From 8af59fd4a366b633bda5e846220a49a04fea95b2 Mon Sep 17 00:00:00 2001 From: skirui-source Date: Wed, 27 Sep 2023 00:31:07 -0700 Subject: [PATCH] rerun cells with new cluser --- .../notebook.ipynb | 833 ++++++------------ .../trained-model_nyctaxi.xgb | Bin 1631819 -> 1633331 bytes .../zipfile_write.zip | Bin 1631967 -> 1633479 bytes 3 files changed, 245 insertions(+), 588 deletions(-) diff --git a/source/examples/xgboost-azure-mnmg-daskcloudprovider/notebook.ipynb b/source/examples/xgboost-azure-mnmg-daskcloudprovider/notebook.ipynb index ff0e78d1..dba2f84b 100644 --- a/source/examples/xgboost-azure-mnmg-daskcloudprovider/notebook.ipynb +++ b/source/examples/xgboost-azure-mnmg-daskcloudprovider/notebook.ipynb @@ -116,75 +116,75 @@ "output_type": "stream", "text": [ "Requirement already satisfied: adlfs in /home/skirui/anaconda3/envs/deployment-docs-dev/lib/python3.9/site-packages (2023.4.0)\n", + "Requirement already satisfied: azure-storage-blob>=12.12.0 in /home/skirui/anaconda3/envs/deployment-docs-dev/lib/python3.9/site-packages (from adlfs) (12.17.0)\n", "Requirement already satisfied: azure-datalake-store<0.1,>=0.0.46 in /home/skirui/anaconda3/envs/deployment-docs-dev/lib/python3.9/site-packages (from adlfs) (0.0.53)\n", + "Requirement already satisfied: azure-core<2.0.0,>=1.23.1 in /home/skirui/anaconda3/envs/deployment-docs-dev/lib/python3.9/site-packages (from adlfs) (1.28.0)\n", "Requirement already satisfied: fsspec>=2021.10.1 in /home/skirui/anaconda3/envs/deployment-docs-dev/lib/python3.9/site-packages (from adlfs) (2023.6.0)\n", "Requirement already satisfied: aiohttp>=3.7.0 in /home/skirui/anaconda3/envs/deployment-docs-dev/lib/python3.9/site-packages (from adlfs) (3.8.5)\n", "Requirement already satisfied: azure-identity in /home/skirui/anaconda3/envs/deployment-docs-dev/lib/python3.9/site-packages (from adlfs) (1.13.0)\n", - 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" \"retrieveDatetime\": \"2023-09-26T18:52:57.6913718Z\",\n", - " \"signature\": \"76XJFI56UNBCU6YVRHS2HL6INB3EA4WC5OF2VLW65B6PP2K6VA527NUXCQEZZXII7DMJQRYWJ6YTWGALNVDRRFNPP36RCYI464NHKHA\",\n", + " \"retrieveDatetime\": \"2023-09-27T06:49:56.1917399Z\",\n", + " \"signature\": \"DMNAJRI7YWDBPHCQSPUE6KS3732W6NXADZCHX32LSRWLZGEYVXDMITRUSS4B52H2W6RTODZJ7BABIVPRPI5WA2FXQZRTDXFA53AUIWI\",\n", " \"systemData\": {\n", - " \"createdAt\": \"2023-09-26T18:53:00.070127+00:00\",\n", + " \"createdAt\": \"2023-09-27T06:49:59.000248+00:00\",\n", " \"createdBy\": \"fc4f4a6b-4041-4b1c-8249-854d68edcf62\",\n", " \"createdByType\": \"ManagedIdentity\",\n", - " \"lastModifiedAt\": \"2023-09-26T18:53:00.070127+00:00\",\n", + " \"lastModifiedAt\": \"2023-09-27T06:49:59.000248+00:00\",\n", " \"lastModifiedBy\": \"fc4f4a6b-4041-4b1c-8249-854d68edcf62\",\n", " \"lastModifiedByType\": \"ManagedIdentity\"\n", " },\n", " \"type\": \"Microsoft.MarketplaceOrdering/offertypes\"\n", "}\n", - "\u001b[32mCommand ran in 6.416 seconds (init: 0.150, invoke: 6.265)\u001b[0m\n" + "\u001b[32mCommand ran in 6.780 seconds (init: 0.148, invoke: 6.633)\u001b[0m\n" ] } ], @@ -712,8 +712,8 @@ "Network interface ready\n", "Using Marketplace VM image with a Plan\n", "Creating VM\n", - "Created VM dask-cd718e4c-scheduler\n", - "Waiting for scheduler to run at 4.154.137.246:8786\n", + "Created VM dask-e1db89fb-scheduler\n", + "Waiting for scheduler to run at 20.80.185.4:8786\n", "Scheduler is running\n" ] }, @@ -737,10 +737,10 @@ "Network interface ready\n", "Using Marketplace VM image with a Plan\n", "Creating VM\n", - "Created VM dask-cd718e4c-worker-7b9b2ff2\n", - "Created VM dask-cd718e4c-worker-3cf05f03\n", - "CPU times: user 1.55 s, sys: 312 ms, total: 1.86 s\n", - "Wall time: 5min 51s\n" + "Created VM dask-e1db89fb-worker-396d96a3\n", + "Created VM dask-e1db89fb-worker-f3e80672\n", + "CPU times: user 1.38 s, sys: 534 ms, total: 1.92 s\n", + "Wall time: 7min 24s\n" ] } ], @@ -769,52 +769,6 @@ "cell_type": "code", "execution_count": 8, "metadata": {}, - "outputs": [], - "source": [ - "client = Client(cluster)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Uncomment if you only have the scheduler with n_workers=0 and want to scale the workers separately.\n", - "# %%time\n", - "# client.cluster.scale(n_workers)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Wait till all the workers are up. This will wait for `n_workers` number of VMs to be up." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 5.47 ms, sys: 0 ns, total: 5.47 ms\n", - "Wall time: 28.8 ms\n" - ] - } - ], - "source": [ - "%%time\n", - "client.wait_for_workers(2)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, "outputs": [ { "data": { @@ -823,7 +777,7 @@ "
\n", "
\n", "

Client

\n", - "

Client-6c9c49fe-5c9f-11ee-ac2b-80e82cd32958

\n", + "

Client-04e77b37-5d04-11ee-99d6-80e82cd32958

\n", " \n", "\n", " \n", @@ -836,7 +790,7 @@ " \n", " \n", " \n", " \n", " \n", @@ -854,11 +808,11 @@ " \n", "
\n", "

AzureVMCluster

\n", - "

c5594946

\n", + "

4ba0a35f

\n", "
\n", - " Dashboard: http://4.154.137.246:8787/status\n", + " Dashboard: http://20.80.185.4:8787/status\n", "
\n", " \n", " \n", "
\n", - " Dashboard: http://4.154.137.246:8787/status\n", + " Dashboard: http://20.80.185.4:8787/status\n", " \n", " Workers: 4\n", @@ -885,11 +839,11 @@ "
\n", "
\n", "

Scheduler

\n", - "

Scheduler-da7b39f6-8d7b-4580-9521-8667cea57940

\n", + "

Scheduler-9c563196-caf1-4498-aa20-4c4557aa1a47

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\n", - " Comm: tls://10.5.0.26:8786\n", + " Comm: tls://10.5.0.29:8786\n", " \n", " Workers: 4\n", @@ -897,7 +851,7 @@ "
\n", - " Dashboard: http://10.5.0.26:8787/status\n", + " Dashboard: http://10.5.0.29:8787/status\n", " \n", " Total threads: 4\n", @@ -905,7 +859,7 @@ "
\n", - " Started: 6 minutes ago\n", + " Started: 7 minutes ago\n", " \n", " Total memory: 440.42 GiB\n", @@ -926,12 +880,12 @@ "
\n", "
\n", " \n", - "

Worker: dask-cd718e4c-worker-3cf05f03-0

\n", + "

Worker: dask-e1db89fb-worker-396d96a3-0

\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -969,47 +923,6 @@ " \n", "\n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", "\n", "
\n", - " Comm: tls://10.5.0.28:37831\n", + " Comm: tls://10.5.0.30:35833\n", " \n", " Total threads: 1\n", @@ -939,7 +893,7 @@ "
\n", - " Dashboard: http://10.5.0.28:44385/status\n", + " Dashboard: http://10.5.0.30:43943/status\n", " \n", " Memory: 110.11 GiB\n", @@ -947,13 +901,13 @@ "
\n", - " Nanny: tls://10.5.0.28:43437\n", + " Nanny: tls://10.5.0.30:37243\n", "
\n", - " Local directory: /tmp/dask-scratch-space/worker-laurl35q\n", + " Local directory: /tmp/dask-scratch-space/worker-jk35m4vu\n", "
\n", - " Tasks executing: \n", - " \n", - " Tasks in memory: \n", - "
\n", - " Tasks ready: \n", - " \n", - " Tasks in flight: \n", - "
\n", - " CPU usage: 0.0%\n", - " \n", - " Last seen: Just now\n", - "
\n", - " Memory usage: 590.09 MiB\n", - " \n", - " Spilled bytes: 0 B\n", - "
\n", - " Read bytes: 7.72 kiB\n", - " \n", - " Write bytes: 13.27 kiB\n", - "
\n", "
\n", @@ -1021,12 +934,12 @@ "
\n", "
\n", " \n", - "

Worker: dask-cd718e4c-worker-3cf05f03-1

\n", + "

Worker: dask-e1db89fb-worker-396d96a3-1

\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -1064,47 +977,6 @@ " \n", "\n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", "\n", "
\n", - " Comm: tls://10.5.0.28:46499\n", + " Comm: tls://10.5.0.30:39857\n", " \n", " Total threads: 1\n", @@ -1034,7 +947,7 @@ "
\n", - " Dashboard: http://10.5.0.28:36329/status\n", + " Dashboard: http://10.5.0.30:39981/status\n", " \n", " Memory: 110.11 GiB\n", @@ -1042,13 +955,13 @@ "
\n", - " Nanny: tls://10.5.0.28:42893\n", + " Nanny: tls://10.5.0.30:33125\n", "
\n", - " Local directory: /tmp/dask-scratch-space/worker-rz4dg7ou\n", + " Local directory: /tmp/dask-scratch-space/worker-gog2yjq8\n", "
\n", - " Tasks executing: \n", - " \n", - " Tasks in memory: \n", - "
\n", - " Tasks ready: \n", - " \n", - " Tasks in flight: \n", - "
\n", - " CPU usage: 2.0%\n", - " \n", - " Last seen: Just now\n", - "
\n", - " Memory usage: 590.06 MiB\n", - " \n", - " Spilled bytes: 0 B\n", - "
\n", - " Read bytes: 2.07 kiB\n", - " \n", - " Write bytes: 11.07 kiB\n", - "
\n", "
\n", @@ -1116,12 +988,12 @@ "
\n", "
\n", " \n", - "

Worker: dask-cd718e4c-worker-7b9b2ff2-0

\n", + "

Worker: dask-e1db89fb-worker-f3e80672-0

\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -1159,47 +1031,6 @@ " \n", "\n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", "\n", "
\n", - " Comm: tls://10.5.0.27:33135\n", + " Comm: tls://10.5.0.31:40645\n", " \n", " Total threads: 1\n", @@ -1129,7 +1001,7 @@ "
\n", - " Dashboard: http://10.5.0.27:36693/status\n", + " Dashboard: http://10.5.0.31:38957/status\n", " \n", " Memory: 110.11 GiB\n", @@ -1137,13 +1009,13 @@ "
\n", - " Nanny: tls://10.5.0.27:44451\n", + " Nanny: tls://10.5.0.31:35155\n", "
\n", - " Local directory: /tmp/dask-scratch-space/worker-rkwt_wd2\n", + " Local directory: /tmp/dask-scratch-space/worker-jqle2l9n\n", "
\n", - " Tasks executing: \n", - " \n", - " Tasks in memory: \n", - "
\n", - " Tasks ready: \n", - " \n", - " Tasks in flight: \n", - "
\n", - " CPU usage: 4.0%\n", - " \n", - " Last seen: Just now\n", - "
\n", - " Memory usage: 594.46 MiB\n", - " \n", - " Spilled bytes: 0 B\n", - "
\n", - " Read bytes: 611.7517621464344 B\n", - " \n", - " Write bytes: 3.26 kiB\n", - "
\n", "
\n", @@ -1211,12 +1042,12 @@ "
\n", "
\n", " \n", - "

Worker: dask-cd718e4c-worker-7b9b2ff2-1

\n", + "

Worker: dask-e1db89fb-worker-f3e80672-1

\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -1254,47 +1085,6 @@ " \n", "\n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", "\n", "
\n", - " Comm: tls://10.5.0.27:36605\n", + " Comm: tls://10.5.0.31:42211\n", " \n", " Total threads: 1\n", @@ -1224,7 +1055,7 @@ "
\n", - " Dashboard: http://10.5.0.27:41189/status\n", + " Dashboard: http://10.5.0.31:41035/status\n", " \n", " Memory: 110.11 GiB\n", @@ -1232,13 +1063,13 @@ "
\n", - " Nanny: tls://10.5.0.27:35583\n", + " Nanny: tls://10.5.0.31:42491\n", "
\n", - " Local directory: /tmp/dask-scratch-space/worker-iclmame3\n", + " Local directory: /tmp/dask-scratch-space/worker-vwjtj063\n", "
\n", - " Tasks executing: \n", - " \n", - " Tasks in memory: \n", - "
\n", - " Tasks ready: \n", - " \n", - " Tasks in flight: \n", - "
\n", - " CPU usage: 2.0%\n", - " \n", - " Last seen: Just now\n", - "
\n", - " Memory usage: 592.07 MiB\n", - " \n", - " Spilled bytes: 0 B\n", - "
\n", - " Read bytes: 611.8039865679604 B\n", - " \n", - " Write bytes: 3.26 kiB\n", - "
\n", "
\n", @@ -1315,30 +1105,31 @@ "
" ], "text/plain": [ - "" + "" ] }, - "execution_count": 10, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "client = Client(cluster)\n", "client" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'a5edf7b5-fd91-4f6a-abf5-160d4cf9'" + "'1a90f19e-b224-4cad-ab48-94ea8cad'" ] }, - "execution_count": 11, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -1349,7 +1140,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 10, "metadata": { "scrolled": true }, @@ -1357,16 +1148,44 @@ { "data": { "text/plain": [ - "'6824de00-5107-4513-8b9c-bcc0d191'" + "'1fd09c90-c798-44f8-8a47-a2641d0a'" ] }, - "execution_count": 17, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "client.cluster.workers[1].admin_password" + "client.cluster.workers[0].admin_password" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Uncomment if you only have the scheduler with n_workers=0 and want to scale the workers separately.\n", + "# %%time\n", + "# client.cluster.scale(n_workers)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Wait till all the workers are up. This will wait for `n_workers` number of VMs to be up." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "client.wait_for_workers(2)" ] }, { @@ -1378,192 +1197,192 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'address': 'tls://10.5.0.26:8786',\n", - " 'id': 'Scheduler-da7b39f6-8d7b-4580-9521-8667cea57940',\n", + "{'address': 'tls://10.5.0.29:8786',\n", + " 'id': 'Scheduler-9c563196-caf1-4498-aa20-4c4557aa1a47',\n", " 'services': {'dashboard': 8787},\n", - " 'started': 1695754666.812146,\n", + " 'started': 1695797770.0297768,\n", " 'type': 'Scheduler',\n", - " 'workers': {'tls://10.5.0.27:33135': {'gpu': {'memory-total': 17179869184,\n", + " 'workers': {'tls://10.5.0.30:35833': {'gpu': {'memory-total': 17179869184,\n", " 'name': 'Tesla V100-PCIE-16GB'},\n", - " 'host': '10.5.0.27',\n", - " 'id': 'dask-cd718e4c-worker-7b9b2ff2-0',\n", - " 'last_seen': 1695755308.5944703,\n", - " 'local_directory': '/tmp/dask-scratch-space/worker-rkwt_wd2',\n", + " 'host': '10.5.0.30',\n", + " 'id': 'dask-e1db89fb-worker-396d96a3-0',\n", + " 'last_seen': 1695798249.4152472,\n", + " 'local_directory': '/tmp/dask-scratch-space/worker-jk35m4vu',\n", " 'memory_limit': 118225672192,\n", " 'metrics': {'bandwidth': {'total': 100000000,\n", " 'types': {},\n", " 'workers': {}},\n", - " 'cpu': 2.0,\n", - " 'digests_total_since_heartbeat': {'latency': 0.004858493804931641,\n", - " 'tick-duration': 0.500096321105957},\n", - " 'event_loop_interval': 0.020026397705078126,\n", + " 'cpu': 4.0,\n", + " 'digests_total_since_heartbeat': {'latency': 0.0039136409759521484,\n", + " 'tick-duration': 0.500333309173584},\n", + " 'event_loop_interval': 0.019992027282714844,\n", " 'gpu': {'memory-used': 598867968,\n", " 'utilization': 0},\n", " 'gpu_memory_used': 598867968,\n", " 'gpu_utilization': 0,\n", " 'host_disk_io': {'read_bps': 0.0,\n", - " 'write_bps': 16810337.47516862},\n", - " 'host_net_io': {'read_bps': 613.2082065822599,\n", - " 'write_bps': 3350.6017039396684},\n", + " 'write_bps': 0.0},\n", + " 'host_net_io': {'read_bps': 611.5512045867728,\n", + " 'write_bps': 3341.5477583956995},\n", " 'managed_bytes': 0,\n", - " 'memory': 625156096,\n", + " 'memory': 623390720,\n", " 'num_fds': 86,\n", " 'rmm': {'rmm-total': 0,\n", " 'rmm-used': 0},\n", " 'spilled_bytes': {'disk': 0,\n", " 'memory': 0},\n", " 'task_counts': {},\n", - " 'time': 1695755308.0910525,\n", + " 'time': 1695798248.910828,\n", " 'transfer': {'incoming_bytes': 0,\n", " 'incoming_count': 0,\n", " 'incoming_count_total': 0,\n", " 'outgoing_bytes': 0,\n", " 'outgoing_count': 0,\n", " 'outgoing_count_total': 0}},\n", - " 'name': 'dask-cd718e4c-worker-7b9b2ff2-0',\n", - " 'nanny': 'tls://10.5.0.27:44451',\n", + " 'name': 'dask-e1db89fb-worker-396d96a3-0',\n", + " 'nanny': 'tls://10.5.0.30:37243',\n", " 'nthreads': 1,\n", " 'resources': {},\n", - " 'services': {'dashboard': 36693},\n", + " 'services': {'dashboard': 43943},\n", " 'status': 'running',\n", " 'type': 'Worker'},\n", - " 'tls://10.5.0.27:36605': {'gpu': {'memory-total': 17179869184,\n", + " 'tls://10.5.0.30:39857': {'gpu': {'memory-total': 17179869184,\n", " 'name': 'Tesla V100-PCIE-16GB'},\n", - " 'host': '10.5.0.27',\n", - " 'id': 'dask-cd718e4c-worker-7b9b2ff2-1',\n", - " 'last_seen': 1695755309.0630434,\n", - " 'local_directory': '/tmp/dask-scratch-space/worker-iclmame3',\n", + " 'host': '10.5.0.30',\n", + " 'id': 'dask-e1db89fb-worker-396d96a3-1',\n", + " 'last_seen': 1695798249.4130971,\n", + " 'local_directory': '/tmp/dask-scratch-space/worker-gog2yjq8',\n", " 'memory_limit': 118225672192,\n", " 'metrics': {'bandwidth': {'total': 100000000,\n", " 'types': {},\n", " 'workers': {}},\n", " 'cpu': 2.0,\n", - " 'digests_total_since_heartbeat': {'latency': 0.0047109127044677734,\n", - " 'tick-duration': 0.49942922592163086},\n", - " 'event_loop_interval': 0.020009045600891114,\n", + " 'digests_total_since_heartbeat': {'latency': 0.004378795623779297,\n", + " 'tick-duration': 0.49997711181640625},\n", + " 'event_loop_interval': 0.019992733001708986,\n", " 'gpu': {'memory-used': 598867968,\n", " 'utilization': 0},\n", " 'gpu_memory_used': 598867968,\n", " 'gpu_utilization': 0,\n", " 'host_disk_io': {'read_bps': 0.0,\n", - " 'write_bps': 16774763.127036663},\n", - " 'host_net_io': {'read_bps': 611.9105239955448,\n", - " 'write_bps': 3343.5110984331727},\n", + " 'write_bps': 0.0},\n", + " 'host_net_io': {'read_bps': 612.2709225359564,\n", + " 'write_bps': 3345.48033490235},\n", " 'managed_bytes': 0,\n", - " 'memory': 621473792,\n", + " 'memory': 617656320,\n", " 'num_fds': 86,\n", " 'rmm': {'rmm-total': 0,\n", " 'rmm-used': 0},\n", " 'spilled_bytes': {'disk': 0,\n", " 'memory': 0},\n", " 'task_counts': {},\n", - " 'time': 1695755308.5604067,\n", + " 'time': 1695798248.909559,\n", " 'transfer': {'incoming_bytes': 0,\n", " 'incoming_count': 0,\n", " 'incoming_count_total': 0,\n", " 'outgoing_bytes': 0,\n", " 'outgoing_count': 0,\n", " 'outgoing_count_total': 0}},\n", - " 'name': 'dask-cd718e4c-worker-7b9b2ff2-1',\n", - " 'nanny': 'tls://10.5.0.27:35583',\n", + " 'name': 'dask-e1db89fb-worker-396d96a3-1',\n", + " 'nanny': 'tls://10.5.0.30:33125',\n", " 'nthreads': 1,\n", " 'resources': {},\n", - " 'services': {'dashboard': 41189},\n", + " 'services': {'dashboard': 39981},\n", " 'status': 'running',\n", " 'type': 'Worker'},\n", - " 'tls://10.5.0.28:37831': {'gpu': {'memory-total': 17179869184,\n", + " 'tls://10.5.0.31:40645': {'gpu': {'memory-total': 17179869184,\n", " 'name': 'Tesla V100-PCIE-16GB'},\n", - " 'host': '10.5.0.28',\n", - " 'id': 'dask-cd718e4c-worker-3cf05f03-0',\n", - " 'last_seen': 1695755308.6291673,\n", - " 'local_directory': '/tmp/dask-scratch-space/worker-laurl35q',\n", - " 'memory_limit': 118225670144,\n", + " 'host': '10.5.0.31',\n", + " 'id': 'dask-e1db89fb-worker-f3e80672-0',\n", + " 'last_seen': 1695798249.7065253,\n", + " 'local_directory': '/tmp/dask-scratch-space/worker-jqle2l9n',\n", + " 'memory_limit': 118225672192,\n", " 'metrics': {'bandwidth': {'total': 100000000,\n", " 'types': {},\n", " 'workers': {}},\n", - " 'cpu': 2.0,\n", - " 'digests_total_since_heartbeat': {'latency': 0.004083871841430664,\n", - " 'tick-duration': 0.5008234977722168},\n", - " 'event_loop_interval': 0.020003509521484376,\n", + " 'cpu': 4.0,\n", + " 'digests_total_since_heartbeat': {'latency': 0.0037021636962890625,\n", + " 'tick-duration': 0.4992666244506836},\n", + " 'event_loop_interval': 0.020013856887817382,\n", " 'gpu': {'memory-used': 598867968,\n", " 'utilization': 0},\n", " 'gpu_memory_used': 598867968,\n", " 'gpu_utilization': 0,\n", " 'host_disk_io': {'read_bps': 0.0,\n", - " 'write_bps': 8384838.092946665},\n", - " 'host_net_io': {'read_bps': 611.7249623398017,\n", - " 'write_bps': 3342.4971798436222},\n", + " 'write_bps': 29385631.375439122},\n", + " 'host_net_io': {'read_bps': 7885.455761722707,\n", + " 'write_bps': 13551.31655225871},\n", " 'managed_bytes': 0,\n", - " 'memory': 619945984,\n", + " 'memory': 621834240,\n", " 'num_fds': 86,\n", " 'rmm': {'rmm-total': 0,\n", " 'rmm-used': 0},\n", " 'spilled_bytes': {'disk': 0,\n", " 'memory': 0},\n", " 'task_counts': {},\n", - " 'time': 1695755308.1364117,\n", + " 'time': 1695798249.1979365,\n", " 'transfer': {'incoming_bytes': 0,\n", " 'incoming_count': 0,\n", " 'incoming_count_total': 0,\n", " 'outgoing_bytes': 0,\n", " 'outgoing_count': 0,\n", " 'outgoing_count_total': 0}},\n", - " 'name': 'dask-cd718e4c-worker-3cf05f03-0',\n", - " 'nanny': 'tls://10.5.0.28:43437',\n", + " 'name': 'dask-e1db89fb-worker-f3e80672-0',\n", + " 'nanny': 'tls://10.5.0.31:35155',\n", " 'nthreads': 1,\n", " 'resources': {},\n", - " 'services': {'dashboard': 44385},\n", + " 'services': {'dashboard': 38957},\n", " 'status': 'running',\n", " 'type': 'Worker'},\n", - " 'tls://10.5.0.28:46499': {'gpu': {'memory-total': 17179869184,\n", + " 'tls://10.5.0.31:42211': {'gpu': {'memory-total': 17179869184,\n", " 'name': 'Tesla V100-PCIE-16GB'},\n", - " 'host': '10.5.0.28',\n", - " 'id': 'dask-cd718e4c-worker-3cf05f03-1',\n", - " 'last_seen': 1695755308.6400685,\n", - " 'local_directory': '/tmp/dask-scratch-space/worker-rz4dg7ou',\n", - " 'memory_limit': 118225670144,\n", + " 'host': '10.5.0.31',\n", + " 'id': 'dask-e1db89fb-worker-f3e80672-1',\n", + " 'last_seen': 1695798249.5185316,\n", + " 'local_directory': '/tmp/dask-scratch-space/worker-vwjtj063',\n", + " 'memory_limit': 118225672192,\n", " 'metrics': {'bandwidth': {'total': 100000000,\n", " 'types': {},\n", " 'workers': {}},\n", " 'cpu': 2.0,\n", - " 'digests_total_since_heartbeat': {'latency': 0.0037758350372314453,\n", - " 'tick-duration': 0.5004556179046631},\n", - " 'event_loop_interval': 0.020004029273986815,\n", + " 'digests_total_since_heartbeat': {'latency': 0.0039021968841552734,\n", + " 'tick-duration': 0.4993259906768799},\n", + " 'event_loop_interval': 0.019996361732482912,\n", " 'gpu': {'memory-used': 598867968,\n", " 'utilization': 0},\n", " 'gpu_memory_used': 598867968,\n", " 'gpu_utilization': 0,\n", " 'host_disk_io': {'read_bps': 0.0,\n", - " 'write_bps': 8396786.04977224},\n", - " 'host_net_io': {'read_bps': 612.596638495995,\n", - " 'write_bps': 3347.2600639389007},\n", + " 'write_bps': 33689279.12993749},\n", + " 'host_net_io': {'read_bps': 7912.202242801052,\n", + " 'write_bps': 13597.280925492661},\n", " 'managed_bytes': 0,\n", - " 'memory': 619941888,\n", + " 'memory': 625152000,\n", " 'num_fds': 86,\n", " 'rmm': {'rmm-total': 0,\n", " 'rmm-used': 0},\n", " 'spilled_bytes': {'disk': 0,\n", " 'memory': 0},\n", " 'task_counts': {},\n", - " 'time': 1695755308.1476307,\n", + " 'time': 1695798249.0098388,\n", " 'transfer': {'incoming_bytes': 0,\n", " 'incoming_count': 0,\n", " 'incoming_count_total': 0,\n", " 'outgoing_bytes': 0,\n", " 'outgoing_count': 0,\n", " 'outgoing_count_total': 0}},\n", - " 'name': 'dask-cd718e4c-worker-3cf05f03-1',\n", - " 'nanny': 'tls://10.5.0.28:42893',\n", + " 'name': 'dask-e1db89fb-worker-f3e80672-1',\n", + " 'nanny': 'tls://10.5.0.31:42491',\n", " 'nthreads': 1,\n", " 'resources': {},\n", - " 'services': {'dashboard': 36329},\n", + " 'services': {'dashboard': 41035},\n", " 'status': 'running',\n", " 'type': 'Worker'}}}\n" ] @@ -1606,19 +1425,19 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'tls://10.5.0.27:33135': {'status': 'OK'},\n", - " 'tls://10.5.0.27:36605': {'status': 'OK'},\n", - " 'tls://10.5.0.28:37831': {'status': 'OK'},\n", - " 'tls://10.5.0.28:46499': {'status': 'OK'}}" + "{'tls://10.5.0.30:35833': {'status': 'OK'},\n", + " 'tls://10.5.0.30:39857': {'status': 'OK'},\n", + " 'tls://10.5.0.31:40645': {'status': 'OK'},\n", + " 'tls://10.5.0.31:42211': {'status': 'OK'}}" ] }, - "execution_count": 14, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -1666,7 +1485,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -1768,24 +1587,8 @@ }, { "cell_type": "code", - "execution_count": 28, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "library/cuml", - "library/dask-cudf", - "library/numpy", - "library/dask", - "tools/dask-cloudprovider", - "cloud/azure/azure-vm", - "dataset/nyc-taxi", - "data-format/parquet", - "data-storage/adls" - ] - }, + "execution_count": 14, + "metadata": {}, "outputs": [], "source": [ "def persist_train_infer_split(\n", @@ -1933,7 +1736,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -1948,7 +1751,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Wall clock time taken for ETL and persisting : 75.51355690800119 s\n" + "Wall clock time taken for ETL and persisting : 76.94258410600014 s\n" ] } ], @@ -1967,7 +1770,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -1976,7 +1779,7 @@ "48817562" ] }, - "execution_count": 30, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -1994,7 +1797,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -2135,7 +1938,7 @@ "294464 0.0 " ] }, - "execution_count": 31, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -2168,7 +1971,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -2208,14 +2011,14 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Wall clock time taken for this cell : 8.79627256601816 s\n" + "Wall clock time taken for this cell : 8.763767230033409 s\n" ] } ], @@ -2230,26 +2033,6 @@ "print(f\"Wall clock time taken for this cell : {toc-tic} s\")" ] }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "xgb_gpu_model" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -2259,7 +2042,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -2291,7 +2074,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -2300,7 +2083,7 @@ "DoneAndNotDoneFutures(done=set(), not_done=set())" ] }, - "execution_count": 38, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -2312,45 +2095,14 @@ }, { "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "120249 12.0\n", - "157207 5.0\n", - "230041 10.5\n", - "403916 8.5\n", - "153919 11.5\n", - " ... \n", - "52284 7.0\n", - "488390 5.0\n", - "320576 19.0\n", - "270255 15.0\n", - "587681 6.5\n", - "Name: fareAmount, Length: 5426301, dtype: float32" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "_y_test" - ] - }, - { - "cell_type": "code", - "execution_count": 40, + "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Wall clock time taken for xgb.dask.predict : 1.701935115037486 s\n" + "Wall clock time taken for xgb.dask.predict : 1.5908975210040808 s\n" ] } ], @@ -2379,14 +2131,14 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Wall clock time taken for inplace inference : 2.482840881042648 s\n" + "Wall clock time taken for inplace inference : 2.646754012966994 s\n" ] } ], @@ -2401,7 +2153,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 25, "metadata": { "editable": true, "slideshow": { @@ -2415,8 +2167,8 @@ "output_type": "stream", "text": [ "Calculating MSE\n", - "Workflow Complete - RMSE: 2.2928023\n", - "Wall clock time taken for this cell : 0.02508155204122886 s\n" + "Workflow Complete - RMSE: 2.2904203\n", + "Wall clock time taken for this cell : 0.014574272034224123 s\n" ] } ], @@ -2445,7 +2197,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -2466,46 +2218,45 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "dict_keys(['tls://10.5.0.27:33135', 'tls://10.5.0.27:36605', 'tls://10.5.0.28:37831', 'tls://10.5.0.28:46499'])\n" + "dict_keys(['tls://10.5.0.30:35833', 'tls://10.5.0.30:39857', 'tls://10.5.0.31:40645', 'tls://10.5.0.31:42211'])\n" ] } ], "source": [ "workers = client.has_what().keys()\n", "print(workers)\n", - "\n", "n_workers = len(workers)\n", "n_partitions = n_workers" ] }, { "cell_type": "code", - "execution_count": 107, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ - "def checkOrMakeLocalDir():\n", + "def unzipFile(zipname):\n", " worker = get_worker()\n", + " import zipfile\n", " import os\n", "\n", - " if not os.path.exists(worker.local_directory):\n", - " os.makedirs(worker.local_directory)\n", + " with zipfile.ZipFile(os.path.join(worker.local_directory, zipname)) as zf:\n", + " zf.extractall(worker.local_directory)\n", "\n", "\n", - "def unzipFile(zipname):\n", + "def checkOrMakeLocalDir():\n", " worker = get_worker()\n", - " import zipfile\n", " import os\n", "\n", - " with zipfile.ZipFile(os.path.join(worker.local_directory, zipname)) as zf:\n", - " zf.extractall(worker.local_directory)\n", + " if not os.path.exists(worker.local_directory):\n", + " os.makedirs(worker.local_directory)\n", "\n", "\n", "def workerModelInit(model_file):\n", @@ -2513,8 +2264,9 @@ " import os\n", "\n", " worker = get_worker()\n", - " filename = os.path.join(worker.local_directory, model_file)\n", - " worker.data[\"fil_model\"] = ForestInference.load(filename, model_type=\"xgboost\")\n", + " worker.data[\"fil_model\"] = ForestInference.load(\n", + " filename=os.path.join(worker.local_directory, model_file), model_type=\"xgboost\"\n", + " )\n", "\n", "\n", "def predict(input_df):\n", @@ -2529,20 +2281,16 @@ " zf = zipfile.ZipFile(zip_file_name, mode=\"w\")\n", " zf.write(f\"./{model_file_name}\")\n", " zf.close()\n", - "\n", " # check to see if local directory present in workers\n", " # if not present make it\n", " fut = client.submit(checkOrMakeLocalDir)\n", " wait(fut)\n", - "\n", " # upload the zip file in workers\n", " fut = client.upload_file(f\"./{zip_file_name}\")\n", " wait(fut)\n", - "\n", " # unzip file in the workers\n", " fut = client.submit(unzipFile, zip_file_name)\n", " wait(fut)\n", - "\n", " # load model using FIL in workers\n", " fut = client.submit(workerModelInit, model_file_name)\n", " wait(fut)" @@ -2557,15 +2305,15 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 39.4 ms, sys: 4.61 ms, total: 44.1 ms\n", - "Wall time: 408 ms\n" + "CPU times: user 34 ms, sys: 24.9 ms, total: 58.9 ms\n", + "Wall time: 3.64 s\n" ] } ], @@ -2583,16 +2331,9 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": 32, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Wall clock time taken for this cell: 0.18047992000356317 s\n" - ] - }, { "name": "stderr", "output_type": "stream", @@ -2600,34 +2341,35 @@ "/home/skirui/anaconda3/envs/rapids-23.08/lib/python3.10/site-packages/dask/dataframe/core.py:7047: FutureWarning: Meta is not valid, `map_partitions` and `map_overlap` expects output to be a pandas object. Try passing a pandas object as meta or a dict or tuple representing the (name, dtype) of the columns. In the future the meta you passed will not work.\n", " warnings.warn(\n" ] + }, + { + "ename": "KeyError", + "evalue": "'fil_model'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[32], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m tic \u001b[38;5;241m=\u001b[39m timer()\n\u001b[1;32m 2\u001b[0m predictions \u001b[38;5;241m=\u001b[39m X_infer\u001b[38;5;241m.\u001b[39mmap_partitions(predict, meta\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfloat\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;66;03m# this is like MPI reduce\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m y_pred \u001b[38;5;241m=\u001b[39m \u001b[43mpredictions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompute\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m wait(y_pred)\n\u001b[1;32m 5\u001b[0m toc \u001b[38;5;241m=\u001b[39m timer()\n", + "Cell \u001b[0;32mIn[30], line 23\u001b[0m, in \u001b[0;36mpredict\u001b[0;34m()\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpredict\u001b[39m(input_df):\n\u001b[1;32m 21\u001b[0m \u001b[38;5;66;03m# this function will run in each worker and predict \u001b[39;00m\n\u001b[1;32m 22\u001b[0m worker \u001b[38;5;241m=\u001b[39m get_worker()\n\u001b[0;32m---> 23\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m worker\u001b[38;5;241m.\u001b[39mdata[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfil_model\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mpredict(input_df)\n", + "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/dask_cuda/device_host_file.py:269\u001b[0m, in \u001b[0;36m__getitem__\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: 'fil_model'" + ] } ], "source": [ - "# Submit the predict function to the Dask cluster\n", - "predict_future = client.submit(predict, X_infer)\n", - "\n", - "# Map the predict_future to partitions of X_infer\n", "tic = timer()\n", - "predictions = X_infer.map_partitions(lambda df: predict_future, meta=\"float\")\n", + "predictions = X_infer.map_partitions(predict, meta=\"float\") # this is like MPI reduce\n", "y_pred = predictions.compute()\n", "wait(y_pred)\n", "toc = timer()\n", - "print(f\"Wall clock time taken for this cell: {toc - tic} s\")" + "print(f\"Wall clock time taken for this cell : {toc-tic} s\")" ] }, { "cell_type": "code", - "execution_count": 111, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "It took 0.18047992000356317 seconds to predict on 5426301 rows using FIL distributedly on each worker\n" - ] - } - ], + "outputs": [], "source": [ "rows_csv = X_infer.iloc[:, 0].shape[0].compute()\n", "print(\n", @@ -2637,42 +2379,9 @@ }, { "cell_type": "code", - "execution_count": 112, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "ename": "ValueError", - "evalue": "Unsupported dtype object", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[112], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m tic \u001b[38;5;241m=\u001b[39m timer()\n\u001b[0;32m----> 2\u001b[0m score \u001b[38;5;241m=\u001b[39m \u001b[43mmean_squared_error\u001b[49m\u001b[43m(\u001b[49m\u001b[43my_pred\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_y_test\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m toc \u001b[38;5;241m=\u001b[39m timer()\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFinal - RMSE: \u001b[39m\u001b[38;5;124m\"\u001b[39m, np\u001b[38;5;241m.\u001b[39msqrt(score))\n", - "File \u001b[0;32m~/anaconda3/envs/rapids-23.08/lib/python3.10/site-packages/cuml/internals/api_decorators.py:190\u001b[0m, in \u001b[0;36m_make_decorator_function..decorator_function..decorator_closure..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 188\u001b[0m ret \u001b[38;5;241m=\u001b[39m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 189\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 190\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 192\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m cm\u001b[38;5;241m.\u001b[39mprocess_return(ret)\n", - "File \u001b[0;32mregression.pyx:200\u001b[0m, in \u001b[0;36mcuml.metrics.regression.mean_squared_error\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mregression.pyx:111\u001b[0m, in \u001b[0;36mcuml.metrics.regression._prepare_input_reg\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32m~/anaconda3/envs/rapids-23.08/lib/python3.10/site-packages/nvtx/nvtx.py:101\u001b[0m, in \u001b[0;36mannotate.__call__..inner\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 98\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(func)\n\u001b[1;32m 99\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minner\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 100\u001b[0m libnvtx_push_range(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mattributes, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdomain\u001b[38;5;241m.\u001b[39mhandle)\n\u001b[0;32m--> 101\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 102\u001b[0m libnvtx_pop_range(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdomain\u001b[38;5;241m.\u001b[39mhandle)\n\u001b[1;32m 103\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", - "File \u001b[0;32m~/anaconda3/envs/rapids-23.08/lib/python3.10/site-packages/cuml/internals/input_utils.py:369\u001b[0m, in \u001b[0;36minput_to_cuml_array\u001b[0;34m(X, order, deepcopy, check_dtype, convert_to_dtype, check_mem_type, convert_to_mem_type, safe_dtype_conversion, check_cols, check_rows, fail_on_order, force_contiguous)\u001b[0m\n\u001b[1;32m 281\u001b[0m \u001b[38;5;129m@nvtx_annotate\u001b[39m(\n\u001b[1;32m 282\u001b[0m message\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcommon.input_utils.input_to_cuml_array\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 283\u001b[0m category\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutils\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 298\u001b[0m force_contiguous\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m 299\u001b[0m ):\n\u001b[1;32m 300\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 301\u001b[0m \u001b[38;5;124;03m Convert input X to CumlArray.\u001b[39;00m\n\u001b[1;32m 302\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 367\u001b[0m \n\u001b[1;32m 368\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 369\u001b[0m arr \u001b[38;5;241m=\u001b[39m \u001b[43mCumlArray\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_input\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 370\u001b[0m \u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 371\u001b[0m \u001b[43m \u001b[49m\u001b[43morder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morder\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 372\u001b[0m \u001b[43m \u001b[49m\u001b[43mdeepcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdeepcopy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 373\u001b[0m \u001b[43m \u001b[49m\u001b[43mcheck_dtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcheck_dtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 374\u001b[0m \u001b[43m \u001b[49m\u001b[43mconvert_to_dtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconvert_to_dtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 375\u001b[0m \u001b[43m \u001b[49m\u001b[43mcheck_mem_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcheck_mem_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 376\u001b[0m \u001b[43m \u001b[49m\u001b[43mconvert_to_mem_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconvert_to_mem_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 377\u001b[0m \u001b[43m \u001b[49m\u001b[43msafe_dtype_conversion\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msafe_dtype_conversion\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 378\u001b[0m \u001b[43m \u001b[49m\u001b[43mcheck_cols\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcheck_cols\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 379\u001b[0m \u001b[43m \u001b[49m\u001b[43mcheck_rows\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcheck_rows\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 380\u001b[0m \u001b[43m \u001b[49m\u001b[43mfail_on_order\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfail_on_order\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 381\u001b[0m \u001b[43m \u001b[49m\u001b[43mforce_contiguous\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforce_contiguous\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 382\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 383\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 384\u001b[0m shape \u001b[38;5;241m=\u001b[39m arr\u001b[38;5;241m.\u001b[39m__cuda_array_interface__[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mshape\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n", - "File \u001b[0;32m~/anaconda3/envs/rapids-23.08/lib/python3.10/site-packages/cuml/internals/memory_utils.py:87\u001b[0m, in \u001b[0;36mwith_cupy_rmm..cupy_rmm_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 85\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m GPU_ENABLED:\n\u001b[1;32m 86\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m cupy_using_allocator(rmm_cupy_allocator):\n\u001b[0;32m---> 87\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 88\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", - "File \u001b[0;32m~/anaconda3/envs/rapids-23.08/lib/python3.10/site-packages/nvtx/nvtx.py:101\u001b[0m, in \u001b[0;36mannotate.__call__..inner\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 98\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(func)\n\u001b[1;32m 99\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minner\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 100\u001b[0m libnvtx_push_range(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mattributes, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdomain\u001b[38;5;241m.\u001b[39mhandle)\n\u001b[0;32m--> 101\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 102\u001b[0m libnvtx_pop_range(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdomain\u001b[38;5;241m.\u001b[39mhandle)\n\u001b[1;32m 103\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", - "File \u001b[0;32m~/anaconda3/envs/rapids-23.08/lib/python3.10/site-packages/cuml/internals/array.py:1117\u001b[0m, in \u001b[0;36mCumlArray.from_input\u001b[0;34m(cls, X, order, deepcopy, check_dtype, convert_to_dtype, check_mem_type, convert_to_mem_type, safe_dtype_conversion, check_cols, check_rows, fail_on_order, force_contiguous)\u001b[0m\n\u001b[1;32m 1109\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m 1110\u001b[0m (X \u001b[38;5;241m<\u001b[39m target_dtype_range\u001b[38;5;241m.\u001b[39mmin) \u001b[38;5;241m|\u001b[39m (X \u001b[38;5;241m>\u001b[39m target_dtype_range\u001b[38;5;241m.\u001b[39mmax)\n\u001b[1;32m 1111\u001b[0m )\u001b[38;5;241m.\u001b[39many():\n\u001b[1;32m 1112\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;32m 1113\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mData type conversion on values outside\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1114\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m representable range of target dtype\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1115\u001b[0m )\n\u001b[1;32m 1116\u001b[0m arr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m(\n\u001b[0;32m-> 1117\u001b[0m \u001b[43marr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_output\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1118\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_dtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconvert_to_dtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1119\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_mem_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconvert_to_mem_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1120\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m,\n\u001b[1;32m 1121\u001b[0m order\u001b[38;5;241m=\u001b[39mrequested_order,\n\u001b[1;32m 1122\u001b[0m index\u001b[38;5;241m=\u001b[39mindex,\n\u001b[1;32m 1123\u001b[0m validate\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m 1124\u001b[0m )\n\u001b[1;32m 1126\u001b[0m make_copy \u001b[38;5;241m=\u001b[39m force_contiguous \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m arr\u001b[38;5;241m.\u001b[39mis_contiguous\n\u001b[1;32m 1128\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m 1129\u001b[0m \u001b[38;5;129;01mnot\u001b[39;00m fail_on_order \u001b[38;5;129;01mand\u001b[39;00m order \u001b[38;5;241m!=\u001b[39m arr\u001b[38;5;241m.\u001b[39morder \u001b[38;5;129;01mand\u001b[39;00m order \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mK\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1130\u001b[0m ) \u001b[38;5;129;01mor\u001b[39;00m make_copy:\n", - "File \u001b[0;32m~/anaconda3/envs/rapids-23.08/lib/python3.10/site-packages/cuml/internals/memory_utils.py:87\u001b[0m, in \u001b[0;36mwith_cupy_rmm..cupy_rmm_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 85\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m GPU_ENABLED:\n\u001b[1;32m 86\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m cupy_using_allocator(rmm_cupy_allocator):\n\u001b[0;32m---> 87\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 88\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", - "File \u001b[0;32m~/anaconda3/envs/rapids-23.08/lib/python3.10/site-packages/nvtx/nvtx.py:101\u001b[0m, in \u001b[0;36mannotate.__call__..inner\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 98\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(func)\n\u001b[1;32m 99\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minner\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 100\u001b[0m libnvtx_push_range(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mattributes, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdomain\u001b[38;5;241m.\u001b[39mhandle)\n\u001b[0;32m--> 101\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 102\u001b[0m libnvtx_pop_range(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdomain\u001b[38;5;241m.\u001b[39mhandle)\n\u001b[1;32m 103\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", - "File \u001b[0;32m~/anaconda3/envs/rapids-23.08/lib/python3.10/site-packages/cuml/internals/array.py:625\u001b[0m, in \u001b[0;36mCumlArray.to_output\u001b[0;34m(self, output_type, output_dtype, output_mem_type)\u001b[0m\n\u001b[1;32m 618\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39masarray(\n\u001b[1;32m 619\u001b[0m \u001b[38;5;28mself\u001b[39m, dtype\u001b[38;5;241m=\u001b[39moutput_dtype, order\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39morder\n\u001b[1;32m 620\u001b[0m )\n\u001b[1;32m 621\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m cp\u001b[38;5;241m.\u001b[39masnumpy(\n\u001b[1;32m 622\u001b[0m cp\u001b[38;5;241m.\u001b[39masarray(\u001b[38;5;28mself\u001b[39m, dtype\u001b[38;5;241m=\u001b[39moutput_dtype, order\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39morder),\n\u001b[1;32m 623\u001b[0m order\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39morder,\n\u001b[1;32m 624\u001b[0m )\n\u001b[0;32m--> 625\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43moutput_mem_type\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mxpy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43masarray\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 626\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_dtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43morder\u001b[49m\n\u001b[1;32m 627\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 629\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m output_type \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnumba\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 630\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m cuda\u001b[38;5;241m.\u001b[39mas_cuda_array(\n\u001b[1;32m 631\u001b[0m cp\u001b[38;5;241m.\u001b[39masarray(\u001b[38;5;28mself\u001b[39m, dtype\u001b[38;5;241m=\u001b[39moutput_dtype, order\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39morder)\n\u001b[1;32m 632\u001b[0m )\n", - "File \u001b[0;32m~/anaconda3/envs/rapids-23.08/lib/python3.10/site-packages/cupy/_creation/from_data.py:75\u001b[0m, in \u001b[0;36masarray\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m 49\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21masarray\u001b[39m(a, dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, order\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 50\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Converts an object to array.\u001b[39;00m\n\u001b[1;32m 51\u001b[0m \n\u001b[1;32m 52\u001b[0m \u001b[38;5;124;03m This is equivalent to ``array(a, dtype, copy=False, order=order)``.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 73\u001b[0m \n\u001b[1;32m 74\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m---> 75\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_core\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morder\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32mcupy/_core/core.pyx:2376\u001b[0m, in \u001b[0;36mcupy._core.core.array\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mcupy/_core/core.pyx:2400\u001b[0m, in \u001b[0;36mcupy._core.core.array\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mcupy/_core/core.pyx:2527\u001b[0m, in \u001b[0;36mcupy._core.core._array_default\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mValueError\u001b[0m: Unsupported dtype object" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "tic = timer()\n", "score = mean_squared_error(y_pred, _y_test)\n", @@ -2680,61 +2389,9 @@ "print(\"Final - RMSE: \", np.sqrt(score))" ] }, - { - "cell_type": "code", - "execution_count": 87, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(dtype('O'), dtype('float32'))" - ] - }, - "execution_count": 87, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y_pred.dtype, _y_test.dtype" - ] - }, - { - "cell_type": "code", - "execution_count": 88, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 ef{n}-4_io(>4<0x;qu1~G%h18yyLSWqzjZ|a zC#Oos_D8A%)UNFRNn8K_+U7fc=#bJvS)iviUvc;)r^6rVq0A6tO?dh<14LJxe&dJ3 zb~4=wlu6|&?f7|pmb{fm+j3Sgov+fKe2qMo9m)Zz+j1QCDdz;!tF7+$k)4hsH}RMLq->iK=)k$k#lilXC(p9e`ImqKAZoA4m{uI}av+vVLUde#tpaS+ zMk%PY*Nd|U+K`vafXhPKF;g4mpz^6aTuZqExFVz+OGFw6UEfMjWk}tY<8Y1TD&Qc9 z+M#o!>s}SA=Jn{@@H)uB;Odal9?p$kn;KBfR32U*xfZxKMAyFc{g_00&FG$?A1t!* zmHy@M8p?IBTo+Q>!|O}0YdxqwM8~qH-B~MMTe$(28$wEsm9^9BZGkw5a?Q_OvEcQW ztys1}N{%ba(>-B_c!+Y29VmZM2N2a>MDUY+&i*_Wfw%R>yBM9 ziF7}OK#ja!{kbuTxToaC;3kl|--$fkXHB6{NZp520Sf0$;MjmScwS;IN 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