content (approximately several tens of millions of pictures and texts together). About 100-500 pieces of content are added and changed per day.
I haven't worked much with AWS, but in this case the model needs to be deployed to AWS somehow. Of course, I have the model and the entire project locally, I can write an API app and make a Docker container.
The question is, what is the best method to deploy this application on AWS? The best in terms of speed and ease of implementation (for me as an AWS beginner), as well as cost optimization, taking into account the number of requests for the application.
I've seen different possibilities, from simply deploying the application on EC2 (probably the easiest option) to using SageMaker. Also Kubernetes and ECS...
I'd recommend using SageMaker Hosting endpoint if you need to be able to run vectorization in near-real time any time of the day, or in a SageMaker Training job if you can run vectorization batched, for example once every few hour.
For both systems you can use pre-defined Framework containers and SDK to which you pass a Python code and optionally requirements.txt, or you can create your own image.
I've been reading some articles regarding this topic and have preliminary thoughts as what I should do with it, but still want to see if anyone can share comments if you have more experience with running machine learning on AWS. I was doing a project for a professor at school, and we decided to use AWS. I need to find a cost-effective and efficient way to deploy a forecasting model on it.
What we want to achieve is:
read the data from S3 bucket monthly (there will be new data coming in every month),
run a few python files (.py) for custom-built packages and install dependencies (including the files, no more than 30kb),
produce predicted results into a file back in S3 (JSON or CSV works), or push to other endpoints (most likely to be some BI tools - tableau etc.) - but really this step can be flexible (not web for sure)
First thought I have is AWS sagemaker. However, we'll be using "fb prophet" model to predict the results, and we built a customized package to use in the model, therefore, I don't think the notebook instance is gonna help us. (Please correct me if I'm wrong) My understanding is that sagemaker is a environment to build and train the model, but we already built and trained the model. Plus, we won't be using AWS pre-built models anyways.
Another thing is if we want to use custom-built package, we will need to create container image, and I've never done that before, not sure about the efforts to do that.
2nd option is to create multiple lambda functions
one that triggers to run the python scripts from S3 bucket (2-3 .py files) every time a new file is imported into S3 bucket, which will happen monthly.
one that trigger after the python scripts are done running and produce results and save into S3 bucket.
3rd option will combine both options:
- Use lambda function to trigger the implementation on the python scripts in S3 bucket when the new file comes in.
- Push the result using sagemaker endpoint, which means we host the model on sagemaker and deploy from there.
I am still not entirely sure how to put pre-built model and python scripts onto sagemaker instance and host from there.
I'm hoping whoever has more experience with AWS service can help give me some guidance, in terms of more cost-effective and efficient way to run model.
Thank you!!
I would say it all depends on how heavy your model is / how much data you're running through it. You're right to identify that Lambda will likely be less work. It's quite easy to get a lambda up and running to do the things that you need, and Lambda has a very generous free tier. The problem is:
Lambda functions are fundamentally limited in their processing capacity (they timeout after max 15 minutes).
Your model might be expensive to load.
If you have a lot of data to run through your model, you will need multiple lambdas. Multiple lambdas means you have to load your model multiple times, and that's wasted work. If you're working with "big data" this will get expensive once you get through the free tier.
If you don't have much data, Lambda will work just fine. I would eyeball it as follows: assuming your data processing step is dominated by your model step, and if all your model interactions (loading the model + evaluating all your data) take less than 15min, you're definitely fine. If they take more, you'll need to do a back-of-the-envelope calculation to figure out whether you'd leave the Lambda free tier.
Regarding Lambda: You can literally copy-paste code in to setup a prototype. If your execution takes more than 15min for all your data, you'll need a method of splitting your data up between multiple Lambdas. Consider Step Functions for this.
SageMaker is a set of services that each is responsible for a different part of the Machine Learning process. What you might want to use is the hosted version of Jupyter notebooks in SageMaker. You get a lot of freedom in the size of the instance that you are using (CPU/GPU, memory, and disk), and you can install various packages on that instance (such as FB Prophet). If you need it once a month, you can stop and start the notebook instances between these times and "Run all" the cells in your notebooks on this instance. It will only cost you the minutes of execution.
regarding the other alternatives, it is not trivial to run FB Prophet in Lambda due to the size limit of the libraries that you can install on Lambda (to avoid too long cold start). You can also use ECS (container Service) where you can have much larger images, but you need to know how to build a Docker image of your code and endpoint to be able to call it.
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)
I got some data, which is 3.2 million entries in a csv file. I'm trying to use CNN estimator in tensorflow to train the model, but it's very slow. Everytime I run the script, it got stuck, like the webpage(localhost) just refuse to respond anymore. Any recommendations? (I've tried with 22 CPUs and I can't increase it anymore)
Can I just run it and use a thread, like the command line python xxx.py & to keep the process going? And then go back to check after some time?
Google offers serverless machine learning with TensorFlow for precisely this reason. It is called Cloud ML Engine. Your workflow would basically look like this:
Develop the program to train your neural network on a small dataset that can fit in memory (iron out the bugs, make sure it works the way you want)
Upload your full data set to the cloud (Google Cloud Storage or BigQuery or &c.) (documentation reference: training steps)
Submit a package containing your training program to ML Cloud (this will point to the location of your full data set in the cloud) (documentation reference: packaging the trainer)
Start a training job in the cloud; this is serverless, so it will take care of scaling to as many machines as necessary, without you having to deal with setting up a cluster, &c. (documentation reference: submitting training jobs).
You can use this workflow to train neural networks on massive data sets - particularly useful for image recognition.
If this is a little too much information, or if this is part of a workflow that you'll be doing a lot and you want to get a stronger handle on it, Coursera offers a course on Serverless Machine Learning with Tensorflow. (I have taken it, and was really impressed with the quality of the Google Cloud offerings on Coursera.)
I am sorry for answering even though I am completely igonorant to what datalab is, but have you tried batching?
I am not aware if it is possible in this scenario, but insert maybe only 10 000 entries in one go and do this in so many batches that eventually all entries have been inputted?
I'm trying to use AWS EC2's GPU to train deep learning models. My question is how ML practitioners would usually store datasets for training a model on EC2 (Are there best practices?). For now, I'd like to access datasets on my local PC but would it be possible? If so, could you point me a source or give me a direction?
Some specifics:
Machine learning task: Image classification such as CNNs
Data size: 1 million images (mostly 63*63) but I’m using sample datasets for research phase
Training/predicting frequency: Not often (for research phase)
Thanks!