Advanced Topics

Expressions

Most TPV properties can be expressed as Python expressions. The rule of thumb is that all string expressions are evaluated as python f-strings, and all integers or boolean expressions are evaluated as python code blocks. For example, cpu, cores and mem are evaluated as python code blocks, as they evaluate to integer/float values. However, env and params are evaluated as f-strings, as they result in string values. This is to improve the readability and syntactic simplicity of TPV config files.

At the point of evaluating these functions, there is an evaluation context, which is a default set of variables that are available to that expression. The following default variables are available to all expressions:

Default evaluation context

Variable	Description
app	the Galaxy App object
tool	the Galaxy tool object
user	the current Galaxy user object
job	the Galaxy job object
mapper	the TPV mapper object, which can be used to access parsed TPV configs
entity	the TPV entity being currently evaluated. Can be a combined entity.
self	an alias for the current TPV entity.

Custom evaluation contexts

These are user defined context values that can be defined globally, or locally at the level of each entity. Any defined context value is available as a regular variable at the time the entity is evaluated.

Special evaluation contexts

In addition to the defaults above, additional context variables are available at different steps.

gpu, core and mem expressions - these are evaluated in order, and thus can be referred to in that same order. For example, gpu expressions cannot refer to core and mem, as they have not been evaluated yet. cpu expressions can be based on gpu values. mem expressions can refer to both cores and gpus.

env and param expressions - env expressions can be based on gpu, cores or mem. param expressions can additional refer to evaluated env expressions.

rank functions - these can refer to all prior expressions, and are additional passed in a candidate_destinations array, which is a list of matching TPV destinations.

Properties that do not support expressions

Some properties do not support expressions. These are primarily:

• max_accepted_cores, max_accepted_mem and max_accepted_gpus, which can only be defined on destinations. This is because when a combined entity is matched with a destination, concrete values are required.

• tags defined on entities

Evaluation by expression type

The simple rule of thumb here is that all string expressions are evaluated as python f-strings, and all integers or boolean expressions are evaluated as python code blocks. If evaluated as an f-string, the expressions must be a single line and must evaluate to a string. If evaluated as a code-block, expressions may span multiple lines of arbitrary Python code, but the last line must be an expression that evaluates to the expected return type (The return statement should not and cannot be used)

Field	Evaluated As	Expected type
gpus	code block	float
cores	code block	float
mem	code block	float
env	f-strings	string
params	f-strings	string
min_gpus	code block	float
min_cores	code block	float
min_mem	code block	float
max_gpus	code block	float
max_cores	code block	float
max_mem	code block	float
rank	code block	list of destinations
context	not evaluated	string
scheduling tags	not evaluated	string
inherits	not evaluated	string
max_accepted_gpus	not evaluated	float
max_accepted_cores	not evaluated	float
max_accepted_mem	not evaluated	float
if	code block	bool
rules	not evaluated	list of rules
execute	code block	void
fail	f-string	string
resubmit	f-strings	string

Scheduling

TPV offers several mechanisms for controlling scheduling, all of which are optional. In its simplest form, no scheduling constraints would be defined at all, in which case the entity would schedule on the first available destination. Admins can use scheduling tags to exert additional control over which destinations jobs can schedule on. Scheduling tags fall into one of four categories, (required, preferred, accepted, rejected), ranging from indicating a requirement for a particular entity, to indicating complete aversion.

Tag Type	Description
require	required tags must match up for scheduling to occur. For example, if a tool is marked as requiring the high-mem tag, only destinations that are tagged as requiring, preferring or accepting the high-mem tag would be considering for scheduling.
prefer	prefer tags are ranked higher that accept tags when scheduling decisions are made.
accept	accept tags can be used to indicate that a entity can match up or support another entity, even if not preferentially.
reject	reject tags cannot be present for scheduling to occur. For example, if a tool is marked as rejecting the pulsar tag, only destinations that do not have that tag are considered for scheduling. If two entities have the same reject tag, they still repel each other.

Scheduling tag compatibility table

Tag Type	Require	Prefer	Accept	Reject	Not Tagged
Require	✓	✓	✓	✕	✕
Prefer	✓	✓	✓	✕	✓
Accept	✓	✓	✓	✕	✓
Reject	✕	✕	✕	✕	✓
Not Tagged	✕	✓	✓	✓	✓

Scheduling by tag match

Scheduling tags can be used to model anything from compatibility with a destination, to permissions to execute a tool. (e.g. a tool can be tagged as requiring the “restricted” tag, and users can be tagged as rejecting the “restricted” tag by default. Then, only users who are specifically marked as requiring, tolerating, or preferring the “restricted” tag can execute that tool. Of course, the destination must also be marked as not rejecting the “restricted” tag.

Scheduling by rules

Rules can be used to conditionally modify any entity requirement. Rules can be given an ID, which can subsequently be used by an inheriting entity to override the rule. If no ID is specified, a unique ID is generated, and the rule can no longer be overridden. Rules are typically evaluated through an if clause, which specifies the logical condition under which the rule matches. If the rule matches, cores, memory, scheduling tags etc. can be specified to override inherited values. The special clause fail can be used to immediately fail the job with an error message. The execute clause can be used to execute an arbitrary code block on rule match.

Scheduling by custom ranking functions

The default rank function sorts destinations by scoring how well the tags match the job’s requirements. As this may often be too simplistic, the rank function can be overridden by specifying a custom rank clause. The rank clause can contain an arbitrary code block, which can do the desired sorting, for example by determining destination load by querying the job manager, influx statistics etc. The final statement in the rank clause must be the list of sorted destinations.