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118 | 118 | "cell_type": "code",
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119 | 119 | "execution_count": null,
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120 | 120 | "metadata": {
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121 |
| - "name": "job", |
122 | 121 | "gather": {
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123 | 122 | "logged": 1634855420019
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124 |
| - } |
| 123 | + }, |
| 124 | + "name": "job" |
125 | 125 | },
|
126 | 126 | "outputs": [],
|
127 | 127 | "source": [
|
|
134 | 134 | " compute=\"cpu-cluster\",\n",
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135 | 135 | " instance_count=2,\n",
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136 | 136 | " # distribution = {\"type\": \"mpi\", \"process_count_per_instance\": 1},\n",
|
137 |
| - " distribution={\n", |
138 |
| - " \"type\": \"tensorflow\",\n", |
139 |
| - " \"parameter_server_count\": 1,\n", |
140 |
| - " \"worker_count\": 2,\n", |
141 |
| - " \"added_property\": 7,\n", |
142 |
| - " },\n", |
| 137 | + " # distribution={\n", |
| 138 | + " # \"type\": \"tensorflow\",\n", |
| 139 | + " # \"parameter_server_count\": 1, # for legacy TensorFlow 1.x\n", |
| 140 | + " # \"worker_count\": 2,\n", |
| 141 | + " # \"added_property\": 7,\n", |
| 142 | + " # },\n", |
143 | 143 | " # distribution = {\n",
|
144 | 144 | " # \"type\": \"pytorch\",\n",
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145 | 145 | " # \"process_count_per_instance\": 4,\n",
|
|
151 | 151 | ")\n",
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152 | 152 | "\n",
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153 | 153 | "# can also set the distribution in a separate step and using the typed objects instead of a dict\n",
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154 |
| - "job.distribution = TensorFlowDistribution(parameter_server_count=1, worker_count=2)" |
| 154 | + "job.distribution = TensorFlowDistribution(worker_count=2)" |
155 | 155 | ]
|
156 | 156 | },
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157 | 157 | {
|
|
183 | 183 | "returned_job = ml_client.create_or_update(job)"
|
184 | 184 | ]
|
185 | 185 | },
|
| 186 | + { |
| 187 | + "cell_type": "code", |
| 188 | + "execution_count": null, |
| 189 | + "metadata": {}, |
| 190 | + "outputs": [], |
| 191 | + "source": [ |
| 192 | + "# Wait until the job completes\n", |
| 193 | + "ml_client.jobs.stream(returned_job.name)" |
| 194 | + ] |
| 195 | + }, |
186 | 196 | {
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187 | 197 | "cell_type": "markdown",
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188 | 198 | "metadata": {},
|
|
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