I was taking a look at Hub—the dataset format for AI—and noticed that hub integrates with GCP and AWS. I was wondering if it also supported integrations with MinIO.
I know that Hub allows you to directly stream datasets from cloud storage to ML workflows but I’m not sure which ML workflows it integrates with.
I would like to use MinIO over S3 since my team has a self-hosted MinIO instance (aka it's free).
Hub allows you to load data from anywhere. Hub works locally, on Google Cloud, MinIO, AWS as well as Activeloop storage (no servers needed!). So, it allows you to load data and directly stream datasets from cloud storage to ML workflows.
You can find more information about storage authentication in the Hub docs.
Then, Hub allows you to stream data to PyTorch or TensorFlow with simple dataset integrations as if the data were local since you can connect Hub datasets to ML frameworks.
I've been enjoying the free colab TPUs and I am looking to upgrade to the GCP ones, but I am a little concerned about the time limits for TPU colabs, I heard colab only allows a certain number of hours for each user.
So I am wondering if I could just use a CPU or GPU instance, and connect to the TPU on my GCP.
I am beginner with tensorflow and now in a project where I need to deploy distributed production platform for tensorflow. I appreciate if I could get some help to clarify my thought.
Reading the online doument, and youtube,
I understood that main components for distributed production are below.
TFX (Tensorflow extended) built with python 3.x
Pipeline: Apache Beam
Orchestrator: Apache Airflow or Kubeflow
However for orchestrator, I assume that there are pros and cons for both components but which one is the de facto standard for TFX ?
The guide mainly focus in Airflow so I thought this might be the one but kubeflow seems to be new so it might be the new challenger.
Note: The current revision of this user guide primarily discusses deployment on a bare-metal system using Apache Airflow for orchestration.
Thanks,
Yu
I think Kubernetes/Kubeflow is the best orchestrator, however, it brings a lot of upfront costs in setting up and managing your own cluster.
Google just released VertexAI pipelines which is a managed (serverless) service where GCP manages Kubernetes under the covers for you and you can just focus on writing pipeline code.
I highly recommend using it as if very affordable and straight forward to set up. https://cloud.google.com/vertex-ai/docs/pipelines/introduction
In my company we saved millions of dollars in operational and maintenance costs by using VertexAI pipelines.
Just to complete this answer, there are some cons associated with VertexAI. It is a Pre-GA offering so there are still some small issues here and there that I run into but I would say its 90% functional and we are using it to orchestrate our end-to-end machine learning workflows as well as automating some of our analytics and data validation workloads.
I have a Tensorflow model which is working perfectly fine on my laptop (Tf 1.8 on OS HighSierra). However, I wanted to scale my operations up and use Amazon's Virtual Machine to run predictions faster. What is the best way to use my saved model and classify images in jpeg format which are stored locally? Thank you!
you have two options:
1) Start a virtual machine on AWS (known as an Amazon EC2 instance). You can pick from many different instance types, including GPU instances. You'll have full administrative access on this machine, meaning that you can copy you TF model to it and predict just like you would on your own machine.
More details on getting started with EC2 here: https://aws.amazon.com/ec2/getting-started/
I would also recommend using the Deep Learning Amazon Machine Image, which bundles all the popular ML/DL tools as well as the NVIDIA environment for GPU training/prediction : https://aws.amazon.com/machine-learning/amis/
2) If you don't want to manage virtual machines, I'd recommend looking at Amazon SageMaker. You'll be able to import your TF model and to deploy it on fully-managed infrastructure for prediction.
Here's a sample notebook showing you how to bring your own TF model to SageMaker: https://github.com/awslabs/amazon-sagemaker-examples/blob/master/advanced_functionality/tensorflow_iris_byom/tensorflow_BYOM_iris.ipynb
Hope this helps.
While working on Udacity Deep Learning assignments, I encountered memory problem. I need to switch to a cloud platform. I worked with AWS EC2 before but now I would like to try Google Cloud Platform (GCP). I will need at least 8GB memory. I know how to use docker locally but never tried it on the cloud.
Is there any ready-made solution for running Tensorflow on GCP?
If not, which service (Compute Engine or Container Engine) would make it easier to get started?
Any other tip is also appreciated!
Summing up the answers:
AI Platform Notebooks - One click Jupyter Lab environment
Deep Learning VMs images - Raw VMs with ML libraries pre-installed
Deep Learning Container Images - Containerized versions of the DLVM images
Cloud ML
Manual installation on Compute Engine. See instructions below.
Instructions to manually run TensorFlow on Compute Engine:
Create a project
Open the Cloud Shell (a button at the top)
List machine types: gcloud compute machine-types list. You can change the machine type I used in the next command.
Create an instance:
gcloud compute instances create tf \
--image container-vm \
--zone europe-west1-c \
--machine-type n1-standard-2
Run sudo docker run -d -p 8888:8888 --name tf b.gcr.io/tensorflow-udacity/assignments:0.5.0 (change the image name to the desired one)
Find your instance in the dashboard and edit default network.
Add a firewall rule to allow your IP as well as protocol and port tcp:8888.
Find the External IP of the instance from the dashboard. Open IP:8888 on your browser. Done!
When you are finished, delete the created cluster to avoid charges.
This is how I did it and it worked. I am sure there is an easier way to do it.
More Resources
You might be interested to learn more about:
Google Cloud Shell
Container-Optimized Google Compute Engine Images
Google Cloud SDK for a more responsive shell and more.
Good to know
"The contents of your Cloud Shell home directory persist across projects between all Cloud Shell sessions, even after the virtual machine terminates and is restarted"
To list all available image versions: gcloud compute images list --project google-containers
Thanks to #user728291, #MattW, #CJCullen, and #zain-rizvi
Google Cloud Machine Learning is open to the world in Beta form today. It provides TensorFlow as a Service so you don't have to manage machines and other raw resources. As part of the Beta release, Datalab has been updated to provide commands and utilities for machine learning. Check it out at: http://cloud.google.com/ml.
Google has a Cloud ML platform in a limited Alpha.
Here is a blog post and a tutorial about running TensorFlow on Kubernetes/Google Container Engine.
If those aren't what you want, the TensorFlow tutorials should all be able to run on either AWS EC2 or Google Compute Engine.
You now can also use pre-configured DeepLearning images. They have everything that is required for the TensorFlow.
This is an old question but there's are new, even easier options now:
If you want to run TensorFlow with Jupyter Lab
GCP AI Platform Notebooks, which gives you on-click access to a Jupyter Lab Notebook with Tensorflow pre-installed (you can also use Pytorch, R, or a few other libraries instead if you prefer).
If you just want to use a raw VM
If you don't care about Jupyer Lab and just want a raw VM with Tensorflow pre-installed, you can instead create a VM using GCP's Deep Learning VM Image. These DLVM images give you a VM with Tensorflow pre-installed and are all setup to use GPUs if you want. (The AI Platform Notebooks use these DLVM images under the hood)
If you'd like to run it on both your laptop and the cloud
Finally, if you want to be able to run tensorflow both on your personal laptop and in the cloud and are comfortable using Docker, you can use GCP's Deep Learning Container Images. It contains the exact same setup as the DLVM images, but packaged as a container instead, so you can launch these anywhere you like.
Extra benefit: If you're running this container image on your laptop, it's 100% free :D
Im not sure there if there is a need for you to stay on the Google Cloud platform. If you are able to use other products you might save a lot of time, and some money.
If you are using TensorFLow I would recommend a platform called TensorPort. It is exclusively for TesnorFlow and is the easy platform I am aware of. Code and data are loaded with git and they provide a python module for automatic toggling of paths between remote and your local machine. They also provide some boiler plate code for setting up distributed computing if you need it. Hope this helps.