Introducing eADL XS | The next-level in fleet wide dataloading automation

Loading large volumes of data into Snowflake no longer requires external cloud storage or extra credentials. With the latest release of our SSIS Integration Toolkit, the Snowflake Destination component now natively supports Snowflake Internal Stages, delivering a streamlined bulk loading experience fully contained within your existing Snowflake environment.

A data loading utility to reduce requests to a backend via batching and caching.. Latest version: 2.2.2, last published: 2 years ago. Start using dataloader in your project by running `npm i dataloade...

When loading data into Salesforce, it is extremely common to have errors occur. Here is a list of 10 of the most common errors you will see, what the errors mean, and how to resolve them.

At GPU scale, failures are routine. See how smart dataloading and checkpointing keep training fast between failures and cheap to recover after them.

At GPU scale, failures are routine. See how smart dataloading and checkpointing keep training fast between failures and cheap to recover after them.

🔥 Introduction

Data loading is a crucial aspect of implementing Odoo in any business environment. When setting up a new Odoo instance or migrating data from legacy systems, you'll often need to import large volumes of records efficiently. Fortunately, Odoo provides flexible options for creating records using XML and CSV files. In this comprehensive tutorial, I'll explain how to create records using these file formats, highlight their differences, and provide practical examples to help you master data loading in Odoo.

Fix DB2 mass load error code -668 reason code 1. This guide details a solution using global temporary tables to bypass the error and enable successful SELECT operations on your MASS_TABLE.

Thanks John After reading both posts, its not clear that there is a resolution to the reported behavior. I see that Quentin recommended `dataset.with_format("torch")`. I tried this, but it slows down the dataloading compared to the dataset with the “with_format” method, almost by a factor of 2. I have spent some time observing the different behavior of the two methods. Without the “with_format” it loads data in very quickly, but gets hung-up at certain points, at which the dataloader is suspended few seconds, sometimes tens of seconds, while when using “with_format” it never gets hung-up, but as I previously said is only half as fast. Both methods take up considerable memory, although “with_format” a little less. Furthermore whether using “with_format” or not, dataloading with streaming appears to need 5-6 GB per worker, and may be growing and shrinking unpredictably throughout training. Is there anyone that can give me a rationalization for this behavior during streaming? If it is streaming, why is the memory footprint still kinda large. A single batch has maybe 20,000 float32s, and thus should have a running memory footprint on the order of MB, not GB, right?

I’m trying to train a model on a large (600GB on disk) dataset of pre-computed embedding vectors. Training is i/o bound (loading embeddings from disk). I’m trying to increase the speed of dataloading by using multiple workers, but running into memory issues. Some benchmarks: Using 32 workers, I get about 3hr/epoch but OOM about 45 minutes in Using 0 workers, I get about 8hr/epoch with no memory issues Using 32>n>0 workers slows down the rate of memory consumption, but doesn’t stop the core issue. I believe this issue is caused by pytorch’s dataloader workers replicating memory in-process (link), but I don’t know how to stop that. Is there a fix to this, or a way to periodically restart pytorch dataloader workers to clear memory? Some notes: Datasets are formatted for pytorch before training Changing to an iterable dataset slows down the rate of memory accumulation for the same number of workers, but doesn’t stop it this is using: torch==2.2.1 transformers==4.51.3 datasets==2.19.1 accelerate==1.6.0