GPUJob
A Job whose compute requirement is expressed in GPUs and which therefore must run on a GPUServer.
Params
name
A human readable description of the object.
server
GPUServer that processes the job.
An instance of GPUServer.
data_transferred
Total bytes uploaded plus downloaded over the network for one invocation of the job.
Unit: kilobyte.
data_stored
Net change in stored data per invocation. Positive values only. Data deletion is handled by Storage.data_storage_duration
Unit: kilobyte_stored.
request_duration
How long the job takes to process from start to finish on the server.
Unit: second.
compute_needed
GPU consumed by one invocation of the job, expressed in GPUs held for the request duration.
Unit: gpu.
ram_needed
GPU memory held by one invocation of the job for its full duration.
Unit: megabyte_ram.
Backwards links
Calculated attributes
hourly_occurrences_per_usage_pattern
Hourly count of job invocations broken down by usage pattern, derived from when each usage pattern's journeys start and at what point in the journey this job is triggered.
Example value: {
UsagePattern usage pattern (60c504): 26298 values from 2025-01-01 00:00:00+00:00 to 2028-01-01 18:00:00+00:00 in :
first 10 vals [7, 2, 3, 1, 5, 7, 6, 4, 8, 5],
last 10 vals [6, 3, 3, 4, 1, 5, 2, 6, 3, 5],
}
Depends directly on:
through the following calculations:
You can also visit the link to Hourly occurrences in usage pattern’s full calculation graph.
hourly_avg_occurrences_per_usage_pattern
Hourly count of job invocations averaged with respect to job duration, so a job that runs longer than an hour contributes a fractional occurrence to several modeling buckets.
Example value: {
UsagePattern usage pattern (60c504): 26298 values from 2025-01-01 00:00:00+00:00 to 2028-01-01 18:00:00+00:00 in :
first 10 vals [0.00194, 0.000556, 0.000833, 0.000278, 0.00139, 0.00194, 0.00167, 0.00111, 0.00222, 0.00139],
last 10 vals [0.00167, 0.000833, 0.000833, 0.00111, 0.000278, 0.00139, 0.000556, 0.00167, 0.000833, 0.00139],
}
Depends directly on:
through the following calculations:
You can also visit the link to Average hourly occurrences in usage pattern’s full calculation graph.
hourly_data_transferred_per_usage_pattern
Hourly volume of data transferred over the network by this job, broken down by usage pattern.
Example value: {
UsagePattern usage pattern (60c504): 26298 values from 2025-01-01 00:00:00+00:00 to 2028-01-01 18:00:00+00:00 in kB:
first 10 vals [1050, 300, 450, 150, 750, 1050, 900, 600, 1200, 750],
last 10 vals [900, 450, 450, 600, 150, 750, 300, 900, 450, 750],
}
Depends directly on:
- Average hourly occurrences in usage pattern
- Sum of all data uploads and downloads by request
- Request duration
through the following calculations:
You can also visit the link to Hourly data transferred in usage pattern’s full calculation graph.
hourly_data_stored_per_usage_pattern
Hourly net change in storage volume caused by this job, broken down by usage pattern.
Example value: {
UsagePattern usage pattern (60c504): 26298 values from 2025-01-01 00:00:00+00:00 to 2028-01-01 18:00:00+00:00 in kB stored:
first 10 vals [700, 200, 300, 100, 500, 700, 600, 400, 800, 500],
last 10 vals [600, 300, 300, 400, 100, 500, 200, 600, 300, 500],
}
Depends directly on:
through the following calculations:
You can also visit the link to Hourly data stored in usage pattern’s full calculation graph.
hourly_avg_occurrences_across_usage_patterns
Total hourly count of duration-averaged job invocations summed over every usage pattern.
Example value: 26298 values from 2025-01-01 00:00:00+00:00 to 2028-01-01 18:00:00+00:00 in :
first 10 vals [0.00194, 0.000556, 0.000833, 0.000278, 0.00139, 0.00194, 0.00167, 0.00111, 0.00222, 0.00139],
last 10 vals [0.00167, 0.000833, 0.000833, 0.00111, 0.000278, 0.00139, 0.000556, 0.00167, 0.000833, 0.00139]
Depends directly on:
through the following calculations:
You can also visit the link to Hourly average occurrences across usage patterns’s full calculation graph.
hourly_data_transferred_across_usage_patterns
Total hourly volume of data transferred over the network by this job, summed over every usage pattern.
Example value: 26298 values from 2025-01-01 00:00:00+00:00 to 2028-01-01 18:00:00+00:00 in kB:
first 10 vals [1050, 300, 450, 150, 750, 1050, 900, 600, 1200, 750],
last 10 vals [900, 450, 450, 600, 150, 750, 300, 900, 450, 750]
Depends directly on:
through the following calculations:
You can also visit the link to Hourly data transferred across usage patterns’s full calculation graph.
hourly_data_stored_across_usage_patterns
Total hourly net change in storage volume caused by this job, summed over every usage pattern.
Example value: 26298 values from 2025-01-01 00:00:00+00:00 to 2028-01-01 18:00:00+00:00 in kB stored:
first 10 vals [700, 200, 300, 100, 500, 700, 600, 400, 800, 500],
last 10 vals [600, 300, 300, 400, 100, 500, 200, 600, 300, 500]
Depends directly on:
through the following calculations:
You can also visit the link to Hourly data stored across usage patterns’s full calculation graph.
fabrication_impact_repartition_weights
Weights used to attribute fabrication-phase emissions of upstream impact sources to each container of this object.
Example value: {
UsageJourneyStep 20 min streaming (264451): 26298 values from 2025-01-01 00:00:00+00:00 to 2028-01-01 18:00:00+00:00 in :
first 10 vals [2.33, 0.667, 1, 0.333, 1.67, 2.33, 2, 1.33, 2.67, 1.67],
last 10 vals [2, 1, 1, 1.33, 0.333, 1.67, 0.667, 2, 1, 1.67],
}
Depends directly on:
through the following calculations:
You can also visit the link to 20 min streaming weight in impact repartition’s full calculation graph.
fabrication_impact_repartition_weight_sum
Sum of fabrication impact repartition weights, used as the denominator when normalising into per-container shares.
Example value: 26298 values from 2025-01-01 00:00:00+00:00 to 2028-01-01 18:00:00+00:00 in :
first 10 vals [2.33, 0.667, 1, 0.333, 1.67, 2.33, 2, 1.33, 2.67, 1.67],
last 10 vals [2, 1, 1, 1.33, 0.333, 1.67, 0.667, 2, 1, 1.67]
Depends directly on:
through the following calculations:
You can also visit the link to Fabrication impact repartition weights sum’s full calculation graph.
fabrication_impact_repartition
Normalised share of fabrication-phase emissions that this object attributes to each container.
Example value: {
UsageJourneyStep 20 min streaming (264451): 26298 values from 2025-01-01 00:00:00+00:00 to 2028-01-01 18:00:00+00:00 in :
first 10 vals [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
last 10 vals [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
}
Depends directly on:
through the following calculations:
You can also visit the link to fabrication impact attribution to 20 min streaming’s full calculation graph.
usage_impact_repartition_weights
Weights used to attribute usage-phase emissions of upstream impact sources to each container of this object.
Example value: {
UsageJourneyStep 20 min streaming (264451): 26298 values from 2025-01-01 00:00:00+00:00 to 2028-01-01 18:00:00+00:00 in ·g/kWh:
first 10 vals [198, 56.7, 85, 28.3, 142, 198, 170, 113, 227, 142],
last 10 vals [170, 85, 85, 113, 28.3, 142, 56.7, 170, 85, 142],
}
Depends directly on:
through the following calculations:
You can also visit the link to 20 min streaming weight in impact repartition’s full calculation graph.
usage_impact_repartition_weight_sum
Sum of usage impact repartition weights, used as the denominator when normalising into per-container shares.
Example value: 26298 values from 2025-01-01 00:00:00+00:00 to 2028-01-01 18:00:00+00:00 in ·g/kWh:
first 10 vals [198, 56.7, 85, 28.3, 142, 198, 170, 113, 227, 142],
last 10 vals [170, 85, 85, 113, 28.3, 142, 56.7, 170, 85, 142]
Depends directly on:
through the following calculations:
You can also visit the link to Usage impact repartition weights sum’s full calculation graph.
usage_impact_repartition
Normalised share of usage-phase emissions that this object attributes to each container.
Example value: {
UsageJourneyStep 20 min streaming (264451): 26298 values from 2025-01-01 00:00:00+00:00 to 2028-01-01 18:00:00+00:00 in :
first 10 vals [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
last 10 vals [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
}
Depends directly on:
through the following calculations:
You can also visit the link to usage impact attribution to 20 min streaming’s full calculation graph.