Experiment Information
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CPU Info
0 | |
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python_version | 3.7.4.final.0 (64 bit) |
cpuinfo_version | [5, 0, 0] |
arch | X86_64 |
bits | 64 |
count | 32 |
raw_arch_string | x86_64 |
vendor_id | GenuineIntel |
brand | Intel(R) Xeon(R) CPU E5-2630 v3 @ 2.40GHz |
hz_advertised | 2.4000 GHz |
hz_actual | 2.6000 GHz |
hz_advertised_raw | [2400000000, 0] |
hz_actual_raw | [2599968000, 0] |
stepping | 2 |
model | 63 |
family | 6 |
flags | [abm, acpi, aes, aperfmperf, apic, arat, arch_perfmon, avx, avx2, bmi1, bmi2, bts, clflush, cmov, constant_tsc, cqm, cqm_llc, cqm_occup_llc, cx16, cx8, dca, de, ds_cpl, dtes64, dtherm, dts, eagerfpu, epb, ept, erms, est, f16c, flexpriority, fma, fpu, fsgsbase, fxsr, ht, ida, invpcid, invpcid_single, kaiser, lahf_lm, lm, mca, mce, mmx, monitor, movbe, msr, mtrr, nonstop_tsc, nopl, nx, pae, pat, pbe, pcid, pclmulqdq, pdcm, pdpe1gb, pebs, pge, pln, pni, popcnt, pse, pse36, pts, rdrand, rdtscp, rep_good, retpoline, sdbg, sep, smep, smx, ss, sse, sse2, sse4_1, sse4_2, ssse3, syscall, tm, tm2, tpr_shadow, tsc, tsc_adjust, tsc_deadline_timer, vme, vmx, vnmi, vpid, x2apic, xsave, xsaveopt, xtopology, xtpr] |
l3_cache_size | 20480 KB |
l2_cache_size | 256 KB |
l1_data_cache_size | 32 KB |
l1_instruction_cache_size | 32 KB |
usable_cpus | 16 |
GPU Info
0 | |
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name | TITAN V |
total_memory | 12036 MiB |
driver_version | 410.78 |
cuda_version | 10.0 |
Package Info
0 | |
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0 | xmltodict 0.12.0 |
1 | wrapt 1.11.2 |
2 | wheel 0.33.6 |
3 | Werkzeug 0.16.0 |
4 | wcwidth 0.1.7 |
5 | wasabi 0.2.2 |
6 | urllib3 1.25.3 |
7 | ujson 1.35 |
8 | typing 3.7.4 |
9 | typepy 0.6.0 |
10 | traitlets 4.3.2 |
11 | tqdm 4.30.0 |
12 | torchvision 0.4.0+cu92 |
13 | torchtext 0.4.0 |
14 | torch 1.2.0+cu92 |
15 | thop 0.0.31.post1910280903 |
16 | thinc 7.0.4 |
17 | textstat 0.5.6 |
18 | termcolor 1.1.0 |
19 | tensorpack 0.9.8 |
20 | tensorflow 1.13.1 |
21 | tensorflow-gpu 1.14.0 |
22 | tensorflow-estimator 1.14.0 |
23 | tensorboard 1.14.0 |
24 | tabulate 0.8.5 |
25 | tabledata 0.9.1 |
26 | syllapy 0.7.0 |
27 | subword-nmt 0.3.6 |
28 | stevedore 1.30.1 |
29 | stanfordnlp 0.2.0 |
30 | stable-baselines 2.7.0 |
31 | srsly 0.0.5 |
32 | SQLAlchemy 1.3.5 |
33 | spacy 2.1.4 |
34 | spacy-readability 1.4.1 |
35 | soupsieve 1.9.3 |
36 | six 1.12.0 |
37 | Shapely 1.6.4.post2 |
38 | setuptools 41.4.0 |
39 | scipy 1.3.1 |
40 | sacremoses 0.0.35 |
41 | requests 2.22.0 |
42 | repoze.lru 0.7 |
43 | regex 2019.8.19 |
44 | ratelim 0.1.6 |
45 | pyzmq 18.1.0 |
46 | PyYAML 5.1.1 |
47 | pytz 2019.1 |
48 | python-editor 1.0.4 |
49 | python-dateutil 2.8.0 |
50 | pytablewriter 0.46.1 |
51 | pyrsistent 0.15.2 |
52 | Pyphen 0.9.5 |
53 | pyperclip 1.7.0 |
54 | pyparsing 2.4.0 |
55 | PyLaTeX 1.3.0 |
56 | Pygments 2.4.2 |
57 | pyglet 1.3.2 |
58 | pybullet 2.5.1 |
59 | py3nvml 0.2.3 |
60 | py-cpuinfo 5.0.0 |
61 | ptyprocess 0.6.0 |
62 | psutil 5.6.3 |
63 | protobuf 3.9.2 |
64 | prompt-toolkit 2.0.9 |
65 | prettytable 0.7.2 |
66 | preshed 2.0.1 |
67 | plac 0.9.6 |
68 | pip 19.1.1 |
69 | Pillow 6.2.1 |
70 | pickleshare 0.7.5 |
71 | pexpect 4.7.0 |
72 | pbr 5.3.1 |
73 | pathvalidate 0.29.0 |
74 | parso 0.5.1 |
75 | pandas 0.24.2 |
76 | ordered-set 3.1.1 |
77 | optuna 0.15.0 |
78 | opt-einsum 3.1.0 |
79 | opencv-python 4.1.0.25 |
80 | nvidia-ml-py3 7.352.0 |
81 | numpy 1.17.2 |
82 | murmurhash 1.0.2 |
83 | msgpack 0.6.2 |
84 | msgpack-numpy 0.4.4.3 |
85 | msgfy 0.0.7 |
86 | mpi4py 3.0.2 |
87 | morph-net 0.2.1 |
88 | mock 3.0.5 |
89 | mkl-service 2.3.0 |
90 | mkl-random 1.1.0 |
91 | mkl-fft 1.0.14 |
92 | mbstrdecoder 0.8.1 |
93 | matplotlib 3.1.0 |
94 | MarkupSafe 1.1.1 |
95 | Markdown 3.1.1 |
96 | Mako 1.0.13 |
97 | lark-parser 0.7.3 |
98 | kiwisolver 1.1.0 |
99 | Keras-Preprocessing 1.1.0 |
100 | Keras-Applications 1.0.8 |
101 | jsonschema 3.0.1 |
102 | jsonpickle 1.2 |
103 | joblib 0.13.2 |
104 | jedi 0.14.1 |
105 | ipython 7.6.1 |
106 | ipython-genutils 0.2.0 |
107 | idna 2.8 |
108 | h5py 2.10.0 |
109 | gym 0.13.0 |
110 | grpcio 1.24.0 |
111 | gpustat 0.5.0 |
112 | google-pasta 0.1.7 |
113 | gin-config 0.1.4 |
114 | geocoder 1.38.1 |
115 | gast 0.2.2 |
116 | future 0.17.1 |
117 | essential-generators 0.9.2 |
118 | enum34 1.1.6 |
119 | en-core-web-sm 2.1.0 |
120 | deepdiff 4.0.8 |
121 | decorator 4.4.0 |
122 | DataProperty 0.43.1 |
123 | Cython 0.29.13 |
124 | cymem 2.0.2 |
125 | cycler 0.10.0 |
126 | contextlib2 0.5.5 |
127 | ConfigArgParse 0.14.0 |
128 | commentjson 0.8.1 |
129 | colorlog 4.0.2 |
130 | colorama 0.4.1 |
131 | cmd2 0.9.14 |
132 | cloudpickle 1.2.1 |
133 | cliff 2.15.0 |
134 | Click 7.0 |
135 | chardet 3.0.4 |
136 | certifi 2019.9.11 |
137 | bs4 0.0.1 |
138 | blis 0.2.4 |
139 | blessings 1.7 |
140 | beautifulsoup4 4.8.0 |
141 | baselines 0.1.6 |
142 | backcall 0.1.0 |
143 | attrs 19.1.0 |
144 | atari-py 0.2.0 |
145 | astor 0.8.0 |
146 | arrow 0.15.2 |
147 | apex 0.1 |
148 | alembic 1.0.11 |
149 | absl-py 0.8.0 |
150 | pyrouge 0.1.3 |
151 | torchcule 0.1.0 |
152 | experiment-impact-tracker 0.1 |
Carbon Info
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Realtime Carbon Intensity Data Source | http://www.caiso.com/outlook/SP/History/<date>/co2.csv |
Realtime Carbon Intensity Average During Exp | 236.21 |
Region Average Carbon Intensity | 250.733 |
Region Average Carbon Intensity Source | https://github.com/tmrowco/electricitymap-contrib/blob/master/config/co2eq_parameters.json (ElectricityMap Average, 2019) |
Assumed PUE | 1.58 |
Compute Region | California, United States of America |
Experiment Impact Tracker Version | 0.1.1 |
Stats
0 | 1 | |
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0 | Key | Value |
1 | cpu_hours | 18.4724 |
2 | gpu_hours | 1.77998 |
3 | estimated_carbon_impact_kg | 0.191527 |
4 | total_power | 0.792211 |
5 | kw_hr_gpu | 0.280559 |
6 | kw_hr_cpu | 0.220841 |
7 | exp_len_hours | 7.46777 |
8 | average_realtime_carbon_intensity | 236.21 |
9 | AverageReturn | 17.5221 |
10 | AsymptoticReturn | 21 |
11 | AverageReturnPerkWh | 22.118 |
12 | AverageEpisodeLength | 1769.75 |
13 | AverageMaximumEpisodeLength | 1848 |
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