Update Dask_JUWELS.ipynb

116c2d2a · Tim Kreuzer · 3442c015 · 116c2d2a
Commit 116c2d2a authored 5 years ago by Tim Kreuzer
--- a/001-Extensions/Dask_JUWELS.ipynb
+++ b/001-Extensions/Dask_JUWELS.ipynb
@@ -22,7 +22,7 @@
   "outputs": [],
   "source": [
    "queue = \"batch\"  #  batch, gpus, develgpus, etc.\n",
-    "project = \"cstvs\"  # your project: zam, training19xx, etc."
+    "project = \"zam\"  # your project: zam, training19xx, etc."
   ]
  },
  {

 %% Cell type:markdown id: tags:

 # Dask Extension

 %% Cell type:markdown id: tags:

 This notebook will give you a short introduction into the Dask Extension on JURECA. It allows you to run Jobs on the compute nodes, even if your JupyterLab is running interactively on the login node.
 First you have to define on which project and partition it should be running.

 %% Cell type:code id: tags:

 ``` python
 queue = "batch"  #  batch, gpus, develgpus, etc.
-project = "cstvs"  # your project: zam, training19xx, etc.
+project = "zam"  # your project: zam, training19xx, etc.
 ```

 %% Cell type:markdown id: tags:

 # Monte-Carlo Estimate of $\pi$

 We want to estimate the number $\pi$ using a [Monte-Carlo method](https://en.wikipedia.org/wiki/Pi#Monte_Carlo_methods) exploiting that the area of a quarter circle of unit radius is $\pi/4$ and that hence the probability of any randomly chosen point in a unit square to lie in a unit circle centerd at a corner of the unit square is $\pi/4$ as well.  So for N randomly chosen pairs $(x, y)$ with $x\in[0, 1)$ and $y\in[0, 1)$, we count the number $N_{circ}$ of pairs that also satisfy $(x^2 + y^2) < 1$ and estimage $\pi \approx 4 \cdot N_{circ} / N$.

 [<img src="https://upload.wikimedia.org/wikipedia/commons/8/84/Pi_30K.gif"
     width="50%"
     align=top
     alt="PI monte-carlo estimate">](https://en.wikipedia.org/wiki/Pi#Monte_Carlo_methods)

 %% Cell type:markdown id: tags:

 ## Core Lessons

 - setting up SLURM (and other jobqueue) clusters
 - Scaling clusters
 - Adaptive clusters

 %% Cell type:markdown id: tags:

 ## Set up a Slurm cluster

 We'll create a SLURM cluster and have a look at the job-script used to start workers on the HPC scheduler.

 %% Cell type:code id: tags:

 ``` python
 import dask
 from dask.distributed import Client
 from dask_jobqueue import SLURMCluster
 import os

 cluster = SLURMCluster(
    cores=24,
    processes=2,
    memory="100GB",
    shebang="#!/usr/bin/env bash",
    queue=queue,
    dashboard_address=":56755",
    walltime="00:30:00",
    local_directory="/tmp",
    death_timeout="15s",
    interface="ib1",
    log_directory=f'{os.environ["HOME"]}/dask_jobqueue_logs/',
    project=project,
 )
 ```

 %% Cell type:code id: tags:

 ``` python
 print(cluster.job_script())
 ```

 %% Cell type:code id: tags:

 ``` python
 client = Client(cluster)
 client
 ```

 %% Cell type:markdown id: tags:

 ## You can visit the Dask Dashboard at the following url:
 ```
 https://jupyter-jsc.fz-juelich.de/user/<user_name>/<lab_name>/proxy/<port>/status
 ```

 %% Cell type:markdown id: tags:

 ## You can integrate it into your JupyterLab environment by putting the link into the Dask Extension

 %% Cell type:markdown id: tags:

 !["Dask"](https://zam10183.zam.kfa-juelich.de/hub/static/images/dask2.png "dask")

 %% Cell type:markdown id: tags:

 Afterwards you can press on the orange buttons to open a new tab in your JupyterLab Environment.

 %% Cell type:markdown id: tags:

 ## Scale the cluster to two nodes

 A look at the Dashboard reveals that there are no workers in the clusetr.  Let's start 4 workers (in 2 SLURM jobs).

 For the distiction between _workers_ and _jobs_, see [the Dask jobqueue docs](https://jobqueue.dask.org/en/latest/howitworks.html#workers-vs-jobs).

 %% Cell type:code id: tags:

 ``` python
 cluster.scale(4)  # scale to 4 _workers_
 ```

 %% Cell type:markdown id: tags:

 ## The Monte Carlo Method

 %% Cell type:code id: tags:

 ``` python
 import dask.array as da
 import numpy as np


 def calc_pi_mc(size_in_bytes, chunksize_in_bytes=200e6):
    """Calculate PI using a Monte Carlo estimate."""

    size = int(size_in_bytes / 8)
    chunksize = int(chunksize_in_bytes / 8)

    xy = da.random.uniform(0, 1, size=(size / 2, 2), chunks=(chunksize / 2, 2))

    in_circle = (xy ** 2).sum(axis=-1) < 1
    pi = 4 * in_circle.mean()

    return pi


 def print_pi_stats(size, pi, time_delta, num_workers):
    """Print pi, calculate offset from true value, and print some stats."""
    print(
        f"{size / 1e9} GB\n"
        f"\tMC pi: {pi : 13.11f}"
        f"\tErr: {abs(pi - np.pi) : 10.3e}\n"
        f"\tWorkers: {num_workers}"
        f"\t\tTime: {time_delta : 7.3f}s"
    )
 ```

 %% Cell type:markdown id: tags:

 ## The actual calculations

 We loop over different volumes of double-precision random numbers and estimate $\pi$ as described above.

 %% Cell type:code id: tags:

 ``` python
 from time import time, sleep
 ```

 %% Cell type:code id: tags:

 ``` python
 for size in (1e9 * n for n in (1, 10, 100)):

    start = time()
    pi = calc_pi_mc(size).compute()
    elaps = time() - start

    print_pi_stats(
        size, pi, time_delta=elaps, num_workers=len(cluster.scheduler.workers)
    )
 ```

 %% Cell type:markdown id: tags:

 ## Is it running?

 %% Cell type:markdown id: tags:

 To check if something has been started for you just use the following command in a terminal:
 ```
 squeue | grep ${USER}
 ```

 %% Cell type:markdown id: tags:

 ## Scaling the Cluster to twice its size

 We increase the number of workers by 2 and the re-run the experiments.

 %% Cell type:code id: tags:

 ``` python
 new_num_workers = 2 * len(cluster.scheduler.workers)

 print(f"Scaling from {len(cluster.scheduler.workers)} to {new_num_workers} workers.")

 cluster.scale(new_num_workers)

 sleep(10)
 ```

 %% Cell type:code id: tags:

 ``` python
 client
 ```

 %% Cell type:markdown id: tags:

 ## Re-run same experiments with doubled cluster

 %% Cell type:code id: tags:

 ``` python
 for size in (1e9 * n for n in (1, 10, 100)):

    start = time()
    pi = calc_pi_mc(size).compute()
    elaps = time() - start

    print_pi_stats(
        size, pi, time_delta=elaps, num_workers=len(cluster.scheduler.workers)
    )
 ```

 %% Cell type:markdown id: tags:

 ## Automatically Scaling the Cluster

 We want each calculation to take only a few seconds.  Dask will try to add more workers to the cluster when workloads are high and remove workers when idling.

 _**Watch** how the cluster will scale down to the minimum a few seconds after being made adaptive._

 %% Cell type:code id: tags:

 ``` python
 ca = cluster.adapt(minimum=4, maximum=100)

 sleep(4)  # Allow for scale-down
 ```

 %% Cell type:code id: tags:

 ``` python
 client
 ```

 %% Cell type:markdown id: tags:

 ## Repeat the calculation from above with larger work loads

 (And watch the dash board!)

 %% Cell type:code id: tags:

 ``` python
 for size in (n * 1e9 for n in (1, 10, 100)):

    start = time()
    pi = calc_pi_mc(size, min(size / 1000, 500e6)).compute()
    elaps = time() - start

    print_pi_stats(
        size, pi, time_delta=elaps, num_workers=len(cluster.scheduler.workers)
    )

    sleep(20)  # allow for scale-down time
 ```