This repository contains job traces of the 1st party deep learning training workloads in Microsoft's internal ITP clusters. The traces are used in "ElasticFlow: An Elastic Serverless Training Platform for Distributed Deep Learning" in ASPLOS '23.
- Duration: 2 months from 2021-03-04 to 2021-05-03
- Total number of clusters: 10
- Total number of jobs: 69,351
- Total GPU horus: 1,632,719.7
data/raw-traces.tar.gz
contains raw trace data collected from ITP clusters.
There are 10 csv files, each file represents one cluster.
Detailed descriptions for each column are as follows:
Column | Description |
---|---|
job_id |
Job unique id, the UUID is re-generated after trace is collected. |
submission_time |
Job submission time, relative time in seconds to the beginning of collection time. |
duration |
Job duration in seconds. |
num_gpu |
Number of GPUs requested/used by the job. |
Here's an example for each csv:
job_id,submission_time,duration,num_gpu
6b2d9ccc-6279-403c-e1a2-cfe7810d5c57,16566,3890,4
data/elasticflow-traces.tar.gz
contains extra randomly generated data based on raw trace data.
The traces are actually used in ElasticFlow experiments, 10 csv files are used for simulation and 195job csv is used to run in real testbed.
Detailed descriptions for each column are as follows:
Column | Description |
---|---|
job_id |
Job unique id, the UUID is re-generated after trace is collected. |
submission_time |
Job submission time, relative time in seconds to the beginning of collection time. |
num_iteration |
Randomly generated itertion number for the job's training. |
model_name |
Randomly generated model name for the job. |
deadline |
Generated deadline based on specific rules. |
batch_size |
Generated batch size for the job's training. |
num_gpu |
Number of GPUs requested/used by the job. |
duration |
Job duration in seconds. |
Here's an example for each csv:
job_id,submission_time,num_iteration,model_name,deadline,batch_size,num_gpu,duration
6b2d9ccc-6279-403c-e1a2-cfe7810d5c57,16566,40285,bert,21347,64,4,3890
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