tune | Tools for tidy parameter

If we look at the documentation of last_fit() We see that split must be

An rsplit object created from `rsample::initial_split().

You accidentally passed the cross-validation folds object stock_folds into split but you should have passed rsplit object stock_split instead

While I do not know how to deal with this problem directly, I had a somewhat similar issue(and solved). The difference is:

• I use fairseq
• I can run my code on google colab with 1 GPU
• Got RuntimeError: unable to mmap 280 bytes from file : Cannot allocate memory (12) immediately when I tried to run it on multiple GPUs.

From the other people's code, I found that he uses python -m torch.distributed.launch -- ... to run fairseq-train, and I added it to my bash script and the RuntimeError is gone and training is going.

So I guess if you can run with 21000 samples, you may use torch.distributed to make whole data into small batches and distribute them to several workers.

You can use sklearn to calculate f1_score

Why so?

Some debugging will help here. Take the first line for example:

Raster or bitmap fonts are represented in a number of different ways and there are bitmap font file standards that have been developed for both Linux and Windows. However raw data representation of bitmap fonts in programming language source code seems to vary depending on:

• the memory architecture of the target computer,
• the architecture and communication pathways to the display controller,
• character glyph height and width in pixels and
• the amount of memory for bitmap storage and what measures are taken to make that as small as possible.

A brief overview of bitmap fonts

A generic bitmap is a block of data in which individual bits are used to indicate a state of either on or off. One use of a bitmap is to store image data. Character glyphs can be created and stored as a collection of images, one for each character in the character set, so using a bitmap to encode and store each character image is a natural fit.

Bitmap fonts are bitmaps used to indicate how to display or print characters by turning on or off pixels or printing or not printing dots on a page. See Wikipedia Bitmap fonts

A bitmap font is one that stores each glyph as an array of pixels (that is, a bitmap). It is less commonly known as a raster font or a pixel font. Bitmap fonts are simply collections of raster images of glyphs. For each variant of the font, there is a complete set of glyph images, with each set containing an image for each character. For example, if a font has three sizes, and any combination of bold and italic, then there must be 12 complete sets of images.

A brief history of using bitmap fonts

The earliest user interface terminals such as teletype terminals used dot matrix printer mechanisms to print on rolls of paper. With the development of Cathode Ray Tube terminals bitmap fonts were readily transferable to that technology as dots of luminescence turned on and off by a scanning electron gun.

Earliest bitmap fonts were of a fixed height and width with the bitmap acting as a kind of stamp or pattern to print characters on the output medium, paper or display tube, with a fixed line height and a fixed line width such as the 80 columns and 24 lines of the DEC VT-100 terminal.

With increasing processing power, a more sophisticated typographical approach became available with vector fonts used to improve displayed text quality and provide improved scaling while also reducing memory required to describe the character glyphs.

In addition, while a matrix of dots or pixels worked fairly well for languages such as English, written languages with complex glyph forms were poorly served by bitmap fonts.

Representation of bitmap fonts in source code

There are a number of bitmap font file formats which provide a way to represent a bitmap font in a device independent description. For an example see Wikipedia topic - Glyph Bitmap Distribution Format

The Glyph Bitmap Distribution Format (BDF) by Adobe is a file format for storing bitmap fonts. The content takes the form of a text file intended to be human- and computer-readable. BDF is typically used in Unix X Window environments. It has largely been replaced by the PCF font format which is somewhat more efficient, and by scalable fonts such as OpenType and TrueType fonts.

Other bitmap standards such as XBM, Wikipedia topic - X BitMap, or XPM, Wikipedia topic - X PixMap, are source code components that describe bitmaps however many of these are not meant for bitmap fonts specifically but rather other graphical images such as icons, cursors, etc.

As bitmap fonts are an older format many times bitmap fonts are wrapped within another font standard such as TrueType in order to be compatible with the standard font subsystems of modern operating systems such as Linux and Windows.

However embedded systems that are running on the bare metal or using an RTOS will normally need the raw bitmap character image data in the form similar to the XBM format. See Encyclopedia of Graphics File Formats which has this example:

Following is an example of a 16x16 bitmap stored using both its X10 and X11 variations. Note that each array contains exactly the same data, but is stored using different data word types:

Indeed, Regularizations are constraints that are added to the loss function. The model when minimizing the loss function will have to also minimize the regularization term. Hence, This will reduce the model variance as it cannot overfit.

Acceptable parameters for l2_regularization are often on a logarithmic scale between 0 and 0.1, such as 0.1, 0.001, 0.0001.

TL;DR

.parallelStream() does not guarantee multi threading since it will create only threads equals to number of cores you have. To really enforce multiple threads doing this simultaneously, you need to use .parallelStream() with ForkJoinPool.

High Performance using ForkJoinPool

Below is a list of parameters which I found helpful in tuning of the job, I will keep appending this with whenever I found out use case for a parameter

Parameter What to look for spark.scheduler.mode FAIR or FIFO, This decides how you want to allocate executors to jobs executor-memory Check OOM in executors if you find they are going OOM probably this is the reason or check for executor-cores values, wheather they are too small causing the load on executors
https://spoddutur.github.io/spark-notes/distribution_of_executors_cores_and_memory_for_spark_application.html driver-memory If you are doing a collect kind of operation (i.e. any operation that sends data back to Driver) then look for tuning this value executor-cores Its value really depends on what kind of processing you are looking for is it a multi-threaded approach/ light process. The below doc can help you to understand it better
https://spoddutur.github.io/spark-notes/distribution_of_executors_cores_and_memory_for_spark_application.html spark.default.parallelism This helped us a quite bit in reducing job execution time, initially run the job without this value & observe what value is default set by the cluster (it does base on cluster size). If you see the value too high then try to reduce it, We generally reduced it to the below logic

number of Max core nodes * number of threads per machine + number of Max on-demand nodes * number of threads per machine + number of Max spot nodes * number of threads per machine spark.sql.shuffle.partitions This value is used when your job is doing quite shuffling of data e.g. DF with cross joins or inner join when it's not repartitioned on joins clause dynamic executor allocation This helped us quite a bit from the pain of allocating the exact number of the executors to the job. Try to tune below spark.dynamicAllocation.minExecutors To start your application these numbers of executors are needed else it will not start. This is quite helpful when you don't want to make your job crawl on 1 or 2 available executors spark.dynamicAllocation.maxExecutors Max amount of executors can be used to ensure the job does not end up consuming all cluster resources in case its multi-job cluster running parallel jobs spark.dynamicAllocation.initialExecutors This is quite helpful when the driver is doing some initial job before spawning the jobs to executors e.g. listing the files in a folder so it will delete only those files at end of the job. This ensures you can provide min executors but can get a head start with fact know that driver is going to take some time to start spark.dynamicAllocation.executorIdleTimeout This is also helpful in the above-mentioned case where the driver is doing some work & has nothing to assign to the executors & you don't want them to time out causing reallocation of executors which will take some time
https://spark.apache.org/docs/2.4.5/configuration.html#dynamic-allocation Trying to reduce the number of files created while writing the partitions As our data is read by different executors while writing each executor will write its own file. This will end up in creating a large number of small files & in intern the query on those will be heavy. There are 2 ways to do it
Coalesce: This will try to do minimum shuffle across the executors & will create an un-even file size
repartition: This will do a shuffle of data & creates files with ~ equal size
https://stackoverflow.com/questions/31610971/spark-repartition-vs-

coalescemaxRecordsPerFile: This parameter is helpful in informing spark, how many records per file you are looking for When you are joining small DF with large DF Check if you can use broadcasting of the small DF by default Spark use the sort-merge join, but if your table is quite low in size see if you can broadcast those variables
https://towardsdatascience.com/the-art-of-joining-in-spark-dcbd33d693c
How one can hint spark to use broadcasting: https://stackoverflow.com/a/37487575/814074
Below parameters you need to look for doing broadcast joins are spark.sql.autoBroadcastJoinThreshold This helps spark to understand for a given size of DF whether to used broadcast join or not spark.driver.maxResultSize Max result will be returned to the driver so it can broadcast them driver-memory As the driver is doing broadcasting of result this needs to be bigger spark.network.timeout spark.executor.heartbeatInterval This helps in the case where you see an abrupt termination of executors from drivers, there could be multiple reasons but if nothing is specifically found you can check on these parameters
https://spark.apache.org/docs/2.4.5/configuration.html#execution-behavior Data is Skewed across customers Try to find out a way where you can trigger the jobs for descending order of volume per customer. This ensures you that cluster will be well occupied during the initial run & long-running customer gets some time while small customers are completing their job. Also, you can drop the customer if no data is present for a given customer to reduce the load on the cluster