How to Migrate minto.v0 Experiment to minto.v1#
This notebook is a guide to help you migrate your minto.v0 experiment to minto.v1.
import minto
import numpy as np
import jijmodeling as jm
tsp = minto.problems.tsp.QuadTSP()
tsp_problem = tsp.problem()
tsp_data = tsp.data(n=8)
minto.v0 experiment#
You can use the v0.x.x version of the minto in v1.x.x with minto.v1.
In the following, we create a simple experiment using minto.v0 and then migrate it to minto.v1.
import jijmodeling_transpiler as jmt
compiled_instance = jmt.core.compile_model(tsp_problem, tsp_data)
qubo_builder = jmt.core.pubo.transpile_to_pubo(compiled_instance)
qubo, _ = qubo_builder.get_qubo_dict()
import minto.v0
import openjij as oj
sampler = oj.SASampler()
sweeps_list = np.arange(100, 1000, 100)
exp_v0 = minto.v0.Experiment("v0_exp")
for sweeps in sweeps_list:
with exp_v0.run():
response = sampler.sample_qubo(qubo, num_reads=10, num_sweeps=sweeps)
sampleset = jmt.core.pubo.openjij_decode.decode_from_openjij(
response,
qubo_builder,
compiled_instance
)
sampleset = jm.experimental.from_old_sampleset(sampleset)
exp_v0.log_result("result", sampleset)
exp_v0.log_parameter("sweeps", sweeps)
exp_v0.table(enable_sampleset_expansion=False)
| experiment_name | run_id | sweeps | result | |
|---|---|---|---|---|
| 0 | v0_exp | 0 | 100 | (Sample(run_id="4b9b8389-a331-4ddd-a8d7-a9afbd... |
| 1 | v0_exp | 1 | 200 | (Sample(run_id="380588c0-e95c-474a-911a-5dd2ed... |
| 2 | v0_exp | 2 | 300 | (Sample(run_id="2e84fa06-08b5-4818-9b39-1f7e06... |
| 3 | v0_exp | 3 | 400 | (Sample(run_id="6cf101e2-0673-488a-a21f-2f7dd4... |
| 4 | v0_exp | 4 | 500 | (Sample(run_id="c02d1bc0-8535-4ecd-beb1-770e04... |
| 5 | v0_exp | 5 | 600 | (Sample(run_id="13ee025e-99dc-4c2e-aefa-aed347... |
| 6 | v0_exp | 6 | 700 | (Sample(run_id="2ca17c58-8b0e-4604-ada9-3110ec... |
| 7 | v0_exp | 7 | 800 | (Sample(run_id="a55c41cb-1ecf-4df2-8d0c-3af435... |
| 8 | v0_exp | 8 | 900 | (Sample(run_id="11207a98-f05f-4376-ad49-9a84c6... |
Migrate from minto.v0 to minto.v1#
To migrate from minto.v0 to minto.v1, you need to use migrate_to_v1_from_v0 function.
from minto.migrator.v0tov1 import migrate_to_v1_from_v0
exp_v1 = migrate_to_v1_from_v0(exp_v0)
[2025-08-01 22:35:29] π Starting experiment 'v0_exp'
[2025-08-01 22:35:29] ββ π Environment: OS: Linux 6.6.93+, CPU: Intel(R) Xeon(R) CPU @ 2.80GHz (4 cores), Memory: 15.6 GB, Python: 3.11.10
[2025-08-01 22:35:29] ββ π Environment Information
[2025-08-01 22:35:29] ββ OS: Linux 6.6.93+
[2025-08-01 22:35:29] ββ Platform: Linux-6.6.93+-x86_64-with-glibc2.35
[2025-08-01 22:35:29] ββ CPU: Intel(R) Xeon(R) CPU @ 2.80GHz (4 cores)
[2025-08-01 22:35:29] ββ Memory: 15.6 GB
[2025-08-01 22:35:29] ββ Architecture: x86_64
[2025-08-01 22:35:29] ββ Python: 3.11.10
[2025-08-01 22:35:29] ββ Key Package Versions:
[2025-08-01 22:35:29] ββ π Created run #0
[2025-08-01 22:35:29] ββ π Parameter: sweeps = 100
[2025-08-01 22:35:29] ββ π SampleSet 'result': 0 samples
[2025-08-01 22:35:29] ββ β
Run #0 completed (0.1s)
[2025-08-01 22:35:29] ββ π Created run #1
[2025-08-01 22:35:29] ββ π Parameter: sweeps = 200
[2025-08-01 22:35:29] ββ π SampleSet 'result': 0 samples
[2025-08-01 22:35:29] ββ β
Run #1 completed (0.0s)
[2025-08-01 22:35:29] ββ π Created run #2
[2025-08-01 22:35:29] ββ π Parameter: sweeps = 300
[2025-08-01 22:35:29] ββ π SampleSet 'result': 0 samples
[2025-08-01 22:35:29] ββ β
Run #2 completed (0.0s)
[2025-08-01 22:35:29] ββ π Created run #3
[2025-08-01 22:35:29] ββ π Parameter: sweeps = 400
[2025-08-01 22:35:29] ββ π SampleSet 'result': 0 samples
[2025-08-01 22:35:29] ββ β
Run #3 completed (0.0s)
[2025-08-01 22:35:29] ββ π Created run #4
[2025-08-01 22:35:29] ββ π Parameter: sweeps = 500
[2025-08-01 22:35:29] ββ π SampleSet 'result': 0 samples
[2025-08-01 22:35:29] ββ β
Run #4 completed (0.0s)
[2025-08-01 22:35:29] ββ π Created run #5
[2025-08-01 22:35:29] ββ π Parameter: sweeps = 600
[2025-08-01 22:35:29] ββ π SampleSet 'result': 0 samples
[2025-08-01 22:35:29] ββ β
Run #5 completed (0.0s)
[2025-08-01 22:35:29] ββ π Created run #6
[2025-08-01 22:35:29] ββ π Parameter: sweeps = 700
[2025-08-01 22:35:29] ββ π SampleSet 'result': 0 samples
[2025-08-01 22:35:29] ββ β
Run #6 completed (0.0s)
[2025-08-01 22:35:29] ββ π Created run #7
[2025-08-01 22:35:29] ββ π Parameter: sweeps = 800
[2025-08-01 22:35:29] ββ π SampleSet 'result': 0 samples
[2025-08-01 22:35:29] ββ β
Run #7 completed (0.0s)
[2025-08-01 22:35:29] ββ π Created run #8
[2025-08-01 22:35:29] ββ π Parameter: sweeps = 900
[2025-08-01 22:35:29] ββ π SampleSet 'result': 0 samples
[2025-08-01 22:35:29] ββ β
Run #8 completed (0.0s)
exp_v1.get_run_table()
| sampleset_result | parameter | metadata | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| num_samples | obj_mean | obj_std | obj_min | obj_max | feasible | name | sweeps | run_id | elapsed_time | |
| run_id | ||||||||||
| 0 | 10 | 2.703292 | 0.271523 | 2.362134 | 3.326194 | 10 | result | 100 | 0 | 0.055799 |
| 1 | 10 | 2.851875 | 0.347497 | 2.466858 | 3.553749 | 10 | result | 200 | 1 | 0.004895 |
| 2 | 10 | 2.638969 | 0.639893 | 1.452414 | 3.518039 | 4 | result | 300 | 2 | 0.004403 |
| 3 | 10 | 2.609291 | 0.195442 | 2.341296 | 3.037372 | 10 | result | 400 | 3 | 0.004015 |
| 4 | 10 | 2.307913 | 0.680801 | 0.975667 | 3.268000 | 1 | result | 500 | 4 | 0.003958 |
| 5 | 10 | 2.618542 | 0.413288 | 2.049099 | 3.277035 | 4 | result | 600 | 5 | 0.004461 |
| 6 | 10 | 2.781062 | 0.317559 | 2.281351 | 3.230759 | 10 | result | 700 | 6 | 0.004591 |
| 7 | 10 | 2.405925 | 0.673386 | 1.297302 | 3.662775 | 2 | result | 800 | 7 | 0.004686 |
| 8 | 10 | 2.710594 | 0.301820 | 2.281351 | 3.202480 | 10 | result | 900 | 8 | 0.004513 |
Then you can save the migrated experiment using .save() or .save_as_ommx_archive function.
# exp_v1.save() # save the experiment at disk
# exp_v1.save_as_ommx_archive("v1_exp.ommx") # save the experiment as an ommx archive