Backend API¶
As it is currently setup, user code at toolchain blocks running on the platform consumes data stored on disk and produces data which is stored on disk so that subsequent code running on the following blocks can operate on the same principles. This approach allows users to potentially configure experiments with a hybrid set of algorithms that execute on different backends. Each backend can be implemented in a different programming language and contain any number of (pre-installed) libraries users can call on their algorithms.
The requirements for BEAT when reading/writing data are:
Ability to manage large and complex data
Portability to allow the use of heterogeneous environments
Based on our experience and on these requirements, we investigated the use of HDF5. Unfortunately, HDF5 is not convenient to handle structures such as arrays of variable-size elements, for instance, array of strings. Therefore, we decided to rely on our own binary format.
This document describes the binary formats in BEAT and the API required by BEAT to handle multiple backend implementations. The package beat.env.python27 provides the reference Python backend implementation based on Python 2.7.
Binary Format¶
Our binary format does not contains information about the format of the data itself, and it is hence necessary to know this format a priori. This means that the format cannot be inferred from the content of a file.
We rely on the following fundamental C-style formats:
int8
int16
int32
int64
uint8
uint16
uint32
uint64
float32
float64
complex64 (first real value, and then imaginary value)
complex128 (first real value, and then imaginary value)
bool (written as a byte)
string
An element of such a basic format is written in the C-style way, using little-endian byte ordering.
Besides, dataformats always consist of arrays or dictionary of such fundamental formats or compound formats.
An array of elements is saved as followed. First, the shape of the array is saved using an uint64 value for each dimension. Next, the elements of the arrays are saved in C-style order.
A dictionary of elements is saved as followed. First, the key are ordered according to the lexicographic ordering. Then, the values associated to each of these keys are saved following this ordering.
The platform is data-driven and always processes chunks of data. Therefore, data are always written by chunks, each chunk being preceded by a text-formated header indicated the start- and end- indices followed by the size (in bytes) of the chunck.
Considering the Python backend of the platform, this binary format has been
successfully implemented using the struct
module.
Filesystem Organization¶
At the filesystem level, each backend shall be organized so it is fully contained in a single-rooted directory tree. The backend installer should not make assumptions about the directory structure of the target operating systems, except, possibly, for the use of stock files. For example:
$ ls /path/to/beat.env.python27
-rw-r--r-- 1 beat beat 2829 Jul 20 19:57 LICENSE
-rw-r--r-- 1 beat beat 7268 Jul 20 19:57 Makefile
-rw-r--r-- 1 beat beat 1852 Jul 20 19:57 README.md
drwxr-xr-x 2 beat beat 4096 Jul 28 16:19 bin/
drwxr-xr-x 2 beat beat 4096 Jul 28 16:19 usr/
drwxr-xr-x 2 beat beat 4096 Jul 28 16:19 src/
...
There is a minimal set of required files in each environment:
Makefile
: A make file or equivalent script should be present to fully build the environment from scratch. This allows BEAT platform administrators to install the environment on a target machine.bin/describe
: This is an executable that takes no arguments and describes the current environment, providing its name, version and a list of pre-installed libraries, toolboxes or any other information that may be relevant to users implementing algorithms for this backend. The output of thedescribe
command should be a parseable JSON string. For example, our reference beat.env.python27 environment returns the following for a call tobin/describe
:{ "name": "Scientific Python 2.7", "version": "0.0.4", "os": [ "Linux", "extatix03", "3.12.14-1-idiap-generic", "#20140313 SMP Thu Mar 13 15:12:40 CET 2014", "x86_64", "" ], "packages": { "beat.core": "0.9.4", "bob": "1.2.2", "matplotlib": "1.4.3", "numpy": "1.9.2", "oset": "0.1.3" } }
Each pair of
name
andversion
for an environment must be unique so that platform users can uniquely select them.bin/execute
: This is an executable that is called by the BEAT infrastructure to execute user code. The executable must be able to receive 2 arguments that correspond to a I/O server address and (temporary) directory containing the following files:$ ls -1 /tmp/beat.A976xy1/ configuration.json #the configuration for the algorithm in JSON format prefix #the prefix with algorithm/libraries/formats required
Optional flags may be provided for administrative purposes, but will not be using for running user code. For example, the reference implementation of
bin/execute
responds this way when passed the-h
optional flag:$ /path/to/beat.env.python27/bin/execute -h Executes a single algorithm. usage: execute [--debug] <addr> <dir> execute (--help) arguments: <addr> Address of the server for I/O requests <dir> Directory containing all configuration required to run the user algorithm options: -h, --help Shows this help message and exit -d, --debug Runs executor in debugging mode
You should strongly consider implemeting similar functionality on your backend to ease debugging in case of problems.
Further to those files, it is prudent to include:
README.rst
: a file containing installation and management instructions. It should preferrably be written using a markup language such as MarkDown (.md
extension) or reStructuredText (.rst
extension). By reading this file, it should be possible for a remote party with a working knowledge of the target operating system, to completely install the environment without external help.The README should also include contact points and, if possible, a bug-tracking link where users can submit bug/update requests.
LICENSE
: a file that describes the usage license for the backend.
Message Passing¶
The BEAT infrastructure communicates with the bin/execute
process via Zero
Message Queue or ZMQ for short. ZMQ provides a portable bidirectional
communication layer between the BEAT infrastructure and the target backend,
with many language bindings, including python bindings.
The user process, which manages the data readout of a given algorithm, sends commands back to the infrastructure for requesting data when needed.
"command"
"argument1"
"..."
"argumentn"
Where command
is the command name to be executed and argument*
are the
corresponding arguments, sent using separate zmq.send()
calls using the
multipart sending technique (as with zmq.SNDMORE
). In order to simplify
representation, we denote multi-message commands in a single line. So, the
command above will be represented in this document such as:
"command" "argument1" "..." "argumentn"
Commands may also exchange binary data. In such a case, we represent it in this
manual using <binary>
. The binary data format is the one defined by our
BaseFormat
class at the package beat.backend.python
.
The next diagram represents some possible states between the BEAT
infrastructure and the execute
process in case of a successful execution:
In the remainder of this section, we describe the various commands, which are supported by this communication protocol.
Command: information (ifo)¶
(User Process -> Infrastructure)
This command asks the infrastructure to return information about the remote data sources queried. The format of this command is:
"ifo name\n"
The infrastructure will answer by writing the following into the input pipe.
"X"
"Start0"
"End0"
...
"StartX-1"
"EndX-1"
where X is the length of the data source and the StartX and EndX are the start and end indexes available through that data source.
Command: get data (get)¶
(User Process -> Infrastructure)
This command asks the infrastructure to return the data at the given index. The format of this command is:
"get X"
where X is the index of the data in the data source.
The infrastructure will answer by writing the following into the input pipe.
"StartX"
"EndX"
"data"
where StartX and EndX and the start and end indexes in the data sources and data is the packed data that the data sources provides.
Command: done (don)¶
(User Process -> Infrastructure)
This command notifies the infrastructure that the execution of the user process has completed (i.e. it will not read or write any further data).
"done float"
Where float
is a valid floating point number that represents the time
wasted waiting for I/O on the user process. This number composes the statistics
for processes.
Once the UP has sent this command, the infrastructure will retrieve the statistics (I/O, CPU and memory) and it will acknowlegde the UP, once this is done with:
"ack"
At this point, the UP is expected to gracefully terminate.
Command: error (err)¶
(User Process -> Infrastructure)
This command notifies the infrastructure that the execution of the user process has err and will not request any further data. A message explaining the error condition is attached.
"err type message"
Once the UP has sent this command, the infrastructure will retrieve the statistics (I/O, CPU and memory) and it will acknowlegde the UP, once this is done with:
"ack"
At this point, the UP is expected to gracefully terminate. The value for
type
maybe set to usr
, indicating the error occurred inside the user
code or anything else, indicating it was a system error (and must be reported
to system administrators). In this case, the user only gets a generic message
indicating a problem with the infrastructure was detected and that system
administrators were informed.