I want to train Yolact on a custom dataset using Google Colab+.
Is it possible to train on Colab+ or does it time out to easily?
Thank you!
Yes, you can train your model on Colab+. The problem is that Colab has a relatively short lifecycle compared with other cloud platforms such as AWS SageMaker or Google Cloud. I run the code below to extend a bit more such time.
%%javascript
function ClickConnect(){
console.log("Working");
document.querySelector("colab-toolbar-button#connect").click()
}setInterval(ClickConnect,50000)
Related
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?
I have created a Vertex AI AutoML image classification model. How can I assess it for overfitting? I assume I should be able to compare training vs validation accuracy but these do not seem to be available.
And if it is overfitting,can I tweak regularization parameters? Is it already doing cross validation? Anything else that can be done? (More data,early stopping, dropouts ie how can these be done?)
Deploy it to endpoint and test result with sample images by uploading to endpoint. If it's overfitting you can see the stats in analysis. You can increase the training sample and retrain your model again to get better result.
In the SageMaker hyper parameter tuning jobs, you can use a RegEx expression to parse your logs and output a objective metric to the web console. Is it possible to do this during a normal training job?
It would be great to have this feature so I don't need to look through all the logs to find the metric.
Thank you for your suggestion! We will incorporate your feedback into our roadmap planning and prioritize this feature accordingly. As always, we deliver a feature as fast as we can if we see strong customer needs in it.
Thanks for using Amazon Sagemaker !!!
Could you just help me with the following points:
Can we train the tensorflow custom object detection model in SageMaker of AWS?
I came across SageMaker's Image classification Algorithm? Can we use it to detect particular objects in Video after training the model?
Confused with the pricing plan of SageMaker. They are saying "you are offered a monthly free tier of 250 hours of t2.medium notebook usage"; Does that mean we can use t2.medium notebook free for 250 hours?
Final AIM is to train a model for custom object detection like we used to train in paperspace or floydhub in very less price.
Thanks in advance.
1- Sure. You can bring any TensorFlow code to SageMaker. https://docs.aws.amazon.com/sagemaker/latest/dg/tf-examples.html
2- This is a classification model (labels only), not a detection model (labels + bounding boxes). Having said that, yes, you can definitely use it to predict frames extracted from a video.
3- Yes, in the first 12 months following the creation of your AWS account.
Hope this helps.
Any TensorFlow model can be used/ported to SageMaker. You can find examples of TensorFlow models ported to SageMaker here https://github.com/awslabs/amazon-sagemaker-examples/tree/master/sagemaker-python-sdk#amazon-sagemaker-examples.
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?