{"_id":"58f5d573cf6b642300b13f74","category":{"_id":"58f5d52d7891630f00fe4e77","project":"55faf11ba62ba1170021a9a7","version":"55faf11ba62ba1170021a9aa","__v":0,"sync":{"url":"","isSync":false},"reference":false,"createdAt":"2017-04-18T08:58:21.978Z","from_sync":false,"order":31,"slug":"data-cruncher","title":"DATA CRUNCHER"},"version":{"_id":"55faf11ba62ba1170021a9aa","project":"55faf11ba62ba1170021a9a7","__v":38,"createdAt":"2015-09-17T16:58:03.490Z","releaseDate":"2015-09-17T16:58:03.490Z","categories":["55faf11ca62ba1170021a9ab","55faf8f4d0e22017005b8272","55faf91aa62ba1170021a9b5","55faf929a8a7770d00c2c0bd","55faf932a8a7770d00c2c0bf","55faf94b17b9d00d00969f47","55faf958d0e22017005b8274","55faf95fa8a7770d00c2c0c0","55faf96917b9d00d00969f48","55faf970a8a7770d00c2c0c1","55faf98c825d5f19001fa3a6","55faf99aa62ba1170021a9b8","55faf99fa62ba1170021a9b9","55faf9aa17b9d00d00969f49","55faf9b6a8a7770d00c2c0c3","55faf9bda62ba1170021a9ba","5604570090ee490d00440551","5637e8b2fbe1c50d008cb078","5649bb624fa1460d00780add","5671974d1b6b730d008b4823","5671979d60c8e70d006c9760","568e8eef70ca1f0d0035808e","56d0a2081ecc471500f1795e","56d4a0adde40c70b00823ea3","56d96b03dd90610b00270849","56fbb83d8f21c817002af880","573c811bee2b3b2200422be1","576bc92afb62dd20001cda85","5771811e27a5c20e00030dcd","5785191af3a10c0e009b75b0","57bdf84d5d48411900cd8dc0","57ff5c5dc135231700aed806","5804caf792398f0f00e77521","58458b4fba4f1c0f009692bb","586d3c287c6b5b2300c05055","58ef66d88646742f009a0216","58f5d52d7891630f00fe4e77","59a555bccdbd85001bfb1442"],"is_deprecated":false,"is_hidden":false,"is_beta":true,"is_stable":true,"codename":"","version_clean":"1.0.0","version":"1.0"},"project":"55faf11ba62ba1170021a9a7","__v":0,"parentDoc":null,"user":"5767bc73bb15f40e00a28777","updates":[],"next":{"pages":[],"description":""},"createdAt":"2017-04-18T08:59:31.582Z","link_external":false,"link_url":"","githubsync":"","sync_unique":"","hidden":false,"api":{"results":{"codes":[]},"settings":"","auth":"required","params":[],"url":""},"isReference":false,"order":3,"body":"## Overview\n\nData Cruncher allows you to enter and execute Python, R or Julia code to perform further analyses on your data on the CGC. This page will explain how you can access Data Cruncher from a project on the CGC, set up an analysis and execute code within the analysis. To be able to run the analysis, you need execute permissions in the project.\n\n### [ 1 ] Access Data Cruncher\n\n1. Open the desired project on the CGC.\nThis project should contain the data that you want to analyze further using Data Cruncher.\n2. From the project's dashboard, click the **Interactive Analysis** tab.\nThe list of available interactive analysis tools opens. \n3. On the **Data Cruncher** card click **Open**.\n[block:image]\n{\n  \"images\": [\n    {\n      \"image\": [\n        \"https://files.readme.io/b427f41-cruncher_card.png\",\n        \"cruncher_card.png\",\n        293,\n        441,\n        \"#eeebec\"\n      ]\n    }\n  ]\n}\n[/block]\nThis takes you to the Data Cruncher home page. If you have previous analyses, they will be listed on this page.\n\n### [ 2 ] Create and set up your analysis\n1. In the top-right corner click **Create new analysis**.\nThe **Create new analysis wizard** is displayed.\n2. On the first screen, name your analysis in the **Analysis name** field.\n3. Click **Next**.\n4. Select the instance for the analysis.\n[block:image]\n{\n  \"images\": [\n    {\n      \"image\": [\n        \"https://files.readme.io/efb167a-cruncher_quickstart_1.png\",\n        \"cruncher_quickstart_1.png\",\n        572,\n        418,\n        \"#ecedec\"\n      ]\n    }\n  ]\n}\n[/block]\nThe list displays the instances along with their disk size, number of vCPUs and memory (shown in brackets). The default instance is **c3.2xlarge** that has **160 GB** of SSD storage, **8 vCPUs** and **15 GB** of RAM. \n[block:callout]\n{\n  \"type\": \"info\",\n  \"body\": \"<a name=\\\"instance-inactivity\\\"></a>Any instance will be stopped after 30 minutes of inactivity within the analysis that is running on the instance. This also includes stopping the analysis and saving all files that meet the automatic saving criteria or have been selected to be saved as project files. Files that do not meet the criteria and are not manually saved to the project will be lost.\"\n}\n[/block]\n5. Click **Next**.\n6. Define the automatic saving criteria:\n * **Ignore the following file types** - Files that have the listed extensions will never be automatically saved when the analysis is stopped. If you need to specify multiple extensions, they are separated by a comma, e.g. `.zip, .log`.\n * **Ignore files larger than** - Files bigger than the specified size will not be automatically saved when the analysis is stopped.\n7. Click **Start the analysis**.\nThe CGC will start acquiring an adequate instance for your analysis, which may take a few minutes. Once an instance is ready, you will be notified.\n[block:callout]\n{\n  \"type\": \"info\",\n  \"body\": \"If you don't have execute permissions in the project where the analysis is being created, the button is labelled **Create the analysis**. This allows you to create the analysis in draft state with the defined settings, but not execute it.\"\n}\n[/block]\n### [ 3 ] Start the analysis\nOnce the CGC has acquired an instance for your analysis, you can open the editor and run your analysis.\n\n1. Click **Open in editor**.\nThe editor opens in a new window.  You can select to create one of the two types of files:\n * **Notebook** - the central element of a Cruncher analysis. It is a document where you can enter your Python, R or Julia code, but also store equations, visualizations and explanatory text.\n * **Text Editor** - used to create any text-based file that you want to have or use during your analysis. For example, if you need to add a JSON file to your analysis files, you can select this option, enter or paste the JSON content and save the file with a **.json** extension.\n2. Click **Notebook**.\n3. Enter the notebook details:\n * **File Name** - The name under which the notebook will be saved.\n * **Kernel** - This is the “computational engine” that executes the code contained in a notebook. Currently available kernels are **Juilia 0.5.0**, **Python 2**, **Python 3** and **R**.\n4. Click **Create**.\nYour notebook is now ready. You can start entering the code or other text content in the first blank cell at the top.\n\n## Where to go from here?\nTo get started with the Data Cruncher editor, read the [Editor quick reference](doc:editor-quick-reference).","excerpt":"","slug":"run-an-analysis-using-data-cruncher","type":"basic","title":"Run an analysis using Data Cruncher"}

Run an analysis using Data Cruncher


## Overview Data Cruncher allows you to enter and execute Python, R or Julia code to perform further analyses on your data on the CGC. This page will explain how you can access Data Cruncher from a project on the CGC, set up an analysis and execute code within the analysis. To be able to run the analysis, you need execute permissions in the project. ### [ 1 ] Access Data Cruncher 1. Open the desired project on the CGC. This project should contain the data that you want to analyze further using Data Cruncher. 2. From the project's dashboard, click the **Interactive Analysis** tab. The list of available interactive analysis tools opens. 3. On the **Data Cruncher** card click **Open**. [block:image] { "images": [ { "image": [ "https://files.readme.io/b427f41-cruncher_card.png", "cruncher_card.png", 293, 441, "#eeebec" ] } ] } [/block] This takes you to the Data Cruncher home page. If you have previous analyses, they will be listed on this page. ### [ 2 ] Create and set up your analysis 1. In the top-right corner click **Create new analysis**. The **Create new analysis wizard** is displayed. 2. On the first screen, name your analysis in the **Analysis name** field. 3. Click **Next**. 4. Select the instance for the analysis. [block:image] { "images": [ { "image": [ "https://files.readme.io/efb167a-cruncher_quickstart_1.png", "cruncher_quickstart_1.png", 572, 418, "#ecedec" ] } ] } [/block] The list displays the instances along with their disk size, number of vCPUs and memory (shown in brackets). The default instance is **c3.2xlarge** that has **160 GB** of SSD storage, **8 vCPUs** and **15 GB** of RAM. [block:callout] { "type": "info", "body": "<a name=\"instance-inactivity\"></a>Any instance will be stopped after 30 minutes of inactivity within the analysis that is running on the instance. This also includes stopping the analysis and saving all files that meet the automatic saving criteria or have been selected to be saved as project files. Files that do not meet the criteria and are not manually saved to the project will be lost." } [/block] 5. Click **Next**. 6. Define the automatic saving criteria: * **Ignore the following file types** - Files that have the listed extensions will never be automatically saved when the analysis is stopped. If you need to specify multiple extensions, they are separated by a comma, e.g. `.zip, .log`. * **Ignore files larger than** - Files bigger than the specified size will not be automatically saved when the analysis is stopped. 7. Click **Start the analysis**. The CGC will start acquiring an adequate instance for your analysis, which may take a few minutes. Once an instance is ready, you will be notified. [block:callout] { "type": "info", "body": "If you don't have execute permissions in the project where the analysis is being created, the button is labelled **Create the analysis**. This allows you to create the analysis in draft state with the defined settings, but not execute it." } [/block] ### [ 3 ] Start the analysis Once the CGC has acquired an instance for your analysis, you can open the editor and run your analysis. 1. Click **Open in editor**. The editor opens in a new window. You can select to create one of the two types of files: * **Notebook** - the central element of a Cruncher analysis. It is a document where you can enter your Python, R or Julia code, but also store equations, visualizations and explanatory text. * **Text Editor** - used to create any text-based file that you want to have or use during your analysis. For example, if you need to add a JSON file to your analysis files, you can select this option, enter or paste the JSON content and save the file with a **.json** extension. 2. Click **Notebook**. 3. Enter the notebook details: * **File Name** - The name under which the notebook will be saved. * **Kernel** - This is the “computational engine” that executes the code contained in a notebook. Currently available kernels are **Juilia 0.5.0**, **Python 2**, **Python 3** and **R**. 4. Click **Create**. Your notebook is now ready. You can start entering the code or other text content in the first blank cell at the top. ## Where to go from here? To get started with the Data Cruncher editor, read the [Editor quick reference](doc:editor-quick-reference).