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27 × Eureka!Is there documentation for (2) available for evaluation?
For now I am trying to achieve (1). But the goal is (2)
I just installed trains[azure]. Since all my data is on Azure. I don't know about StorageManager.
I see that _AzureBlobServiceStorageDriver need to be updated. Anything else?
There already seems to be support for multiple containers in the code.
Is there an example to configure multiple storage accounts?
I have ~100GB of data that I do not wish to upload to the trains-server. Instead, I would like to have them copied only to host machine (azure container) at training time.
The data is in Azure blob storage and will be copied using a custom script just before training starts.
🙂 I could not locate this file!
I did git clone, not pip install
I surely can, will let you know.
Looks like a mongodb and NTFS issue
https://github.com/docker-library/mongo/issues/190
Web server port is modified and changed c:\opt to d:\opt
Checked. Only change I had to make was to increase memory to 4GB. Still there are errors.
I think errors are related to network and permission.
Will try, thank you.
Federated learning is about sending code to where data exists, training local models and aggregating them in a central place.
Can existing design support this or extensions need to be built?
docker volume create --name=mongodata
I use AzureML, and like to try trains.
First, how to setup trains-server on Azure.
And then...
AzureML allows to trains on low prio clusters.
How can I configure and setup low prio training clusters and connect them to trains.
Would be nice to have a reference implementation
where do I run trains-init from?
Also, each task might need its own configuration. Data are usually stored in multiple containers. Rather than a single configuration, there should be possibility to do it per task.
I tried a slightly different approach that seems to work.
docker volume create --name=mongodata
And configured mogodat data in docker-compose file
Above command and yaml file are working in Win10
Sure, let me test its completely working
trains-apiserver | [2020-07-10 13:33:29,269] [8] [ERROR] [trains.updates] Failed obtaining updates
trains-apiserver | Traceback (most recent call last):
trains-apiserver | File "/opt/trains/server/updates.py", line 96, in _check_updates
trains-apiserver | response = self._check_new_version_available()
trains-apiserver | File "/opt/trains/server/updates.py", line 48, in _check_new_version_available
trains-apiserver | uid = Settings.get_by_key("server.uuid")
trains-apiserver | Fil...