26. Execution Statistics#

This table contains the latest execution statistics.

Document	Modified	Method	Run Time (s)	Status
aiyagari_jax	2025-06-23 03:38	cache	71.15	✅
arellano	2025-06-23 03:38	cache	21.98	✅
autodiff	2025-06-23 03:38	cache	13.4	✅
hopenhayn	2025-06-23 03:39	cache	24.45	✅
ifp_egm	2025-06-23 03:41	cache	143.64	✅
intro	2025-06-23 03:41	cache	1.0	✅
inventory_dynamics	2025-06-23 03:41	cache	9.74	✅
inventory_ssd	2025-06-23 04:17	cache	2152.07	✅
jax_intro	2025-06-23 04:18	cache	36.13	✅
jax_nn	2025-06-23 04:19	cache	89.14	✅
job_search	2025-06-23 04:19	cache	9.85	✅
keras	2025-06-23 04:20	cache	27.13	✅
kesten_processes	2025-06-25 01:18	cache	18.07	✅
lucas_model	2025-06-23 04:20	cache	19.54	✅
markov_asset	2025-06-23 04:21	cache	11.31	✅
mle	2025-06-23 04:21	cache	15.05	✅
newtons_method	2025-06-23 04:24	cache	182.87	✅
opt_invest	2025-06-23 04:24	cache	22.32	✅
opt_savings_1	2025-06-23 04:25	cache	46.08	✅
opt_savings_2	2025-06-23 04:25	cache	20.2	✅
overborrowing	2025-06-23 04:26	cache	22.8	✅
short_path	2025-06-23 04:26	cache	3.97	✅
status	2025-06-23 04:26	cache	2.4	✅
troubleshooting	2025-06-23 03:41	cache	1.0	✅
wealth_dynamics	2025-06-23 04:29	cache	155.46	✅
zreferences	2025-06-23 03:41	cache	1.0	✅

These lectures are built on linux instances through github actions that has access to a gpu. These lectures make use of the nvidia T4 card.

You can check the backend used by JAX using:

import jax
# Check if JAX is using GPU
print(f"JAX backend: {jax.devices()[0].platform}")

JAX backend: gpu

and the hardware we are running on:

!nvidia-smi

/home/runner/miniconda3/envs/quantecon/lib/python3.12/pty.py:95: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.
  pid, fd = os.forkpty()

Mon Jun 23 04:26:25 2025       
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 575.51.03              Driver Version: 575.51.03      CUDA Version: 12.9     |
|-----------------------------------------+------------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
|                                         |                        |               MIG M. |
|=========================================+========================+======================|
|   0  Tesla T4                       Off |   00000000:00:1E.0 Off |                    0 |
| N/A   36C    P0             32W /   70W |     109MiB /  15360MiB |      2%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+
                                                                                         
+-----------------------------------------------------------------------------------------+
| Processes:                                                                              |
|  GPU   GI   CI              PID   Type   Process name                        GPU Memory |
|        ID   ID                                                               Usage      |
|=========================================================================================|
|    0   N/A  N/A            6926      C   ...da3/envs/quantecon/bin/python        106MiB |
+-----------------------------------------------------------------------------------------+