GPUJob

Params

name

A human readable description of the object.

server

An instance of GPUServer.

data_transferred

Sum of all data uploads and downloads for request manually defined gpu job in kilobyte.

data_stored

Data stored by request manually defined gpu job in kilobyte.

request_duration

Request duration of manually defined gpu job in second.

compute_needed

Gpus needed on server on premise gpu server to process manually defined gpu job in gpu.

ram_needed

Ram needed on server on premise gpu server to process manually defined gpu job in megabyte_ram.

Backwards links

• UsageJourneyStep

Calculated attributes

hourly_occurrences_per_usage_pattern

Dictionary with UsagePattern as keys and Hourly manually defined gpu job occurrences in usagepattern usage pattern as values, in occurrence.

Example value: {
UsagePattern usage pattern (7703de): 26298 values from 2025-01-01 00:00:00+00:00 to 2028-01-01 18:00:00+00:00 in :
first 10 vals [6, 6, 1, 1, 7, 7, 1, 7, 9, 1],
last 10 vals [7, 1, 3, 1, 7, 5, 6, 3, 3, 4],
}

Depends directly on:

• usage pattern UTC

through the following calculations:

You can also visit the link to Hourly Manually defined GPU job occurrences in UsagePattern usage pattern’s full calculation graph.

hourly_avg_occurrences_per_usage_pattern

Dictionary with UsagePattern as keys and Average hourly manually defined gpu job occurrences in usage pattern as values, in concurrent.

Example value: {
UsagePattern usage pattern (7703de): 26298 values from 2025-01-01 00:00:00+00:00 to 2028-01-01 18:00:00+00:00 in :
first 10 vals [0.00167, 0.00167, 0.000278, 0.000278, 0.00194, 0.00194, 0.000278, 0.00194, 0.0025, 0.000278],
last 10 vals [0.00194, 0.000278, 0.000833, 0.000278, 0.00194, 0.00139, 0.00167, 0.000833, 0.000833, 0.00111],
}

Depends directly on:

• Hourly Manually defined GPU job occurrences in UsagePattern usage pattern
• Request duration of Manually defined GPU job from e-footprint hypothesis

through the following calculations:

You can also visit the link to Average hourly Manually defined GPU job occurrences in usage pattern’s full calculation graph.

hourly_data_transferred_per_usage_pattern

Dictionary with UsagePattern as keys and Hourly data transferred for manually defined gpu job in usage pattern as values, in megabyte.

Example value: {
UsagePattern usage pattern (7703de): 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 [900, 900, 150, 150, 1050, 1050, 150, 1050, 1350, 150],
last 10 vals [1050, 150, 450, 150, 1050, 750, 900, 450, 450, 600],
}

Depends directly on:

• Average hourly Manually defined GPU job occurrences in usage pattern
• Sum of all data uploads and downloads for request Manually defined GPU job from e-footprint hypothesis
• Request duration of Manually defined GPU job from e-footprint hypothesis

through the following calculations:

You can also visit the link to Hourly data transferred for Manually defined GPU job in usage pattern’s full calculation graph.

hourly_data_stored_per_usage_pattern

Dictionary with UsagePattern as keys and Hourly data stored for manually defined gpu job in usage pattern as values, in megabyte.

Example value: {
UsagePattern usage pattern (7703de): 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 [600, 600, 100, 100, 700, 700, 100, 700, 900, 100],
last 10 vals [700, 100, 300, 100, 700, 500, 600, 300, 300, 400],
}

Depends directly on:

• Average hourly Manually defined GPU job occurrences in usage pattern
• Data stored by request Manually defined GPU job from e-footprint hypothesis
• Request duration of Manually defined GPU job from e-footprint hypothesis

through the following calculations:

You can also visit the link to Hourly data stored for Manually defined GPU job in usage pattern’s full calculation graph.

hourly_avg_occurrences_across_usage_patterns

Hourly manually defined gpu job average occurrences across usage patterns in concurrent.

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 [0.00167, 0.00167, 0.000278, 0.000278, 0.00194, 0.00194, 0.000278, 0.00194, 0.0025, 0.000278],
last 10 vals [0.00194, 0.000278, 0.000833, 0.000278, 0.00194, 0.00139, 0.00167, 0.000833, 0.000833, 0.00111]

Depends directly on:

• Average hourly Manually defined GPU job occurrences in usage pattern

through the following calculations:

You can also visit the link to Hourly Manually defined GPU job average occurrences across usage patterns’s full calculation graph.

hourly_data_transferred_across_usage_patterns

Hourly manually defined gpu job data transferred across usage patterns in megabyte.

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 [900, 900, 150, 150, 1050, 1050, 150, 1050, 1350, 150],
last 10 vals [1050, 150, 450, 150, 1050, 750, 900, 450, 450, 600]

Depends directly on:

• Hourly data transferred for Manually defined GPU job in usage pattern

through the following calculations:

You can also visit the link to Hourly Manually defined GPU job data transferred across usage patterns’s full calculation graph.

hourly_data_stored_across_usage_patterns

Hourly manually defined gpu job data stored across usage patterns in megabyte.

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 [600, 600, 100, 100, 700, 700, 100, 700, 900, 100],
last 10 vals [700, 100, 300, 100, 700, 500, 600, 300, 300, 400]

Depends directly on:

• Hourly data stored for Manually defined GPU job in usage pattern

through the following calculations:

You can also visit the link to Hourly Manually defined GPU job data stored across usage patterns’s full calculation graph.

fabrication_impact_repartition_weights

Dictionary with UsageJourneyStep as keys and 20 min streaming weight in manually defined gpu job impact repartition as values, in concurrent.

Example value: {
UsageJourneyStep 20 min streaming (624221): 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, 2, 0.333, 0.333, 2.33, 2.33, 0.333, 2.33, 3, 0.333],
last 10 vals [2.33, 0.333, 1, 0.333, 2.33, 1.67, 2, 1, 1, 1.33],
}

Depends directly on:

• user journey weight in 20 min streaming impact repartition

through the following calculations:

You can also visit the link to 20 min streaming weight in Manually defined GPU job impact repartition’s full calculation graph.

fabrication_impact_repartition_weight_sum

Sum of manually defined gpu job fabrication impact repartition weights in concurrent.

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, 2, 0.333, 0.333, 2.33, 2.33, 0.333, 2.33, 3, 0.333],
last 10 vals [2.33, 0.333, 1, 0.333, 2.33, 1.67, 2, 1, 1, 1.33]

Depends directly on:

• 20 min streaming weight in Manually defined GPU job impact repartition

through the following calculations:

You can also visit the link to Sum of Manually defined GPU job fabrication impact repartition weights’s full calculation graph.

fabrication_impact_repartition

Dictionary with UsageJourneyStep as keys and Manually defined gpu job fabrication impact attribution to 20 min streaming as values, in concurrent.

Example value: {
UsageJourneyStep 20 min streaming (624221): 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:

• 20 min streaming weight in Manually defined GPU job impact repartition
• Sum of Manually defined GPU job fabrication impact repartition weights

through the following calculations:

You can also visit the link to Manually defined GPU job fabrication impact attribution to 20 min streaming’s full calculation graph.

usage_impact_repartition_weights

Dictionary with UsageJourneyStep as keys and 20 min streaming weight in manually defined gpu job impact repartition as values, in concurrent * gram / kilowatt_hour.

Example value: {
UsageJourneyStep 20 min streaming (624221): 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 [170, 170, 28.3, 28.3, 198, 198, 28.3, 198, 255, 28.3],
last 10 vals [198, 28.3, 85, 28.3, 198, 142, 170, 85, 85, 113],
}

Depends directly on:

• user journey weight in 20 min streaming impact repartition

through the following calculations:

You can also visit the link to 20 min streaming weight in Manually defined GPU job impact repartition’s full calculation graph.

usage_impact_repartition_weight_sum

Sum of manually defined gpu job usage impact repartition weights in concurrent * gram / kilowatt_hour.

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 [170, 170, 28.3, 28.3, 198, 198, 28.3, 198, 255, 28.3],
last 10 vals [198, 28.3, 85, 28.3, 198, 142, 170, 85, 85, 113]

Depends directly on:

• 20 min streaming weight in Manually defined GPU job impact repartition

through the following calculations:

You can also visit the link to Sum of Manually defined GPU job usage impact repartition weights’s full calculation graph.

usage_impact_repartition

Dictionary with UsageJourneyStep as keys and Manually defined gpu job usage impact attribution to 20 min streaming as values, in concurrent.

Example value: {
UsageJourneyStep 20 min streaming (624221): 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:

• 20 min streaming weight in Manually defined GPU job impact repartition
• Sum of Manually defined GPU job usage impact repartition weights

through the following calculations:

You can also visit the link to Manually defined GPU job usage impact attribution to 20 min streaming’s full calculation graph.