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 (d71fc8): 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in :
first 10 vals [2, 8, 9, 8, 3, 1, 7, 6, 9, 2],
last 10 vals [3, 8, 9, 2, 8, 6, 5, 2, 6, 3],
}

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 (d71fc8): 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in :
first 10 vals [0.000556, 0.00222, 0.0025, 0.00222, 0.000833, 0.000278, 0.00194, 0.00167, 0.0025, 0.000556],
last 10 vals [0.000833, 0.00222, 0.0025, 0.000556, 0.00222, 0.00167, 0.00139, 0.000556, 0.00167, 0.000833],
}

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 (d71fc8): 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in M:
first 10 vals [2.4, 9.6, 10.8, 9.6, 3.6, 1.2, 8.4, 7.2, 10.8, 2.4],
last 10 vals [3.6, 9.6, 10.8, 2.4, 9.6, 7.2, 6, 2.4, 7.2, 3.6],
}

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 (d71fc8): 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in M:
first 10 vals [1.6, 6.4, 7.2, 6.4, 2.4, 0.8, 5.6, 4.8, 7.2, 1.6],
last 10 vals [2.4, 6.4, 7.2, 1.6, 6.4, 4.8, 4, 1.6, 4.8, 2.4],
}

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 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in :
first 10 vals [0.000556, 0.00222, 0.0025, 0.00222, 0.000833, 0.000278, 0.00194, 0.00167, 0.0025, 0.000556],
last 10 vals [0.000833, 0.00222, 0.0025, 0.000556, 0.00222, 0.00167, 0.00139, 0.000556, 0.00167, 0.000833]

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 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in M:
first 10 vals [2.4, 9.6, 10.8, 9.6, 3.6, 1.2, 8.4, 7.2, 10.8, 2.4],
last 10 vals [3.6, 9.6, 10.8, 2.4, 9.6, 7.2, 6, 2.4, 7.2, 3.6]

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 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in M:
first 10 vals [1.6, 6.4, 7.2, 6.4, 2.4, 0.8, 5.6, 4.8, 7.2, 1.6],
last 10 vals [2.4, 6.4, 7.2, 1.6, 6.4, 4.8, 4, 1.6, 4.8, 2.4]

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 (1417a0): 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in :
first 10 vals [0.667, 2.67, 3, 2.67, 1, 0.333, 2.33, 2, 3, 0.667],
last 10 vals [1, 2.67, 3, 0.667, 2.67, 2, 1.67, 0.667, 2, 1],
}

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 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in :
first 10 vals [0.667, 2.67, 3, 2.67, 1, 0.333, 2.33, 2, 3, 0.667],
last 10 vals [1, 2.67, 3, 0.667, 2.67, 2, 1.67, 0.667, 2, 1]

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 (1417a0): 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+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 (1417a0): 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in ·g/kWh:
first 10 vals [56.7, 227, 255, 227, 85, 28.3, 198, 170, 255, 56.7],
last 10 vals [85, 227, 255, 56.7, 227, 170, 142, 56.7, 170, 85],
}

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 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in ·g/kWh:
first 10 vals [56.7, 227, 255, 227, 85, 28.3, 198, 170, 255, 56.7],
last 10 vals [85, 227, 255, 56.7, 227, 170, 142, 56.7, 170, 85]

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 (1417a0): 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+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.