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| <li class="toctree-l1 current"><a class="current reference internal" href="#">Federated Environment</a><ul> |
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| |
| <section id="federated-environment"> |
| <h1>Federated Environment<a class="headerlink" href="#federated-environment" title="Permalink to this heading"></a></h1> |
| <p>The python SystemDS supports federated execution. |
| To enable this, each of the federated environments have to have |
| a running federated worker.</p> |
| <section id="start-federated-worker"> |
| <h2>Start Federated worker<a class="headerlink" href="#start-federated-worker" title="Permalink to this heading"></a></h2> |
| <p>To start a federated worker, you first have to setup your environment variables. |
| A simple guide to do this is in the SystemDS <a class="reference external" href="https://github.com/apache/systemds/tree/main/bin/">Repository</a>.</p> |
| <p>If that is setup correctly simply start a worker using the following command. |
| Here the <code class="docutils literal notranslate"><span class="pre">8001</span></code> refer to the port used by the worker.</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">systemds</span> <span class="n">WORKER</span> <span class="mi">8001</span> |
| </pre></div> |
| </div> |
| </section> |
| <section id="simple-aggregation-example"> |
| <h2>Simple Aggregation Example<a class="headerlink" href="#simple-aggregation-example" title="Permalink to this heading"></a></h2> |
| <p>In this example we use a single federated worker, and aggregate the sum of its data.</p> |
| <p>First we need to create some data for our federated worker to use. |
| In this example we simply use Numpy to create a <code class="docutils literal notranslate"><span class="pre">test.csv</span></code> file.</p> |
| <p>Currently we also require a metadata file for the federated worker. |
| This should be located next to the <code class="docutils literal notranslate"><span class="pre">test.csv</span></code> file called <code class="docutils literal notranslate"><span class="pre">test.csv.mtd</span></code>. |
| To make both the data and metadata simply execute the following</p> |
| <div class="code python highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Python</span> |
| <span class="kn">import</span> <span class="nn">os</span> |
| |
| <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> |
| |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isdir</span><span class="p">(</span><span class="s2">"temp"</span><span class="p">):</span> |
| <span class="n">os</span><span class="o">.</span><span class="n">mkdir</span><span class="p">(</span><span class="s2">"temp"</span><span class="p">)</span> |
| <span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">],</span> <span class="p">[</span><span class="mi">7</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">9</span><span class="p">]])</span> |
| <span class="n">np</span><span class="o">.</span><span class="n">savetxt</span><span class="p">(</span><span class="s2">"temp/test.csv"</span><span class="p">,</span> <span class="n">a</span><span class="p">,</span> <span class="n">delimiter</span><span class="o">=</span><span class="s2">","</span><span class="p">)</span> |
| <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s2">"temp/test.csv.mtd"</span><span class="p">,</span> <span class="s2">"w"</span><span class="p">)</span> <span class="k">as</span> <span class="n">mtd</span><span class="p">:</span> |
| <span class="n">mtd</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s1">'{ "format":"csv", "header":false, "rows":3, "cols":3 }'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>After creating our data the federated worker becomes able to execute federated instructions. |
| The aggregated sum using federated instructions in python SystemDS is done as follows</p> |
| <div class="code python highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Python</span> |
| <span class="kn">import</span> <span class="nn">logging</span> |
| |
| <span class="kn">from</span> <span class="nn">systemds.context</span> <span class="kn">import</span> <span class="n">SystemDSContext</span> |
| |
| <span class="c1"># Create a federated matrix</span> |
| <span class="c1"># Indicate the dimensions of the data:</span> |
| <span class="c1"># Here the first list in the tuple is the top left Coordinate,</span> |
| <span class="c1"># and the second the bottom left coordinate.</span> |
| <span class="c1"># It is ordered as [col,row].</span> |
| <span class="n">dims</span> <span class="o">=</span> <span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span> |
| |
| <span class="c1"># Specify the address + file path from worker:</span> |
| <span class="n">address</span> <span class="o">=</span> <span class="s2">"localhost:8001/temp/test.csv"</span> |
| |
| <span class="k">with</span> <span class="n">SystemDSContext</span><span class="p">()</span> <span class="k">as</span> <span class="n">sds</span><span class="p">:</span> |
| <span class="n">fed_a</span> <span class="o">=</span> <span class="n">sds</span><span class="o">.</span><span class="n">federated</span><span class="p">([</span><span class="n">address</span><span class="p">],</span> <span class="p">[</span><span class="n">dims</span><span class="p">])</span> |
| <span class="c1"># Sum the federated matrix and call compute to execute</span> |
| <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="n">fed_a</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">compute</span><span class="p">())</span> |
| <span class="c1"># Result should be 45.</span> |
| </pre></div> |
| </div> |
| </section> |
| <section id="multiple-federated-environments"> |
| <h2>Multiple Federated Environments<a class="headerlink" href="#multiple-federated-environments" title="Permalink to this heading"></a></h2> |
| <p>In this example we multiply matrices that are located in different federated environments.</p> |
| <p>Using the data created from the last example we can simulate |
| multiple federated workers by starting multiple ones on different ports. |
| Start with 3 different terminals, and run one federated environment in each.</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">systemds</span> <span class="n">WORKER</span> <span class="mi">8001</span> |
| <span class="n">systemds</span> <span class="n">WORKER</span> <span class="mi">8002</span> |
| <span class="n">systemds</span> <span class="n">WORKER</span> <span class="mi">8003</span> |
| </pre></div> |
| </div> |
| <p>Once all three workers are up and running we can leverage all three in the following example</p> |
| <div class="code python highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Python</span> |
| <span class="kn">import</span> <span class="nn">logging</span> |
| |
| <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> |
| <span class="kn">from</span> <span class="nn">systemds.context</span> <span class="kn">import</span> <span class="n">SystemDSContext</span> |
| |
| <span class="n">addr1</span> <span class="o">=</span> <span class="s2">"localhost:8001/temp/test.csv"</span> |
| <span class="n">addr2</span> <span class="o">=</span> <span class="s2">"localhost:8002/temp/test.csv"</span> |
| <span class="n">addr3</span> <span class="o">=</span> <span class="s2">"localhost:8003/temp/test.csv"</span> |
| |
| <span class="c1"># Create a federated matrix using two federated environments</span> |
| <span class="c1"># Note that the two federated matrices are stacked on top of each other</span> |
| |
| <span class="k">with</span> <span class="n">SystemDSContext</span><span class="p">()</span> <span class="k">as</span> <span class="n">sds</span><span class="p">:</span> |
| <span class="c1"># federated data on three locations</span> |
| <span class="n">fed</span> <span class="o">=</span> <span class="n">sds</span><span class="o">.</span><span class="n">federated</span><span class="p">([</span><span class="n">addr1</span><span class="p">,</span> <span class="n">addr2</span><span class="p">,</span> <span class="n">addr3</span><span class="p">],</span> <span class="p">[</span> |
| <span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">]),</span> |
| <span class="p">([</span><span class="mi">3</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">6</span><span class="p">,</span> <span class="mi">3</span><span class="p">]),</span> |
| <span class="p">([</span><span class="mi">6</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">9</span><span class="p">,</span> <span class="mi">3</span><span class="p">])])</span> |
| <span class="c1"># local matrix to multiply with</span> |
| <span class="n">loc</span> <span class="o">=</span> <span class="n">sds</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span> |
| <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">9</span><span class="p">],</span> |
| <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">9</span><span class="p">],</span> |
| <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">9</span><span class="p">]</span> |
| <span class="p">]))</span> |
| <span class="c1"># Multiply local and federated</span> |
| <span class="n">ret</span> <span class="o">=</span> <span class="n">loc</span> <span class="o">@</span> <span class="n">fed</span> |
| <span class="c1"># execute the lazy script and print</span> |
| <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="n">ret</span><span class="o">.</span><span class="n">compute</span><span class="p">())</span> |
| </pre></div> |
| </div> |
| <p>The print should look like</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[[</span><span class="mf">198.</span> <span class="mf">243.</span> <span class="mf">288.</span><span class="p">]</span> |
| <span class="p">[</span><span class="mf">198.</span> <span class="mf">243.</span> <span class="mf">288.</span><span class="p">]</span> |
| <span class="p">[</span><span class="mf">198.</span> <span class="mf">243.</span> <span class="mf">288.</span><span class="p">]]</span> |
| </pre></div> |
| </div> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>If it does not work, then double check |
| that you have:</p> |
| <p>a csv file, mtd file, and SystemDS Environment is set correctly.</p> |
| </div> |
| </section> |
| <section id="multi-tenant-federated-learning"> |
| <h2>Multi-tenant Federated Learning<a class="headerlink" href="#multi-tenant-federated-learning" title="Permalink to this heading"></a></h2> |
| <p>SystemDS supports Multi-tenant Federated Learning, meaning that multiple |
| coordinators learn on shared federated workers. From another perspective, |
| the federated worker allows multiple coordinators to perform model training |
| simultaneously using the data from the respective federated site. This |
| approach enables the worker to operate in a server-like mode, providing |
| multiple tenants with the ability to learn on the federated data at the same |
| time. Tenant isolation ensures that tenant-specific intermediate results are |
| only accessible by the respective tenant.</p> |
| <section id="limitations"> |
| <h3>Limitations<a class="headerlink" href="#limitations" title="Permalink to this heading"></a></h3> |
| <p>Since the coordinators are differentiated by their IP address in combination |
| with their process ID, the worker is not able to isolate coordinators which |
| share the same IP address and the same process ID. This occurs, for example, |
| when two coordinators are running behind a proxy (same IP address), where |
| both coordinators coincidentally have the same process ID.</p> |
| <p>A second limitation is showing up in networks using the Dynamic Host Protocol |
| (DHCP). Since the federated worker identifies the coordinator based on the |
| IP address, the worker does not re-identify the coordinator when its IP address |
| has changed, i.e., when DHCP renews its IP address.</p> |
| </section> |
| </section> |
| </section> |
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